Publications

Show abstract The increasing awareness of the impact of spontaneous movements on neuronal activity has raised the need to track behavior. We present FreiPose, a versatile learning-based framework to directly capture 3D motion of freely definable points with high precision (median error < 3.5% body length, 41.9% improvement compared to state-of-the-art) and high reliability (82.8% keypoints within < 5% body length error boundary, 72.0% improvement). The versatility of FreiPose is demonstrated in two experiments: (1) By tracking freely moving rats with simultaneous electrophysiological recordings in motor cortex, we identified neuronal tuning to behavioral states and individual paw trajectories. (2) We inferred time points of optogenetic stimulation in rat motor cortex from the measured pose across individuals and attributed the stimulation effect automatically to body parts. The versatility and accuracy of FreiPose open up new possibilities for quantifying behavior of freely moving animals and may lead to new ways of setting up experiments.

Show abstract Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy. It is frequently associated with abnormal MRI findings, which are caused by underlying cellular, structural, and chemical changes at the micro-scale. In the current study, it is investigated to which extent these alterations correspond to imaging features detected by high resolution magnetic resonance imaging in the intrahippocampal kainate mouse model of MTLE. Fixed hippocampal and whole-brain sections of mouse brain tissue from nine animals under physiological and chronically epileptic conditions were examined using structural and diffusion-weighted MRI. Microstructural details were investigated based on a direct comparison with immunohistochemical analyses of the same specimen. Within the hippocampal formation, diffusion streamlines could be visualized corresponding to dendrites of CA1 pyramidal cells and granule cells, as well as mossy fibers and Schaffer collaterals. Statistically significant changes in diffusivities, fractional anisotropy, and diffusion orientations could be detected in tissue samples from chronically epileptic animals compared to healthy controls, corresponding to microstructural alterations (degeneration of pyramidal cells, dispersion of the granule cell layer, and sprouting of mossy fibers). The diffusion parameters were significantly correlated with histologically determined cell densities. These findings demonstrate that high-resolution diffusion-weighted MRI can resolve subtle microstructural changes in epileptic hippocampal tissue corresponding to histopathological features in MTLE.

Show abstract Brain networks store new memories using functional and structural synaptic plasticity. Memory formation is generally attributed to Hebbian plasticity, while homeostatic plasticity is thought to have an ancillary role in stabilizing network dynamics. Here we report that homeostatic plasticity alone can also lead to the formation of stable memories. We analyze this phenomenon using a new theory of network remodeling, combined with numerical simulations of recurrent spiking neural networks that exhibit structural plasticity based on firing rate homeostasis. These networks are able to store repeatedly presented patterns and recall them upon the presentation of incomplete cues. Storing is fast, governed by the homeostatic drift. In contrast, forgetting is slow, driven by a diffusion process. Joint stimulation of neurons induces the growth of associative connections between them, leading to the formation of memory engrams. In conclusion, homeostatic structural plasticity induces a specific type of “silent memories”, different from conventional attractor states.

Show abstract We present a novel, to the best of our knowledge, fabrication process for highly aspherical lenses based on surface deformation due to thermal expansion of a soft polymer, polydimethylsiloxane (PDMS), using laser-structuring, molding, and precise shape optimization. Our fabrication process can be used for almost any lens shape with a large degree of freedom—both individual lenses and dense arrays. We present the design, fabrication, and characterization with examples of four different lenses with 1 mm apertures and surface deviations below 100 nm.

Show abstract We present a highly compact and fast varifocal lens with aspherical tunability based on an active piezo-glass-piezo sandwich membrane. Using an optimized geometry, improved fabrication and compliant elastomer structures together with an index-matched optical fluid, we achieved an outer diameter of just 9 mm (10 mm packaged) for a clear aperture of 7.6 mm. The range of the focal power was -7 m−1 to +6 m−1, with a wavefront error around 100 nm and a response time between 0.1 and 0.15 ms.

Show abstract Intrinsically generated patterns of coupled neuronal activity are associated with the dynamics of specific brain states. Sensory inputs are extrinsic factors that can perturb these intrinsic coupling modes, creating a complex scenario in which forthcoming stimuli are processed. Studying this intrinsic-extrinsic interplay is necessary to better understand perceptual integration and selection. Here, we show that this interplay leads to a reconfiguration of functional cortical connectivity that acts as a mechanism to facilitate stimulus processing. Using audiovisual stimulation in anesthetized ferrets, we found that this reconfiguration of coupling modes is context specific, depending on long-term modulation by repetitive sensory inputs. These reconfigured coupling modes lead to changes in latencies and power of local field potential responses that support multisensory integration. Our study demonstrates that this interplay extends across multiple time scales and involves different types of intrinsic coupling. These results suggest a previously unknown large-scale mechanism that facilitates multisensory integration.

Show abstract Fast-spiking parvalbumin-expressing interneurons (PVIs) and granule cells (GCs) of the dentate gyrus receive layer-specific dendritic inhibition. Its impact on PVI and GC excitability is, however, unknown. By applying whole-cell recordings, GABA uncaging and single-cell-modeling, we show that proximal dendritic inhibition in PVIs is less efficient in lowering perforant path-mediated subthreshold depolarization than distal inhibition but both are highly efficient in silencing PVIs. These inhibitory effects can be explained by proximal shunting and distal strong hyperpolarizing inhibition. In contrast, GC proximal but not distal inhibition is the primary regulator of their excitability and recruitment. In GCs inhibition is hyperpolarizing along the entire somato-dendritic axis with similar strength. Thus, dendritic inhibition differentially controls input-output transformations in PVIs and GCs. Dendritic inhibition in PVIs is suited to balance PVI discharges in dependence on global network activity thereby providing strong and tuned perisomatic inhibition that contributes to the sparse representation of information in GC assemblies.

Show abstract Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here, we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients.

Show abstract Striatal projection neurons, the medium spiny neurons (MSNs), play a crucial role in various motor and cognitive functions. MSNs express either D1 or D2 type dopamine receptors and initiate the direct-pathway (dMSNs) or indirect pathways (iMSNs) of the basal ganglia, respectively. dMSNs have been shown to receive more inhibition than iMSNs from intrastriatal sources. Based on these findings, computational modelling of the striatal network has predicted that under healthy conditions dMSNs should receive more total input than iMSNs. To test this prediction, we analyzed in vivo whole-cell recordings from dMSNs and iMSNs in healthy and dopamine-depleted (6OHDA) anaesthetized mice. By comparing their membrane potential fluctuations, we found that dMSNs exhibited considerably larger membrane potential fluctuations over a wide frequency range. Furthermore, by comparing the spike-triggered average membrane potentials, we found that dMSNs depolarized towards the spike threshold significantly faster than iMSNs did. Together, these findings (in particular the STA analysis) corroborate the theoretical prediction that direct-pathway MSNs receive stronger total input than indirect-pathway neurons. Finally, we found that dopamine-depleted mice exhibited no difference between the membrane potential fluctuations of dMSNs and iMSNs. These data provide new insights into the question how the lack of dopamine may lead to behavioral deficits associated with Parkinson's disease.

Show abstract In this study, the release of fluorescein from a photo‐crosslinked conducting polymer hydrogel made from a hydrogel precursor poly(dimethylacrylamide‐co‐4‐methacryloyloxy benzophenone (5%)‐co‐4‐styrenesulfonate (2.5%)) (PDMAAp) and the conducting polymer poly(3,4‐ethylenedioxythiophene) (PEDOT) is investigated. Fluorescein, here used as a model for a drug, is actively released through application of an electrical trigger signal. The detected quantity is more than six times higher in comparison to that released from a conventional PEDOT/polysterene sulfonate (PSS) system. Release profiles, drug dose, and timing can be tailored by the application of different trigger signals and pretreatments. To demonstrate that the novel drug release system can be used for a drug relevant for local delivery to a neural interface, experiments are furthermore performed with the anti‐inflammatory drug dexamethasone (Dex). The conducting polymer hydrogel facilitates the active release of Dex, in comparison to the previously used PEDOT/Dex. It is suggested that PEDOT/PDMAAp is an interesting alternative for conducting polymer based drug release systems, with the potential to offer more volume for storage, yet retaining the excellent electrochemical properties known for PEDOT electrodes.

Show abstract Lower limb amputation (LLA) destroys the sensory communication between the brain and the external world during standing and walking. Current prostheses do not restore sensory feedback to amputees, who, relying on very limited haptic information from the stump-socket interaction, are forced to deal with serious issues: the risk of falls, decreased mobility, prosthesis being perceived as an external object (low embodiment), and increased cognitive burden. Poor mobility is one of the causes of eventual device abandonment. Restoring sensory feedback from the missing leg of above-knee (transfemoral) amputees and integrating the sensory feedback into the sensorimotor loop would markedly improve the life of patients. In this study, we developed a leg neuroprosthesis, which provided real-time tactile and emulated proprioceptive feedback to three transfemoral amputees through nerve stimulation. The feedback was exploited in active tasks, which proved that our approach promoted improved mobility, fall prevention, and agility. We also showed increased embodiment of the lower limb prosthesis (LLP), through phantom leg displacement perception and questionnaires, and ease of the cognitive effort during a dual-task paradigm, through electroencephalographic recordings. Our results demonstrate that induced sensory feedback can be integrated at supraspinal levels to restore functional abilities of the missing leg. This work paves the way for further investigations about how the brain interprets different artificial feedback strategies and for the development of fully implantable sensory-enhanced leg neuroprostheses, which could drastically ameliorate life quality in people with disability.

Show abstract Emerging virtual reality systems offer intriguing therapeutic possibilities, but their development and use should be guided by ethical priorities that account for the specific vulnerabilities of patients.

Show abstract Cognitive control is proposed to rely on a rostral-to-caudal hierarchy of neural processing within the prefrontal cortex (PFC), with more rostral parts exerting control over more caudal parts. Anatomical and functional data suggest that this hierarchical organization of the PFC may be separated into a ventral and a dorsal component. Furthermore, recent studies indicate that the apex of the hierarchy resides within the mid-lateral rather the rostral PFC. However, investigating the hierarchical aspect of rostro-to-caudal processing requires quantification of the directed interactions between PFC regions. Using functional near-infrared spectroscopy (fNIRS) in a sample of healthy young adults we analyzed directed interactions between rostral and caudal PFC during passive watching of nature documentaries. Directed coherence (DC) as a measure of directed interaction was computed pairwise between 38 channels evenly distributed over the lateral prefrontal convexity. Results revealed an overall predominance of rostral-to-caudal directed interactions in the PFC that further dissociated along a ventro-dorsal axis: Dorsal regions exerted stronger rostro-caudally directed interactions on dorsal than on ventral regions and vice versa. Interactions between ventral and dorsal PFC were stronger from ventral to dorsal areas than vice versa. Results further support the notion that the mid-dorsolateral PFC constitutes the apex of the prefrontal hierarchy. Taken together these data provide novel evidence for parallel dorsal and ventral streams within the rostro-caudal hierarchical organization of the PFC. FNIRS-based analyses of directed interactions put forward a new perspective on the functional architecture of the prefrontal hierarchy and complement previous insights from functional magnetic resonance imaging.

Show abstract Selective gene delivery into subtypes of interneurons remains an important challenge in vector development. Adeno-associated virus (AAV) vector particles are especially promising for intracerebral injections. For cell entry, AAV2 particles are supposed to attach to heparan-sulfate proteoglycans (HSPGs) followed by endocytosis via the AAV receptor (AAVR). Here, we assessed engineered AAV particles deficient in HSPG attachment but competent in recognizing the glutamate receptor 4 (GluA4, also known as GluRD or GRIA4) through a displayed GluA4-specific DARPin (designed ankyrin repeat protein). When injected into the mouse brain, histological evaluation revealed that in various regions, more than 90% of the transduced cells were interneurons, mainly of the parvalbumin-positive subtype. Although part of the selectivity was mediated by the DARPin, the chosen spleen focus-forming virus (SFFV) promoter had contributed as well. Further analysis revealed that the DARPin mediated selective attachment to GluA4-positive cells, whereas gene delivery required expression of AAVR. Our data suggest that cell selectivity of AAV particles can be modified rationally and efficiently through DARPins, but expression of the AAV entry receptor remains essential.

Show abstract Objective: Resonant vibrations of implanted structures during an MRI procedure pose a risk to the patient in the form of soft tissue irritation and degradation of the implant. In this study, the mechanical behaviour of implant structures in air, water, and viscoelastic materials was explored. Methods: The static and dynamic transfer functions of various test samples in air, and immersed in both water and hydrogels, were analysed. The laser-based acquisition method allowed for high angular resolution (10 μDeg) and high dynamic range (0 to 6 kHz) measurements. Additional MRI experiments were conducted to investigate the dependence of vibration strength on MR sequence parameters in combination with the obtained transfer functions. Results: The largest forces were found to be in the μN to mN range, which is comparable to forces applied during implantation. Of additional concern was the damping introduced by viscoelastic tissue, which was less than expected, leading to an underdamped system. In contrast to current wisdom, the imaging experiments demonstrated drastically different vibration amplitudes for identical gradient slope but different timing parameters TR, mainly due to resonant amplification. Conclusion: The results showed that a safe, force-free MR procedure depends not only on the gradient slope, but also and more drastically on the choice of secure timing parameters. Significance: These findings delineate design improvements to achieve longevity of implants, and will lead to increased patient safety during MRI. A prudent choice of mechanical characteristics of implanted structures is sufficient to avoid resonant excitation due to mismatched MR sequence parameters.

Show abstract Smiling, laughing, and overt speech production are fundamental to human everyday communication. However, little is known about how the human brain achieves the highly accurate and differentiated control of such orofacial movement during natural conditions. Here, we utilized the high spatiotemporal resolution of subdural recordings to elucidate how human motor cortex is functionally engaged during control of real-life orofacial motor behaviour. For each investigated movement class—lip licking, speech production, laughing and smiling—our findings reveal a characteristic brain activity pattern within the mouth motor cortex with both spatial segregation and overlap between classes. Our findings thus show that motor cortex relies on sparse and action-specific activation during real-life orofacial behaviour, apparently organized in distinct but overlapping subareas that control different types of natural orofacial movements.

Show abstract Transcranial direct current stimulation (tDCS) is a variant of noninvasive neuromodulation, which promises treatment for brain diseases like major depressive disorder. In experiments, long-lasting aftereffects were observed, suggesting that persistent plastic changes are induced. The mechanism underlying the emergence of lasting aftereffects, however, remains elusive. Here we propose a model, which assumes that tDCS triggers a homeostatic response of the network involving growth and decay of synapses. The cortical tissue exposed to tDCS is conceived as a recurrent network of excitatory and inhibitory neurons, with synapses subject to homeostatically regulated structural plasticity. We systematically tested various aspects of stimulation, including electrode size and montage, as well as stimulation intensity and duration. Our results suggest that transcranial stimulation perturbs the homeostatic equilibrium and leads to a pronounced growth response of the network. The stimulated population eventually eliminates excitatory synapses with the unstimulated population, and new synapses among stimulated neurons are grown to form a cell assembly. Strong focal stimulation tends to enhance the connectivity within new cell assemblies, and repetitive stimulation with well-chosen duty cycles can increase the impact of stimulation even further. One long-term goal of our work is to help in optimizing the use of tDCS in clinical applications.

Show abstract Transcranial direct current stimulation (tDCS) is a variant of noninvasive neuromodulation, which promises treatment for brain diseases like major depressive disorder. In experiments, long-lasting aftereffects were observed, suggesting that persistent plastic changes are induced. The mechanism underlying the emergence of lasting aftereffects, however, remains elusive. Here we propose a model, which assumes that tDCS triggers a homeostatic response of the network involving growth and decay of synapses. The cortical tissue exposed to tDCS is conceived as a recurrent network of excitatory and inhibitory neurons, with synapses subject to homeostatically regulated structural plasticity. We systematically tested various aspects of stimulation, including electrode size and montage, as well as stimulation intensity and duration. Our results suggest that transcranial stimulation perturbs the homeostatic equilibrium and leads to a pronounced growth response of the network. The stimulated population eventually eliminates excitatory synapses with the unstimulated population, and new synapses among stimulated neurons are grown to form a cell assembly. Strong focal stimulation tends to enhance the connectivity within new cell assemblies, and repetitive stimulation with well-chosen duty cycles can increase the impact of stimulation even further. One long-term goal of our work is to help in optimizing the use of tDCS in clinical applications.

Show abstract Behavioral deficits after stroke like apraxia can be related to structural lesions and to a functional state of the underlying network - three factors, reciprocally influencing each other. Combining lesion data, behavioral performance and passive functional activation of the network-of-interest, this study aims to disentangle those mutual influences and to identify 1) activation patterns associated with the presence or absence of acute apraxia in tool-associated actions and 2) the specific impact of lesion location on those activation patterns. Brain activity of 48 patients (63.31 ± 13.68 years, 35 male) was assessed in a fMRI paradigm with observation of tool-related actions during the acute phase after first-ever left-hemispheric stroke (4.83 ± 2.04 days). Behavioral assessment of apraxia in tool-related tasks was obtained independently. Brain activation was compared between patients versus healthy controls and between patient with versus without apraxia. Interaction effects of lesion location (frontal vs parietal) and behavioral performance (apraxia vs no apraxia) were assessed in a 2 × 2 factorial design. Action observation activated the ventro-dorsal parts of the network for cognitive motor function; activation was globally downregulated after stroke. Apraxic compared to non-apraxic patients showed relatively increased activity in bilateral posterior middle temporal gyrus and middle frontal gyrus/superior frontal sulcus. Altered activation occurred in regions for tool-related cognition, corroborating known functions of the ventro-dorsal and ventral streams for praxis, and comprised domain-general areas, functionally related to cognitive control. The interaction analyses revealed different levels of activation in the left anterior middle temporal gyrus in the ventral stream in apraxic patients with frontal compared to parietal lesions, suggesting a modulation of network activation in relation to behavioral performance and lesion location as separate factors. By detecting apraxia-specific activation patterns modulated by lesion location, this study underlines the necessity to combine structural lesion information, behavioral parameters and functional activation to comprehensively examine cognitive functions in acute stroke patients.

Show abstract Background: Following an amputation, the human postural control system develops neuromuscular adaptations to regain an effective postural control. We investigated the compensatory mechanisms behind these adaptations and how sensorimotor integration is affected after a lower-limb transfemoral amputation. METHODS: Center of pressure (CoP) data of 12 unilateral transfemoral amputees and 12 age-matched able-bodied subjects were recorded during quiet standing with eyes open (EO) and closed (EC). CoP adjustments under each leg were recorded to study their contribution to posture control. The spatial structure of the CoP displacements was characterized by measuring the mean distance, the mean velocity of the CoP adjustments, and the sway area. The Entropic Half-Life (EnHL) quantifies the temporal structure of the CoP adjustments and was used to infer disrupted sensory feedback loops in amputees. We expanded the analysis with measures of weight-bearing imbalance and asymmetry, and with two standardized balance assessments, the Berg Balance Scale (BBS) and Timed Up-and-Go (TUG). RESULTS: There was no difference in the EnHL values of amputees and controls when combining the contributions of both limbs (p = 0.754). However, amputees presented significant differences between the EnHL values of the intact and prosthetic limb (p <  0.001). Suppressing vision reduced the EnHL values of the intact (p = 0.001) and both legs (p = 0.028), but not in controls. Vision feedback in amputees also had a significant effect (increase) on the mean CoP distance (p <  0.001), CoP velocity (p <  0.001) and sway area (p = 0.007). Amputees presented an asymmetrical stance. The EnHL values of the intact limb in amputees were positively correlated to the BBS scores (EO: ρ = 0.43, EC: ρ = 0.44) and negatively correlated to the TUG times (EO: ρ = - 0.59, EC: ρ = - 0.69). CONCLUSION: These results suggest that besides the asymmetry in load distribution, there exist neuromuscular adaptations after an amputation, possibly related to the loss of sensory feedback and an altered sensorimotor integration. The EnHL values suggest that the somatosensory system predominates in the control of the intact leg. Further, suppressing the visual system caused instability in amputees, but had a minimal impact on the CoP dynamics of controls. These findings points toward the importance of providing somatosensory feedback in lower-limb prosthesis to reestablish a normal postural control.

Show abstract Objective. Electrochemical microsensors based on noble metals can give essential information on their microenvironment with high spatio-temporal resolution. However, most advanced chemo- and biosensors lack the long-term stability for physiological monitoring of brain tissue beyond an acute application. Noble metal electrodes are widely used as neural interfaces, particularly for stimulating in the central nervous system. Our goal was to recruit already deployed, unmodified noble metal electrodes (Pt, Pt/Ir) as in situ chemical sensors. Approach. With advanced electrochemical sensor methods, we investigated electrode surface processes, oxidizable species and oxygen as an indicator for tissue mass transport. We developed a unique, multi-step, amperometric/potentiometric sensing procedure derived from the investigation of Pt surface processes by chronocoulometry providing fundamental characterization of the electrode itself. Main results. The resulting electrochemical protocol preconditions the electrode, measures oxidizable and reducible species, and the open circuit potential (OCP). A linear, stable sensor performance was demonstrated, also in the presence of proteins, validating signal stability of our cyclic protocol in complex environments. We investigated our sensor protocol with microelectrodes on custom Pt/Ir-wire tetrodes by in vivo measurements in the rat brain for up to four weeks. Results showed that catalytic activity of the electrode is lost over time, but our protocol is repeatedly able to both quantify and restore electrode sensitivity in vivo. Significance. Our approach is highly relevant because it can be applied to any existing Pt electrode. Current methods to assess the brain/electrode microenvironment mainly rely on imaging techniques, histology and analysis of explanted devices, which are often end-point methods. Our procedure delivers online and time-transient information on the chemical microenvironment directly at the electrode/tissue interface of neural implants, gives new insight into the charge transfer processes, and delivers information on the state of the electrode itself addressing long-term electrode degradation.

Show abstract Objective. Electrochemical microsensors based on noble metals can give essential information on their microenvironment with high spatio‐temporal resolution. However, most advanced chemo‐ and biosensors lack the long‐term stability for physiological monitoring of brain tissue beyond an acute application. Noble metal electrodes are widely used as neural interfaces, particularly for stimulating in the central nervous system. Our goal was to recruit already deployed, unmodified noble metal electrodes (Pt, Pt/Ir) as in situ chemical sensors. Approach. With advanced electrochemical sensor methods, we investigated electrode surface processes, oxidizable species and oxygen as an indicator for tissue mass transport. We developed a unique, multi‐step, amperometric/potentiometric sensing procedure derived from the investigation of Pt surface processes by chronocoulometry providing fundamental characterization of the electrode itself. Main results. The resulting electrochemical protocol preconditions the electrode, measures oxidizable and reducible species, and the open circuit potential. A linear, stable sensor performance was demonstrated, also in the presence of proteins, validating signal stability of our cyclic protocol in complex environments. We investigated our sensor protocol with microelectrodes on custom Pt/Ir‐wire tetrodes by in vivo measurements in the rat brain for up to four weeks. Results showed that catalytic activity of the electrode is lost over time, but our protocol is repeatedly able to both quantify and restore electrode sensitivity in vivo. Significance. Our approach is highly relevant because it can be applied to any existing Pt electrode. Current methods to assess the brain/electrode microenvironment mainly rely on imaging techniques, histology and analysis of explanted devices, which are often end‐point methods. Our procedure delivers online and time‐transient information on the chemical microenvironment directly at the electrode/tissue interface of neural implants, gives new insight into the charge transfer processes, and delivers information on the state of the electrode itself addressing long‐term electrode degradation.

Show abstract Optogenetics has revolutionized neurobiological research by allowing to disentangle intricate neuronal circuits at a spatio-temporal precision unmatched by other techniques. Here, we review current advances of optogenetic applications in mammals, especially focusing on freely moving animals. State-of-the-art strategies allow the targeted expression of opsins in neuronal subpopulations, defined either by genetic cell type or neuronal projection pattern. Optogenetic manipulations of these subpopulations become particularly powerful when combined with behavioral paradigms and neurophysiological readout techniques. Thereby, specific roles can be assigned to identified cells. All-optical approaches with the opportunity to write complex three dimensional patterns into neuronal networks have recently emerged. While clinical implications of the new tool set seem tempting, we emphasize here the role of optogenetics for basic research.

