Modeling Effective Connectivity in High-Dimensional Cortical Source Signals

Yuxiao Wang, Chee Ming Ting, Hernando Ombao

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

To study the effective connectivity among sources in a densely voxelated (high-dimensional) cortical surface, we develop the source-space factor VAR model. The first step in our procedure is to estimate cortical activity from multichannel electroencephalograms (EEG) using anatomically constrained brain imaging methods. Following parcellation of the cortical surface into disjoint regions of interest (ROIs), latent factors within each ROI are computed using principal component analysis. These factors are ROI-specific low-rank approximations (or representations) which allow for efficient estimation of connectivity in the high-dimensional cortical source space. The second step is to model effective connectivity between ROIs by fitting a VAR model jointly on all the latent processes. Measures of cortical connectivity, in particular partial directed coherence, are formulated using the VAR parameters. We illustrate the proposed model to investigate connectivity and interactions between cortical ROIs during rest.

Original languageEnglish (US)
Article number7542588
Pages (from-to)1315-1325
Number of pages11
JournalIEEE Journal on Selected Topics in Signal Processing
Volume10
Issue number7
DOIs
StatePublished - Oct 1 2016
Externally publishedYes

Keywords

  • Coherence analysis
  • dimension reduction
  • factor analysis
  • multichannel EEG
  • partial directed coherence
  • principal component analysis
  • vector autoregressive model

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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