Diffusion tensor imaging reliably differentiates patients with schizophrenia from healthy volunteers

Authors

  • Babak A. Ardekani,

    Corresponding author
    1. Center for Advanced Brain Imaging, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
    2. Department of Psychiatry, New York University School of Medicine, New York, New York
    • The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA
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  • Ali Tabesh,

    1. Center for Advanced Brain Imaging, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
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  • Serge Sevy,

    1. The Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
    2. Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
    3. Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, New York
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  • Delbert G. Robinson,

    1. The Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
    2. Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
    3. Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, New York
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  • Robert M. Bilder,

    1. Semel Institute for Neuroscience and Human Behavior, Geffen School of Medicine, UCLA, Los Angeles, California
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  • Philip R. Szeszko

    1. The Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
    2. Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
    3. Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, New York
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Abstract

The objective of this research was to determine whether fractional anisotropy (FA) and mean diffusivity (MD) maps derived from diffusion tensor imaging (DTI) of the brain are able to reliably differentiate patients with schizophrenia from healthy volunteers. DTI and high resolution structural magnetic resonance scans were acquired in 50 patients with schizophrenia and 50 age- and sex-matched healthy volunteers. FA and MD maps were estimated from the DTI data and spatially normalized to the Montreal Neurologic Institute standard stereotactic space. Individuals were divided randomly into two groups of 50, a training set, and a test set, each comprising 25 patients and 25 healthy volunteers. A pattern classifier was designed using Fisher's linear discriminant analysis (LDA) based on the training set of images to categorize individuals in the test set as either patients or healthy volunteers. Using the FA maps, the classifier correctly identified 94% of the cases in the test set (96% sensitivity and 92% specificity). The classifier achieved 98% accuracy (96% sensitivity and 100% specificity) when using the MD maps as inputs to distinguish schizophrenia patients from healthy volunteers in the test dataset. Utilizing FA and MD data in combination did not significantly alter the accuracy (96% sensitivity and specificity). Patterns of water self-diffusion in the brain as estimated by DTI can be used in conjunction with automated pattern recognition algorithms to reliably distinguish between patients with schizophrenia and normal control subjects. Hum Brain Mapp, 2010. © 2010 Wiley-Liss, Inc.

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