Effective connectivity analysis of fMRI and MEG data collected under identical paradigms

Sergey M. Plis, Michael P. Weisend, Eswar Damaraju, Tom Eichele, Andy Mayer, Vincent P. Clark, Terran Lane, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review


Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.

Original languageEnglish (US)
Pages (from-to)1156-1165
Number of pages10
JournalComputers in Biology and Medicine
Issue number12
StatePublished - Dec 1 2011
Externally publishedYes


  • Bayesian networks
  • Effective connectivity
  • FMRI
  • MEG

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Fingerprint Dive into the research topics of 'Effective connectivity analysis of fMRI and MEG data collected under identical paradigms'. Together they form a unique fingerprint.

Cite this