Show abstract BACKGROUND: Intracranial electroencephalography (iEEG) is increasingly used in neuroscientific research. However, the position of the implanted electrodes varies greatly between patients, which makes group analyses particularly difficult. Therefore, an assignment procedure is needed that enables the neuroanatomical information to be obtained for each individual electrode contact. NEW METHOD: Here, we present a MATLAB-based electrode assignment approach for iEEG electrode contacts, implemented in the open-source toolbox ELAS, that allows a hierarchical probabilistic assignment of individual electrode contacts to cytoarchitectonically-defined brain areas. The here presented ELAS consists of two major steps: (I) a pre-assignment to the cerebral lobes and (II) a following probabilistic assignment based on lobe-specific probability maps of the SPM Anatomy Toolbox. RESULTS: We analyzed iEEG data obtained in 14 epilepsy patients with a total of 783 intracranial electrode contacts. The neuroanatomical assignment to cortical brain areas was possible in 72.5% of the electrode contacts that were located on the lateral cortical convexity. COMPARISON WITH EXISTING METHODS: This assignment procedure is to our knowledge the first approach that combines both individual macro-anatomical and cytoarchitectonic probabilistic information. Due to the integration of information about individual anatomical landmarks, incorrect assignments could be avoided in approx. 7% of electrode contacts. CONCLUSION: The present study demonstrates how probabilistic assignment procedures developed for the analysis of neuroimaging data can be adapted to iEEG, which is especially helpful for group analyses. The presented assignment approach is freely available via the open-source toolbox ELAS, including a 3D visualization and a file export for virtual reality setups.

Show abstract Neurons in different layers of sensory cortex generally have different functional properties. But what determines firing rates and tuning properties of neurons in different layers? Orientation selectivity in primary visual cortex (V1) is an interesting case to study these questions. Thalamic projections essentially determine the preferred orientation of neurons that receive direct input. But how is this tuning propagated though layers, and how can selective responses emerge in layers that do not have direct access to the thalamus? Here we combine numerical simulations with mathematical analyses to address this problem. We find that a large-scale network, which just accounts for experimentally measured layer and cell-type specific connection probabilities, yields firing rates and orientation selectivities matching electrophysiological recordings in rodent V1 surprisingly well. Further analysis, however, is complicated by the fact that neuronal responses emerge in a dynamic fashion and cannot be directly inferred from static neuroanatomy, as some connections tend to have unintuitive effects due to recurrent interactions and strong feedback loops. These emergent phenomena can be understood by linearizing and coarse-graining. In fact, we were able to derive a low-dimensional linear dynamical system effectively describing stimulus-driven activity layer by layer. This low-dimensional system explains layer-specific firing rates and orientation tuning by accounting for the different gain factors of the aggregate system. Our theory can also be used to design novel optogenetic stimulation experiments, thus facilitating further exploration of the interplay between connectivity and function.

Show abstract Neural oscillations are increasingly interpreted as transient bursts, yet a method to measure these short-lived events in real-time is missing. Here we present a real-time data analysis system, capable to detect short and narrowband bursts, and demonstrate its usefulness for volitional increase of beta-band burst-rate in rats. This neurofeedback-training induced changes in overall oscillatory power, and bursts could be decoded from the movement of the rats, thus enabling future investigation of the role of oscillatory bursts.

Show abstract Learning to reliably perceive and understand the scene is an integral enabler for robots to operate in the real-world. This problem is inherently challenging due to the multitude of object types as well as appearance changes caused by varying illumination and weather conditions. Leveraging complementary modalities can enable learning of semantically richer representations that are resilient to such perturbations. Despite the tremendous progress in recent years, most multimodal convolutional neural network approaches directly concatenate feature maps from individual modality streams rendering the model incapable of focusing only on the relevant complementary information for fusion. To address this limitation, we propose a mutimodal semantic segmentation framework that dynamically adapts the fusion of modality-specific features while being sensitive to the object category, spatial location and scene context in a self-supervised manner. Specifically, we propose an architecture consisting of two modality-specific encoder streams that fuse intermediate encoder representations into a single decoder using our proposed self-supervised model adaptation fusion mechanism which optimally combines complementary features. As intermediate representations are not aligned across modalities, we introduce an attention scheme for better correlation. In addition, we propose a computationally efficient unimodal segmentation architecture termed AdapNet++ that incorporates a new encoder with multiscale residual units and an efficient atrous spatial pyramid pooling that has a larger effective receptive field with more than 10× fewer parameters, complemented with a strong decoder with a multi-resolution supervision scheme that recovers high-resolution details. Comprehensive empirical evaluations on Cityscapes, Synthia, SUN RGB-D, ScanNet and Freiburg Forest benchmarks demonstrate that both our unimodal and multimodal architectures achieve state-of-the-art performance while simultaneously being efficient in terms of parameters and inference time as well as demonstrating substantial robustness in adverse perceptual conditions.

Show abstract Conventional leg prostheses do not convey sensory information about motion or interaction with the ground to above-knee amputees, thereby reducing confidence and walking speed in the users that is associated with high mental and physical fatigue1,2,3,4. The lack of physiological feedback from the remaining extremity to the brain also contributes to the generation of phantom limb pain from the missing leg5,6. To determine whether neural sensory feedback restoration addresses these issues, we conducted a study with two transfemoral amputees, implanted with four intraneural stimulation electrodes7 in the remaining tibial nerve (ClinicalTrials.gov identifier NCT03350061). Participants were evaluated while using a neuroprosthetic device consisting of a prosthetic leg equipped with foot and knee sensors. These sensors drive neural stimulation, which elicits sensations of knee motion and the sole of the foot touching the ground. We found that walking speed and self-reported confidence increased while mental and physical fatigue decreased for both participants during neural sensory feedback compared to the no stimulation trials. Furthermore, participants exhibited reduced phantom limb pain with neural sensory feedback. The results from these proof-of-concept cases provide the rationale for larger population studies investigating the clinical utility of neuroprostheses that restore sensory feedback.

Show abstract OBJECTIVE: Hand amputation is a highly disabling event, which significantly affects quality of life. An effective hand replacement can be achieved if the user, in addition to motor functions, is provided with the sensations that are naturally perceived while grasping and moving. Intraneural peripheral electrodes have shown promising results toward the restoration of the sense of touch. However, the long-term usability and clinical relevance of intraneural sensory feedback have not yet been clearly demonstrated. METHODS: To this aim, we performed a 6-month clinical study with 3 transradial amputees who received implants of transverse intrafascicular multichannel electrodes (TIMEs) in their median and ulnar nerves. After calibration, electrical stimulation was delivered through the TIMEs connected to artificial sensors in the digits of a prosthesis to generate sensory feedback, which was then used by the subjects while performing different grasping tasks. RESULTS: All subjects, notwithstanding their important clinical differences, reported stimulation-induced sensations from the phantom hand for the whole duration of the trial. They also successfully integrated the sensory feedback into their motor control strategies while performing experimental tests simulating tasks of real life (with and without the support of vision). Finally, they reported a decrement of their phantom limb pain and a general improvement in mood state. INTERPRETATION: The promising results achieved with all subjects show the feasibility of the use of intraneural stimulation in clinical settings. ANN NEUROL 2019;85:137-154.

Show abstract Spatio-temporally controlled drug release based on conducting polymer films offers a powerful technology to improve the tissue integration for implantable neuroprobes. We here explore the release efficiency of such systems in order to improve the understanding of the release mechanism and allow for optimized implementation of this technology into future drug release applications. By exposing drug loaded PEDOT coatings of different thicknesses to a multitude of release signals, along with optimizing the steps during the polymer synthesis, we could identify a highly reproducible electrostatically controlled drug release next to a slow diffusion driven release component. The release efficiency was moreover observed to be higher for a cyclic voltammetry signal in comparison to release driven by a constant potential. Biphasic current pulses, as used during neural stimulation, did not allow for long enough diffusion times to yield efficient active drug expulsion from the polymer films. A quantitative analysis could confirm an overall linear dependency between drug release and film thickness. The amount of drug released in response to the trigger signals was however not linearly correlated with the amount of charge applied. By combining these findings we could develop a model which accurately describes the drug release mechanism from a PEDOT film. The proposed model thereby points the way for how actively controlled, and diffusion related, release can be tuned for obtaining delivery dynamics tailored to specific applications.

Show abstract In this letter, we deal with the reality gap from a novel perspective, targeting transferring deep reinforcement learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, we seek to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home. We propose this as a lightweight, flexible, and efficient solution for visual control, as first, no extra transfer steps are required during the expensive training of DRL agents in simulation; second, the trained DRL agents will not be constrained to being deployable in only one specific real-world environment; and third, the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a simple yet effective shift loss that is agnostic to the downstream task, to constrain the consistency between subsequent frames which is important for consistent policy outputs. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in indoor and outdoor robotics experiments.

Show abstract Future‐oriented directions in neural interface technologies point towards the development of multimodal devices that combine different functionalities such as neural stimulation, neurotransmitter sensing, and drug release within one platform. Conducting polymer hydrogels (CPHs) are suggested as materials for the coating of standard metal electrodes to add functionalities such as local delivery of therapeutic drugs. However, to make such coatings truly useful for multimodal devices, it is necessary to develop process technologies that allow the micropatterning of CPHs onto selected electrode sites. In this study, a wafer‐scale fabrication procedure is presented, which is used to coat the CPH, based on the hydrogel P(DMAA‐co‐5%MABP‐co‐2,5%SSNa) and the conducting polymer poly(3,4‐ethylenedioxythiophene) (PEDOT), onto flexible neural probes. The resulting material has favorable properties for the generation of recording electrodes and in addition offers a convenient platform for biofunctionalization. By controlling the PEDOT content within the hydrogel matrix, charge injection limits of up to 3.7 mC cm−2 are obtained. Long‐term stability is tested by immersing coated samples in phosphate‐buffered saline solution at 37 °C for 1 year. Non‐cytotoxicity of the coatings is confirmed with a direct cell culture test using a fluorescent neuroblastoma cell line.

Show abstract The neuromodulator dopamine plays a key role in motivation, reward-related learning and normal motor function. The different affinity of striatal D1 and D2 dopamine receptor types has been argued to constrain the D1 and D2 signalling pathways to phasic and tonic dopamine signals, respectively. However, this view assumes that dopamine receptor kinetics are instantaneous so that the time courses of changes in dopamine concentration and changes in receptor occupation are basically identical. Here we developed a neurochemical model of dopamine receptor binding taking into account the different kinetics and abundance of D1 and D2 receptors in the striatum. Testing a large range of behaviorally-relevant dopamine signals, we found that the D1 and D2 dopamine receptor populations responded very similarly to tonic and phasic dopamine signals. Furthermore, due to slow unbinding rates, both receptor populations integrated dopamine signals over a timescale of minutes. Our model provides a description of how physiological dopamine signals translate into changes in dopamine receptor occupation in the striatum, and explains why dopamine ramps are an effective signal to occupy dopamine receptors. Overall, our model points to the importance of taking into account receptor kinetics for functional considerations of dopamine signalling. Significance statement Current models of basal ganglia function are often based on a distinction of two types of dopamine receptors, D1 and D2, with low and high affinity, respectively. Thereby, phasic dopamine signals are believed to mostly affect striatal neurons with D1 receptors, and tonic dopamine signals are believed to mostly affect striatal neurons with D2 receptors. This view does not take into account the rates for the binding and unbinding of dopamine to D1 and D2 receptors. By incorporating these kinetics into a computational model we show that D1 and D2 receptors both respond to phasic and tonic dopamine signals. This has implications for the processing of reward-related and motivational signals in the basal ganglia.

Show abstract Potentiometric oxygen monitoring using platinum as the electrode material was enabled by the combination of conventional potentiometry with active prepolarization protocols, what we call active potentiometry. The obtained logarithmic transfer function is well-suited for the measurement of dissolved oxygen in biomedical applications, as the physiological oxygen concentration typically varies over several decades. We describe the application of active potentiometry in phosphate buffered salt solution at different pH and ion strength. Sensitivity was in the range of 60 mV/dec oxygen concentration; the transfer function deviated from logarithmic behavior for smaller oxygen concentration and higher ion strength of the electrolyte. Long-term stability was demonstrated for 60 h. Based on these measurement results and additional cyclic voltammetry investigations a model is discussed to explain the potential forming mechanism. The described method of active potentiometry is applicable to many different potentiometric sensors possibly enhancing sensitivity or selectivity for a specific parameter.

Show abstract Modern deep learning methods are very sensitive to many hyperparameters, and, due to the long training times of state-of-the-art models, vanilla Bayesian hyperparameter optimization is typically computationally infeasible. On the other hand, bandit-based configuration evaluation approaches based on random search lack guidance and do not converge to the best configurations as quickly. Here, we propose to combine the benefits of both Bayesian optimization and bandit-based methods, in order to achieve the best of both worlds: strong anytime performance and fast convergence to optimal configurations. We propose a new practical state-of-the-art hyperparameter optimization method, which consistently outperforms both Bayesian optimization and Hyperband on a wide range of problem types, including high-dimensional toy functions, support vector machines, feed-forward neural networks, Bayesian neural networks, deep reinforcement learning, and convolutional neural networks. Our method is robust and versatile, while at the same time being conceptually simple and easy to implement.

Show abstract This paper reports on 1 the development, characterization, and validation of neural probes serving the growing need of neuroscience for miniaturized tools enabling simultaneous high-resolution recording of neural activity in multiple brain areas. The probes consist of a needle-shaped shaft with a crosssection of 100 × 50 μm2 and a length of 10 or 5 mm emerging from a base with dimensions of only 0.55×1.8 mm2. The shafts carry 1600 and 800 recording sites, respectively, grouped into 50 (respectively 25) blocks of 4 × 8 electrodes with an area of 17 × 17 μm2 each, a pitch of 20 μm, and an electrode-to electrode spacing of 3 μm. The probes are fabricated using a commercial 0.18 μm CMOS process followed by dedicated metallization, passivation, and microfabrication steps. Neural signals are accessible through 32 analog output channels via a hierarchical digital addressing scheme implementing an advanced electronic depth control concept giving the option of multiple scanning modes and offering a switching time of 416 μs at a clock frequency of 1 MHz. All output channels are shielded against each other, whereby crosstalk between neighboring channels is measured to be −58 dB at 1 kHz. Absolute impedance values at 1 kHz of single IrOx and Pt electrodes are 230 ± 38 kOhm and 2.2 ± 0.3 MOhm, respectively. In vivo recordings taking advantage of the new addressing concept for high-resolution recordings from multiple brain regions were successfully performed in anesthetized rats.

Show abstract OBJECTIVE: Rolandic epilepsy (RE) is characterized by typical interictal-electroencephalogram (EEG) patterns mainly localized in centrotemporal and parietooccipital areas. An aberrant intrinsic organization of the default mode network (DMN) due to repeated disturbances from spike-generating areas may be able to account for specific cognitive deficits and behavioral problems in RE. The aim of the present study was to investigate cognitive development (CD) and socioemotional development (SED) in patients with RE during active disease in relation to DMN connectivity and network topology. METHODS: In 10 children with RE and active EEG, CD was assessed using the Wechsler Intelligence Scale for Children-IV (WISC-IV); SED was assessed using the Funf-Faktoren-Fragebogen fur Kinder (FFFK), a Big-Five inventory for the assessment of personality traits in children. Functional connectivity (FC) in the DMN was determined from a 15-minute resting state functional magnetic resonance imaging (fMRI), and network properties were calculated using standard graph-theoretical measures. RESULTS: More severe deficits of verbal abilities tended to be associated with an earlier age at epilepsy onset, but were not directly related to the number of seizures and disease duration. Nonetheless, at the network level, disease duration was associated with alterations of the efficiency and centrality of parietal network nodes and midline structures. Particularly, centrality of the left inferior parietal lobe (IPL) was found to be linked with CD. Reduced centrality of the left IPL and alterations supporting a rather segregated processing within DMN's subsystems was associated with a more favorable CD. A more complicated SED was associated with high seizure frequency and long disease duration, and revealed links with a less favorable CD. SIGNIFICANCE: An impaired CD and - because of their interrelation - SED might be mediated by a common pathomechanism reflected in an aberrant organization, and thus, a potential functional deficit of the DMN. A functional segregation of (left) parietal network nodes from the DMN and a rather segregated processing mode within the DMN might have positive implications/protective value for CD in patients with RE.

Show abstract Recent studies have shown that direct nerve stimulation can be used to provide sensory feedback to hand amputees. The intensity of the elicited sensations can be modulated using the amplitude or frequency of the injected stimuli. However, a comprehensive comparison of the effects of these two encoding strategies on the amputees’ ability to control a prosthesis has not been performed. In this paper, we assessed the performance of two trans-radial amputees controlling a myoelectric hand prosthesis while receiving grip force sensory feedback encoded using either linear modulation of amplitude (LAM) or linear modulation of frequency (LFM) of direct nerve stimulation (namely, bidirectional prostheses). Both subjects achieved similar and significantly above-chance performance when they were asked to exploit LAM or LFM in different tasks. The feedbacks allowed them to discriminate, during manipulation through the robotic hand, objects of different compliances and shapes or different placements on the prosthesis. Similar high performances were obtained when they were asked to apply different levels of force in a random order on a dynamometer using LAM or LFM. In contrast, only the LAM strategy allowed the subjects to continuously modulate the grip pressure on the dynamometer. Furthermore, when long-lasting trains of stimulation were delivered, LFM strategy generated a very fast adaptation phenomenon in the subjects, which caused them to stop perceiving the restored sensations. Both encoding approaches were perceived as very different from the touch feelings of the healthy limb (natural). These results suggest that the choice of specific sensory feedback encodings can have an effect on user performance while grasping. In addition, our results invite the development of new approaches to provide more natural sensory feelings to the users, which could be addressed by a more biomimetic strategy in the future.

Show abstract Optogenetic tools have opened a rich experimental landscape for understanding neural function and disease. Here, we present the first validation of eight optogenetic constructs driven by recombinant adeno-associated virus (AAV) vectors and a WGA-Cre based dual injection strategy for projection targeting in a widely-used New World primate model, the common squirrel monkey Saimiri sciureus. We observed opsin expression around the local injection site and in axonal projections to downstream regions, as well as transduction to thalamic neurons, resembling expression patterns observed in macaques. Optical stimulation drove strong, reliable excitatory responses in local neural populations for two depolarizing opsins in anesthetized monkeys. Finally, we observed continued, healthy opsin expression for at least one year. These data suggest that optogenetic tools can be readily applied in squirrel monkeys, an important first step in enabling precise, targeted manipulation of neural circuits in these highly trainable, cognitively sophisticated animals. In conjunction with similar approaches in macaques and marmosets, optogenetic manipulation of neural circuits in squirrel monkeys will provide functional, comparative insights into neural circuits which subserve dextrous motor control as well as other adaptive behaviors across the primate lineage. Additionally, development of these tools in squirrel monkeys, a well-established model system for several human neurological diseases, can aid in identifying novel treatment strategies.

Show abstract Imitation of tool-use gestures (transitive; e.g., hammering) and communicative emblems (intransitive; e.g., waving goodbye) is frequently impaired after left-hemispheric lesions. We aimed 1) to identify lesions related to deficient transitive or intransitive gestures, 2) to delineate regions associated with distinct error types (e.g., hand configuration, kinematics), and 3) to compare imitation to previous data on pantomimed and actual tool use. Of note, 156 patients (64.3 +/- 14.6 years; 56 female) with first-ever left-hemispheric ischemic stroke were prospectively examined 4.8 +/- 2.0 days after symptom onset. Lesions were delineated on magnetic resonance imaging scans for voxel-based lesion-symptom mapping. First, while inferior-parietal lesions affected both gesture types, specific associations emerged between intransitive gesture deficits and anterior temporal damage and between transitive gesture deficits and premotor and occipito-parietal lesions. Second, impaired hand configurations were related to anterior intraparietal damage, hand/wrist-orientation errors to premotor lesions, and kinematic errors to inferior-parietal/occipito-temporal lesions. Third, premotor lesions impacted more on transitive imitation compared with actual tool use, pantomimed and actual tool use were more susceptible to lesioned insular cortex and subjacent white matter. In summary, transitive and intransitive gestures differentially rely on ventro-dorsal and ventral streams due to higher demands on temporo-spatial processing (transitive) or stronger reliance on semantic information (intransitive), respectively.

Show abstract Thin-film neural devices are an appealing alternative to traditional implants, although their chronic stability remains matter of investigation. In this study, a chronically stable class of thin-film devices for electrocorticography is manufactured incorporating silicon carbide and diamond-like carbon as adhesion promoters between glassy carbon (GC) electrodes and polyimide and between GC and platinum traces. The devices are aged in three solutions—phosphate-buffered saline (PBS), 30 × 10−3 and 150 × 10−3m H2O2/PBS—and stressed using cyclic voltammetry (2500 cycles) and 20 million biphasic pulses. Electrochemical impedance spectroscopy (EIS) and image analysis are performed to detect eventual changes of the electrodes morphology. Results demonstrate that the devices are able to undergo chemically induced oxidative stress and electrical stimulation without failing but actually improving their electrical performance until a steady state is reached. Additionally, cell viability tests are carried out to verify the noncytotoxicity of the materials, before chronically implanting them into rat models. The behavior of the GC electrodes in vivo is monitored through EIS and sensorimotor evoked potential recordings which confirm that, with GC being activated, impedance lowers and quality of recorded signal improves. Histological analysis of the brain tissue is performed and shows no sign of severe immune reaction to the implant.

Show abstract Microglia are yolk sac-derived macrophages residing in the parenchyma of brain and spinal cord, where they interact with neurons and other glial. After different conditioning paradigms and bone marrow (BM) or hematopoietic stem cell (HSC) transplantation, graft-derived cells seed the brain and persistently contribute to the parenchymal brain macrophage compartment. Here we establish that graft-derived macrophages acquire, over time, microglia characteristics, including ramified morphology, longevity, radio-resistance and clonal expansion. However, even after prolonged CNS residence, transcriptomes and chromatin accessibility landscapes of engrafted, BM-derived macrophages remain distinct from yolk sac-derived host microglia. Furthermore, engrafted BM-derived cells display discrete responses to peripheral endotoxin challenge, as compared to host microglia. In human HSC transplant recipients, engrafted cells also remain distinct from host microglia, extending our finding to clinical settings. Collectively, our data emphasize the molecular and functional heterogeneity of parenchymal brain macrophages and highlight potential clinical implications for HSC gene therapies aimed to ameliorate lysosomal storage disorders, microgliopathies or general monogenic immuno-deficiencies.

Show abstract The development of highly accelerated fMRI acquisition techniques has led to novel possibilities to monitor cerebral activity non-invasively and with unprecedented temporal resolutions. With the emergence of dynamic connectivity and its ability to provide a much richer characterization of brain function compared to static measures, fast fMRI may yet play a crucial role in tracking dynamically varying networks. In spite of the dominance of slow hemodynamic contributions to the BOLD signal, high temporal sampling rates nevertheless improve the measurement of physiological noise, yielding an exceptional sensitivity for the detection of periods of transient connectivity at time scales of a few tens of seconds. There is also evidence that relevant BOLD fluctuations are detectable at high frequencies, implying that the benefits of fast fMRI extend beyond the ability to sample nuisance confounds. Here we review the latest technological advancements that have established fast fMRI as an effective acquisition technique, as well as its current and future implications on the analysis of dynamic networks.

Show abstract Glassy carbon (GC) has high potential to serve as a biomaterial in neural applications because it is biocompatible, electrochemically inert and can be incorporated in polyimide-based implantable devices. Miniaturization and applicability of GC is, however, thought to be partially limited by its electrical conductivity. For this study, ultra-conformable polyimide-based electrocorticography (ECoG) devices with different-diameter GC electrodes were fabricated and tested in vitro and in rat models. For achieving conformability to the rat brain, polyimide was patterned in a finger-like shape and its thickness was set to 8 µm. To investigate different electrode sizes, each ECoG device was assigned electrodes with diameters of 50, 100, 200 and 300 µm. They were electrochemically characterized and subjected to 10 million biphasic pulses—for achieving a steady-state—and to X-ray photoelectron spectroscopy, for examining their elemental composition. The electrodes were then implanted epidurally to evaluate the ability of each diameter to detect neural activity. Results show that their performance at low frequencies (up to 300 Hz) depends on the distance from the signal source rather than on the electrode diameter, while at high frequencies (above 200 Hz) small electrodes have higher background noises than large ones, unless they are coated with poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS).

