Cited by 59EEG and Brain-Computer InterfacesFunctional Brain Connectivity StudiesNeural dynamics and brain function

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

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Sohrabpour, Abbas, Ye, Shuai, Worrell, Gregory A., Zhang, Wenbo, He, Bin·2016·IEEE Transactions on Biomedical Engineering

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.

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  • The eConnectome approach has stirred interest in studying pathological networks such as epilepsy, where determining the source that drives seizures is important.

    “The eConnectome approach (104, 105) has stirred interest in studying pathological networks such as epilepsy, where determining the source that drives seizures is of particular importance (106).”

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References 125 · Cited by 59

Authors
Sohrabpour, A., Ye, S., Worrell, G. A., Zhang, W., & He, B.
Affiliations
  • University of Minnesota (US)
  • Mayo Clinic (US)
Corresponding author
He, Bin
Venue
IEEE Transactions on Biomedical Engineering
Published
2016-12
Volume
63
Issue
12
Pages
2474-2487
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
OpenAlex
W2529907792
Cited by
59
References
125
Citations by year
2026: 4 · 2025: 6 · 2024: 7 · 2023: 5 · 2022: 6 · 2021: 5 · 2020: 8 · 2019: 7 · 2018: 8 · 2017: 2 · 2016: 1
FWCI
3.82
Type
journal article
Language
en
Open access
Locations
  • IEEE Transactions on Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • NIH (R01NS096761, R01EB021027, R01EY023101, U01HL117664, S10OD021721)
  • National Science Foundation (CBET-1450956, CBET-1264782)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
Granger causality, brain networks, magnetoencephalography, epilepsy surgery
MeSH terms
Brain, Computer Simulation, Electroencephalography, Epilepsy, Humans, Models, Neurological, Neural Pathways, Magnetoencephalography, Models, Statistical, Connectome

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