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      Biomarkers for identifying first-episode schizophrenia patients using diffusion weighted imaging.

      Brain research. Brain research reviews

      Algorithms, Biological Markers, analysis, Brain, pathology, Diffusion Magnetic Resonance Imaging, methods, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Reproducibility of Results, Schizophrenia, diagnosis, Sensitivity and Specificity

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          Abstract

          Recent advances in diffusion weighted MR imaging (dMRI) has made it a tool of choice for investigating white matter abnormalities of the brain and central nervous system. In this work, we design a system that detects abnormal features (biomarkers) of first-episode schizophrenia patients and then classifies them using these features. We use two different models of the dMRI data, namely, spherical harmonics and the two-tensor model. The algorithm works by first computing several diffusion measures from each model. An affine-invariant representation of each subject is then computed, thus avoiding the need for registration. This representation is used within a kernel based feature selection algorithm to determine the biomarkers that are statistically different between the two populations. Confirmation of how well these biomarkers identify each population is obtained by using several classifiers such as, k-nearest neighbors, Parzen window classifier, and support vector machines to separate 21 first-episode patients from 20 age-matched normal controls. Classification results using leave-many-out cross-validation scheme are given for each representation. This algorithm is a first step towards early detection of schizophrenia.

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          Journal
          20879287
          3091029

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