Abstract
Patients with obsessive compulsive disorder (OCD) exhibit tremendous heterogeneity in structural and functional neuroimaging aberrance. However, most previous studies just focus on group-level aberrance of a single modality ignoring heterogeneity and multimodal features. On that account, we aimed to uncover OCD subtypes integrating structural and functional neuroimaging features with the help of a multiview learning method and examined multimodal aberrance for each subtype. Ninety-nine first-episode untreated patients with OCD and 104 matched healthy controls (HCs) undergoing structural and functional MRI were included in this study. Voxel-based morphometric and amplitude of low-frequency fluctuation (ALFF) were adopted to assess gray matter volumes (GMVs) and the spontaneous neuronal fluctuations respectively. Structural/functional distance network was obtained by calculating Euclidean distance between pairs of regional GMVs/ALFF values across patients. Similarity network fusion, one of multiview learning methods capturing shared and complementary information from multimodal data sources, was used to fuse multimodal distance networks into one fused network. Then spectral clustering was adopted to categorize patients into subtypes. As a result, two robust subtypes were identified. These two subtypes presented opposite GMV aberrance and distinct ALFF aberrance compared with HCs while shared indistinguishable clinical and demographic features. In addition, these two subtypes exhibited opposite structure-function difference correlation reflecting distinct adaptive modifications between multimodal aberrance. Altogether, these results uncover two objective subtypes with distinct multimodal aberrance and provide a new insight into taxonomy of OCD.