Show abstract Neural interfaces for neuroscientific research are nowadays mainly manufactured using standard microsystems engineering technologies which are incompatible with the integration of carbon as electrode material. In this work, we investigate a new method to fabricate graphitic carbon electrode arrays on flexible substrates. The devices were manufactured using infrared nanosecond laser technology for both patterning all components and carbonizing the electrode sites. Two laser pulse repetition frequencies were used for carbonization with the aim of finding the optimum. Prototypes of the devices were evaluated in vitro in 30 mM hydrogen peroxide to mimic the post-surgery oxidative environment. The electrodes were subjected to 10 million biphasic pulses (39.5 μC/cm2) to measure their stability under electrical stress. Their biosensing capabilities were evaluated in different concentrations of dopamine in phosphate buffered saline solution. Raman spectroscopy and x-ray photoelectron spectroscopy analysis show that the atomic percentage of graphitic carbon in the manufactured electrodes reaches the remarkable value of 75%. Results prove that the infrared nanosecond laser yields activated graphite electrodes that are conductive, non-cytotoxic and electrochemically inert. Their comprehensive assessment indicates that our laser-induced carbon electrodes are suitable for future transfer into in vivo studies, including neural recordings, stimulation and neurotransmitters detection.

Show abstract Abstract: Optogenetics opened not only new exciting opportunities to interrogate the nervous system but also requires adequate probes to facilitate these wishes. Therefore, a multidisciplinary effort is essential to match these technical opportunities with biological needs in order to establish a stable and functional material-tissue interface. This in turn can address an optical intervention of the genetically modified, light sensitive cells in the nervous system and recording of electrical signals from single cells and neuronal networks that result in behavioral changes. In this review, we present the state of the art of optoelectronic probes and assess advantages and challenges of the different design approaches. At first, we discuss mechanisms and processes at the material-tissue interface that influence the performance of optoelectronic probes in acute and chronic implantations. We classify optoelectronic probes by their property of delivering light to the tissue: by waveguides or by integrated light sources at the sites of intervention. Both approaches are discussed with respect to size, spatial resolution, opportunity to integrate electrodes for electrical recording and potential interactions with the target tissue. At last, we assess translational aspects of the state of the art. Long-term stability of probes and the opportunity to integrate them into fully implantable, wireless systems are a prerequisite for chronic applications and a transfer from fundamental neuroscientific studies into treatment options for diseases and clinical trials. Highlights: - Miniaturization technologies enable multichannel optoelectronic neural probes. - No external laser is needed when light sources are directly integrated on the probes. - Connectors to recording equipment still limit further miniaturization. - Wireless systems with integrated multiplexers and amplifiers can replace connectors. - Longevity of probes is mandatory for chronic implantation.

Show abstract Deep brain stimulation (DBS) is a successful medical therapy for many treatment resistant neuropsychiatric disorders such as movement disorders; e.g., Parkinson’s disease, Tremor, and dystonia. Moreover, DBS is becoming more and more appealing for a rapidly growing number of patients with other neuropsychiatric diseases such as depression and obsessive compulsive disorder. In spite of the promising outcomes, the current clinical hardware used in DBS does not match the technological standards of other medical applications and as a result could possibly lead to side effects such as high energy consumption and others. By implementing more advanced DBS devices, in fact, many of these limitations could be overcome. For example, a higher channels count and smaller electrode sites could allow more focal and tailored stimulation. In addition, new materials, like carbon for example, could be incorporated into the probes to enable adaptive stimulation protocols by biosensing neurotransmitters in the brain. Updating the current clinical DBS technology adequately requires combining the most recent technological advances in the field of neural engineering. Here, a novel hybrid multimodal DBS probe with glassy carbon microelectrodes on a polyimide thin-film device assembled on a silicon rubber tubing is introduced. The glassy carbon interface enables neurotransmitter detection using fast scan cyclic voltammetry and electrophysiological recordings while simultaneously performing electrical stimulation. Additionally, the presented DBS technology shows no imaging artefacts in magnetic resonance imaging. Thus, we present a promising new tool that might lead to a better fundamental understanding of the underlying mechanism of DBS while simultaneously paving our way towards better treatments.

Show abstract Stereo-electroencephalography depth electrodes, regularly implanted into drug-resistant patients with focal epilepsy to localize the epileptic focus, have a low channel count (6-12 macro- or microelectrodes), limited spatial resolution (0.5-1 cm) and large contact area of the recording sites (~mm2). Thus, they are not suited for high-density local field potential and multiunit recordings. In this paper, we evaluated the long-term electrophysiological recording performance and histocompatibility of a neural interface consisting of 32 microelectrodes providing a physical shape similar to clinical devices. The cylindrically-shaped depth probes made of polyimide (PI) were chronically implanted for 13 weeks into the brain of rats, while cortical or thalamic activity (local field potentials, single-unit and multi-unit activity) was recorded regularly to monitor the temporal change of several features of the electrophysiological performance. To examine the tissue reaction around the probe, neuron-selective and astroglia-selective immunostaining methods were applied. Stable single-unit and multi-unit activity were recorded for several weeks with the implanted depth probes and a weak or moderate tissue reaction was found around the probe track. Our data on biocompatibility presented here and in vivo experiments in non-human primates provide a strong indication that this type of neural probe can be applied in stereo-electroencephalography recordings of up to 2 weeks in humans targeting the localization of epileptic foci providing an increased spatial resolution and the ability to monitor local field potentials and neuronal spiking activity.

Show abstract Vor einigen Jahren waren bunte Aufnahmen unserer grauen Zellen überaus populär. Prominente Vertreter der Neurowissenschaften erweckten mit bildgebenden Verfahren den Eindruck, als ob unser Gehirn kurz vor der Enträtselung stünde. Auf einen solchen Durchbruch warten manche Veränderungsmanager schon sehnsüchtig. Inzwischen ist es um diesen Hype deutlich ruhiger geworden und von den einstigen Protagonisten ist nicht mehr viel zu hören. Es ist also Zeit für eine Bestandsaufnahme. Chefredakteur Martin Claßen hat mit einem der führenden deutschen Neurowissenschaftler, Stefan Rotter, über die Möglichkeiten und Grenzen seines Metiers und die Konsequenzen für das Change Management gesprochen.

Show abstract Patients suffering from neuronal degenerative diseases are increasingly being equipped with neural implants to treat symptoms or restore functions and increase their quality of life. Magnetic resonance imaging (MRI) would be the modality of choice for diagnosis and compulsory post-operative monitoring of such patients. However, interactions between the MR environment and implants pose severe health risks to the patient. Nevertheless, neural implant recipients regularly underwent MRI examinations, and adverse events were reported rarely. This should not imply that the procedures are safe. More than 300.000 cochlear implant recipients are excluded from MRI unless the indication outweighs excruciating pain. For 75.000 DBS recipients quite the opposite holds: MRI is considered essential part of the implantation procedure and some medical centres deliberately exceed safety regulations which they referred to as crucially impractical. MRI related permanent neurological dysfunctions in DBS recipients have occurred in the past when manufacturer recommendations were exceeded. Within the last decades extensive effort has been invested to identify, characterise, and quantify the occurring interactions. Today we are far from a satisfying solution to achieve a safe and beneficial MR procedure for all implant recipients. To contribute, we intend to raise awareness of a growing concern and want to summon the community to stop absurdities and instead improve the situation for the increasing number of patients. Therefore, we review implant safety in the MRI literature from an engineering point of view, with a focus on cochlear and DBS implants as success stories in clinical practice. We briefly explain fundamental phenomena which can lead to patient harm, and point out breakthroughs and errors made. We end with conclusions and strategies to avoid future implants from being contraindicated to MR examinations. We believe that implant recipients should enter MRI, but before doing so, we should make sure that the procedure is reasonable.

Show abstract Social robots can help meet the growing need for rehabilitation assistance; measures for creating and maintaining trust in human-robot interactions should be priorities when designing social robots for rehabilitation.

Show abstract In fMRI, functional connectivity is conventionally characterized by correlations between fMRI time series, which are intrinsically undirected measures of connectivity. Yet, some information about the directionality of network connections can nevertheless be extracted from the matrix of pairwise temporal correlations between all considered time series, when expressed in the frequency-domain as a cross-spectral density matrix. Using a sparsity prior, it then becomes possible to determine a unique directed network topology that best explains the observed undirected correlations, without having to rely on temporal precedence relationships that may not be valid in fMRI. Applying this method on simulated data with 100 nodes yielded excellent retrieval of the underlying directed networks under a wide variety of conditions. Importantly, the method did not depend on temporal precedence to establish directionality, thus reducing susceptibility to hemodynamic variability. The computational efficiency of the algorithm was sufficient to enable whole-brain estimations, thus circumventing the problem of missing nodes that otherwise occurs in partial-brain analyses. Applying the method to real resting-state fMRI data acquired with a high temporal resolution, the inferred networks showed good consistency with structural connectivity obtained from diffusion tractography in the same subjects. Interestingly, this agreement could also be seen when considering high-frequency rather than low-frequency connectivity (average correlation: r = 0.26 for f < 0.3 Hz, r = 0.43 for 0.3 < f < 5 Hz). Moreover, this concordance was significantly better (p<0.05) than for networks obtained with conventional functional connectivity based on correlations (average correlation r = 0.18). The presented methodology thus appears to be well-suited for fMRI, particularly given its lack of explicit dependence on temporal lag structure, and is readily applicable to whole-brain effective connectivity estimation.

Show abstract Synchronous spiking of multiple neurons is a key phenomenon in normal brain function and pathologies. Recently, approaches to record spikes from the intact cortical surface using small high-density arrays of microelectrodes have been reported. It remained unaddressed how epicortical spiking relates to intracortical unit activity. We introduced a mesoscale approach using an array of 64 electrodes with intermediate diameter (250 μm) and combined large-coverage epicortical recordings in ferrets with intracortical recordings via laminar probes. Empirical data and modelling strongly suggest that our epicortical electrodes selectively captured synchronized spiking of neurons in the cortex beneath. As a result, responses to sensory stimulation were more robust and less noisy compared to intracortical activity, and receptive field properties were well preserved in epicortical recordings. This should promote insights into assembly-coding beyond the informative value of subdural EEG or single-unit spiking, and be advantageous to real-time applications in brain-machine interfacing.

Show abstract The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations. As a result, the research challenge shifts from devising algorithms to creating suitable and abundant training data for supervised learning. How to efficiently create such training data? The dominant data acquisition method in visual recognition is based on web data and manual annotation. Yet, for many computer vision problems, such as stereo or optical flow estimation, this approach is not feasible because humans cannot manually enter a pixel-accurate flow field. In this paper, we promote the use of synthetically generated data for the purpose of training deep networks on such tasks. We suggest multiple ways to generate such data and evaluate the influence of dataset properties on the performance and generalization properties of the resulting networks. We also demonstrate the benefit of learning schedules that use different types of data at selected stages of the training process.

Show abstract Background Studies on sleep-spindles are typically based on visual-marks performed by experts, however this process is time consuming and presents a low inter-expert agreement, causing the data to be limited in quantity and prone to bias. An automatic detector would tackle these issues by generating large amounts of objectively marked data. New Method Our goal was to develop a sensitive, precise and robust sleep-spindle detection method. Emphasis has been placed on achieving a consistent performance across heterogeneous recordings and without the need for further parameter fine tuning. The developed detector runs on a single channel and is based on multivariate classification using a support vector machine. Scalp-electroencephalogram recordings were segmented into epochs which were then characterized by a selection of relevant and non-redundant features. The training and validation data came from the Medical Center-University of Freiburg, the test data consisted of 27 records coming from 2 public databases. Results Using a sample based assessment, 53% sensitivity, 37% precision and 96% specificity was achieved on the DREAMS database. On the MASS database, 77% sensitivity, 46% precision and 96% specificity was achieved. The developed detector performed favorably when compared to previous detectors. The classification of normalized EEG epochs in a multidimensional space, as well as the use of a validation set, allowed to objectively define a single detection threshold for all databases and participants. Conclusions The use of the developed tool will allow increasing the data-size and statistical significance of research studies on the role of sleep-spindles.

Show abstract Simultaneous recordings and manipulations of neural circuits that control the behavior of animals is one of the key techniques in modern neuroscience. Rapid advances in optogenetics have led to a variety of probes combining multichannel readout and optogenetic write in. Given the complexity of the brain, it comes as no surprise that the choice of the device is constrained by several factors such as the animal model, the structure and location of the brain area of interest, as well as the behavioral read out. Here we provide an overview of available devices for chronic simultaneous neural recordings and optogenetic manipulation in awake behaving rats. We focus on two fixed arrays and two moveable drives. For both options, we present data from one custom-made (in house) and one commercially available device. Here we provide evidence that simultaneous neural recordings and optogenetic manipulations are feasible with all four tested devices. Further we give detailed information about the recording quality, and also contrast the different features of the probes. As we provide detailed information about equipment and building procedures for combined chronic multichannel readout and optogenetic control with maximum performance at minimized costs, this overview might help especially researchers who want to enter the field of in vivo optophysiology.

Show abstract Stable interconnection to neurons in vivo over long time-periods is critical for the success of future advanced neuroelectronic applications. The inevitable foreign body reaction towards implanted materials challenges the stability and an active intervention strategy would be desirable to treat inflammation locally. Here, we investigate whether controlled release of the anti-inflammatory drug Dexamethasone from flexible neural microelectrodes in the rat hippocampus has an impact on probe-tissue integration over 12 weeks of implantation. The drug was stored in a conducting polymer coating (PEDOT/Dex), selectively deposited on the electrode sites of neural probes, and released on weekly basis by applying a cyclic voltammetry signal in three electrode configuration in fully awake animals. Dex-functionalized probes provided stable recordings and impedance characteristics over the entire chronic study. Histological evaluation after 12 weeks of implantation revealed an overall low degree of inflammation around all flexible probes whereas electrodes exposed to active drug release protocols did have neurons closer to the electrode sites compared to controls. The combination of flexible probe technology with anti-inflammatory coatings accordingly offers a promising approach for enabling long-term stable neural interfaces.

Show abstract This study presents a new conducting polymer hydrogel (CPH) system, consisting of the synthetic hydrogel P(DMAA-co-5%MABP-co-2,5%SSNa) and the conducting polymer (CP) poly(3,4-ethylenedioxythiophene) (PEDOT), intended as coating material for neural interfaces. The composite material can be covalently attached to the surface electrode, can be patterned by a photolithographic process to influence selected electrode sites only and forms an interpenetrating network. The hybrid material was characterized using cyclic voltammetry (CV), impedance spectroscopy (EIS) and X-ray photoelectron spectroscopy (XPS), which confirmed a homogeneous distribution of PEDOT throughout all CPH layers. The CPH exhibited a 2,5 times higher charge storage capacity (CSC) and a reduced impedance when compared to the bare hydrogel. Electrochemical stability was proven over at least 1000 redox cycles. Non-toxicity was confirmed using an elution toxicity test together with a neuroblastoma cell-line. The described material shows great promise for surface modification of neural probes making it possible to combine the beneficial properties of the hydrogel with the excellent electronic properties necessary for high quality neural microelectrodes. STATEMENT OF SIGNIFICANCE: Conductive polymer hydrogels have emerged as a promising new class of materials to functionalize electrode surfaces for enhanced neural interfaces and drug delivery. Common weaknesses of such systems are delamination from the connection surface, and the lack of suitable patterning methods for confining the gel to the selected electrode site. Various studies have reported on conductive polymer hydrogels addressing one of these challenges. In this study we present a new composite material which offers, for the first time, the unique combination of properties: it can be covalently attached to the substrate, forms an interpenetrating network, shows excellent electrical properties and can be patterned via UV-irradiation through a structured mask.

Show abstract We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

Show abstract Hebbian and homeostatic plasticity have been studied extensively in the past, both experimentally and theoretically, but many aspects of their interaction remain to be elucidated. Hebbian plasticity is thought to shape neuronal connectivity during development and learning, whereas homeostatic plasticity would stabilize network activity. Here we investigate another aspect of this interaction, which is whether Hebbian associative properties can also emerge as a network effect from a plasticity rule based on homeostatic principles on the neuronal level. The maturation of cortical networks during sensory experience is an ideal case to explore this question. Excitatory neurons in the visual cortex of rodents have been shown to connect preferentially to neurons that respond to similar visual features. Since this connectivity bias is not existent at the time of eye opening, but only after some weeks of visual experience, it has been suggested that plastic mechanisms are responsible for the changes taking place during sensory stimulation. We consider a structural plasticity rule driven by a homeostasis of firing rate in a recurrent network of leaky integrate-and-fire (LIF) neurons exposed to external input that is modulated by the orientation of a visual stimulus. Our results show that feature specific connectivity, similar to what has been experimentally observed in rodent visual cortex, can emerge out of a random balanced network of LIF neurons with a plasticity rule that is not explicitly dependent on correlations between pre- and postsynaptic neuronal activity. The synaptic association of neurons responding to similar stimulus features occurs as a side-effect of controlling the activity of individual neurons. The experience dependent structural changes that are triggered by simulation are long lasting and decay only slowly when the neurons are exposed again to non modulated external input. arXiv 1706.02912 [q-bio.NC], 2017 (pdf)

Show abstract Medical brain implants for closed-loop interaction with the cerebral cortex promise new treatment options for brain disorders, and thus great efforts are being made to develop devices for long-term application. Closed-loop interaction can be implemented using electrophysiological recording techniques, and can be used to modulate local cortical activity or long-range functional connectivity. In a case study performed in sheep chronically implanted with a novel micro-electrocorticography-based device, we show that (1) open-loop single-pulse electrical stimulation (SPES) elicited the well-known cortico-cortical evoked potentials (CCEPs), and (2) closed-loop repetitive-pulse electrical stimulation (RPES) elicited specific cortico-cortical spectral responses (CCSRs). CCSRs were spatially focalized in the gamma band, compared with beta band independent of RPES frequency. The topography of CCSRs was different compared with CCEPs, suggesting that CCEPs and CCSRs capture different aspects of cortico-cortical connectivity. We propose that CCSRs provide new useful measures of functional connectivity, and that in particular gamma-band CCSRs may be an optimal choice if spatially precise closed-loop interaction is desired. However, the parameter space of micro-electrocorticography stimulation patterns and associated changes in μECoG frequency bands needs to be further explored and many questions remain before closed-loop brain implants can be used in clinical applications.

Show abstract Wireless implants for interaction with the cortex have developed rapidly over the last decade and increasingly meet demands of clinical brain–computer interfaces. For such applications, well-established technologies are available, suitable for recording of neural activity at different spatial scales and adequate for modulating brain activity by cortical electrical stimulation. The incorporation of recording and stimulation into closed-loop systems is a major aim in active, fully implantable medical device design. To reduce clinical long-term implantation risk and to increase the spatial specificity of epicortical recordings and stimulation, micro-electrocorticography is a promising technology. However, currently there is a lack of implants suitable for chronic human clinical applications that utilize micro-electrocorticography and possess closed-loop functionality. Here, we describe the clinical importance of cortical stimulation, give an overview of existing implants that use mainly epicortical recording methods, and present results of a closed-loop micro-electrocorticography system developed for clinical application within a collaborative framework. Finally, we conclude with our vision of future design options in the field of neuroprosthetic devices.

Show abstract Background: Objective assessments of Parkinson’s disease (PD) patients’ motor state using motion capture techniques are still rarely used in clinical practice, even though they may improve clinical management. One major obstacle relates to the large dimensionality of motor abnormalities in PD. We aimed to extract global motor performance measures covering different everyday motor tasks, as a function of a clinical intervention, i.e., deep brain stimulation (DBS) of the subthalamic nucleus. Methods: We followed a data-driven, machine-learning approach and propose performance measures that employ Random Forests with probability distributions. We applied this method to 14 PD patients with DBS switched-off or -on, and 26 healthy control subjects performing the Timed Up and Go Test (TUG), the Functional Reach Test (FRT), a hand coordination task, walking 10-m straight, and a 90° curve. Results: For each motor task, a Random Forest identified a specific set of metrics that optimally separated PD off DBS from healthy subjects. We noted the highest accuracy (94.6%) for standing up. This corresponded to a sensitivity of 91.5% to detect a PD patient off DBS, and a specificity of 97.2% representing the rate of correctly identified healthy subjects. We then calculated performance measures based on these sets of metrics and applied those results to characterize symptom severity in different motor tasks. Task-specific symptom severity measures correlated significantly with each other and with the Unified Parkinson’s Disease Rating Scale (UPDRS, part III, correlation of r2 = 0.79). Agreement rates between different measures ranged from 79.8 to 89.3%. Conclusion: The close correlation of PD patients’ various motor abnormalities quantified by different, task-specific severity measures suggests that these abnormalities are only facets of the underlying one-dimensional severity of motor deficits. The identification and characterization of this underlying motor deficit may help to optimize therapeutic interventions, e.g., to “automatically” adapt DBS settings in PD patients.

Show abstract Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, i.e. learning from the raw data. Now, there is increasing interest in using deep ConvNets for end-to-end EEG analysis. However, little is known about many important aspects of how to design and train ConvNets for end-to-end EEG decoding, and there is still a lack of techniques to visualize the informative EEG features the ConvNets learn. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed movements from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching or surpassing that of the widely-used filter bank common spatial patterns (FBCSP) decoding algorithm. While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta and high gamma frequencies. These methods also proved useful as a technique for spatially mapping the learned features, revealing the topography of the causal contributions of features in different frequency bands to decoding the movement classes. Our study thus shows how to design and train ConvNets to decode movement-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping.

Show abstract Gamma oscillations (30–150 Hz) in neuronal networks are associated with the processing and recall of information. We measured local field potentials in the dentate gyrus of freely moving mice and found that gamma activity occurs in bursts, which are highly heterogeneous in their spatial extensions, ranging from focal to global coherent events. Synaptic communication among perisomatic-inhibitory interneurons (PIIs) is thought to play an important role in the generation of hippocampal gamma patterns. However, how neuronal circuits can generate synchronous oscillations at different spatial scales is unknown. We analyzed paired recordings in dentate gyrus slices and show that synaptic signaling at interneuron-interneuron synapses is distance dependent. Synaptic strength declines whereas the duration of inhibitory signals increases with axonal distance among interconnected PIIs. Using neuronal network modeling, we show that distance-dependent inhibition generates multiple highly synchronous focal gamma bursts allowing the network to process complex inputs in parallel in flexibly organized neuronal centers.

Show abstract Throughout each day, the brain displays transient changes in state, as evidenced by shifts in behavior and vigilance. While the electrophysiological correlates of brain states have been studied for some time, it remains unclear how large-scale cortico-cortical functional connectivity systematically reconfigures across states. Here, we investigate state-dependent shifts in cortical functional connectivity by recording local field potentials (LFPs) during spontaneous behavioral transitions in the ferret using chronically implanted micro-electrocorticographic (µECoG) arrays positioned over occipital, parietal, and temporal cortical regions. To objectively classify brain state, we describe a data-driven approach that projects time-varying LFP spectral properties into brain state space. Distinct brain states displayed markedly different patterns of cross-frequency phase-amplitude coupling and inter-electrode phase synchronization across several LFP frequency bands. The largest across-state differences in functional connectivity were observed between periods of presumed slow-wave and rapid-eye-movement-sleep/active-state, which were characterized by the contrasting phenomena of cortical network fragmentation and global synchronization, respectively. Collectively, our data provide strong evidence that large-scale functional interactions in the brain dynamically reconfigure across behavioral states.

Show abstract We present a variational approach for surface reconstruction from a set of oriented points with scale information. We focus particularly on scenarios with non-uniform point densities due to images taken from different distances. In contrast to previous methods, we integrate the scale information in the objective and globally optimize the signed distance function of the surface on a balanced octree grid. We use a finite element discretization on the dual structure of the octree minimizing the number of variables. The tetrahedral mesh is generated efficiently from the dual structure, and also memory efficiency is optimized, such that robust data terms can be used even on very large scenes. The surface normals are explicitly optimized and used for surface extraction to improve the reconstruction at edges and corners.

Show abstract OBJECTIVE: Technology for localizing epileptogenic brain regions plays a central role in surgical planning. Recent improvements in acquisition and electrode technology have revealed that high-frequency oscillations (HFOs) within the 80-500 Hz frequency range provide the neurophysiologist with new information about the extent of the epileptogenic tissue in addition to ictal and interictal lower frequency events. Nevertheless, two decades after their discovery there remain questions about HFOs as biomarkers of epileptogenic brain and there use in clinical practice. METHODS: In this review, we provide practical, technical guidance for epileptologists and clinical researchers on recording, evaluation, and interpretation of ripples, fast ripples, and very high-frequency oscillations. RESULTS: We emphasize the importance of low noise recording to minimize artifacts. HFO analysis, either visual or with automatic detection methods, of high fidelity recordings can still be challenging because of various artifacts including muscle, movement, and filtering. Magnetoencephalography and intracranial electroencephalography (iEEG) recordings are subject to the same artifacts. SIGNIFICANCE: High-frequency oscillations are promising new biomarkers in epilepsy. This review provides interested researchers and clinicians with a review of current state of the art of recording and identification and potential challenges to clinical translation.

