TY - JOUR
T1 - Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis
T2 - An Electrophysiological Connectome (eConnectome) Approach
AU - Sohrabpour, Abbas
AU - Ye, Shuai
AU - Worrell, Gregory A.
AU - Zhang, Wenbo
AU - He, Bin
N1 - Funding Information:
This work was supported in part by NIH R01NS096761, R01EB021027, R01EY023101, U01HL117664, S10OD021721, and in part by the National Science Foundation CBET-1450956 and CBET-1264782.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/12
Y1 - 2016/12
N2 - 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.
AB - 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.
KW - Directed transfer function (DTF)
KW - Granger causality analysis
KW - dynamic seizure imaging (DSI)
KW - electromagnetic source imaging (ESI)
KW - high-density electroencephalography (EEG)
KW - interictal spikes (IIS)
KW - magnetoencephalography (MEG)
KW - network
UR - http://www.scopus.com/inward/record.url?scp=84999751302&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84999751302&partnerID=8YFLogxK
U2 - 10.1109/TBME.2016.2616474
DO - 10.1109/TBME.2016.2616474
M3 - Article
C2 - 27740473
AN - SCOPUS:84999751302
SN - 0018-9294
VL - 63
SP - 2474
EP - 2487
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 12
M1 - 7588130
ER -