Granger causal time-dependent source connectivity in the somatosensory network

Lin Gao (Corresponding Author), Linda Sommerlade, Brian Coffman, Tongsheng Zhang, Julia M. Stephen, Dichen Li, Jue Wang, Celso Grebogi, Bjoern Olaf Schelter

Research output: Contribution to journalArticle

22 Citations (Scopus)
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Abstract

Exploration of transient Granger causal interactions in neural sources of electrophysiological activities provides deeper insights into brain information processing mechanisms. However, the underlying neural patterns are confounded by time-dependent dynamics, non-stationarity and observational noise contamination. Here we investigate transient Granger causal interactions using source time-series of somatosensory evoked magnetoencephalographic (MEG) elicited by air puff stimulation of right index finger and recorded using 306-channel MEG from 21 healthy subjects. A new time-varying connectivity approach, combining renormalised partial directed coherence with state space modelling, is employed to estimate fast changing information flow among the sources. Source analysis confirmed that somatosensory evoked MEG was mainly generated from the contralateral primary somatosensory cortex (SI) and bilateral secondary somatosensory cortices (SII). Transient Granger causality shows a serial processing of somatosensory information, 1) from contralateral SI to contralateral SII, 2) from contralateral SI to ipsilateral SII, 3) from contralateral SII to contralateral SI, and 4) from contralateral SII to ipsilateral SII. These results are consistent with established anatomical connectivity between somatosensory regions and previous source modeling results, thereby providing empirical validation of the time-varying connectivity analysis. We argue that the suggested approach provides novel information regarding transient cortical dynamic connectivity, which previous approaches could not assess.
Original languageEnglish
Article number10399
JournalScientific Reports
Volume5
DOIs
Publication statusPublished - 21 May 2015

Keywords

  • biomedical engineering
  • network models

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