Prediction of illness severity in patients with major depression using structural MR brain scans

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    Abstract

    Purpose: To develop a model for the prediction of Major Depressive Disorder (MDD) illness severity ratings from individual structural MRI brain scans.

    Materials and Methods: Structural T1-weighted MRI scans were obtained from 30 patients with MDD recruited from two different scanning centers. Self-rated (Beck Depression Inventory; BDI), and clinician-rated (Hamilton Rating Scale for Depression, HRSD), syndrome-specific illness severity ratings were obtained just before scanning. Relevance vector regression (RVR) was used to predict the scores (BDI, HRSD) from T1-weighted MRI scans.

    Results: It was possible to predict the BDI score (correlation between actual score and RVR predicted scores r = 0.694; P < 0.0001), but not the HRSD scores (r = 0.34; P = 0.068) from individual subjects. BDI scores from the most ill patients were predicted more accurately than those from patients who were least ill (standard deviation of difference between predicted and actual scores 2.5 versus 7.4, respectively).

    Conclusion: These data suggest that T1-weighted MRI scans contain sufficient information about neurobiological change in patients with MDD to permit accurate predictions about illness severity, on an individual subject basis, particularly for the most ill patients.

    Original languageEnglish
    Pages (from-to)64-71
    Number of pages8
    JournalJournal of Magnetic Resonance Imaging
    Volume35
    Issue number1
    DOIs
    Publication statusPublished - Jan 2012

    Keywords

    • major depressive disorder
    • relevance vector regression
    • pattern classification
    • multicenter neuroimaging
    • BDI
    • Beck Depression Inventory
    • HRSD
    • Hamilton Depression Rating Scale
    • HIPPOCAMPAL VOLUME
    • CLASSIFICATION
    • MACHINE
    • METAANALYSIS
    • REGRESSION

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