Abstract
Magnetic resonance imaging (MRI) methods have been used to detect cerebral anatomical distinction between obsessive-compulsive disorder (OCD) patients and healthy controls (HC). Machine learning approach allows for the possibility of discriminating patients on the individual level. However, few studies have used this automatic technique based on multiple modalities to identify potential biomarkers of OCD. High-resolution structural MRI and diffusion tensor imaging (DTI) data were acquired from 48 OCD patients and 45 well-matched HC. Gray matter volume (GMV), white matter volume (WMV), fractional anisotropy (FA), and mean diffusivity (MD) were extracted as four features were examined using support vector machine (SVM). Ten brain regions of each feature contributed most to the classification were also estimated. Using different algorithms, the classifier achieved accuracies of 72.08, 61.29, 80.65, and 77.42% for GMV, WMV, FA, and MD, respectively. The most discriminative gray matter regions that contributed to the classification were mainly distributed in the orbitofronto-striatal “affective” circuit, the dorsolateral, prefronto-striatal “executive” circuit and the cerebellum. For WMV feature and the two feature sets of DTI, the shared regions contributed the most to the discrimination mainly included the uncinate fasciculus, the cingulum in the hippocampus, corticospinal tract, as well as cerebellar peduncle. Based on whole-brain volumetry and DTI images, SVM algorithm revealed high accuracies for distinguishing OCD patients from healthy subjects at the individual level. Computer-aided method is capable of providing accurate diagnostic information and might provide a new perspective for clinical diagnosis of OCD.
Highlights
The core characteristics of obsessive-compulsive disorder (OCD) are intrusive recurrent thoughts and/or repetitive behaviors
Abnormalities of gray matter (GM) and white matter (WM) microstructures are considered to be related with its neural pathogenesis
Voxel-based morphometry (VBM) analysis allows the investigation of gray matter volume (GMV) and white matter volume (WMV) in the whole brain [1]
Summary
The core characteristics of obsessive-compulsive disorder (OCD) are intrusive recurrent thoughts and/or repetitive behaviors. Abnormalities of gray matter (GM) and white matter (WM) microstructures are considered to be related with its neural pathogenesis. Nowadays magnetic resonance imaging (MRI) approaches provide a perspective to investigate the neuropathological changes of OCD and allow researchers to identify better biological markers of this disease. Voxel-based morphometry (VBM) analysis allows the investigation of gray matter volume (GMV) and white matter volume (WMV) in the whole brain [1]. Diffusion tensor imaging (DTI), on the other hand, is available to measure the in vivo water molecule diffusion within the WM fibers, which renders more exquisite details on microstructure changes in WM [2]. Fractional anisotropy (FA) and mean diffusivity (MD) are the two most widely used diffusion indices to investigate the pathology of OCD [3, 4]
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