Automated classification of bipolar, schizophrenic, and healthy individuals via multiple spatial ICA functional brain 'modes'

V. Calhoun, G. Pearlson, K. Kiehf

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

    Abstract

    Schizophrenia (SZ) and bipolar disorder (BP) are currently diagnosed on the basis of a constellation of psychiatric symptoms and longitudinal course. The clinical profile of SZ and BP can sometimes look similar, especially when symptoms are overlapping. The determination of a reliable biologically-based indicator of these diseases (a biomarker) would be a significant advance and could provide the groundwork for developing more rigorous tools for differential diagnosis and assignment of treatments. Recently, independent component analysis applied to functional magnetic resonance imaging (fMRI) data has been fruitful in grouping the data into meaningful spatially independent components. We propose an automated way to identify two distinct brain networks or 'modes' which occur in regions implicated in schizophrenia. Following identification, we propose a method for combining two brain modes and develop a supervised classification algorithm for discriminating subjects with bipolar disorder, chronic schizophrenia, and healthy controls. An adaptive threshold applied to pair-wise mean difference images for the two spatial modes is trained to minimize the total error based upon the Euclidean distance between the group mean images and the images to be classified. Using a fully validated leave-one-out approach, results indicate an average sensitivity and specificity of 90% and 95%, respectively. In summary, we show that using features derived from fMRI data with ICA and a supervised classification approach, we can objectively separate diagnostic groups and suggests that combining multiple brain networks may improve our ability to distinguish diseases processes.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
    PublisherIEEE Computer Society
    Pages371-376
    Number of pages6
    ISBN (Print)1424406560, 9781424406562
    DOIs
    StatePublished - Jan 1 2006
    Event2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006 - Maynooth, Ireland
    Duration: Sep 6 2006Sep 8 2006

    Publication series

    NameProceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006

    Other

    Other2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
    CountryIreland
    CityMaynooth
    Period9/6/069/8/06

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software
    • Signal Processing

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