Show abstract Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders require a tedious calibration procedure prior to every session. Several unsupervised classification methods have been proposed that tune the decoder during actual use and as such omit this calibration. Each of these methods has its own strengths and weaknesses. Our aim is to improve overall accuracy of ERP-based BCIs without calibration.Approach. We consider two approaches for unsupervised classification of ERP signals. Learning from label proportions (LLP) was recently shown to be guaranteed to converge to a supervised decoder when enough data is available. In contrast, the formerly proposed expectation maximization (EM) based decoding for ERP-BCI does not have this guarantee. However, while this decoder has high variance due to random initialization of its parameters, it obtains a higher accuracy faster than LLP when the initialization is good.We introduce a method to optimally combine these two unsupervised decoding methods, letting one method’s strengths compensate for the weaknesses of the other and vice versa. The new method is compared to the aforementioned methods in a resimulation of an experiment with a visual speller.Main Results. Analysis of the experimental results shows that the new method exceeds the performance of the previous unsupervised classification approaches in terms of ERP classification accuracy and symbol selection accuracy during the spelling experiment. Furthermore, the method shows less dependency on random initialization of model parameters and is consequently more reliable.Significance. Improving the accuracy and subsequent reliability of calibrationless BCIs makes these systems more appealing for frequent use.

Show abstract The relative timing of action potentials in neurons recorded from local cortical networks often shows a non-trivial dependence, which is then quantified by cross-correlation functions. Theoretical models emphasize that such spike train correlations are an inevitable consequence of two neurons being part of the same network and sharing some synaptic input. For non-linear neuron models, however, explicit correlation functions are difficult to compute analytically, and perturbative methods work only for weak shared input. In order to treat strong correlations, we suggest here an alternative non-perturbative method. Specifically, we study the case of two leaky integrate-and-fire neurons with strong shared input. Correlation functions derived from simulated spike trains fit our theoretical predictions very accurately. Using our method, we computed the non-linear correlation transfer as well as correlation functions that are asymmetric due to inhomogeneous intrinsic parameters or unequal input.

Show abstract We train generative 'up-convolutional' neural networks which are able to generate images of objects given object style, viewpoint, and color. We train the networks on rendered 3D models of chairs, tables, and cars. Our experiments show that the networks do not merely learn all images by heart, but rather find a meaningful representation of 3D models allowing them to assess the similarity of different models, interpolate between given views to generate the missing ones, extrapolate views, and invent new objects not present in the training set by recombining training instances, or even two different object classes. Moreover, we show that such generative networks can be used to find correspondences between different objects from the dataset, outperforming existing approaches on this task.

Show abstract Shakey the robot was one of the first autonomous robots that showed impressive capabilities of navigation and mobile manipulation. Since then, robotics research has made great progress, showing more and more capable robotic systems for a large variety of application domains and tasks. In this letter, we look back on decades of research by rebuilding Shakey with modern robotics technology in the open-source Shakey 2016 system. Hereby, we demonstrate the impact of research by showing that ideas from the original Shakey are still alive in state-of-the-art systems, while robotics in general has improved to deliver more robust and more capable software and hardware. Our Shakey 2016 system has been implemented on real robots and leverages mostly open-source software. We experimentally evaluate the system in real-world scenarios on a PR2 robot and a Turtlebot-based robot and particularly investigate the development effort. The experiments documented in this letter demonstrate that results from robotics research are readily available for building complex robots such as Shakey within a short amount of time and little effort.

Show abstract Pairs of neurons in brain networks often share much of the input they receive from other neurons. Due to essential nonlinearities of the neuronal dynamics, the consequences for the correlation of the output spike trains are generally not well understood. Here we analyze the case of two leaky integrate-and-fire neurons using an approach which is nonperturbative with respect to the degree of input correlation. Our treatment covers both weakly and strongly correlated dynamics, generalizing previous results based on linear response theory.

Show abstract Somatostatin-expressing-interneurons (SOMIs) in the dentate gyrus (DG) control formation of granule cell (GC) assemblies during memory acquisition. Hilar-perforant-path-associated interneurons (HIPP cells) have been considered to be synonymous for DG-SOMIs. Deviating from this assumption, we show two functionally contrasting DG-SOMI-types. The classical feedback-inhibitory HIPPs distribute axon fibers in the molecular layer. They are engaged by converging GC-inputs and provide dendritic inhibition to the DG circuitry. In contrast, SOMIs with axon in the hilus, termed hilar interneurons (HILs), provide perisomatic inhibition onto GABAergic cells in the DG and project to the medial septum. Repetitive activation of glutamatergic inputs onto HIPP cells induces long-lasting-depression (LTD) of synaptic transmission but long-term-potentiation (LTP) of synaptic signals in HIL cells. Thus, LTD in HIPPs may assist flow of spatial information from the entorhinal cortex to the DG, whereas LTP in HILs may facilitate the temporal coordination of GCs with activity patterns governed by the medial septum.

Show abstract Accurate simulations of peripheral nerve recordings are needed to develop improved neuroprostheses. Previous models of peripheral nerves contained simplifications whose effects have not been investigated. We created a novel detailed finite element (FE) model of a peripheral nerve, and used it to carry out a sensitivity analysis of several model parameters. To construct the model, in vivo recordings were obtained in a rat sciatic nerve using an 8-channel nerve cuff electrode, after which the nerve was imaged using magnetic resonance imaging (MRI). The FE model was constructed based on the MRI data, and included progressive branching of the fascicles. Neural pathways were defined in the model for the tibial, peroneal and sural fascicles. The locations of these pathways were selected so as to maximize the correlations between the simulated and in vivo recordings. The sensitivity analysis showed that varying the conductivities of neural tissues had little influence on the ability of the model to reproduce the recording patterns obtained experimentally. On the other hand, the increased anatomical detail did substantially alter the recording patterns observed, demonstrating that incorporating fascicular branching is an important consideration in models of nerve cuff recordings. The model used in this study constitutes an improved simulation tool and can be used in the design of neural interfaces.

Show abstract With the continued miniaturisation of portable embedded systems, wireless EEG recording techniques are becoming increasingly prevalent in animal behavioural research. However, in spite of their versatility and portability, they have seldom been used inside water-maze tasks designed for rats. As such, a novel 3D printed implant and waterproof connector is presented, which can facilitate wireless water-maze EEG recordings in freely-moving rats, using a commercial wireless recording system (W32; Multichannel Systems). As well as waterproofing the wireless system, battery, and electrode connector, the implant serves to reduce movement-related artefacts by redistributing movement-related forces away from the electrode connector. This implant/connector was able to successfully record high-qual- ity LFP in the hippocampo-striatal brain regions of rats as they undertook a procedural- learning variant of the double-H water-maze task. Notably, there were no significant perfor- mance deficits through its use when compared with a control group across a number of met- rics including number of errors and speed of task completion. Taken together, this method can expand the range of measurements that are currently possible in this diverse area of behavioural neuroscience, whilst paving the way for integration with more complex behaviours.

Show abstract Direct current (DC) stimulation can be used to influence the orientation and migratory behavior of cells and results in cellular electrotaxis. Experimental work on such phenomena commonly relies on electrochemical dissolution of silver:silver–chloride (Ag:AgCl) electrodes to provide the stimulation via salt bridges. The strong ionic flow can be expected to influence the cell culture environment. In order to shed more light on which effects that must be considered, and possibly counter balanced, we here characterize a typical DC stimulation system. Silver migration speed was determined by stripping voltammetry. pH variability with stimulation was measured by ratiometric image analysis and conductivity alterations were quantified via two electrode impedance spectroscopy. It could be concluded that pH shifts towards more acidic values, in a linear manner with applied charge, after the buffering capability of the culture medium is exceeded. In contrast, the influence on conductivity was of negligible magnitude. Silver ions could enter the culture chamber at low concentrations long before a clear effect on the viability of the cultured cells could be observed. A design rule of 1 cm salt bridge per C of stimulation charge transferred was however sufficient to ensure separation between cells and silver at all times.

Show abstract Abstract There has been substantial progress over the last decade towards miniaturizing implantable microelectrodes for use in Active Implantable Medical Devices (AIMD). Compared to the rapid development and complexity of electrode miniaturization, methods to monitor and assess functional integrity and electrical functionality of these electrodes, particularly during long term stimulation, have not progressed to the same extent. Evaluation methods that form the gold standard, such as stimulus pulse testing, cyclic voltammetry and electrochemical impedance spectroscopy, are either still bound to laboratory infrastructure (impractical for long term in vivo experiments) or deliver no comprehensive insight into the material’s behaviour. As there is a lack of cost effective and practical predictive measures to understand long term electrode behaviour in vivo, material investigations need to be performed after explantation of the electrodes. We propose the analysis of the electrode and its environment in situ, to better understand and correlate the effects leading to electrode failure. The derived knowledge shall eventually lead to improved electrode designs, increased electrode functionality and safety in clinical applications. In this paper, the concept, design and prototyping of a sensor framework used to analyse the electrode’s behaviour and to monitor diverse electrode failure mechanisms, even during stimulation pulses, is presented. We focused on the electronic circuitry and data acquisition techniques required for a conceptual multi-sensor system. Functionality of single modules and a prototype framework have been demonstrated, but further work is needed to convert the prototype system into an implantable device. In vitro studies will be conducted first to verify sensor performance and reliability.

Show abstract Connectome genetics seeks to uncover how genetic factors shape brain functional connectivity; however, the causal impact of a single gene's activity on whole-brain networks remains unknown. We tested whether the sole targeted deletion of the mu opioid receptor gene (Oprm1) alters the brain connectome in living mice. Hypothesis-free analysis of combined resting-state fMRI diffusion tractography showed pronounced modifications of functional connectivity with only minor changes in structural pathways. Fine-grained resting-state fMRI mapping, graph theory, and intergroup comparison revealed Oprm1-specific hubs and captured a unique Oprm1 gene-to-network signature. Strongest perturbations occurred in connectional patterns of pain/aversion-related nodes, including the mu receptor-enriched habenula node. Our data demonstrate that the main receptor for morphine predominantly shapes the so-called reward/aversion circuitry, with major influence on negative affect centers.

Show abstract OBJECTIVES: In simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), artifacts on the EEG arise from the switching of magnetic field gradients in the MR scanner. These artifacts depend on head position, and are, therefore, difficult to remove in the presence of subject motion. In this study, gradient artifacts are modeled by multiple templates extracted from externally recorded motion information.METHODS: Gradient artifact correction was performed in EEG-fMRI recordings by estimating artifactual templates modulated by slowly varying splines, as well as head position information. The EEG signal quality was then compared following two common methods: averaged artifact subtraction (AAS) and optimal basis sets (OBS).RESULTS: Artifact correction using multiple templates estimated from splines or motion time courses outperformed the existing AAS and OBS approaches, as quantified by root-mean-square power across gradient epochs. Improvements were mostly seen in posterior EEG channels, where most of the residual artifacts are seen following the AAS and OBS methods. Residual spectral power was comparable to that of EEG signals recorded without fMRI scanning.CONCLUSION: Gradient artifacts can be well modeled by multiple templates estimated from head position information, resulting in an effective artifact removal.SIGNIFICANCE: This method can facilitate EEG-fMRI of uncooperative subjects in whom motion is inevitable, for example, to investigate high-frequency EEG activity in which gradient artifacts are particularly prominent.

Show abstract This paper describes the complete mathematical optimization process of an inductive powering system suitable for the application within implanted biomedical systems. The optimization objectives are thereby size, energy efficiency, and tissue absorption. Within the first step, the influence of the operational frequency on the given quantities is computed by means of finite element simulations, yielding a compromise of power transfer efficiency of the wireless link and acceptable tissue heating in terms of the specific absorption rate. All simulations account for the layered structure of the human head, modeling the dielectric properties with Cole-Cole dispersion effects. In the second step, the relevant coupling and loss effects of the transmission coils are modeled as a function of the geometrical design parameters, enabling a noniterative and comprehensible mathematical derivation of the optimum coil geometry given an external size constraint. Further investigations of the optimum link design also consider high-permeability structures being applied to the primary coil, enhancing the efficiency by means of an increased mutual inductance. Thereby, a final link efficiency of 80% at a coil separation distance of 5 mm and 20% at 20 mm using a 10-mm planar receiving coil can be achieved, contributing to a higher integration density of multichannel brain implanted sensors. Moreover, the given procedure does not only give insight into the optimization of the coil design, but also provides a minimized set of mathematical expressions for designing a highly efficient primary side coil driver and for selecting the components of the secondary side impedance matching. All mathematical models and descriptions have been verified by simulation and concluding measurements.

Show abstract Abstract Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817–830, 2017. © 2016 Wiley Periodicals, Inc.

Show abstract An innovative micro thermoelectric generator (lTEG) fabrication process has been developed. Two selectively dissolvable photoresists and galvanostatic electrodeposition are used to grow p- and n-type thermoelectric materials as well as the upper and lower contacts of the µTEGs onto a single substrate. Two particular features ofthe process are the usage ofa multilamination technique to create structures for legs and contacts, as well as an industrial pick and placer (P&P), which allows dispensing of a second, selectively dissolvable, photoresist to protect certain areas during material deposition. This allows sequential electrochemical deposition oftwo different thermoelectric materials on a single substrate, without further costly and time-consuming process steps. The process therefore provides a highly flexible fabrication platform for research and development.

Show abstract Polyimide based shaft electrodes were coated with a bioresorbable layer to stiffen them for intracortical insertion and to reduce the mechanical mismatch between the target tissue and the implanted device after degradation of the coating. Molten saccharose was used as coating material. In a proof-of-concept study, the electrodes were implanted into the cortex of Wistar rats and the insertion forces during implantation were recorded. Electrochemical impedance spectroscopy was performed immediately after implantation and up to 13 weeks after implantation to monitor the tissue responsetotheimplantedelectrodes.Therecordedspectrawere modeled with an equivalent circuit to differentiate the influence of the single components. In one rat, a peak in the encapsulation resistance was observable after two weeks of implantation, indicating the peak of the acute inflammatory response. In another rat, the lowest resistances were observed after four weeks, indicating the termination of the acute inflammatory response. Multiunitactivity was recorded with an adequatesignaltonoiseratiotoallowspikesorting.Histology was performed after 7, 45 and 201 days of implantation. The results showed the highest tissue reaction after 45 days and confirmed impedance data that acute inflammatory reactions terminate over time.

Show abstract The objective of this work is to produce a laser- fabricated polymer-metal-polymer electrode with the merit of a carbon-based coating as the active site. A 10 μm-thick layer of parylene-C is used serving as the insulation layer in which the active site is locally laser-pyrolyzed. Our preliminary results show that the proposed method is promising in terms of fabrication feasibility and desired electrochemical capabilities.

Show abstract Over the past decades, optical technologies have entered neural implant technologies. Applications such as optogenetics, near-infrared spectroscopy (NIRS), and direct-near-infrared stimulation (NIS) request technical devices that combine electrical and optical recording as well as stimulation capabilities using light sources and/or optical sensors. Optoprobes are the technical devices that meet these requirements. This paper provides basic insights into optogenetic mechanisms, the background of NIRS and NIS, and focuses on fundamental requirements of technical systems from a biological background. The state of the art of optoprobes is reviewed and attention is drawn on the potential long-term stability of these technical devices for chronic neural implants. Further, material selection for stiff and flexible devices, applicable light sources, waveguide and coupling concepts, packaging paradigms as well as system assembly and integration aspects are discussed in view of biocompatible and biostable devices. This paper also considers the physical background of light scattering and heat generation when light sources are implanted into biological tissue.

Show abstract Conducting polymers (CPs) have frequently been described as outstanding coating materials for neural microelectrodes, providing significantly reduced impedance or higher charge injection compared to pure metals. Usability has until now, however, been limited by poor adhesion of polymers like poly(3,4-ethylenedioxythiophene) (PEDOT) to metallic substrates, ultimately precluding long-term applications. The aim of this study was to overcome this weakness of CPs by introducing two novel adhesion improvement strategies that can easily be integrated with standard microelectrode fabrication processes. Iridium Oxide (IrOx) demonstrated exceptional stability for PEDOT coatings, resulting in polymer survival over 10 000 redox cycles and 110 days under accelerated aging conditions at 60 °C. Nanostructured Pt was furthermore introduced as a purely mechanical adhesion promoter providing 10-fold adhesion improvement compared to smooth Pt substrates by simply altering the morphology of Pt. This layer can be realized in a very simple process that is compatible with any electrode design, turning nanostructured Pt into a universal adhesion layer for CP coatings. By the introduction of these adhesion-promoting strategies, the weakness of CP-based neural probes can ultimately be eliminated and true long-term stable use of PEDOT on neural probes will be possible in future electrode generations.

Show abstract Magnetic resonance imaging (MRI) was used to gain in vivo insight into load-induced displacements of inner plant tissues making a non-invasive and non-destructive stress and strain analysis possible. The central aim of this study was the identification of a possible load-adapted orientation of the vascular bundles and their fibre caps as the mechanically relevant tissue in branch-stem-attachments of Dracaena marginata. The complex three-dimensional deformations that occur during mechanical loading can be analysed on the basis of quasi-three-dimensional data representations of the outer surface, the inner tissue arrangement (meristem and vascular system), and the course of single vascular bundles within the branch-stem-attachment region. In addition, deformations of vascular bundles could be quantified manually and by using digital image correlation software. This combination of qualitative and quantitative stress and strain analysis leads to an improved understanding of the functional morphology and biomechanics of D. marginata, a plant that is used as a model organism for optimizing branched technical fibre-reinforced lightweight trusses in order to increase their load bearing capacity.

Show abstract Goal: To identify and overcome barriers to creating new neurotechnologies capable of restoring both motor and sensory function in individuals with neurological conditions. Methods: This report builds upon the outcomes of a joint workshop between the US National Science Foundation and the German Research Foundation on New Perspectives in Neuroengineering and Neurotechnology convened in Arlington, VA, USA, November 13–14, 2014. Results: The participants identified key technological challenges for recording and manipulating neural activity, decoding, and interpreting brain data in the presence of plasticity, and early considerations of ethical and social issues pertinent to the adoption of neurotechnologies. Conclusions: The envisaged progress in neuroengineering requires tightly integrated hardware and signal processing efforts, advances in understanding of physiological adaptations to closed-loop interactions with neural devices, and an open dialog with stakeholders and potential end-users of neurotechnology. Significance: The development of new neurotechnologies (e.g., bidirectional brain–computer interfaces) could significantly improve the quality of life of people living with the effects of brain or spinal cord injury, or other neurodegenerative diseases. Focused efforts aimed at overcoming the remaining barriers at the electrode tissue interface, developing implantable hardware with on-board computation, and refining stimulation methods to precisely activate neural tissue will advance both our understanding of brain function and our ability to treat currently intractable disorders of the nervous system.

Show abstract As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, different users typically have their own preferences, for example with respect to arranging objects on different shelves. As many of these preferences depend on a variety of factors including personal taste, cultural background, or common sense, it is challenging for an expert to pre-program a robot in order to accommodate all potential users. At the same time, it is impractical for robots to constantly query users about how they should perform individual tasks. In this work, we present an approach to learn patterns in user preferences for the task of tidying up objects in containers, e.g. shelves or boxes. Our method builds upon the paradigm of collaborative filtering for making personalized recommendations and relies on data from different users which we gather using crowdsourcing. To deal with novel objects for which we have no data, we propose a method that compliments standard collaborative filtering by leveraging information mined from the Web. When solving a tidy-up task, we first predict pairwise object preferences of the user. Then, we subdivide the objects in containers by modeling a spectral clustering problem. Our solution is easy to update, does not require complex modeling, and improves with the amount of user data. We evaluate our approach using crowdsourcing data from over 1200 users and demonstrate its effectiveness for two tidy-up scenarios. Additionally, we show that a real robot can reliably predict user preferences using our approach.

Show abstract We propose a framework for building electrophysiological predictors of single-trial motor performance variations, exemplified for SVIPT, a sequential isometric force control task suitable for hand motor rehabilitation after stroke. Electroencephalogram (EEG) data of 20 subjects with mean age of 53 years was recorded prior to and during 400 trials of SVIPT. They were executed within a single session with the non-dominant left hand, while receiving continuous visual feedback of the produced force trajectories. The behavioral data showed strong trial-by-trial performance variations for five clinically relevant metrics, which accounted for reaction time as well as for the smoothness and precision of the produced force trajectory. 18 out of 20 tested subjects remained after preprocessing and entered offline analysis. Source Power Comodulation (SPoC) was applied on EEG data of a short time interval prior to the start of each SVIPT trial. For 11 subjects, SPoC revealed robust oscillatory EEG subspace components, whose bandpower activity are predictive for the performance of the upcoming trial. Since SPoC may overfit to non-informative subspaces, we propose to apply three selection criteria accounting for the meaningfulness of the features. Across all subjects, the obtained components were spread along the frequency spectrum and showed a variety of spatial activity patterns. Those containing the highest level of predictive information resided in and close to the alpha band. Their spatial patterns resemble topologies reported for visual attention processes as well as those of imagined or executed hand motor tasks. In summary, we identified subject-specific single predictors that explain up to 36% of the performance fluctuations and may serve for enhancing neuroergonomics of motor rehabilitation scenarios.

Show abstract The theory on the glymphatic convection mechanism of cerebrospinal fluid holds that cardiac pulsations in part pump cerebrospinal fluid from the peri-arterial spaces through the extracellular tissue into the peri-venous spaces facilitated by aquaporin water channels. Since cardiac pulses cannot be the sole mechanism of glymphatic propulsion, we searched for additional cerebrospinal fluid pulsations in the human brain with ultra-fast magnetic resonance encephalography. We detected three types of physiological mechanisms affecting cerebral cerebrospinal fluid pulsations: cardiac, respiratory, and very low frequency pulsations. The cardiac pulsations induce a negative magnetic resonance encephalography signal change in peri-arterial regions that extends centrifugally and covers the brain in ≈1 Hz cycles. The respiratory ≈0.3 Hz pulsations are centripetal periodical pulses that occur dominantly in peri-venous areas. The third type of pulsation was very low frequency (VLF 0.001-0.023 Hz) and low frequency (LF 0.023-0.73 Hz) waves that both propagate with unique spatiotemporal patterns. Our findings using critically sampled magnetic resonance encephalography open a new view into cerebral fluid dynamics. Since glymphatic system failure may precede protein accumulations in diseases such as Alzheimer's dementia, this methodological advance offers a novel approach to image brain fluid dynamics that potentially can enable early detection and intervention in neurodegenerative diseases.

Show abstract Highlights • A novel 3D-printed headstage was developed for protecting skull-mounted implants in rodents. • The socket allowed for successful chronic pair-housing of rats following stereotaxic surgery. • Rats were able to carry out a range of normal behaviours, with no significant implant damage observed. • This implant can help to improve the well-being of post-surgical rats, whilst reducing the cost of rodent upkeep

Show abstract The possibility to release drugs from conducting polymers, like polypyrrole or poly(3,4‐ethylenedioxythiophene) (PEDOT), has been described and investigated for a variety of different substances during the last years, showing a wide interest in these release systems. A point that has not been looked at so far however is the possibility of other substances, next to the intended ones, leaving the polymer film under the high voltage excursions during redox sweeping. In this study we target this weakness of commonly used detection methods by implementing a high precision analytical method (high‐performance liquid chromatography) that allows a separation and subsequently a detailed quantification of all possible release products. We could identify a significantly more complex release behavior for a PEDOT:Dex system than has been assumed so far, revealing the active release of the monomer upon redox activation. The released EDOT could thereby be shown to result from the bulk material, causing a considerable loss of polymer (>10% during six release events) that could partly account for the observed degradation or delamination effects of drug‐eluting coatings. The monomer leakage was found to be substantially higher for a PEDOT:Dex film compared to a PEDOT:PSS sample. This finding indicates an overestimation of drug release if side products are mistaken for the actual drug mass. Moreover the full picture of released substances implements the need for further studies to reduce the monomer leakage and identify possible adverse effects, especially in the perspective of releasing an anti‐inflammatory substance for attenuation of the foreign body reaction toward implanted electrodes.

