Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/622083
Title: Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach
Authors: Abbas Sohrabpour;Shuai Ye;Gregory A. Worrell;Wenbo Zhang;Bin He
subject: magnetoencephalography (MEG)|Directed transfer function (DTF)|Granger causality analysis|electromagnetic source imaging (ESI)|high-density electroencephalography (EEG)|dynamic seizure imaging (DSI)|network|interictal spikes (IIS)
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/150160
http://localhost/handle/Hannan/622083
ISSN: 0018-9294
1558-2531
volume: 63
issue: 12
Appears in Collections:2016

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Title: Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach
Authors: Abbas Sohrabpour;Shuai Ye;Gregory A. Worrell;Wenbo Zhang;Bin He
subject: magnetoencephalography (MEG)|Directed transfer function (DTF)|Granger causality analysis|electromagnetic source imaging (ESI)|high-density electroencephalography (EEG)|dynamic seizure imaging (DSI)|network|interictal spikes (IIS)
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/150160
http://localhost/handle/Hannan/622083
ISSN: 0018-9294
1558-2531
volume: 63
issue: 12
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7588130.pdf1.11 MBAdobe PDFThumbnail
Preview File
Title: Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach
Authors: Abbas Sohrabpour;Shuai Ye;Gregory A. Worrell;Wenbo Zhang;Bin He
subject: magnetoencephalography (MEG)|Directed transfer function (DTF)|Granger causality analysis|electromagnetic source imaging (ESI)|high-density electroencephalography (EEG)|dynamic seizure imaging (DSI)|network|interictal spikes (IIS)
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/150160
http://localhost/handle/Hannan/622083
ISSN: 0018-9294
1558-2531
volume: 63
issue: 12
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7588130.pdf1.11 MBAdobe PDFThumbnail
Preview File