Directed Functional Brain Connectivity Based on EEG Source Imaging: Methodology and Application to Temporal Lobe Epilepsy

Objective: The importance of functional brain connectivity to study physiological and pathological brain activity has been widely recognized. Here, we aimed to 1) review a methodological pipeline to investigate directed functional connectivity between brain regions using source signals derived from high-density EEG; 2) elaborate on some methodological challenges; and 3) apply this pipeline to temporal lobe epilepsy (TLE) patients and healthy controls to investigate directed functional connectivity differences in the theta and beta frequency bands during EEG epochs without visible pathological activity. Methods: The methodological pipeline includes: EEG acquisition and preprocessing, electrical-source imaging (ESI) using individual head models and distributed inverse solutions, parcellation of the gray matter in regions of interest, fixation of the dipole orientation for each region, computation of the spectral power in the source space, and directed functional connectivity estimation using Granger-causal modeling. We specifically analyzed how the signal-to-noise ratio (SNR) changes using different approaches for the dipole orientation fixation. We applied this pipeline to 20 left TLE patients, 20 right TLE patients, and 20 healthy controls. Results: Projecting each dipole to the predominant dipole orientation leads to a threefold SNR increase as compared to the norm of the dipoles. By comparing connectivity in TLE versus controls, we found significant frequency-specific outflow differences in physiologically plausible regions. Conclusion: The results suggest that directed functional connectivity derived from ESI can help better understand frequency-specific resting-state network alterations underlying focal epilepsy. Significance: EEG-based directed functional connectivity could contribute to the search of new biomarkers of this disorder.

[1]  Laura Astolfi,et al.  The physiological plausibility of time-varying Granger-causal modeling: Normalization and weighting by spectral power , 2014, NeuroImage.

[2]  Laura Astolfi,et al.  Tracking the Time-Varying Cortical Connectivity Patterns by Adaptive Multivariate Estimators , 2008, IEEE Transactions on Biomedical Engineering.

[3]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[4]  Hans Hallez,et al.  Accurate epileptogenic focus localization through time-variant functional connectivity analysis of intracranial electroencephalographic signals , 2011, NeuroImage.

[5]  Alois Schlögl,et al.  The Electroencephalogram and the Adaptive Autoregressive Model: Theory and Applications , 2000 .

[6]  Karl J. Friston,et al.  Anatomically Informed Basis Functions for EEG Source Localization: Combining Functional and Anatomical Constraints , 2002, NeuroImage.

[7]  Bin He,et al.  Neocortical seizure foci localization by means of a directed transfer function method , 2010, Epilepsia.

[8]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[9]  M. Kahana,et al.  Theta returns , 2001, Current Opinion in Neurobiology.

[10]  Alois Schlögl,et al.  A comparison of multivariate autoregressive estimators , 2006, Signal Process..

[11]  G. A. Miller,et al.  Comparison of different cortical connectivity estimators for high‐resolution EEG recordings , 2007, Human brain mapping.

[12]  F. D. Silva,et al.  EEG and MEG: Relevance to Neuroscience , 2013, Neuron.

[13]  Bin He,et al.  Cortical Imaging of Event-Related (de)Synchronization During Online Control of Brain-Computer Interface Using Minimum-Norm Estimates in Frequency Domain , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Gregory A. Worrell,et al.  Ictal source analysis: Localization and imaging of causal interactions in humans , 2007, NeuroImage.

[15]  Laura Astolfi,et al.  Connectome : A MATLAB toolbox for mapping and imaging of brain , 2010 .

[16]  Georgia Ramantani,et al.  Electrical source imaging in cortical malformation–related epilepsy: A prospective EEG‐SEEG concordance study , 2014, Epilepsia.

[17]  F. Lazeyras,et al.  Contralateral medial temporal lobe damage in right but not left temporal lobe epilepsy: a 1H magnetic resonance spectroscopy study , 2003, Journal of neurology, neurosurgery, and psychiatry.

[18]  Bin He,et al.  Estimation of Time-Varying Connectivity Patterns Through the Use of an Adaptive Directed Transfer Function , 2008, IEEE Transactions on Biomedical Engineering.

[19]  W. Drongelen,et al.  Identification of epileptogenic foci from causal analysis of ECoG interictal spike activity , 2009, Clinical Neurophysiology.

[20]  Jean Daunizeau,et al.  Concepts of Connectivity and Human Epileptic Activity , 2011, Front. Syst. Neurosci..

[21]  Kensuke Sekihara,et al.  MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG , 2011, Comput. Intell. Neurosci..

[22]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.

[23]  Laura Astolfi,et al.  Spectrally weighted Granger-causal modeling: Motivation and applications to data from animal models and epileptic patients , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[24]  Christoph M. Michel,et al.  Head model and electrical source imaging: A study of 38 epileptic patients☆ , 2014, NeuroImage: Clinical.