Show abstract A typical Go/No-Go decision is suggested to be implemented in the brain via the activation of the direct or indirect pathway in the basal ganglia. Medium spiny neurons (MSNs) in the striatum, receiving input from cortex and projecting to the direct and indirect pathways express D1 and D2 type dopamine receptors, respectively. Recently, it has become clear that the two types of MSNs markedly differ in their mutual and recurrent connectivities as well as feedforward inhibition from FSIs. Therefore, to understand striatal function in action selection, it is of key importance to identify the role of the distinct connectivities within and between the two types of MSNs on the balance of their activity. Here, we used both a reduced firing rate model and numerical simulations of a spiking network model of the striatum to analyze the dynamic balance of spiking activities in D1 and D2 MSNs. We show that the asymmetric connectivity of the two types of MSNs renders the striatum into a threshold device, indicating the state of cortical input rates and correlations by the relative activity rates of D1 and D2 MSNs. Next, we describe how this striatal threshold can be effectively modulated by the activity of fast spiking interneurons, by the dopamine level, and by the activity of the GPe via pallidostriatal backprojections. We show that multiple mechanisms exist in the basal ganglia for biasing striatal output in favour of either the `Go' or the `No-Go' pathway. This new understanding of striatal network dynamics provides novel insights into the putative role of the striatum in various behavioral deficits in patients with Parkinson's disease, including increased reaction times, L-Dopa-induced dyskinesia, and deep brain stimulation-induced impulsivity.

Show abstract In the absence of sensory stimulation or motor output, the brain exhibits complex spatiotemporal patterns of intrinsically generated neural activity. Analysis of ongoing brain dynamics has identified the prevailing modes of cortico-cortical interaction; however, little is known about how such patterns of intrinsically generated activity are correlated between cortical and subcortical brain areas. We investigate the correlation structure of ongoing cortical and superior colliculus (SC) activity across multiple spatial and temporal scales. Ongoing cortico-tectal interaction was characterized by correlated fluctuations in the amplitude of delta, spindle, low gamma, and high-frequency oscillations (>100 Hz). Of these identified coupling modes, topographical patterns of high-frequency coupling were the most consistent with patterns of anatomical connectivity, reflecting synchronized spiking within cortico-tectal networks. Cortico-tectal coupling at high frequencies was temporally parcellated by the phase of slow cortical oscillations and was strongest for SC-cortex channel pairs that displayed overlapping visual spatial receptive fields. Despite displaying a high degree of spatial specificity, cortico-tectal coupling in lower-frequency bands did not match patterns of cortex-to-SC anatomical connectivity. Collectively, our findings demonstrate that neural activity is spontaneously coupled between cortex and SC, with high- and low-frequency modes of coupling reflecting direct and indirect cortico-tectal interactions, respectively.

Show abstract Extracellular, large scale in vivo recording of neural activity is mandatory for elucidating the interaction of neurons within large neural networks at the level of their single unit activity. Technological achievements in MEMS-based multichannel electrode arrays offer electrophysiological recording capabilities that go far beyond those of classical wire electrodes. Despite their impressive channel counts, recording systems with modest interconnection overhead have been demonstrated thanks to the hybrid integration of CMOS circuitry for signal preprocessing and data handling. The number of addressable channels is increased even further by a switch matrix for electrode selection co-integrated along the slender probe shafts. When realized by IC fabrication technologies, these probes offer highest recording site densities along the entire shaft length.

Show abstract A cost effective bottom-up process for the fabrication of micro thermoelectric generators (μTEGs) was developed. It is based on a novel fabrication method involving a selectively sacrificial photoresist for the sequential galvanostatic electrodeposition of thermoelectric materials. The use of an industrial pick and placer (P&P) for dispensing the second photoresist allows for accurate and flexible μTEG designs. The process makes use of Ordyl® as a negative dry film photoresist template and sequential lamination steps for shaping all thermoelectric legs and contacts. All structures of the μTEG are generated in one photoresist multi-layer - this represents the most significant advantage of the process. The process uses a minimum of clean room processing for the preparation of pre-structured substrates for electrodeposition and therefore provides a cost-effective, highly flexible fabrication platform for research and development.

Show abstract As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, learning our preferences is a nontrivial problem, as many of them stem from a variety of factors including personal taste, cultural background, or common sense. Obviously, such factors are hard to formulate or model a priori. In this paper, we present a solution for tidying up objects in containers, e.g., shelves or boxes, by following user preferences. We learn the user preferences using collaborative filtering based on crowdsourced and mined data. First, we predict pairwise object preferences of the user. Then, we subdivide the objects in containers by modeling a spectral clustering problem. Our solution is easy to update, does not require complex modeling, and improves with the amount of user data. We evaluate our approach using crowdsoucing data from over 1,200 users and demonstrate its effectiveness for two tidy-up scenarios. Additionally, we show that a real robot can reliably predict user preferences using our approach.

Show abstract We are interested in the analysis of very large continuous-time Markov chains (CTMCs) with many distinct rates. Such models arise naturally in the context of the reliability analysis, e.g., of computer networks performability analysis, of power grids, of computer virus vulnerability, and in the study of crowd dynamics. We use abstraction techniques together with novel algorithms for the computation of bounds on the expected final and accumulated rewards in continuous-time Markov decision processes (CTMDPs). These ingredients are combined in a partly symbolic and partly explicit (symblicit) analysis approach. In particular, we circumvent the use of multi-terminal decision diagrams, because the latter do not work well if facing a large number of different rates. We demonstrate the practical applicability and efficiency of the approach on two case studies.

Show abstract Animal models are an essential testbed for new devices on their path from the bench to the patient. Po- tential impairments by brain stimulation are often investi- gated in water mazes to study spatial memory and learn- ing. Video camera based tracking systems exist to quan- tify rodent behaviour, but reflections of ambient light- ing on the water surface and artefacts due to the waves caused by the swimming animal cause errors. This often requires tweaking of algorithms and parameters, or even potentially modifying the lab setup. In the following, we provide a simple solution to alleviate these problem using a combination of region based tracking and independent multimodal background subtraction (IMBS) without having to tweak a plethora of parameters.

Show abstract In this paper, we present a novel mapping system that robustly generates highly accurate 3-D maps using an RGB-D camera. Our approach requires no further sensors or odometry. With the availability of low-cost and light-weight RGB-D sensors such as the Microsoft Kinect, our approach applies to small domestic robots such as vacuum cleaners, as well as flying robots such as quadrocopters. Furthermore, our system can also be used for free-hand reconstruction of detailed 3-D models. In addition to the system itself, we present a thorough experimental evaluation on a publicly available benchmark dataset. We analyze and discuss the influence of several parameters such as the choice of the feature descriptor, the number of visual features, and validation methods. The results of the experiments demonstrate that our system can robustly deal with challenging scenarios such as fast camera motions and feature-poor environments while being fast enough for online operation. Our system is fully available as open source and has already been widely adopted by the robotics community.

Show abstract Conducting polymer films offer a convenient route for the functionalization of implantable microelectrodes without compromising their performance as excellent recording units. A micron thick coating, deposited on the surface of a regular metallic electrode, can elute anti-inflammatory drugs for the treatment of glial scarring as well as growth factors for the support of surrounding neurons. Electro-activation of the polymer drives the release of the substance and should ideally provide a reliable method for controlling quantity and timing of release. Driving signals in the form of a constant potential (CP), a slow redox sweep or a fast pulse are all represented in literature. Few studies present such release in vivo from actual recording and stimulating microelectronic devices. It is essential to bridge the gap between studies based on release in vitro, and the intended application, which would mean release into living and highly delicate tissue. In the biological setting, signals are limited both by available electronics and by the biological safety. Driving signals must not be harmful to tissue and also not activate the tissue in an uncontrolled manner. This review aims at shedding more light on how to select appropriate driving parameters for the polymer electrodes for the in vivo setting. It brings together information regarding activation thresholds for neurons, as well as injury thresholds, and puts this into context with what is known about efficient driving of release from conducting polymer films.

Show abstract The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections ("communication through resonance"). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks.

Show abstract We demonstrate and discuss the fabrication of cross-plane microthermoelectric generators with electrochemically deposited thermoelectric materials. A new process based on two layers of photoresist is presented to deposit the legs on the final substrate. The n-type Bi 2 Te 3 , Cu, and p-type Sb x Te y are integrated into the generator. The deposition of antimony telluride is performed to the largest thickness reported to date. The influence of thermal annealing on the material properties is studied. A new flip-chip reflow soldering process with Bi 57 Sn 42 Ag 1 soldering paste is presented that allows for enhanced thermal coupling of the generators. The manufactured generators are electrically and thermally fully characterized. They generate up to 1.63 μW cm -2 K -2 , which corresponds to a maximum power density of 2434.4 μW cm -2 .

Show abstract EEG-fMRI is a unique method to combine the high temporal resolution of EEG with the high spatial resolution of MRI to study generators of intrinsic brain signals such as sleep grapho-elements or epileptic spikes. While the standard EPI sequence in fMRI experiments has a temporal resolution of around 2.5-3s a newly established fast fMRI sequence called MREG (Magnetic-Resonance-Encephalography) provides a temporal resolution of around 100ms. This technical novelty promises to improve statistics, facilitate correction of physiological artifacts and improve the understanding of epileptic networks in fMRI. The present study compares simultaneous EEG-EPI and EEG-MREG analyzing epileptic spikes to determine the yield of fast MRI in the analysis of intrinsic brain signals. Patients with frequent interictal spikes (>3/20min) underwent EEG-MREG and EEG-EPI (3T, 20min each, voxel size 3×3×3mm, EPI TR=2.61s, MREG TR=0.1s). Timings of the spikes were used in an event-related analysis to generate activation maps of t-statistics. (FMRISTAT, |t|>3.5, cluster size: 7 voxels, p<0.05 corrected). For both sequences, the amplitude and location of significant BOLD activations were compared with the spike topography. 13 patients were recorded and 33 different spike types could be analyzed. Peak T-values were significantly higher in MREG than in EPI (p<0.0001). Positive BOLD effects correlating with the spike topography were found in 8/29 spike types using the EPI and in 22/33 spikes types using the MREG sequence. Negative BOLD responses in the default mode network could be observed in 3/29 spike types with the EPI and in 19/33 with the MREG sequence. With the latter method, BOLD changes were observed even when few spikes occurred during the investigation. Simultaneous EEG-MREG thus is possible with good EEG quality and shows higher sensitivity in regard to the localization of spike-related BOLD responses than EEG-EPI. The development of new methods of analysis for this sequence such as modeling of physiological noise, temporal analysis of the BOLD signal and defining appropriate thresholds is required to fully profit from its high temporal resolution.

Show abstract Understanding the intrinsic circuit-level functional organization of the brain has benefited tremendously from the advent of resting-state fMRI (rsfMRI). In humans, resting-state functional network has been consistently mapped and its alterations have been shown to correlate with symptomatology of various neurological or psychiatric disorders. To date, deciphering the mouse brain functional connectivity (MBFC) with rsfMRI remains a largely underexplored research area, despite the plethora of human brain disorders that can be modeled in this specie. To pave the way from pre-clinical to clinical investigations we characterized here the intrinsic architecture of mouse brain functional circuitry, based on rsfMRI data acquired at 7T using the Cryoprobe technology. High-dimensional spatial group independent component analysis demonstrated fine-grained segregation of cortical and subcortical networks into functional clusters, overlapping with high specificity onto anatomical structures, down to single gray matter nuclei. These clusters, showing a high level of stability and reliability in their patterning, formed the input elements for computing the MBFC network using partial correlation and graph theory. Its topological architecture conserved the fundamental characteristics described for the human and rat brain, such as small-worldness and partitioning into functional modules. Our results additionally showed inter-modular interactions via "network hubs". Each major functional system (motor, somatosensory, limbic, visual, autonomic) was found to have representative hubs that might play an important input/output role and form a functional core for information integration. Moreover, the rostro-dorsal hippocampus formed the highest number of relevant connections with other brain areas, highlighting its importance as core structure for MBFC.

Show abstract Exchange of thoughts by means of expressive speech is fundamental to human communication. However, the neuronal basis of real-life communication in general, and of verbal exchange of ideas in particular, has rarely been studied until now. Here, our aim was to establish an approach for exploring the neuronal processes related to cognitive “idea” units (IUs) in conditions of non-experimental speech production. We investigated whether such units corresponding to single, coherent chunks of speech with syntactically-defined borders, are useful to unravel the neuronal mechanisms underlying real-world human cognition. To this aim, we employed simultaneous electrocorticography (ECoG) and video recordings obtained in pre-neurosurgical diagnostics of epilepsy patients. We transcribed non-experimental, daily hospital conversations, identified IUs in transcriptions of the patients' speech, classified the obtained IUs according to a previously-proposed taxonomy focusing on memory content, and investigated the underlying neuronal activity. In each of our three subjects, we were able to collect a large number of IUs which could be assigned to different functional IU subclasses with a high inter-rater agreement. Robust IU-onset-related changes in spectral magnitude could be observed in high gamma frequencies (70–150 Hz) on the inferior lateral convexity and in the superior temporal cortex regardless of the IU content. A comparison of the topography of these responses with mouth motor and speech areas identified by electrocortical stimulation showed that IUs might be of use for extraoperative mapping of eloquent cortex (average sensitivity: 44.4%, average specificity: 91.1%). High gamma responses specific to memory-related IU subclasses were observed in the inferior parietal and prefrontal regions. IU-based analysis of ECoG recordings during non-experimental communication thus elicits topographically- and functionally-specific effects. We conclude that segmentation of spontaneous real-world speech in linguistically-motivated units is a promising strategy for elucidating the neuronal basis of mental processing during non-experimental communication.

Show abstract Activation detection in functional Magnetic Resonance Imaging (fMRI) typically assumes the hemodynamic response to neuronal activity to be invariant across brain regions and subjects. Reports of substantial variability of the morphology of blood-oxygenation-level-dependent (BOLD) responses are accumulating, suggesting that the use of a single generic model of the expected response in general linear model (GLM) analyses does not provide optimal sensitivity due to model misspecification. Relaxing assumptions of the model can limit the impact of hemodynamic response function (HRF) variability, but at a cost on model parsimony. Alternatively, better specification of the model could be obtained from a priori knowledge of the HRF of a given subject, but the effectiveness of this approach has only been tested on simulation data. Using fast BOLD fMRI, we characterized the variability of hemodynamic responses to a simple event-related auditory-motor task, as well as its effect on activation detection with GLM analyses. We show the variability to be higher between subjects than between regions and variation in different regions to correlate from one subject to the other. Accounting for subject-related variability by deriving subject-specific models from responses to the task in some regions lead to more sensitive detection of responses in other regions. We applied the approach to epilepsy patients, where task-derived patient-specific models provided additional information compared to the use of a generic model for the detection of BOLD responses to epileptiform activity identified on scalp electro-encephalogram (EEG). This work highlights the importance of improving the accuracy of the model for detecting neuronal activation with fMRI, and the fact that it can be done at no cost to model parsimony through the acquisition of independent a priori information about the hemodynamic response.

Show abstract Objective. Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. Approach. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)–(d) are investigated. Main results. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. Significance. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

Show abstract Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.

Show abstract Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user’s intentions and the delivery of nearly “natural” sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and “life-like” quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.

Show abstract Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain–computer interfaces (BCIs). Approach . Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. Main results . We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel str

Show abstract Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI’s classifier can learn to decode the individual’s brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model.

Show abstract Mit bildhaften Vergleichen versuchen Philosophen und Wissenschaftler seit der Antike, die Arbeits­weise des menschlichen Gehirns zu beschreiben. Diese Metaphern sind Kinder ihrer jeweiligen Zeit. Sie spiegeln den aktuellen Stand der Technik wider und prägen somit die Vorstellung vom menschlichen Geist. Die Begriffsschablonen können helfen, die Komplexität des Gehirns besser zu verstehen. Indem sie eine bestimmte Eigenschaft hervorheben, unter­schlagen sie allerdings andere Aspekte, die für das Verständnis ebenso wichtig sein könnten.

Show abstract In optogenetics, neurons are genetically modified to become sensitive to light and thus, they can be stimulated or inhibited by light of certain wavelengths. In this work, we describe the fabrication of a polymer-based shaft electrode as a tool for optogenetics. This device can conduct light as well as fluids to a target brain region and record electrical neural signals from the same part of the tissue simultaneously. It is intended to facilitate optogenetic in vivo experiments with those novel multimodal neural probes or polymer optrodes. We used microsystems technology to integrate an SU-8 based waveguide and fluidic channel into a polyimide-based electrode shaft to allow simultaneous optical stimulation, fluid delivery, and electrophysiological recording in awake behaving animals. In a first acute proof-of-concept experiment in genetically modified mice, our device recorded single unit activity that was modulated by laser light transmitted into the tissue via the integrated waveguide.

Show abstract Neuronal networks confront researchers with an overwhelming complexity of interactions between their elements. A common approach to understanding neuronal processing is to reduce complexity by defining subunits and infer their functional role by selectively modulating them. However, this seemingly straightforward approach may lead to confusing results if the network exhibits parallel pathways leading to recurrent connectivity. We demonstrate limits of the selective modulation approach and argue that, even though highly successful in some instances, the approach fails in networks with complex connectivity. We argue to refine experimental techniques by carefully considering the structural features of the neuronal networks involved. Such methods could dramatically increase the effectiveness of selective modulation and may lead to a mechanistic understanding of principles underlying brain function.

Show abstract A major challenge in neuroscience is to accurately decipher in vivo the entire brain circuitry (connectome) at a microscopic level. Currently, the only methodology providing a global noninvasive window into structural brain connectivity is diffusion tractography. The extent to which the reconstructed pathways reflect realistic neuronal networks depends, however, on data acquisition and postprocessing factors. Through a unique combination of approaches, we designed and evaluated herein a framework for reliable fiber tracking and mapping of the living mouse brain connectome. One important wiring scheme, connecting gray matter regions and passing fiber-crossing areas, was closely examined: the lemniscal thalamocortical (TC) pathway. We quantitatively validated the TC projections inferred from in vivo tractography with correlative histological axonal tracing in the same wild-type and reeler mutant mice. We demonstrated noninvasively that changes in patterning of the cortical sheet, such as highly disorganized cortical lamination in reeler, led to spectacular compensatory remodeling of the TC pathway.

Show abstract In many species, spatial navigation is supported by a network of place cells that exhibit increased firing whenever an animal is in a certain region of an environment. Does this neural representation of location form part of the spatiotemporal context into which episodic memories are encoded? We recorded medial temporal lobe neuronal activity as epilepsy patients performed a hybrid spatial and episodic memory task. We identified place-responsive cells active during virtual navigation and then asked whether the same cells activated during the subsequent recall of navigation-related memories without actual navigation. Place-responsive cell activity was reinstated during episodic memory retrieval. Neuronal firing during the retrieval of each memory was similar to the activity that represented the locations in the environment where the memory was initially encoded.

Show abstract Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

Show abstract Variable responses of neuronal networks to repeated sensory or electrical stimuli reflect the interaction of the stimulus' response with ongoing activity in the brain and its modulation by adaptive mechanisms, such as cognitive context, network state, or cellular excitability and synaptic transmission capability. Here, we focus on reliability, length, delays, and variability of evoked responses with respect to their spatial distribution, interaction with spontaneous activity in the networks, and the contribution of GABAergic inhibition. We identified network-intrinsic principles that underlie the formation and modulation of spontaneous activity and stimulus-response relations with the use of state-dependent stimulation in generic neuronal networks in vitro. The duration of spontaneously recurring network-wide bursts of spikes was best predicted by the length of the preceding interval. Length, delay, and structure of responses to identical stimuli systematically depended on stimulus timing and distance to the stimulation site, which were described by a set of simple functions of spontaneous activity. Response length at proximal recording sites increased with the duration of prestimulus inactivity and was best described by a saturation function y(t) = A(1 − e−αt). Concomitantly, the delays of polysynaptic late responses at distant sites followed an exponential decay y(t) = Be−βt + C. In addition, the speed of propagation was determined by the overall state of the network at the moment of stimulation. Disinhibition increased the number of spikes/network burst and interburst interval length at unchanged gross firing rate, whereas the response modulation by the duration of prestimulus inactivity was preserved. Our data suggest a process of network depression during bursts and subsequent recovery that limit evoked responses following distinct rules. We discuss short-term synaptic depression due to depletion of neurotransmitter vesicles as an underlying mechanism. The seemingly unreliable patterns of spontaneous activity and stimulus-response relations thus follow a predictable structure determined by the interdependencies of network structures and activity states. electrical stimulation of nervous tissue is used increasingly in the treatment of central nervous system disorders, e.g., by deep brain stimulation, in neuroprosthetic devices aiding sensory perception, as well as in examining the biophysiological properties of single cells and the function of neuronal networks. Whereas the reproducible responses of directly stimulated individual neurons consist of precisely timed single action potentials (APs) or trains of APs under specific conditions (Bryant and Segundo 1976; Mainen and Sejnowski 1995; but see Gal et al. 2010), stimulation in recurrent networks elicits multiphasic responses. These typically consist of: 1) a fast excitatory component of precise and reliable firing by antidromic or monosynaptic activation of local neurons with delays between 2 and 20 ms, 2) a transition phase with low activity thought to be mediated by inhibitory neurons, and 3) a delayed excitatory component driven by recurrent polysynaptic activation (Butovas and Schwarz 2003; Eccles et al. 1974; Fanselow and Nicolelis 1999; Rowland and Jaeger 2008; Wagenaar et al. 2004). More physiological sensory responses induced by, e.g., foot tapping, whisker deflection, or air puffs unfold comparable dynamics in various brain regions of awake and anesthetized animals (Cody et al. 1981; Fanselow and Nicolelis 1999; Rowland and Jaeger 2005). Across stimulation trials, however, the variability of timing and duration of the response, as well as the number and distribution of neurons involved, is typically high (Azouz and Gray 1999; Jones et al. 2007). On short time scales, temporal nonstationarities by modulation of activity and excitability tend to prevail and modify response amplitude, latency, and spatial spread (Kisley and Gerstein 1999; Petersen et al. 2003). In addition, the activity state of the neocortex at stimulus onset may dominate trial-by-trial variability (Arieli et al. 1996; Hasenstaub et al. 2007). Although these influences are known in a general sense, they have not been assessed quantitatively, and the prediction of stimulation outcomes for individual stimuli given during autonomous network dynamics is unreliable. Predictable stimulus-response relations, however, become increasingly important to control adequate functionality of neurotechnological devices, which seems incompatible with trial-by-trial variability. To identify the general rules of the interaction between ongoing activity and stimulus-response relations, independent of a specific tissue architecture, function, or sensory stimulus, we analyzed spontaneous and evoked activity dynamics in generic neuronal networks in vitro. These networks exhibit spontaneous spiking with typical patterns and long-term modulation (Wagenaar et al. 2006) that defines the network state with which different stimuli may interact. Here, we focus on reliability, length, delays, and variability of evoked responses with respect to their spatial distribution, interaction with spontaneous activity in the networks, and the contribution of GABAergic inhibition. We asked which interactions arise between weak, local electrical pulses and network state and how they influence evoked responses. Specifically, we developed quantitative models that show how the state of the network at the moment of stimulation determines response length and delay. Closed-loop stimulation relative to ongoing activity significantly reduced trial-by-trial variability and enabled us to examine systematically the influence of network inhibition and short-term plasticity on stimulus-response relations.

Show abstract Theoretische Neurowissenschaftler versuchen, Hirnerkrankungen wie Morbus Parkinson am Computer zu modellieren. Ein von Freiburger Ingenieuren und Mathematikern ersonnenes Computermodell kann die bei Parkinsonpatienten auftretenden Aktivitätsschwankungen in bestimmten Hirnarealen nachbilden. Das Modell sagt vorher, dass eine tiefe Hirnstimulation die krankhaften Oszillationen auch mit deutlich weniger Reizen unterdrücken könnte.

Show abstract A central motif in neuronal networks is convergence, linking several input neurons to one target neuron. In visual cortex, convergence renders target neurons responsive to complex stimuli. Yet, convergence typically sends multiple stimuli to a target, and the behaviorally relevant stimulus must be selected. We used two stimuli, activating separate electrocorticographic V1 sites, and both activating an electrocorticographic V4 site equally strongly. When one of those stimuli activated one V1 site, it gamma synchronized (60-80 Hz) to V4. When the two stimuli activated two V1 sites, primarily the relevant one gamma synchronized to V4. Frequency bands of gamma activities showed substantial overlap containing the band of interareal coherence. The relevant V1 site had its gamma peak frequency 2-3 Hz higher than the irrelevant V1 site and 4-6 Hz higher than V4. Gamma-mediated interareal influences were predominantly directed from V1 to V4. We propose that selective synchronization renders relevant input effective, thereby modulating effective connectivity.

Show abstract Emerging technological developments in nano- and microsystems engineering have delivered powerful tools for neuroscience research over the last 50 years. However, only a few neural implants have been transferred into clinical practice. Challenges and opportunities for translational research are discussed herein.

