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

@article{Sohrabpour2016NoninvasiveES,
  title={Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach},
  author={Abbas Sohrabpour and Shuai Ye and Gregory A. Worrell and Wenbo Zhang and Bin He},
  journal={IEEE Transactions on Biomedical Engineering},
  year={2016},
  volume={63},
  pages={2474-2487}
}
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… CONTINUE READING

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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 ) .
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 ) .
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 ) .
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 ) .
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 ) .
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 ) .
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 ) .
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 .
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 .
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 ) .
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 .
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 ) .
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 ) .
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 ) .
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