[25]  Laura Astolfi,et al.  Early recurrence and ongoing parietal driving during elementary visual processing , 2015, Scientific Reports.

[26]  Stefan Haufe,et al.  A critical assessment of connectivity measures for EEG data: A simulation study , 2013, NeuroImage.

[27]  Bin He,et al.  Seizure source imaging by means of FINE spatio-temporal dipole localization and directed transfer function in partial epilepsy patients , 2012, Clinical Neurophysiology.

[28]  J. Gotman,et al.  Patterns of altered functional connectivity in mesial temporal lobe epilepsy , 2012, Epilepsia.

[29]  J. Schoffelen,et al.  Source connectivity analysis with MEG and EEG , 2009, Human brain mapping.

[30]  P. Dudchenko.  The hippocampus as a cognitive map , 2010 .

[31]  Huafu Chen,et al.  Altered spontaneous neuronal activity of the default-mode network in mesial temporal lobe epilepsy , 2010, Brain Research.

[32]  R. Passingham.  The hippocampus as a cognitive map J. O'Keefe & L. Nadel, Oxford University Press, Oxford (1978). 570 pp., £25.00 , 1979, Neuroscience.

[33]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[34]  A. Cichocki,et al.  Estimate of Causality Between Independent Cortical Spatial Patterns During Movement Volition in Spinal Cord Injured Patients , 2007, Brain Topography.

[35]  Katarzyna J. Blinowska,et al.  Review of the methods of determination of directed connectivity from multichannel data , 2011, Medical & Biological Engineering & Computing.

[36]  F. Vollenweider,et al.  Localization of MDMA‐induced brain activity in healthy volunteers using low resolution brain electromagnetic tomography (LORETA) , 2001, Human brain mapping.

[37]  Margitta Seeck,et al.  Dynamic directed interictal connectivity in left and right temporal lobe epilepsy , 2015, Epilepsia.

[38]  C. Michel,et al.  Electric source imaging of interictal activity accurately localises the seizure onset zone , 2013, Journal of Neurology, Neurosurgery & Psychiatry.

[39]  E. Carrette,et al.  Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization , 2014, Progress in Neurobiology.

[40]  A. Schnider,et al.  Rapid consolidation and the human hippocampus: Intracranial recordings confirm surface EEG , 2011, Hippocampus.

[41]  Christoph M. Michel,et al.  Spatiotemporal Analysis of Multichannel EEG: CARTOOL , 2011, Comput. Intell. Neurosci..

[42]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[43]  Roberto D. Pascual-Marqui,et al.  Electrical Neuroimaging: Multichannel frequency and time-frequency analysis , 2009 .

[44]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[45]  Katarzyna J. Blinowska,et al.  Determination of EEG activity propagation: pair-wise versus multichannel estimate , 2004, IEEE Transactions on Biomedical Engineering.

[46]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[47]  M. Brázdil,et al.  An optimized voxel‐based morphometric study of gray matter changes in patients with left‐sided and right‐sided mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE/HS) , 2010, Epilepsia.

[48]  Mark P Richardson,et al.  Large scale brain models of epilepsy: dynamics meets connectomics , 2012, Journal of Neurology, Neurosurgery & Psychiatry.

[49]  Yvonne Höller,et al.  Altered directed functional connectivity in temporal lobe epilepsy in the absence of interictal spikes: A high density EEG study , 2016, Epilepsia.

[50]  Hans Hallez,et al.  Ictal‐onset localization through connectivity analysis of intracranial EEG signals in patients with refractory epilepsy , 2013, Epilepsia.

[51]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[52]  BrunetDenis,et al.  Spatiotemporal analysis of multichannel EEG , 2011 .

[53]  F. Babiloni,et al.  Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function , 2005, NeuroImage.

[54]  D Le Bihan,et al.  Bilateral hemispheric alteration of memory processes in right medial temporal lobe epilepsy , 2002, Journal of neurology, neurosurgery, and psychiatry.

[55]  J. O’Keefe,et al.  Phase relationship between hippocampal place units and the EEG theta rhythm , 1993, Hippocampus.

[56]  Christoph M. Michel,et al.  Towards the utilization of EEG as a brain imaging tool , 2012, NeuroImage.

[57]  Claudio Pollo,et al.  Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients , 2011, Brain : a journal of neurology.

[58]  Christoph M. Michel,et al.  Electrical neuroimaging based on biophysical constraints , 2004, NeuroImage.

[59]  Laura Astolfi,et al.  Assessing cortical functional connectivity by partial directed coherence: simulations and application to real data , 2006, IEEE Transactions on Biomedical Engineering.

[60]  Georgia Ramantani,et al.  Catching the Invisible: Mesial Temporal Source Contribution to Simultaneous EEG and SEEG Recordings , 2014, Brain Topography.

[61]  W. M. Carey,et al.  Digital spectral analysis: with applications , 1986 .

[62]  Steven L. Bressler,et al.  Wiener–Granger Causality: A well established methodology , 2011, NeuroImage.