Show abstract The widespread use of conducting polymers, especially poly(3,4-ethylene dioxythiophene) (PEDOT), within the space of bioelectronics has enabled improvements, both in terms of electrochemistry and functional versatility, of conventional metallic electrodes. This short review aims to provide an overview of how PEDOT coatings have contributed to functionalizing existing bioelectronics, the challenges which meet conducting polymer coatings from a regulatory and stability point of view and the possibilities to bring PEDOT-based coatings into large-scale clinical applications. Finally, their potential use for enabling new technologies for the field of bioelectronics as biodegradable, stretchable and slow-stimulation materials will be discussed.

Show abstract Over the past decades, optical technologies have entered neural implant technologies. Applications such as optogenetics, near-infrared spectroscopy (NIRS), and direct-near-infrared stimulation (NIS) request technical devices that combine electrical and optical recording as well as stimulation capabilities using light sources and/or optical sensors. Optoprobes are the technical devices that meet these requirements. This paper provides basic insights into optogenetic mechanisms, the background of NIRS and NIS, and focuses on fundamental requirements of technical systems from a biological background. The state of the art of optoprobes is reviewed and attention is drawn on the potential long-term stability of these technical devices for chronic neural implants. Further, material selection for stiff and flexible devices, applicable light sources, waveguide and coupling concepts, packaging paradigms as well as system assembly and integration aspects are discussed in view of biocompatible and biostable devices. This paper also considers the physical background of light scattering and heat generation when light sources are implanted into biological tissue.

Show abstract Neural prostheses are technical systems that interface nerves to treat the symptoms of neurological diseases and to restore sensory of motor functions of the body. Success stories have been written with the cochlear implant to restore hearing, with spinal cord stimulators to treat chronic pain as well as urge incontinence, and with deep brain stimulators in patients suffering from Parkinson's disease. Highly complex neural implants for novel medical applications can be miniaturized either by means of precision mechanics technologies using known and established materials for electrodes, cables, and hermetic packages or by applying microsystems technologies. Examples for both approaches will be introduced and discussed. Electrode arrays for recording of electrocorticograms during presurgical epilepsy diagnosis have been manufactured using approved materials and a marking laser to achieve an integration density that is adequate in the context of brain machine interfaces, e.g. on the motor cortex. Microtechnologies have to be used for further miniaturization to develop polymer-based flexible and light weighted electrode arrays to interface the peripheral and central nervous system. Polyimide as substrate and insulation material will be discussed as well as several application examples for nerve interfaces like cuffs, filament like electrodes and large arrays for subdural implantation.

Show abstract Two big trends are leading the way to a new generation of thin-film electrocorticography (ECoG) micro electrode arrays (MEAs): miniaturization, which combines higher electrode densities with thinner substrates for conformability purposes; and the pursuit to extend the recording frequency band to 1 kHz and beyond (recording of spikes). When combining these two trends, however, the frequency-dependent phenomenon of crosstalk emerges as a possible setback to the so desired spatial selectivity. In this work, high in vivo coherences at 1 kHz between electrodes with neighboring tracks are reported when using the MuSA (Multi-Species Array) as recording ECoG MEA on rats. These results suggest a high degree of crosstalk between closely routed electrodes, even if placed far apart on the array, and are corroborated by coherence plots of control recordings in vitro in phosphate buffered saline (PBS). As means to estimate the combined leakage resistance and capacitance between the signal lines and the targeted brain tissue, an impedance spatial sweep over the 32 tracks routing the MuSA electrodes is performed in PBS in a two-electrode electrochemical impedance spectroscopy setup. This study should raise awareness of crosstalk as an important aspect to consider when aiming for high-quality, high-density and high-frequency neural recordings.

Show abstract Micro-electrode arrays for electrocorticography (ECoG) represent the best compromise between invasiveness and signal quality, as they are surface devices that still allow high sensitivity recordings. In this work, an assessment of different technical aspects determining the ultimate performance of ultra-conformable polyimide-based μECoG arrays is conducted via a finite element model, impedance spectroscopy measurements and recordings of sensorimotor evoked potentials (SEPs) in rats. The finite element model proves that conformability of thin-film arrays can be achieved with polyimide, a non-stretchable material, by adjusting its thickness according to the curvature of the targeted anatomical area. From the electrochemical characterization of the devices, intrinsic thermal noise of platinum and gold electrodes is estimated to be 3–5 μV. Results show that electrode size and in vitro impedance do not influence the amplitude of the recorded SEPs. However, the use of a reference on-skull (a metal screw), as compared to reference on-array (a metal electrode surrounding the recording area), provides higher-amplitude SEPs. Additionally, the incorporation of a grounded metal shield in the thin-film devices limits crosstalk between tracks and does not compromise the recording capabilities of the arrays.

Show abstract Intercranial EEG (icEEG) electrodes are implanted for pre-surgical assessment of cortical electrical activity in patients with epilepsy. MRI helps to localize the implant with respect to the individual’s anatomy and fMRI can improve understanding of the neuropathology. However, the magnetic susceptibility artifacts caused by the metal components of commercially available implants produce MRI artifacts that compromise the results especially in the direct vicinity of the implants. Next generation “thin-film” implants which feature 100x less metal thickness can mitigate these artifacts, but thin-film implants produce inconspicuous MRI signal voids in many clinical MRI sequences. Imperatively, physicians need to know the implant position and the value of EEG increases with the precision of electrode localization. Therefore, we investigated various concentrations of super paramagnetic iron oxide (SPIO) nanoparticles (NP) to label thin-film implants for localization in MRI using a range of sequences. In particular, we aim to create a marker that conspicuously renders the implant, enables spatial localization of the individual electrodes, and keeps disruptive imaging artifacts small.

Show abstract The lack of sensory feedback during grasping is a very important limitation of current hand prostheses, which affects their everyday usability. In the last years several research groups have demonstrated that nerve stimulation by implantable peripheral nerve interfaces can be reliably used to restore sensory feedback to upper limb amputees. They have shown that direct neural stimulation of peripheral nerves can effectively provide tactile information to the amputees, controlling the sensation intensity by modulating either the amplitude or the frequency of the injected stimuli. However, efforts are still necessary to identify encoding strategies converting tactile information into neural stimulation patterns capable of eliciting percepts that are both felt as natural and effective for prosthesis control. In this study, we compared the naturalness and efficacy of a set of encoding strategies based on biomimetic (model-driven) frequency modulation, amplitude modulation, or combinations of both. Such strategies were used to deliver neural stimulation to a trans-radial amputee implanted with intraneural electrodes (TIMEs). Frequency modulation was based on a biomimetic model (TouchSim) able to reproduce nerve activation patterns of the multifaceted mechanics of the skin and mechano-transduction. It was perceived as more natural, while amplitude modulation enabled better performance in tasks requiring fine identification of the applied force. Notably, hybrid encoding strategies involving both amplitude and frequency modulation were able to convey at least as much information as the amplitude modulation (for the completion of tasks), and were perceived at least as natural as the frequency modulation. The hybrid strategies improved the gross manual dexterity of the subjects during functional tasks while maintaining high manual accuracy. They also improved the level of prosthesis embodiment and reduced abnormal body perceptions of the phantom limb (“telescoping”). Encoding strategies based on the combination of biomimetic frequency modulation and amplitude modulation are able to provide highly sensitive and natural percepts and should be preferred in bidirectional prosthesis use.

Show abstract Treatments of neurological disorders utilizing active implantable devices which interact with the activity of the brain are demonstrating increasingly promising advances. Next to the continuous improvement of established therapies for movement disorders, Epilepsy and chronic pain, new therapies for depression, paralysis and many more are under investigation. The current technology available for the development of these treatments is derived from the first active implants, the cardiac pacemakers, developed in mid to late 20th century: battery powered devices with few channels and limited intelligence. The Brain Interchange technology, developed in a joint effort by the University of Freiburg and CorTec, is a new system, enabling battery-free, intelligent closed-loop applications with up to 32 channels in its first version. The implantable part, including a novel hermetic encapsulation, custom electronics and firmware, were presented last year. The electrode technology will be presented in a companion poster. Here we present the progress in the development of the external parts and the software of the system and discuss potential applications. The first version of the external parts of the system manages power and communication with the implant, as well as the software for controlling the system. Physically, these parts consist of a head piece, a relay- and a controller unit. The main functions, accessible via a graphical user interface, are: managing recording and stimulation, measuring impedance and reading out humidity, temperature, supply voltage and unique ID of the implant. A filter pipeline for signal processing and feature extraction, suitable for computationally demanding closed-loop algorithms, and the control of external devices with minimal latency has also been implemented. For future collaboration partners programmable interfaces for C++, Matlab and Python are available. The development has been done under ISO-13485 and according to EN 62304. Potential applications range from closed-loop stimulation for the treatment of Parkinson’s disease or Epilepsy to the control of assistive technology in chronic paralysis or for rehabilitation purposes. Further applications could lie in closed-loop treatments in the peripheral nervous system.

Show abstract Increasing the accessibility of autonomous robots also for inexperienced users requires user-friendly and highlevel control opportunities of robotic systems. While automated planning is able to decompose a complex task into a sequence of steps which reaches an intended goal, it is difficult to formulate such a goal without knowing the internals of the planning system and the exact capabilities of the robot. This becomes even more important in dynamic environments in which manipulable objects are subject to change. In this paper, we present an adaptive control interface which allows users to specify goals based on an internal world model by incrementally building referring expressions to the objects in the world. We consider fetch-and-carry tasks and automatically deduce potential high-level goals from the world model to make them available to the user. Based on its perceptions our system can react to changes in the environment by adapting the goal formulation within the domain-independent planning system.

Show abstract Abstract-Implantable, closed-loop devices for automated early detection and stimulation of epileptic seizures are promising treatment options for patients with severe epilepsy that cannot be treated with traditional means. Most approaches for early seizure detection in the literature are, however, not optimized for implementation on ultra-low power microcontrollers required for long-term implantation. In this paper we present a convolutional neural network for the early detection of seizures from intracranial EEG signals, designed specifically for this purpose. In addition, we investigate approximations to comply with hardware limits while preserving accuracy. We compare our approach to three previously proposed convolutional neural networks and a feature-based SVM classifier with respect to detection accuracy, latency and computational needs. Evaluation is based on a comprehensive database with long-term EEG recordings. Key ResultThe proposed method outperforms the other detectors with a median sensitivity of 0.96, false detection rate of 10.1 per hour and median detection delay of 3.7 seconds, while being the only approach suited to be realized on a low power microcontroller due to its parsimonious use of computational and memory resources.

Show abstract An innovative fabrication process of glass waveguides on silicon substrates for miniaturized implants is presented. Thin glass was bonded on oxidized silicon wafers and patterned using wet etching. Multimode waveguides with different shapes and a low surface roughness as well as low scattering of light were successfully fabricated. For efficient coupling of light and accurate alignment, KOH-grooves were etched in the silicon with respect to the glass waveguides to attach optical fibers from external light sources. Towards higher biostability, several coating and cladding materials were evaluated in accelerated in vitro tests in 60°C PBS. TiO2, SiC polyimide, Parylene C and SU-8 showed a very stable optical transmittance after 320 days in accelerated aging while PECVD Si3N4 showed significant changes within the first days.

Show abstract The high complexity of the biological response to implanted materials builds a serious barrier against implanted recording and stimulation electrode arrays to succeed in clini- cally relevant chronic studies. Some of the cell and molecular inte ractions and the ir contribution to inflammation and de vice failure are s till uncle ar. The inte rre late d me chanis ms le ading to tissue damage and electrode array failure during simultaneous faradaic, electrochemical reactions and biological response under electrical stimulation are not understood sufficiently. One variable, with which inflammatory and electrode surface processes can be analyzed and assessed, is the pH change in the immediate environment of the material-tissue interface. Here, the greatest challenges are in the biocompatibility and in-vivo long-term stability of selected sensor materials, the measure- ment of small transient pH oscillations and positioning of the sensor at a defined and nearest possible distance in the mi- crometer range, to the site of activity without the pH sensing being affected by the material-tissue interactions itself. This work represents the in-situ measurement of local and transient pH changes at a pulsed electrode with an embedded in-vivo compatible pH sensor and therein differentiating from current approaches of pH sensing during electrical stimulation.

Show abstract In dynamic environments, learned controllers are supposed to take motion into account when selecting the action to be taken. However, in existing reinforcement learning works motion is rarely treated explicitly; it is rather assumed that the controller learns the necessary motion representation from temporal stacks of frames implicitly. In this paper, we show that for continuous control tasks learning an explicit representation of motion clearly improves the quality of the learned controller in dynamic scenarios. We demonstrate this on common benchmark tasks (Walker, Swimmer, Hopper), on target reaching and ball catching tasks with simulated robotic arms, and on a dynamic single ball juggling task. Moreover, we find that when equipped with an appropriate network architecture, the agent can, on some tasks, learn motion features also with pure reinforcement learning, without additional supervision.

Show abstract Cervical spinal cord injury (SCI) and stroke severely impact grasping movements required for activities of daily living. Intraneural peripheral nerve stimulation enables specific activation of passing fibers. This paradigm has restored precise leg movements in animal models of SCI and selective sensation in human amputees. Intraneural peripheral nerve stimulation may also restore fine grasping in paralyzed hands, but this possibility has not been investigated. Here, we assess the feasibility of using intrafascicular electrical stimulation of peripheral nerves to produce precise hand movements in the non-human primate (NHP). We first extensively characterized the branching points of the median, radial, and ulnar nerves to their target muscles in the adult macaca fascicularis in order to identify the optimal implantation site for intraneural electrodes. We then reconstructed the tridimensional structure of the identified portion of each nerve in order to analyze the fascicular organization within the nerve at this level. Additionally, we assessed the distribution of motor fibers within the fascicles using immunohistochemistry. The obtained data was used to build realistic computational models of intraneural peripheral nerve stimulation for each nerve. The simulations confirmed the advantages of using intrafascicular electrodes to induce precise hand movements, demonstrating the possibility to selectively recruit patches of motor fibers without a priori knowledge of the electrode placement. We validated these results during electrophysiology experiments using transverse intrafascicular multichannel electrodes (TIMEs) implanted in the nerves of anesthetized NHPs. The stimulation achieved a selective recruitment of wrist and finger flexors and extensors in a reproducible manner across animals. We exploited these results to determine stimulation sequences that aimed at reproducing the muscle activation patterns underlying different grasping movements. For this, we mapped the obtained muscle recruitment maps to continuous EMG recordings during behavioral experiments and automatically optimized the selection of stimulation channels for each phase of the movement such as to reproduce the desired EMG pattern. Taken together, these results suggest encouraging evidences for the usability of the TIMEs to restore fine hand control after paralysis.

Show abstract Brain implants are increasingly used in neuroscientific research and medical applications. The requirements for such implants are diverse due to different experimental paradigms, scientific problem to address and demands by the researcher or clinician. To overcome these requirements, brain implants that can be built in a customized fashion might be beneficial. We previously introduced such a research grade wireless brain implant, developed exclusively using off-the-shelf components, which allows for quick and customized assembly. To verify the operability of the device during recording and stimulation, we present neurophysiological data obtained in an ovine animal model. During general anesthesia, the 63-channel µECoG electrode array was placed on the cortex of the sheep brain and both auditory and electrical stimuli were used to evoke neurophysiological responses which were recorded at a sampling rate of 4 kHz. Experiments performed in this study were conducted according to EU Directive 2010/63/EU and approved by the Animal Committee of the University of Freiburg and the Regierungspraesidium Freiburg, Germany. We show that our off-the-shelf research grade brain implant is capable to reliably record neurophysiological brain activity and electrically stimulate, evoking cortico-cortical responses, under in vivo conditions. The obtained neurophysiological activity showed clear responses with distinct spatio-temporal patterns to both auditory stimuli and cortical electrical stimulation, the latter with response patterns systematically depending on the exact stimulation site. In addition, spectral analysis revealed a neurophysiological frequency profile of the recorded activity which is in agreement with the well-known frequency power-law, i.e., the frequency-dependent linear decrease of log-log absolute spectral power. The presented neurophysiological data are in agreement with previously published recording and stimulation results obtained in sheep using different implantable devices that were not off-the-shelf. The results and their neurobiological implications as presented here highlight that off-the-shelf component brain interfacing devices are feasible and thus open up new avenues for implant-based research, especially when flexible requirements have to be addressed, as it is often the case in basic neuroscientific research. In the future we want to expand our approach to higher channel counts (128-channel high-resolution µECoG electrode arrays), higher recording sampling rate, and functional systems beyond the auditory system, as further use cases of our implant concept in neurotechnological research.

Show abstract As research becomes more and more data intensive, manag-ing this data becomes a major challenge in any organization.At university level there is seldom a unified data managementsystem in place. The general approach to storing data in suchenvironments is to deploy network storage. Each member canstore their data organized to their own likings in their ded-icated location on the network. Additionally, users tend tostore data in distributed manner such as on private devices,portable storage, or public and private repositories. Addingto this complexity, it is common for university departmentsto have high fluctuation of staff, resulting in major loss ofinformation and data on an employee’s departure. A com-mon scenario then is that it is known that certain data hasalready been created via experiments or simulation. However,it can not be retrieved, resulting in a repetition of generation,which is costly and time-consuming. Additionally, as of re-cent years, publishers and funding agencies insist on storing,sharing, and reusing existing research data. We show howdigital preservation can help group leaders and their employ-ees cope with these issues, by introducing our own archivalsystem OntoRAIS.

Show abstract This work presents reliability investigations of silicone gasket as solid underfill for interconnection interfaces in hybrid implant systems with high channel count flexible electrode arrays and hermetically packed electronics. The gasket is fabricated by laser structuring thin sheet of silicone rubber. The surface activation of silicone sheet ensures mechanical bonds with the mating surfaces thereby improving the mechanical stability of the assembly and the insulation of the interconnects. The gasket samples with 10 × 10 openings for interconnect pads, each with diameter of 270 μm and a center to center pitch size of 490 μm, were sandwiched between a polyimide array and a metallized ceramic substrate. The gasket maintained high insulation impedance of 15 ± 0.30 MΩ between the adjacent interconnects with markedly capacitive behavior (phase angle, -89 °) after 17 weeks in soaked conditions under accelerated aging at 60 °C. The gasket also survived electrical stresses and sustained high impedance (10.93 MΩ with phase angle of -88 °) when subjected to constant 3 VDC for 100 days.

Show abstract Alternating current stimulation (ACS) provides a versatile tool for modulating brain activity and presents a promising strategy for the treatment of neurological disorders like Parkinson’s disease or epilepsy. Stimulation of neural tissue at low-frequency however poses new challenges on conventional electrode materials which support limited charge transfer in the desired frequency range, from less than 0.1 Hz to several tens of Hz. In our study we address this challenge by investigating the charge transfer properties of PEDOT/PSS coatings for low-frequency applications, focusing on the impact of the polymer bulk. PEDOT films of various thicknesses were exposed to low-frequency as well as DC stimulation in vitro and compared to Pt and IrOx electrodes as controls. The charge injection performance of the metallic substrates could be substantially improved already by a thin PEDOT coating. Additionally a linear dependency between charge injection and polymer thickness suggests that PEDOT coatings are promising as materials for future ACS applications.

Show abstract Leg amputation destroys the communication between brain and environment during walking. Leg amputees rely on practically inexistent and often uncomfortable haptic feedback from the stump-socket interaction to monitor ground and obstacles contact, climb stairs, or walk in challenging environments. The lack of sensory feedback causes specific impairments to subjects that do not perceive the prosthesis as part of their body and risk falls, have decreased mobility, increased cognitive burden during walking resulting in prosthesis abandonment. In hand amputees, to restore the bidirectional communication, nerve interfaces have directly linked sensors readout from robotic hands to direct stimulation of nerves above injury. We believe that this strategy could also restore sensations from missing legs, with many scientific and technological barriers to overcome. It has never been proved that the electrical stimulation of the leg nerves by implantable neural interfaces can induce reliable sensations from missing leg and foot. In this work we developed a leg prosthesis restoring sensory feedback by means of direct nerve stimulation injected through transversal intraneural electrodes (TIME) implanted in the sciatic nerve. The stimulation was driven by the readout of pressure sensors placed under the prosthetic foot, and an encoder embedded in the prosthetic knee. We assessed the capability of 3 transfemoral amputees to recognize, blindfolded and acoustically insulated, the location where the prosthetic foot was touched and the degree of flexion of the prosthetic knee. The subjects were asked to recognize only touch, only flexion and then both conditions at the same time. We compared the performance when sensory feedback was restored and when no nerve stimulation was delivered. We found that single and double condition-tasks were executed on average respectively with a success rate of about 85% and 75%, when sensory feedback was provided. Without nerve stimulation the average success rate dropped to 25%.

Show abstract The design of microelectrodes for electrical stimulation or recording of neuronal activity increasingly focuses on biocompatibility. Still, implantation of a cortical probe evokes a sustained sterile inflammatory response (SIR) within brain tissue. This manifests in activation of microglia, astrocytic scarring, neurodegeneration, and other molecular and morphological changes that may ultimately lead to failure of the device. To visualize the extent of the spatially limited tissue response, immunohistochemistry (IHC) is the well-established method of choice. But while qualitative assessment of resulting microscopic images can be easily accomplished, quantitative analysis poses a larger challenge to the investigator. This is partly due to the large number of images required to adequately represent the SIR, but also accounted for by high variability within and across tissue sections and animals. To this point there have been several publications that employed custom-built codes for quantification of IHC signals at the implantation sites of cortical electrodes. However, these differ greatly with respect to their evaluation methods and are not provided for open access. Hence, it is difficult to compare the extent of the SIR across different or even identical electrode types utilized by different groups. We therefore aimed at creating a semi-automated quantification method that allows fast and flexible delineation of the signal-intensity distribution from the site of lesion towards healthy brain tissue, based on the widely used software tools ImageJ and MATLAB. In this study cortical implantation of lithographically fabricated, flexible polyimide probes (10µm thickness with iridium oxide electrodes) was performed on adult Sprague Dawley rats. After different survival times transversal tissue sections were immunohistochemically stained and imaged with an epifluorescence microscope. Subsequent signal quantification was performed in concentric distance bins with flexibly adaptable size (the shape of the latter is determined by the respective cavity shape of the individual image). Processing artifacts can optionally be selected and excluded from the analysis. Apart from that normalization parameters can be modified towards the needs of the study design. This also yields the possibility of using differently processed and/or imaged tissue sections within one experimental population. Our approach provides a novel, straightforward tool for quantification of IHC-images that is versatile and adaptable for various purposes.

Show abstract In 1997, deep brain stimulation (DBS) was approved by the FDA for treatment of essential tremor. In the following decades neuromodulation of the CNS became a active field and was applied for treating different conditions. Similar to the technological progress of cardiac pacemakers, concepts were developed to adapt the stimulation to the patient's need, making the devices responsive. Today, two of these closed-loop devices are approved for clinical use, Medtronic Activa PC+S and Neuropace RNS. Both devices work with eight electrode contacts on the surface or deep inside the brain and permit delivery of electrical stimuli initiated, or modified in intensity, based on neural recordings. Here, we present a closed-loop device that overcomes current application limitation by increasing the electrode contact number, minimizing the closed-loop response time and transferring the closed-loop algorithms to a device outside the body, allowing maximum freedom for clinical research. The design is inspired by today's cochlear implants: The implant is wirelessly powered by a body-external transceiver. Cortical electrode arrays and DBS electrodes can be connected to the hermetically packaged implanted electronics. The device records synchronously from 32 electrode contacts at 1kS/s (16bit) at a pass band of 0.5 to 450Hz. Data are wirelessly streamed to the body-external transceiver, which is connected to a laptop-PC, running the control software. The software can send instructions to the implant to generate electrical stimuli of up to 6mA on each of the 32 electrode contacts. Typically, it takes some 10ms for closing the loop of recording and recording-based stimulation, strongly depended on the signal analysis and decision-taking algorithms used. The system was implanted in sheep (approved by the Regierungspraesidium Freiburg, Germany and the Animal Ethics Committee of the University of Freiburg) to investigate long-term functionality and biological acceptance. Excellent robustness of the implanted hardware, good biological acceptance and stable recording signal quality could be demonstrated. We present the latest results from the animal studies and technical improvements developed based on prior results. In conclusion, the implant system presented has the potential for researching closed-loop therapies for the central nervous system. The validations towards clearance for clinical studies are currently on the way.

Show abstract The variety of "`ready-to-use'" implantable recording and stimulation systems commercially available for neuroscience is very limited and fabrication of custom made implants is commonly considered expensive and time consuming. We present a circuit design that allows cost efficient and fast translation of available components into fully wireless implants. As demonstration fully wireless implantable bidirectional neural interfaces are presented which are made of commercial off-the-shelf components (COTS) only. It is demonstrated that they are competitive to currently available state-of-the-art systems regarding size and performance.

