Relevant for Research Area

A - Foundations




Tools to record brain activity at high resolution and to focally stimulate the brain based on the recorded signals are the basis of novel strategies of therapeutical intervention in patients. Individual brain areas do not function in isolation, however, they rather form networks comprising several dynamically interacting cortical and subcortical regions. The main objective of our project is to identify these networks from recorded signals and suggest efficient ways to bidirectionally interfere with them. To this end, we have developed a novel computational method to reconstruct the underlying effective network structure from measurements of human brain activity and tested it for two recording methods that optimally complement each other: Electrocorticography (ECoG) and MR-Encephalography (MREG). Based on resting state and motor state data sets we identified and characterized directed “causal” network structures and possible network hubs (nodes with high indegree and outdegree) that are expected to allow particularly efficient readout and stimulation in scenarios as described in the LiNC and SEAM platforms. Of crucial importance for the safe application of our new reconstruction method to human brain networks is a trustworthy validation of its performance. One possibility is to compare the networks reconstructed from resting state activity with structural data that represent the “ground truth”. In the case of ECoG, we used numerical simulations of biophysically realistic spiking networks to test the reconstruction performance on artificial surrogate data, with very promising preliminary results. In the case of MREG, we employed simulations of simple autoregressive processes for calibration, but also considered diffusion tensor imaging (DTI) and compared the resulting structural information with the networks reconstructed from dynamic MREG signals acquired in human, yielding promising results.

Publications and Achievements

Peer reviewed publications

Kern M et al (2013) Heart cycle-related effects on event-related potentials, spectral power changes, and connectivity patterns in the human ECoG. NeuroImage 81: 178–190

Lennartz C, Schiefer J, Rotter S, Hennig J, LeVan P (2018) Sparse Estimation of Resting- State Effective Connectivity from fMRI Cross-Spectra. Frontiers in Neuroscience 12: 287

Pernice V, Rotter S (2013) Reconstruction of sparse connectivity in neural networks from spike train covariances. Journal of Statistical Mechanics P03008

Proulx S, Safi-Harb M, LeVan P, An D, Watanabe S, Gotman J (2014). Increased sensitivity of fast BOLD fMRI with a subject-specific hemodynamic response function and application to epilepsy. NeuroImage 93 Pt 1: 59-73

Schiefer J, Niederbühl A, Pernice V, Lennartz C, LeVan P, Hennig J, Rotter S (2018) From Correlation to Causation: Estimation of Effective Connectivity from Continuous Brain Signals based on Zero-Lag Covariance. PLOS Computational Biology 14(3): e1006056

Conference papers

Arand C, Schiefer J, Rotter S, Akin B, Hennig J, LeVan P (2016) Covariance-based estimation of cerebral effective connectivity from fast BOLD-fMRI. Proceedings of 22nd Annual Meeting of OHBM, Geneva, Switzerland

Kern M, Ruescher H et al (2018d) Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study. Computational Cognitive Neuroscience, Philadelphia, 2018, paper number 1096

Lee HL, Assländer J, LeVan P, Hennig J (2014b) Frequency-dependent resting-state connectivity and network disintegration in brain hub regions. Proceedings 20th Annual Meeting of OHBM, Hamburg, Germany

Lee HL, Assländer J, LeVan P, Hennig J (2014a) Resting-state functional hubs at multiple frequencies revealed by MR-Encephalography. Proceedings 22nd Annual Meeting of ISMRM, Milan, Italy

Lee HL, Assländer J, LeVan P, Hennig J (2015) Dynamic Wavelet Coherence Maps and Frequency-Dependent Connectivity Strength in Default Mode Network. Proceedings 23rd Annual Meeting of ISMRM, Toronto, Canada

Lennartz C, Akin B, Hennig J, LeVan P (2017) A model-free approach for HRF estimation from resting state fMRI data. Proceedings 23rd Annual Meeting of OHBM, Vancouver, Canada

Niederbühl A, Pernice V, Rotter S (2014) Inferring causation from correlation in sparse networks. ECML 2014, Neural Connectomics Workshop – From Imaging to Connectivity. Nancy, France

Pernice V, Niederbühl A, Levan P, Hennig J, Rotter S (2014) Inference of sparse effective connectivity from resting state fMRI. Bernstein Conference 2014

Schiefer J, Arand C, Hennig J, LeVan P, Rotter S (2016) Inferring directed whole-brain connectivity from fast BOLD-fMRI signals. Bernstein Conference 2016

Schiefer J, Rotter S (2016) Inference of cerebral network structure. 2nd International Conference on Mathematical NeuroScience 2016

Schiefer J, Schäfer L, Ball T, Rotter S (2015) Inferring Brain Connectivity from ECoG Signals: A Simulation Study. Bernstein Conference 2015, 10.12751/nncn.bc2015.0171

Unpublished manuscripts

Kern M, Behnke J et al (2018a) ELAS: an SPM toolbox for hierarchical probabilistic neuroanatomical assignment and 3-D visualization of intracranial electrodes. NeuroImage, in a final state of preparation

Kern M et al (2018b) Sparse and action-specific motor cortical control of real life laughing, smiling and speech production. Nature Communications Biology, minor revision in a final state of preparation

Kern M et al (2018c) Gamma band responses in human early visual areas during blinks and saccades: an ECoG study. Journal of Neuroscience, in a final state of preparation

Schiefer J, Schäfer L, Rotter S (2018) Estimating connectivity from simulated ECoG signals. Manuscript in preparation.