Welcome to CDC Stacks | Diffusion Tensor Imaging Reliably Differentiates Patients With Schizophrenia from Healthy Volunteers - 33179 | CDC Public Access
Stacks Logo
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
 
 
Help
Clear All Simple Search
Advanced Search
Diffusion Tensor Imaging Reliably Differentiates Patients With Schizophrenia from Healthy Volunteers
Filetype[PDF - 518.99 KB]


Details:
  • Pubmed ID:
    20205252
  • Pubmed Central ID:
    PMC2896986
  • Funding:
    K01 MH001990/MH/NIMH NIH HHS/United States
    K01 MH001990-05/MH/NIMH NIH HHS/United States
    M01 PR018535/PR/OCPHP CDC HHS/United States
    MH01990/MH/NIMH NIH HHS/United States
    MH60004/MH/NIMH NIH HHS/United States
    MH60374/MH/NIMH NIH HHS/United States
    MH76995/MH/NIMH NIH HHS/United States
    P30 MH060575/MH/NIMH NIH HHS/United States
    P30 MH074543/MH/NIMH NIH HHS/United States
    P30 MH074543-05/MH/NIMH NIH HHS/United States
    P50 MH080173-02/MH/NIMH NIH HHS/United States
    R01 MH060004/MH/NIMH NIH HHS/United States
    R01 MH060004-09/MH/NIMH NIH HHS/United States
    R01 MH060374/MH/NIMH NIH HHS/United States
    R01 MH060374-05/MH/NIMH NIH HHS/United States
    R01 MH076995/MH/NIMH NIH HHS/United States
    R01 MH076995-03/MH/NIMH NIH HHS/United States
    R03EB8201/EB/NIBIB NIH HHS/United States
  • Document Type:
  • Collection(s):
  • Description:
    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.