Higher dimensional fMRI connectivity dynamics show reduced dynamism in schizophrenia patients

Robyn L. Miller, Maziar Yaesoubi, Vince D. Calhoun, Shruti Gopal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Assessments of functional connectivity between brain networks is a fixture of resting state fMRI research. Until very recently most of this work proceeded from an assumption of stationarity in resting state network connectivity. In the last few years however, interest in moving beyond this simplifying assumption has grown considerably. Applying group temporal independent component analysis (tICA) to a set of time-varying functional network connectivity (FNC) matrices derived from a large multi-site fMRI dataset (N=314; 163 healthy, 151 schizophrenia patients), we obtain a set of five basic correlation patterns (component spatial maps (SMs)) from which observed FNCs can be expressed as mutually independent linear combinations, ie. the coefficient on each SM in the linear combination is statistically independent of the others. We study dynamic properties of network connectivity as they are reflected in this five-dimensional space, and report stark differences in connectivity dynamics between schizophrenia patients and healthy controls

Original languageEnglish (US)
Title of host publicationProceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014
PublisherIEEE Computer Society
ISBN (Print)9781479941506
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event4th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014 - Tubingen, Germany
Duration: Jun 4 2014Jun 6 2014

Publication series

NameProceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014

Other

Other4th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014
CountryGermany
CityTubingen
Period6/4/146/6/14

Keywords

  • dynamics
  • fMRI
  • independent component analysis
  • network connectivity
  • schizophrenia

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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