Show abstract Decoding the human brain state with BCI methods can be seen as a building block for human-machine interaction, providing a noisy but objective, low-latency information channel including human reactions to the environment. Specifically in the context of autonomous driving, human judgement is relevant in high-level scene understanding. Despite advances in computer vision and scene understanding, it is still challenging to go from the detection of traffic events to the detection of hazards. We present a preliminary study on hazard perception, implemented in the context of natural driving videos. These have been augmented with artificial events to create potentially hazardous driving situations. We decode brain signals from electroencephalography (EEG) in order to classify single events into hazardous and non-hazardous ones. We find that event-related responses can be discriminated and the classification of events yields an AUC of 0.79. We see these results as a step towards incorporating EEG feedback into more complex, real-world tasks.

Show abstract We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet architectures recently shown to decode task-related information from EEG at least as well as established algorithms designed for this purpose. In decoding EEG pathology, both ConvNets reached substantially better accuracies (about 6% better, ~85% vs. ~79%) than the only published result for this dataset, and were still better when using only 1 minute of each recording for training and only six seconds of each recording for testing. We used automated methods to optimize architectural hyperparameters and found intriguingly different ConvNet architectures, e.g., with max pooling as the only nonlinearity. Visualizations of the ConvNet decoding behavior showed that they used spectral power changes in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside other features, consistent with expectations derived from spectral analysis of the EEG data and from the textual medical reports. Analysis of the textual medical reports also highlighted the potential for accuracy increases by integrating contextual information, such as the age of subjects. In summary, the ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic.

Show abstract In this paper we consider the problem of robot navigation in simple maze-like environments where the robot has to rely on its onboard sensors to perform the navigation task. In particular, we are interested in solutions to this problem that do not require localization, mapping or planning. Additionally, we require that our solution can quickly adapt to new situations (e.g., changing navigation goals and environments). To meet these criteria we frame this problem as a sequence of related reinforcement learning tasks. We propose a successor-feature-based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances. Our algorithm substantially decreases the required learning time after the first task instance has been solved, which makes it easily adaptable to changing environments. We validate our method in both simulated and real robot experiments with a Robotino and compare it to a set of baseline methods including classical planning-based navigation.

Show abstract Maintaining the insulation between adjacent electrical interconnections is critical for the success of active implantable medical device. Underfilling and globtop coating of dense arrays of microfabricated interconnects pose a big reliability risk. Contamination, voids and the difficulty to deposit liquid underfillers with the appropriate adhesive behavior remains a major hurdle when developing miniaturized high-channel neural interfaces. We approach a bottom up process to fabricate adhesion promoting, graded interfaces on the bottom and top side of polyimide-based microelectrode arrays. This allows the use of pre-molded silicone rubber gaskets as dry underfill material and silicone rubber as globtop material. In this work we present the layer deposition approach to solve the difficulties of providing a double-sided, inversely oriented layer stack for an adhering silicon-oxide termination on polyimide substrates. By introducing a sacrificial polyimide layer, we permit high temperature depositions of the required layers allowing a release of the fabricated stack at the desired interface. Long term stable silicone rubber underfill and overcoat is thus achievable despite the use of polyimide substrates. The fabricated samples showed better adhesion to silicone rubber even after storing in phosphate buffered saline (PBS) at 85 °C for 18 hours and at 60°C for 72 hours. The Fourier Transform infrared (FTIR) spectrum also revealed the integrity of the structural stack after detachment from the release layer. The fabrication of double side layer stacks increases the confidence in long term stability of interconnects in polyimide electrodes.

Show abstract Long-term stability of neural interfaces is a challenge that has still to be overcome. In this study, we manufactured a highly stable multi-layer thin-film class of carbon-based devices for electrocorticography (ECoG) incorporating silicon carbide (SiC) and amorphous carbon (DLC) as adhesion promoters between glassy carbon (GC) electrodes and polyimide (PI) substrate and between PI and platinum (Pt) traces. We aged the thin-film electrodes in 30 mM H2O2 at 39 °C for one week - to mimic the effects of post-surgery inflammatory reaction - and subsequently stressed them with 2500 CV cycles. We additionally performed stability tests stimulating the electrodes with 15 million biphasic pulses. Finally, we implanted the electrodes for 6 weeks into rat models and optically characterized the explanted devices. Results show that the fabricated ECoG devices were able to withstand the in vitro and in vivo tests without significant change in impedance and morphology.

Show abstract In the field of neural prostheses, much attention has lately been given to the long-term performance not only of the electronic components but also of the parts directly interfacing with the nervous system. Neural interfaces have, in fact, a critical role in chronic applications, where they have to outlast the highly humid and oxidative body environment without undergoing delamination, corroding and without losing their functionality over time. Among all, carbon has been proved to be the material with the highest potential to contemporary serve as biomaterial for recording nerve cells activity, electrically stimulating them and, in addition, for selectively detecting the presence of neurotransmitters or other electrically active biomolecules. However, the feasibility of the fabrication method - with respect to process complexity and cost - is a factor of great importance and it is not always easy to accomplish with carbon electrodes. In this work, we present a new method to manufacture thin-film microelectrode arrays (MEAs) with laser-induced carbon active sites made from parylene c coatings on platinum iridium tracks. Such MEAs are manufactured without the need for cleanroom and MEMS processes. Prototypes of these carbon electrodes were evaluated first in vitro in hydrogen peroxide to mimic the post-surgery oxidative environment due to the acute inflammatory reaction to the implant. Electrodes were stimulated using biphasic pulses to prove their stability under electrical stress and testes with respect to their biosensing capabilities on different concentrations of dopamine in PBS. Results show that our laser-induced carbon electrodes do not deteriorate under chemical and electrochemical loads. They were able to detect different dopamine levels in vitro. These new laser-induced carbon electrodes show promising potential to successfully be implanted in vivo and be used for long-term neural applications for recording, stimulation and biochemical sensing.

Show abstract The technology for miniaturization of bioelectronics is making great progress, and the interest for high density electrode recordings in the neural systems is continually increasing. High density recording of, e.g., the cortical activity could help scientists to elucidate the language of the brain and further increase our understanding of the behavior of the cells in the neural system. Furthermore, a high density electrode implant would also increase the possibility to choose specific electrodes, e.g. in the proximity of the desired neural target, or active sites instead of silent. Also, the ability to map larger surface areas with high density arrays could help growing the understanding for the connectivity between different regions in the brain. In our group we have developed several structures based on the polymer polyimide, which is a flexible, stable and biocompatible polymer, therefore excellent for neural probes, especially for long-term applications with high demands on reliability. Designs have been targeted to animal models of turtles, rats, ferrets, cats and macaque monkeys. Modular, finger-based designs adapted well to the two-dimensionally curved structure of the brain surface even though the substrate material itself is not stretchable. Electrode size and pitch have been adapted to the size of the target structures. Array variations comprised 36, 64, 96, 192 and 252 electrode sites. Several high density ECoG arrays have been fabricated and implanted into primates. These showed good long-term stability and both single-unit activity, as well as multi-unit activity and local field potentials could be recorded via platinum and iridium oxide thin-film metal sites. Signal-to-noise ratios were sufficiently high over months and degraded only slowly. Results on stability and functionality are promising and consistent with other translational studies on peripheral nerves.

Show abstract Miniaturization of electrodes is a prerequisite of selective and targeted interaction with single neurons, enabling more applications in the continuously growing field of neuroprostheses. Miniaturization in all three dimensions of the electrical contact sites should maintain or increase longevity and electrical functionality. The thin-film metallization of the electrode site, which is only a couple of hundreds of nanometers thick, has to withstand high chemical load through the corrosive environment in the body and the electrochemical processes during electrical stimulation in vivo. Platinum (Pt), which is known to be chemically inert and mechanical stable as bulk material shows a lack of chemical and mechanical integrity applied in thin-film microelectrodes. In our study we investigated failure mechanisms of thin-film Pt electrodes under conditions of electrode aging and electrical stimulation in different physiological media. To understand and eventually overcome stability loss, we investigated the intrinsic structural stress and deformations that arose from mechanical loading through chemical impact and electrical stimulation using optical microscopy and white-light interferometry. Electrochemical measurements indicated oxidation and surface roughening as two of the degradation processes in thin-film electrodes. From the results presumptions about the underlying microstructural changes were made.

Show abstract We study motion planning problems where agents move inside environments that are not fully observable and subject to uncertainties. The goal is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications. Such problems are naturally modeled by partially observable Markov decision processes (POMDPs). Because of the potentially huge or even infinite belief space of POMDPs, verification and strategy synthesis is in general computationally intractable. We tackle this difficulty by exploiting typical structural properties of such scenarios; for instance, we assume that agents have the ability to observe their own positions inside an evironment. Ambiguity in the state of the environment is abstracted into non-deterministic choices over the possible states of the environment. Technically, this abstraction transforms POMDPs into probabilistic two-player games (PGs). For these PGs, efficient verification tools are able to determine strategies that approximate certain measures on the POMDP. If an approximation is too coarse to provide guarantees, an abstraction refinement scheme further resolves the belief space of the POMDP. We demonstrate that our method improves the state of the art by orders of magnitude compared to a direct solution of the POMDP.

Show abstract Nanostructured materials exhibit large electrochemical surface areas and are thus of high interest for neural interfaces where low impedance and high charge transfer characteristics are desired. While progress in nanotechnology successively enabled smaller feature sizes and thus improved electrochemical properties, concerns were raised with respect to the mechanical stability of such nano structures for use in neural applications. In our study we address these concerns by investigating the mechanical and electrochemical stability of nanostructured platinum. Neural probes with nano-Pt were exposed to exaggerated stress tests resembling insertion into neural tissue over 60 mm distance or long-term stimulation over 240 M biphasic current pulses. Thereby only insignificant changes in electrochemical properties and morphological appearance could be observed in response to the test, proving that nanostructured platinum exhibits outstanding stability. With this finding, a major concern in using nanostructured materials for interfacing neural tissue could be eliminated, demonstrating the high potential of nanostructured platinum for neuroprosthetic devices.

Show abstract The combination of implantable neural electrodes and fMRI holds great potential for better understanding the human brain. However, the image acquisition - especially in the vicinity of the implants - is compromised by artifacts caused by metal components. In this work we address this issue by studying different types of devices in terms of designs and materials, and by quantifying their MRI artifacts. Doing so we demonstrate the quasi artifact-free behavior of a hybrid probe combining surface and penetrating carbon electrodes into a single sheet of polyimide, after comparing it with conventional implants in high field MRI and clinical fMRI.

Show abstract The loss of a limb permanently disrupts daily living activities. Prosthetic devices are an alternative to partially circumvent this disability. The lack of sensory feedback of current prosthetic options limits their acceptance and usability rate. In the upper limb, somatosensory percepts are essential for proper object manipulation, while in the lower counterparts proprioceptive and cutaneous sensations are required to maintain balance and stable gait. Restoral of sensory feedback also improves the sense of embodiment of prosthetic limbs, which positively impacts user satisfaction. The introduction of surgically targeted muscle reinnervation (TMR) led to promising outcomes in terms of controllability of prosthetic devices, and in providing a novel channel to restore sensory information. TMR consists on re-routing the remaining peripheral nerves from the amputee’s stump to the chest area. After transferring the nerves into the chest, afferent and efferent fibers reinnervate the hosting muscles, amplifying the signals from the efferent pathways, and providing a more selective channel for activating afferent fibers. We have previously demonstrated that electrical stimulation of peripheral nerves using implanted intrafascicular electrodes elicits natural sensory feedback during real-time, bidirectional control of a prosthetic hand. In this study, we propose that after TMR, the afferent reinnervated fibers can be electrically stimulated to restore natural somatosensations. We further propose that this stimulation can be provided wirelessly by capacitive coupling through the chest skin, eliminating the need for percutaneous cables and implanted electronics. An electrode implanted in the inner side of the skin picks up a portion of the current flowing inside bodily tissue when two surface electrodes located in the vicinity of the reinnervated muscles are electrically stimulated. Practically, this implanted electrode is electrically connected to the fibers reinnervated in the chest muscles such to transfer the collected current and depolarize the surrounding nerve axons. We simulate different stimulation paradigms of electrical currents travelling through the skin to the sensory fibers. Activation outputs of reinnervated afferent fibers of the peripheral nerves are then analyzed using a hybrid model of the electrical field generated by the stimulation. The outcomes of this proof-of-concept prototype as well as the implication of this novel technique for restoring natural sensory feedback in the amputee are discussed.

Show abstract Continuous control of high-dimensional systems can be achieved by current state-of-the-art reinforcement learning methods such as the Deep Deterministic Policy Gradient algorithm, but needs a significant amount of data samples. For real-world systems, this can be an obstacle since excessive data collection can be expensive, tedious or lead to physical damage. The main incentive of this work is to keep the advantages of model-free Q-learning while minimizing real-world interaction by the employment of a dynamics model learned in parallel. To counteract adverse effects of imaginary rollouts with an inaccurate model, a notion of uncertainty is introduced, to make use of artificial data only in cases of high uncertainty. We evaluate our approach on three simulated robot tasks and achieve faster learning by at least 40 per cent in comparison to vanilla DDPG with multiple updates.

Show abstract After the development of a single-sided fabrication process for intrafascicular parylene C based electrode arrays tests showed that an increase in integration density can only be achieved by a double-side process. The process uses 25 μm thick platinum iridium foil, which is thinned down with the laser and sandwiched between two 10 μm thick parylene C layers. Utilizing a picosecond laser (355 nm Nd:YVO4) it was possible to fabricate 40 μm thick electrodes that can be implanted directly in the nerve without relying on additional support layers like chitosan or silk. The fabricated samples feature three 80 μm diameter electrodes on each side and a large ground electrode that is opened to both sides. Impedance mismatches from front to back side as a result of the fabrication process are compensated by electrochemical deposition of nanostructured platinum. This step makes it possible to bring the impedances of the small electrodes down to the range of just a few kΩ at 1 kHz and illustrate the additionally gained surface due to the picosecond laser ablation on the front side electrodes. The safely injectable charge per pulse was found to be 635.75 μC/cm2 for such coated electrodes. Optical investigations show that this fabrication process offers an alternative to established lithographic processes for thin and flexible electrode arrays in neural implants.

Show abstract The objective characterization of human motion is required in a variety of fields including competitive sports, rehabilitation and the detection of motor deficits. Nowadays, typically human experts evaluate the motor behavior. These evaluations are based on their individual experience which leads to a low inter- and intra-expert reliability. Standardized tests improve on the reliability but are still prone to subjective ratings and require human expert knowledge. This paper presents a novel method to characterize the motor state of Parkinson patients using full body motion capturing data based on a combination of multiple metrics. Our approach merges various metrics with a Random Forest and uses a probabilistic formulation to compute a one-dimensional measure for the performed motion. We present an application of our approach to the problem of relating subject motion to different classes like healthy subjects and Parkinson disease patients with deep brain stimulation switched on or off. In the experimental session we show that our measure leads to high classification rates and high entropy values for real-world data. Besides, we show that our method discriminates between Parkinson’s subjects (with and without stimulation) and healthy persons as good as the Unified Parkinson’s Disease Rating Scale (UPDRS).

Show abstract Robotic assistants have the potential to greatly improve our quality of life by supporting us in our daily activities. A service robot acting autonomously in an indoor environment is faced with very complex tasks. Consider the problem of pouring a liquid into a cup, the robot should first determine if the cup is empty or partially filled. RGB-D cameras provide noisy depth measurements which depend on the opaqueness and refraction index of the liquid. In this paper, we present a novel probabilistic approach for estimating the fill-level of a liquid in a cup using an RGB-D camera. Our approach does not make any assumptions about the properties of the liquid like its opaqueness or its refraction index. We develop a probabilistic model using features extracted from RGB and depth data. Our experiments demonstrate the robustness of our method and an improvement over the state of the art.

Show abstract Motion analysis is important in a broad range of contexts, including animation, bio-mechanics, robotics and experiments investigating animal behavior. For applications, in which tracking accuracy is one of the main require- ments, passive optical motion capture systems are widely used. Many skeleton tracking methods based on such systems use a predefined skeleton model, which is scaled once in the initialization step to the individual size of the character to be tracked. However, there are remarkable differences in the bone length relations across gender and even more across mammal races. In practice, the optimal skeleton model has to be determined in a manual and time-consuming process. In this paper, we reformulate this task as an optimization problem aiming to rescale a rough hierarchical skeleton structure to optimize probabilistic skeleton tracking performance. We solve this optimization problem by means of state-of-the-art black- box optimization methods based on sequential model-based Bayesian optimization (SMBO). We compare different SMBO methods on three real-world datasets with an animal and humans, demonstrating that we can automatically find skeleton structures for previously unseen mammals. The same methods also allow an automated choice of a suitable starting frame for initializing tracking.

Show abstract State-of-the-art neural microprobes contain hundreds of electrodes within a single shaft. Due to hardware and wiring restrictions, it is usually only possible to measure a small subset of the available electrodes simultaneously. The selection of the best channels is typically performed offline either manually or automatically. However, having a fixed selection for long-term observation does not allow the system to react to changes in the neural activity, and may therefore lead to the loss of important information. In this paper, we formulate the process of autonomously selecting the best subset of electrodes as a combinatorial multi-armed bandit problem with non-stationary rewards, thus allowing the probe to adapt its selection policies online. In order to minimize exploratory actions of the probe, we furthermore take advantage of the existing dependencies between neighboring channels. Our approach is an adaptation of the discounted upper confidence bounds (D-UCB) algorithm, and identifies the electrodes providing the largest amount of non-redundant information. To the best of our knowledge, this is the first online approach for the problem of electrode selection. In extensive experiments, we demonstrate that our solution is not only able to converge towards an average optimal selection policy, but it is also able to react to changes in the neural activity or to damages of the recording electrodes.

Show abstract Neural probes are designed to selectively record from or stimulate nerve cells. In optogenetics it is desirable to build miniaturized and long-term stable optical neural probes, in which the light sources can be directly and chronically implanted into the animals to allow free movement and behavior. Because of the size and the beam shape of the available light sources, it is difficult to target single cells as well as spatially localized networks. We therefore investigated design considerations for packages, which encapsulate the light source hermetically and have integrated hemispherical lens structures that enable to focus the light onto the desired region, by optical simulations. Integration of a biconvex lens into the package lid (diameter = 300 μm, material: silicon carbide) increased the averaged absolute irradiance ηA by 298 % compared to a system without a lens and had a spot size of around 120 μm. Solely integrating a plano-convex lens (same diameter and material) results in an ηA of up to 227 %.

Show abstract Lifetime estimation of implanted electronics in hermetic packages requires the prediction of the humidity induced lifetime. Classical approaches are limited in applications where miniaturized packages and a buffered humidity at low values are being utilized. An approach to overcome these limitations is described and investigations on suitable materials and measurement setups are presented. The findings support the usability of a Zeolite-silicone based desiccant system as a dielectric for a new type of online sensor. The future exploitation of this new sensing principle can allow the monitoring and prediction of humidity conditions inside of highly reliable miniaturized hermetic implantable packages.

Show abstract n this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p= 0.0033) when using the multimodal signals features as compared to pure EEG features.

Show abstract The ability to track skeletal movements is impor- tant in a variety of applications including animation, biological studies and animal experiments. To detect even small move- ments, such a method should provide highly accurate estimates. Besides that it should not impede the mammal in its motion. This motivates the usage of a passive optical motion capture system. Thereby the main challenges are the initialization, the association of the unlabeled markers to their corresponding segment also across the frames, and the estimation of the skeleton configuration. While many existing approaches can deal with the latter two problems, they typically need a specific pose for initialization. This is rather unpractical in the context of animal tracking and often requires a manual initialization process. In this paper, we present an approach to reliably track animals and humans in marker-based optical motion capture systems with freely attached markers. Our method is also able to perform an automatic initialization without any pre- or post-processing of the data. To achieve this, our approach utilizes a large database of previously observed poses. We present our algorithm and its evaluation on real-world data sets with an animal and humans. The results demonstrate that our initialization method performs accurately for the most kind of initial poses and our tracking approach outperforms a popular fully automatic skeleton tracking method especially with respect to the smoothness of the motion.

Show abstract This paper summarizes the recent research on tunable aspherical micro optics in regard to applications in the life sciences. Particular emphasis is placed on adaptive lenses and a very special class among them, conical lenses, so-called axicons. While various mechanisms to tune the asphericity are reviewed, the focus of the discussion is placed on piezo¬electric actuation because of its speed and thermal actuation because of its design freedom. The major appli¬cations in life sciences are microscopy, cell handling, and neurosciences. Possible further applications are referred to as well.

Show abstract In situ monitoring of tissue temperature during in vivo experiments can be of great advantage. It could allow the detection of inflammation development and possible infections caused by the implanted devices, the prevention of overheating due to electrical or optogenetical stimulation and the recording of effected vasodilation and increased metabolism. In this work, we present the characterization of platinum temperature sensors integrated in thin-film polyimide substrates. Three different sensor types were fabricated, with the intent to match a Pt 100, a Pt 1000 and a Pt 5000, all of them showing the typical characteristic curve of platinum for the electrical resistance as a function of temperature. Sensitivities of 0.2 Ω/°C, 1.7 Ω/°C and 8.8 Ω/°C were determined for the different sensor types. With the Pt 5000 samples temperature changes of less than 0.5 °C could be detected reliably, independent of the ambiance being air or water. Good in vivo behavior of the platinum sensors is assumed as the fabrication process was not altered compared to established polyimide-based electrode arrays.

Show abstract Pathological changes in excitability of cortical tissue commonly underlie the initiation and spread of seizure activity in patients suffering from epilepsy. Accordingly, monitoring excitability and controlling its degree using antiepileptic drugs (AEDs) is of prime importance for clinical care and treatment. To date, adequate measures of excitability and action of AEDs have been difficult to identify. Recent insights into ongoing cortical activity have identified global levels of phase synchronization as measures that characterize normal levels of excitability and quantify any deviation therefrom. Here, we explore the usefulness of these intrinsic measures to quantify cortical excitability in humans. First, we observe a correlation of such markers with stimulation-evoked responses suggesting them to be viable excitability measures based on ongoing activity. Second, we report a significant covariation with the level of AED load and a wake-dependent modulation. Our results indicate that excitability in epileptic networks is effectively reduced by AEDs and suggest the proposed markers as useful candidates to quantify excitability in routine clinical conditions overcoming the limitations of electrical or magnetic stimulation. The wake-dependent time course of these metrics suggests a homeostatic role of sleep, to rebalance cortical excitability.

Show abstract Introduction Optogenetics have enabled new possibilities in neuroscience during the last years . However, the development of suitable micro - optrodes still needs to be improved and often lacks in fulfilling important requirements such as l ong - term stability or miniaturization . This work presents an approach for developing test struc tures for implantable optrodes using wafer - level microtechnology and thin glass substrates for multimode waveguides and packages. Methods A 25 μm thin aluminum - borosilicate glass was chosen as a substrate. Different cleaning processes (O 2 - p lasma, H 2 O 2 , H 2 SO 4 , HCl, standard solvents) were tested to remov e all particles fro m the glass’ surface. The average amount of particles on the glass surface was determined for each test ed proces s by means of optical inspection in defined fields on the sub- strate . A wafer - level process was developed based on glass foils and silicon wafers. Waveguides were designed using the thin glass substrate as core and SiO 2 layers as cladding material. A SiO 2 layer was deposited on a silicon wafer with plasma enhanced chemical vapor deposition (PECVD). Afterwards, the glass was bonded to the wafer. Photolithography and reactive ion etching were used to structure the layers. A second layer of PECVD SiO 2 covers the waveguides and closes the cladding. The roughness of the waveguide’s end facets was examined using scanning electron microscopy. Results The fragility of the glass foils limits the applicability of different cle aning procedures. Applying ultrasound to the wet cleaning processes enhanced the cleaning results , especially with combined etchants and was found to remove more particles than dry cleaning. Clean surfaces allowed successful bonding of the glass to the wafer. Conclusion T hin glass foils could suc cessfully be integra ted in the wafer - level process for developing microtechnical optrodes . T his process could be used to realize hermetic micro - packages with integrated waveguides for long - term stable implantable optrodes using wafer level packaging

Show abstract It is expected that autonomous vehicles capable of driving without human supervision will be released to market within the next decade. For user acceptance, such vehicles should not only be safe and reliable, they should also provide a comfortable user experience. However, individual perception of comfort may vary considerably among users. Whereas some users might prefer sporty driving with high accelerations, others might prefer a more relaxed style. Typically, a large number of parameters such as acceleration profiles, distances to other cars, speed during lane changes, etc., characterize a human driver's style. Manual tuning of these parameters may be a tedious and error-prone task. Therefore, we propose a learning from demonstration approach that allows the user to simply demonstrate the desired style by driving the car manually. We model the individual style in terms of a cost function and use feature-based inverse reinforcement learning to find the model parameters that fit the observed style best. Once the model has been learned, it can be used to efficiently compute trajectories for the vehicle in autonomous mode. We show that our approach is capable of learning cost functions and reproducing different driving styles using data from real drivers.

