Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

• Published in: IEEE transactions on biomedical engineering 2016-12, Vol.63 (12), p.2474-2487
• Main Authors: Sohrabpour, Abbas, Ye, Shuai, Worrell, Gregory A., Zhang, Wenbo, He, Bin
• Format: Article
• Language: English
• Subjects: Algorithms; Brain; Brain - diagnostic imaging; Brain - physiology; Brain - physiopathology; Brain models; Causality; Cointegration analysis; Computer Simulation; Connectivity analysis; Connectome - methods; Directed transfer function (DTF); dynamic seizure imaging (DSI); EEG; Electroencephalography; Electroencephalography - methods; electromagnetic source imaging (ESI); Electromagnetics; Epilepsy; Epilepsy - physiopathology; Errors; Granger causality analysis; high-density electroencephalography (EEG); Humans; Imaging; Imaging techniques; interictal spikes (IIS); Localization; Magnetoencephalography; magnetoencephalography (MEG); Magnetoencephalography - methods; Manganese; Mathematical models; Medical imaging; Models, Neurological; Models, Statistical; network; Networks; Neural networks; Neural Pathways - diagnostic imaging; Neural Pathways - physiology; Neural Pathways - physiopathology; Neuroimaging; Nodes; Patients; Simulation; Surgery; Time series analysis
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2016.2616474

Objective : Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Methods : Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG). Results : Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Conclusion : Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). Significance : The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.