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However, most previous studies just focus on group\u2010level 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\u2010nine first\u2010episode untreated patients with OCD and 104 matched healthy controls (HCs) undergoing structural and functional MRI were included in this study. Voxel\u2010based morphometric and amplitude of low\u2010frequency 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\u2013function 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.<\/jats:p>","DOI":"10.1002\/hbm.25951","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T07:27:49Z","timestamp":1655796469000},"page":"4254-4265","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Two distinct subtypes of obsessive compulsive disorder revealed by a framework integrating multimodal neuroimaging information"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7331-0315","authenticated-orcid":false,"given":"Shaoqiang","family":"Han","sequence":"first","affiliation":[{"name":"Department of Magnetic Resonance Imaging the First Affiliated Hospital of Zhengzhou University  China"},{"name":"Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province  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