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The paper presents a method of creating abnormality classifiers learned from Diffusion Tensor Imaging (DTI) data of a population of patients and controls. The score produced by the classifier can be used to aid in diagnosis as it quantifies the degree of pathology. Using anatomically meaningful features computed from the DTI data we train a non-linear support vector machine (SVM) pattern classifier. The method begins with high dimensional elastic registration of DT images followed by a feature extraction step that involves creating a feature by concatenating average anisotropy and diffusivity values in anatomically meaningful regions. Feature selection is performed via a mutual information based technique followed by sequential elimination of the features. A non-linear SVM classifier is then constructed by training on the selected features. The classifier assigns each test subject with a probabilistic abnormality score that indicates the extent of pathology. In this study, abnormality classifiers were created for two populations; one consisting of schizophrenia patients (SCZ) and the other with individuals with autism spectrum disorder (ASD). A clear distinction between the SCZ patients and controls was achieved with 90.62% accuracy while for individuals with ASD, 89.58% classification accuracy was obtained. The abnormality scores clearly separate the groups and the high classification accuracy indicates the prospect of using the scores as a diagnostic and prognostic marker.

We study morphologic characteristics and age-related changes in patients with schizophrenia to investigate whether abnormal neurodevelopment and brain structure have a role in the pathophysiological course of this disease.Our data consist of a set of cranial magnetic resonance images of 46 patients with schizophrenia and age- and sex-matched healthy controls. We deformed a template brain image to our set of subject images. Jacobian fields of these deformations were reduced to sets of 52 normalized region volumes for each subject by using a neuroanatomic atlas. Normalized regional volumes of the control and patient groups were compared by using Student t-test, and age correlation of each region volume was calculated for the two groups. All results were corrected for multiple comparisons by using permutation testing. We used a classifier based on support vector machines and a feature selection method to determine our ability to discriminate brains of controls from those of patients.Analysis of normalized region volumes shows enlargement of the third ventricle in patients. The age-correlation study showed a significant positive correlation in the third ventricle and right thalamus of controls, but not patients. Using an average of 6.5 features, our classifier was able to correctly identify 72% of patients and 70% of controls.In addition to enlargement of the third ventricle, brains of patients with schizophrenia show a different pattern of age-related changes.

Abnormal neurodevelopment may play a role in the pathophysiology of schizophrenia. We used deformation-based morphometry to examine voxel-wise age-related changes in patients with schizophrenia compared with healthy brains.We used a set of skull-stripped brains from an image database of cranial magnetic resonance images. We then deformed a template brain to the rest of the brains creating a set of deformation fields. Using the Jacobian values of these deformation fields, we calculated the voxel-wise t-score for comparison of controls with patients. We also calculated the voxel-wise Pearson correlation of Jacobian with age for both controls and patients.By examining the volume renderings of these statistical fields, we found that healthy people undergo age-related expansion of the ventricles, the surrounding periventricular white matter, and a corresponding decline in the frontal lobes and cingulate gyrus. In contrast, patients show much less of this age-related expansion of the ventricles and less atrophy in the cerebral cortex. In addition, patients have larger ventricles and reduced volume in the frontal/parietal lobes.These constellations of findings suggest that otherwise normal age-related ventricular enlargement and cortical loss occurs in schizophrenia patients, albeit at an earlier age.