A maximal-correlation approach using ICA for testing functional network connectivity applied to schizophrenia

Madiha J. Jafri, Godfrey D. Pearlson, Vince D. Calhoun

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

    Abstract

    There has been a growing interest in analyzing brain activation differences between patients and controls by studying resting-state fMRI brain networks. Functional connectivity of the resting brain has been studied by analyzing correlation differences in time courses among seed voxels, regions, or volume of interest with other voxels of the brain in patients versus controls. Spatial differences have also been analyzed among component maps derived from independent component analysis (ICA) in patients with schizophrenia and in healthy controls. However, the relationship among ICA component time courses, (which we define as functional network connectivity), has not been studied. We propose a novel technique to determine FNC applied to schizophrenia which does not rely on the time series of specific brain voxels or regions of interest and instead focuses upon the connectivity between functional networks (components) estimated from ICA using maximal correlation between component time series.

    Original languageEnglish (US)
    Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
    Subtitle of host publicationFrom Nano to Macro - Proceedings
    Pages468-471
    Number of pages4
    DOIs
    StatePublished - Nov 27 2007
    Event2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States
    Duration: Apr 12 2007Apr 15 2007

    Publication series

    Name2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

    Other

    Other2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
    Country/TerritoryUnited States
    CityArlington, VA
    Period4/12/074/15/07

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Medicine(all)

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