Show abstract Although the neurological impairments of Parkinson’s disease (PD) patients are well known to go along with motor control deficits, e.g., tremor, rigidity, and reduced movement, not much is known about the motor control parameters affected by the disease. In this paper, we therefore present a novel approach to human motions analysis using motor control strategies with joint weight parameterization. We record the motions of healthy subjects and PD patients performing a hand coordination task with the whole-body XSens MVN motion capture system. For our motion strategy analysis we then follow a two step approach. First, we perform a complexity reduction by mapping the recorded human motions to a simplified kinematic model of the upper body. Second, we reproduce the recorded motions using a Jacobian weighted damped least squares controller with adaptive joint weights. We developed a method to iteratively learn the joint weights of the controller with the mapped human joint trajectories as reference input. Finally, we use the learned joint weights for a quantitative comparison between the motion control strategies of healthy subjects and PD patients. Other than expected from clinical experience, we found that the joint weights are almost evenly distributed along the arm in the PD group. In contrast to that, the proximal joint weights of the healthy subjects are notably larger than the distal ones.

Show abstract In grasping tasks carried out with humanoids, knowledge about the robot’s reachable workspace is important. Without this knowledge, it might be necessary to repeatedly adapt the stance location and call an inverse kinematics solver before a valid robot configuration to reach a given grasping pose can be found. In this paper, we present an approach to select an optimal stance location in SE(2) for a humanoid robot’s feet relative to a desired grasp pose. We use a precomputed representation of the robot’s reachable workspace that stores quality information in addition to spatial data. By inverting this representation we obtain a so-called inverse reachability map (IRM) containing a collection of potential stance poses for the robot. The generated IRM can subsequently be used to select a statically stable, collision-free stance configuration to reach a given grasping target. We evaluated our approach with a Nao humanoid in simulation and in experiments with the real robot. As the experiments show, using our approach optimal stance poses can easily be obtained. Furthermore, the IRM leads to a substantially increased success rate of reaching grasping poses compared to other meaningful foot placements within the vicinity of the desired grasp.

Show abstract We consider the safety verification of controllers obtained via machine learning. This is an important problem as the employed machine learning techniques work well in practice, but cannot guarantee safety of the produced controller, which is typically represented as an artificial neural network. Nevertheless, such methods are used in safety-critical environments. In this paper we take a typical control problem, namely the Cart Pole System (a.k.a. inverted pendulum), and a model of its physical environment and study safety verification of this system. To do so, we use bounded model checking (BMC). The created formulas are solved with the SMT-solver iSAT3. We examine the problems that occur during solving these formulas and show that extending the solver by special deduction routines can reduce both memory consumption and computation time on such instances significantly. This constitutes a first step towards verification of machine-learned controllers, but a lot of challenges remain.

Show abstract We present an Internet-based brain-computer interface (BCI) for controlling an intelligent robotic device with autonomous reinforcement-learning. BCI control was achieved through dry-electrode electroencephalography (EEG) obtained during imaginary movements. Rather than using low-level direct motor control, we employed a high-level control scheme of the robot, acquired via reinforcement learning, to keep the users cognitive load low while allowing control a reachinggrasping task with multiple degrees of freedom. High-level commands were obtained by classification of EEG responses using an artificial neural network approach utilizing timefrequency features and conveyed through an intuitive user interface. The novel combination of a rapidly operational dry electrode setup, autonomous control and Internet connectivity made it possible to conveniently interface subjects in an EEG laboratory with remote robotic devices in a closed-loop setup with online visual feedback of the robots actions to the subject. The same approach is also suitable to provide homebound patients with the possibility to control state-of-the-art robotic devices currently confined to a research environment. Thereby, our BCI approach could help severely paralyzed patients by facilitating patient-centered research of new means of communication, mobility and independence.

Show abstract IEEE 2014 International Symposium on Optomechatronic Technologies (ISOT 2014)

Show abstract Purpose: Activation of astrocytes is a hallmark of hippocampal sclerosis in patients with mesial temporal lobe epilepsy. However, the specific role of activated astrocytes in the epileptic brain is discussed controversially. We have previously shown that activation of astrocytes by a single, defined stimulus enhances their neuroprotective properties. We injected ciliary neurotrophic factor (CNTF) prior to an epilepsy-inducing injection of kainate (KA) and found that epilepsy-related brain damage was ameliorated and epileptiform activity reduced. In the present study we investigated the underlying molecular mechanisms. Method: Adult C57Bl/6 mice received either a single CNTF injection into the dorsal hippocampus or sequential injections of CNTF and KA (CNTF+KA) followed by real-time qPCR analysis or immunohistochemistry for glial glutamate transporters (GLT1/GLAST), glutamine synthetase (GS), inwardly-rectifying K+ channel (Kir 4.1) or connexin 43 and 30 (Cx43/Cx30) to characterize molecular changes of preactivated astrocytes. Results: We show that intrahippocampal injection of CNTF induces a rapid and sustained activation of astrocytes reflected by up-regulation of glial fibrillary acidic protein (GFAP). Moreover, CNTF signaling via phosphorylation and nuclear translocation of STAT3 as part of the JAK/STAT pathway was specifically activated in GFAP-positive astrocytes. Real-time RT-PCR analysis revealed that CNTF-mediated pre-activation of astrocytes followed by KA injection resulted in a significant up-regulation of Cx43 and Cx30 mRNAs indicating enhanced coupling properties of astrocytes via gap junctions. Moreover GLT1/GLAST and GS mRNA expression was significantly enhanced pointing to improved glutamate clearance from the synaptic cleft. Furthermore, Kir4.1 mRNA was significantly higher expressed in CNTF+KA-injected animals. Complementary immunocytochemistry reveaIed that up-regulation of all these mRNAs occurred exclusively in astrocytes. Conclusion: In summary, our results indicate that activation of astrocytes prior to an excitotoxic injury leads to molecular changes indicative of the observed neuroprotective and anti-epileptic action.

Show abstract Electrical stimulation of the brain is used to treat neurological disorders. Yet it is unknown how to find stimulation patterns that produce desired results with the least interference. Towards this goal, we tested a generic closed-loop paradigm that autonomously optimizes stimulation settings. We used neuronal networks coupled to a reinforcement learning based controller to maximize response lengths.

Show abstract The perforant path provides the major excitatory hippocampal input from the entorhinal cortex and degenerative alterations are associated with cognitive dysfunctions including Alzheimer`s disease. However, the molecular mechanisms for guidance and target specificity are unclear. We examined in this context the immunoglobulin superfamily cell adhesion molecule Neuronal Growth Regulator 1 (NEGR1) in mice. In situ hybridization demonstrated Negr1 expression both in entorhinal cortex projection neurons and in targeted hippocampal granule cells. NEGR1 overexpression in NSC-34 cells stimulated neurite growth and attracted axons of co-cultured primary neurons suggesting that homophilic interaction of NEGR1 promotes neuronal connectivity. Organotypic slice co-cultures of entorhinal cortices from actin-EGFP mice and NEGR1-deficient hippocampi indicated that NEGR1 is required for axon growth and guidance of entorhinal axons to granule cells. DiI tracing of the perforant path in NEGR1-deficient mice revealed fasciculation and growth abnormalities including aberrant projections of entorhinal axons into the hilar region. Following single injection of the GABA-A receptor antagonist pentylenetetrazole NEGR1-deficient mice exhibited increased seizure susceptibility. Subtle abnormalities were observed in behavioral tasks. These results involve NEGR1 in the development of entorhinal-hippocampal connectivity. Malformations of the perforant path may contribute to increased seizure susceptibility and behavioral abnormalities in NEGR1-deficient mice.

Show abstract Purpose: Loss of interneurons is considered as a reason for hyperexcitability of the hippocampus in temporal lobe epilepsy (TLE). Here we used a focal mouse model for TLE to characterize the vulnerability of interneurons along the septotemporal axis of the hippocampus and to record epileptic activity in corresponding areas. Method: Adult C57Bl/6 mice received a unilateral injection of kainate into the septal hippocampus which induced recurrent epileptic seizures, hippocampal sclerosis and granule cell dispersion (GCD). We implanted four electrodes along the septotemporal axis of the hippocampus to measure local field potentials. In situ hybridization for glutamate decarboxylase 67 (GAD67) mRNA and immunolabeling for parvalbumin, neuropeptide Y (NPY) and GAD65 was used to characterize changed inhibition in the whole hippocampus. Results: We show that epileptiform activity is not strongest at the injection site with most prominent cell death and GCD but in the intermediate hippocampus which seems histologically less affected. Quantification of GAD67 mRNA-positive cells revealed a significant loss of inhibitory interneurons close to the injection site and a reduction in the intermediate hippocampus. Yet, different interneurons populations showed differential vulnerability: Parvalbumin-positive cells were lost on a substantially larger extent than NPY-positive interneurons. Remarkably, granule cells showed a compensatory reaction to epileptiform activity characterized by an upregulation of GAD67 mRNA in cell bodies, GAD65 protein in mossy fiber synapses and NPY in mossy fibers. Conclusion: Together with our previous study on neurogenesis (Häussler et al., Cerebral Cortex 2013, 22(1):26-36) these data indicate that the intermediate hippocampus comprises a network with higher epileptogenicity than the dorsal, sclerotic hippocampus. This might be due to a shifted excitation-inhibition balance originating from the reduction in inhibitory interneurons and the addition of newborn, hyperexcitable granule cells. Acknowledgements: Supported as part of the Excellence Cluster ‘BrainLinks-BrainTools’ by the German Research Foundation, grant EXC1086; Scientific Society Freiburg.

Show abstract Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition. The feature representation learned by our algorithm achieves classification results matching or outperforming the current state-of-the-art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101).

Show abstract Within this paper a novel approach for the development of hermetic electrical feedthroughs is introduced. So far, every vertical feedthrough induces at the feedthroughs' interfaces possible paths for water to leak across the hermetic barrier into the hermetic package. The presented approach is based on the diffusion of platinum into silicon, locally changing the electrical behavior of the substrate due to the induced impurities. This method avoids destroying the bulk material, in this case silicon, preserving the hermetic barrier environment. Different n-type silicon substrates were investigated for their usability through various diffusion experiments under two gas atmospheres: Argon/hydrogen and pure nitrogen. A significant change in silicon behavior could be shown for one of the used substrates. The current flowing through the bulk could be decreased by a factor of around 12 for an argon/hydrogen atmosphere and by around 10 for pure nitrogen. The current directly correlates with a local increase of the substrates' resistance, demonstrating the possibility of adapting the electrical properties of a substrate to create insulating areas within a conductive substrate.

Show abstract The iSAT algorithm aims at solving boolean combinations of linear and non-linear arithmetic constraint formulas (including transcendental functions), and thus is suitable to verify safety properties of systems consisting of both, linear and non-linear behaviour. The iSAT algorithm tightly integrates interval constraint propagation into the conflict-driven clause-learning framework. During the solving process, this may result in a huge implication graph. This paper presents a method to compress the implication graph on-the-fly. Experiments demonstrate that this method is able to reduce the overall memory footprint up to an order of magnitude.

Show abstract The mammalian cerebral cortex is a remarkably complex structure, and establishment of cortical neural circuitries requires its unique laminar organization. During perinatal development, newborn pyramidal neurons migrate along radial glia fibers, to create the six-layered structure of the neocortex. Disruption in neural migration can lead to brain malformations with functional consequences on proper wiring of the neuronal network, as already described in neurodevelopmental disorders such as Autism Spectrum Disorders (ASD). Common knowledge indicates cell-adhesion molecules (CAMs) as essential for proper neural migration. Neuronal growth regulator 1 (Negr1) is a CAM, and NEGR1 gene mutations have been recently associated to ASD. By in utero electroporation coupled with RNA interference (siRNA), we downregulated Negr1 levels in late-born pyramidal neurons migrating to the superficial layers of the neocortex. We found that Negr1 siRNA caused ectopic positioning of neurons concentrated at the border between layer 5 and layer 4 of the somatosensory cortex. Downregulation of Negr1 did not cause migration defects in the motor or prefrontal cortices. We found that FGFR2 (also associated to autism) and its partner NCAM physically and functionally interact with Negr1. Here, we proved that downregulation of NCAM in utero resulted in a strikingly similar phenotype on neuronal migration as found for Negr1. Interestingly, downregulation of Negr1 in the embryonic somatosensory cortex resulted in decreased number of ultrasound vocalizations in pups. These data suggest that Negr1/FGFR2/NCAM complex is necessary for proper neuronal migration of pyramidal neurons in the somatosensory cortex, indicating a possible role for this complex in autism.

Show abstract Purpose: In mesio-temporal lobe epilepsy (MTLE) the hippocampal network is pathologically restructured resulting in the emergence of epileptiform activity (EA). EA occurs transiently and alternates with putatively “normal” activity patterns. In healthy animals, hippocampal activity is dominated by network oscillations that couple information transfer between hippocampal subregions and shape the firing probability of single neurons. Previously we showed phase-shifted coupling of the theta-rhythm between the medial entorhinal cortex (MEC) and the sclerotic dentate gyrus (DG) in epileptic mice. Whether phase-shifted oscillations are accompanied by shifted neuronal firing across hippocampal fields or locally with respect to ongoing network oscillations is investigated here. Both conditions would severely alter information processing in the epileptic hippocampal formation. Method: We recorded local field potential rhythms and multi-unit activity in freely behaving epileptic animals using the intrahippocampal kainate mouse model of MTLE. We chronically implanted multi-site silicon electrode arrays sampling the whole entorhinal-hippocampal (EC-HC) loop. Results: We show that neurons in all investigated substructures of the EC-HC loop fired phase-coupled to theta and gamma oscillations. Coupling to theta rhythm was comparable in strength and phase-angle across healthy and epileptic groups. Furthermore, coupling properties of neurons recorded in the weakly-sclerotic DG, a region showing highest epileptogenicity, were comparable to those in the non-sclerotic DG. While the phase-coupling of neurons persisted in epileptic mice, the average firing rate of cells from the DG and parahippocampal region was increased. Conclusion: Hippocampal neuronal firing is modulated with the local theta-rhythm. Thus, a phase-shift between the theta-rhythms of the MEC and DG implies a mismatch of neuronal firing across these structures that could tune the hippocampal network towards seizure susceptibility via pathological plasticity. Support: BMBF (FKZ 01GQ0420, 01GQ0830), DFG within the Cluster of Excellence BrainLinks-BrainTools (EXC1086), EC (EFRE - TIGER)

Show abstract Objective: Focal cortical dysplasia (FCD), a cortical malformation arising during prenatal development, is a major cause of pharmaco-resistant focal epilepsy thus frequently becoming object to neurosurgical resection. However, little is known about the histopathologic and molecular phenotypes underlying the cortical dyslamination patterns in human FCD. The structural, molecular and cellular characterization of an impaired cortical composition and a possible coherence between neuron-specific alterations and epileptogenicity of FCD are subject of the current study. Methods: Layer-specific protein expression (Reelin, Calbindin, Calretinin, SMI32, Parvalbumin, TLE4) was studied by immunohistological techniques on paraffin-embedded sections including double immunolabeling in neuropathologically confirmed mild FCD Type 1 and 2a (n=15), FCD2b (n=29) and was compared to a control group with (n=6) or without epilepsy (n=3 post mortem cases). Neuron-specific protein levels were analyzed by quantitative Western blot analysis. Following systematic quantification of neocortical neuronal density and neuron-specific protein levels statistical analysis of layer-specific neuronal subpopulations was performed according to age at surgery and brain region. Results: Even in severe dyslamination of FCD2b with impaired laminar assignment particularly of layer 3 and 5 pyramidal cells we found a rudimentary preservation of laminar structure using lamina-specific markers. The quantitative analysis of layer-specific neuronal subpopulations revealed a highly significant, age-related decrease in distinct interneuron subpopulations especially of Parvalbumin-positive interneurons primarily located in layer 4 whereas supragranular interneurons expressing Calbindin and Calretinin were only marginally affected. Furthermore, TLE4-positive projection neurons in layer 6 were increased in numbers. Conclusion: Our findings suggest that cortical dyslamination is associated with disturbances in cell proliferation or differentiation, but not primarily with a general migration defect. A differential vulnerability of especially deep-layered interneurons and an increase of distinct layer-specific neurons, respectively, results in a profound, age-related alteration of the neocortical neuronal composition in FCD.

Show abstract In the subgranular zone (SGZ) of the dentate gyrus new neurons are generated throughout life by a sophisticated sequence of proliferation, differentiation and migration. To investigate possible factors affecting SGZ neurogenesis under pathological conditions, we used organotypic hippocampal slice cultures (OHSC) as they are a commonly used in vitro system and, interestingly, gradually lose their neurogenic capacity. Since OHSC share key features with CNS pathologies (e.g. strong glial activation), we hypothesized that the degree of glial activation could limit the extent of neurogenesis. To address this question, we analyzed the expression of factors regulating neurogenesis or glial cell activation in OHSC by RT-qPCR. We found that most neurogenic genes were down-regulated, whereas factors released by activated glial cells were strongly induced. To monitor neurogenic dynamics in OHSC we used transgenic mice, in which EGFP is exclusively expressed in immature neurons under the pro-opiomelanocortin (POMC) promotor. Measuring EGFP signal intensity revealed a rapid decrease within the first week of culturing. To identify the glial influence, we enhanced glial reactivity by the treatment with ciliary neurotrophic factor or diminished it by applying a P2Y12 receptor antagonist or indomethacin. The degree of glial activation determined the neurogenic outcome. Therefore, our results highlight the important role of a balanced activation of glial cells for hippocampal neurogenesis. This could be of particular medical interest, as most CNS diseases are accompanied by glial activation, which could impede the regenerative potential of neurogenesis. Supported by the DFG within the Cluster of Excellence “BrainLinks-BrainTools” (DFG grant EXC1086).

Show abstract In this paper, we present a system that enables humanoid robots to imitate complex whole-body motions of humans in real time. In our approach, we use a compact human model and consider the positions of the endeffectors as well as the center of mass as the most important aspects to imitate. Our system actively balances the center of mass over the support polygon to avoid falls of the robot, which would occur when using direct imitation. For every point in time, our approach generates a statically stable pose. Hereby, we do not constrain the configurations to be in double support. Instead, we allow for changes of the support mode according to the motions to imitate. To achieve safe imitation, we use retargeting of the robot’s feet if necessary and find statically stable configurations by inverse kinematics. We present experiments using human data captured with an Xsens MVN motion capture system. The results show that a Nao humanoid is able to reliably imitate complex whole-body motions in real time, which also include extended periods of time in single support mode, in which the robot has to balance on one foot.

Show abstract Due to the progressive development, there is a significant demand of passively powered implants in biomedical research. Small dimensions make it possible to place the devices into inaccessible or critical positions. As consequence, the coupling factor between reader and implant antenna is slight. This work therefore provides a novel analysis of inductive coupled systems based on numerical methods for determining a proper coil configuration. Keywords: inductive coupling, wireless power transfer, three antenna systems, deep brain stimulation.

Show abstract This work is motivated by (1) a practical application which automatically generates test patterns for integrated circuits and (2) the observation that off-the-shelf state-of-the-art pseudo-Boolean solvers have difficulties in solving instances with huge pseudo-Boolean constraints as created by our application. Derived from the SMT solver iSAT3 we present the solver iSAT3p that on the one hand allows the efficient handling of huge pseudo-Boolean constraints with several thousand summands and large integer coefficients. On the other hand, experimental results demonstrate that at the same time iSAT3p is competitive or even superior to other solvers on standard pseudo-Boolean benchmark families.

Show abstract Many studies on neurogenesis have shown that in the adult dentate gyrus (DG) progenitor cells proliferate and give rise to young neurons integrating into the neuronal network. The DG neurogenic niche can be modulated by external stimuli, e.g. physical activity which is the best described physiological stimulus for neurogenesis in adult mice. So far, mice have been exposed to wheel-running for >14 days to activate neurogenesis. Here, we used shorter running periods to describe the timeline of running-induced neurogenesis and to characterize early changes in the DG. Adult C57Bl/6N mice had free access to running wheels for two or seven days. Subsequently, we performed immunocytochemistry for glial fibrillary acidic protein (GFAP) and Nestin to label stem cells, doublecortin (DCX) for young neurons and Ki-67 for proliferating cells. To detect changes in the molecular characteristics of the neurogenic niche, we performed real-time RT-qPCR for neurotrophic factors (BDNF, BMP4). We show that short running periods are sufficient to activate neurogenesis, as indicated by a continuous increase in DCX labeling. In contrast, GFAP- and Nestin-positive cells were reduced after two days, demonstrating that stem cell proliferation and differentiation do not arise simultaneously. Instead, we hypothesize that proliferation succeeds progenitor differentiation after a period of exhaustion of the stem cell pool, as confirmed by Ki-67 staining. Real-time RT-qPCR analysis revealed a modulated expression of niche factors BDNF and BMP4 already after 2 days privileging differentiation. In summary, our data point to a two-phasic mechanism involved in early running-induced neurogenesis. Support: MOTI-VATE program, Medical Faculty Freiburg.

Show abstract Purpose: Focal cortical dysplasias (FCD) are local malformations of the human neocortex, originating during pre- and perinatal brain development. They are frequent causes of medically intractable focal epilepsy, associated with a high seizure frequency. To date little is known about the pathomechanisms leading to the architectural and functional abnormalities associated with FCD. Mostly, morphological studies have been carried out to analyze the pathology of the disease. In this study, a whole transcriptome screening was performed to understand the molecular mechanisms leading to this cortical malformation. Method: Human Gene 1.0 ST arrays (Affymetrix) were used for microarray analysis on dysplastic and non-dysplastic temporal lobe tissue, obtained from children (n=7; mean age 2; range 1-5 years) and adult patients (n= 7; mean age 19; range 6-36 years) who had undergone surgical treatment due to intractable epilepsy or low grade tumor resection (n=8; mean age 17; range 1-27 years). Microarray data were validated by real-time PCR, in situ hybridization and immunohistochemistry for genes of interest. Results: The whole transcriptome screening of dysplastic compared to non-dysplastic temporal neocortex revealed that approximately 0.1% of genes are differentially expressed, with the majority being down-regulated in FCD. In particular, genes affecting oligodendrocyte differentiation and myelination were found to be down-regulated in dysplastic temporal lobes of children and adults. These data could be confirmed by real-time PCR. Accordingly, myelin basic protein-expressing cells were drastically reduced in dysplastic cortex. Conclusion: Our transcriptome screening revealed that only a relatively low number of genes (0.1%) are differentially expressed in the dysplastic temporal neocortex when analyzed in the chronic stage of the disease. Nevertheless, we found a significantly reduced expression of myelin-associated genes indicating a disturbance of oligodendrocyte differentiation and myelin sheet formation/maintenance in FCD. Supported by the Deutsche Forschungsgemeinschaft (DFG FA 775/2-1)

Show abstract Dentate granule cells receive their major excitatory input from the entorhinal cortex (EC) via the perforant path. Although pathological reorganization associated with mesial temporal lobe epilepsy (i.e. neuronal cell loss, granule cell dispersion, mossy fiber sprouting) has been studied extensively, it remains uncertain whether the entorhinal input is altered under epileptic conditions. To address this question adult C57Bl/6 or transgenic Thy1-EGFP mice received a unilateral, intrahippocampal kainate or saline injection, followed by stereotaxic infusion of biotinylated dextran amine (BDA), an anterograde neuronal tracer, into the medial EC 14 days later. After a survival period of 21 days, brain sections were immunohistochemically stained for BDA and vGLUT-1, labeling the traced fibers and their presynaptic terminals. First, we mapped the projection from the medial EC to the outer molecular layer of the hippocampus, namely the medial perforant path (MPP), confirming the topographic results of previous tracing studies. Interestingly, following kainate treatment, the width of the projection remained unchanged, although dentate granule cells dispersed broadly. In turn, the density of traced fibers was reduced and high-power confocal imaging revealed axonal varicosities within the MPP. Yet to be quantified, a preliminary assessment of synaptic densities pointed to a decline of MPP terminals, projecting onto postsynaptic dendrites, while postsynaptic spines may be upregulated. In conclusion, our findings suggest that under epileptic conditions the MPP is preserved on a macroscopic scale, but it appears altered on the level of individual synapses. Supported by the DFG within the Cluster of Excellence “BrainLinks-BrainTools” (DFG grant EXC1086).