Exploring the Epileptic Brain Network using Time-Variant Effective Connectivity and Graph Theory

Silvia Storti, Ilaria Boscolo Galazzo, Sehresh Khan, Paolo Manganotti, Gloria Menegaz

Research output: Contribution to journalArticlepeer-review


The application of time-varying measures of causality between source time series can be very informative to elucidate the direction of communication among the regions of an epileptic brain. The aim of the study was to identify the dynamic patterns of epileptic networks in focal epilepsy by applying multivariate adaptive directed transfer function (ADTF) analysis and graph-theory to high-density electroencephalographic (hdEEG) recordings. The cortical network was modeled after source reconstruction and topology modulations were detected during interictal spikes. First a distributed linear inverse solution, constrained to the individual grey matter, was applied to the averaged spikes and the mean source activity over 112 regions, as identified by the Harvard-Oxford Atlas, was calculated. Then, ADTF, a dynamic measure of causality, was used to quantify the connectivity strength between pairs of regions acting as nodes in the graph, and the measure of node centrality was derived. The proposed analysis was effective in detecting the focal regions as well as in characterizing the dynamics of the spike propagation, providing evidence of the fact that node centrality is a reliable feature for the identification of the epileptogenic zones. Validation was performed by multi-modal analysis as well as from surgical outcomes. In conclusion, the time-variant connectivity analysis applied to the epileptic patients can distinguish the generator of the abnormal activity from the propagation spread and identify the connectivity pattern over time.

Original languageEnglish
Article number7563891
JournalIEEE Journal of Biomedical and Health Informatics
Issue number99
Publication statusPublished - 2016
Externally publishedYes


  • adaptive directed transfer function
  • Brain connectivity
  • centrality
  • Epilepsy

ASJC Scopus subject areas

  • Biotechnology
  • Medicine(all)
  • Computer Science Applications
  • Health Information Management
  • Electrical and Electronic Engineering


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