Two multimodal neuroimaging subtypes of obsessive-compulsive disorder disclosed by semi-supervised machine learning.

Obsessive-compulsive disorder (OCD) is a highly heterogeneous mental condition with a diverse symptom. Existing studies classified OCD on the basis of conventional phenomenology-based taxonomy ignoring the fact that the same subtype identified in accordance with clinical symptom may have different mechanisms and treatment responses. This research involved 50 medicine-free patients with OCD and 50 matched healthy controls (HCs). All the participants were subjected to structural and functional magnetic resonance imaging (MRI). Voxel-based morphometry (VBM) and amplitude of low frequency fluctuation (ALFF) were used to evaluate gray matter volume (GMV) and spontaneous neuronal activities at rest respectively. Similarity network fusion (SNF) was utilized to integrate GMVs and spontaneous neuronal activities, and heterogeneity by discriminant analysis was applied to characterise OCD subtypes. Two OCD subtypes were identified: Subtype 1 exhibited decreased GMVs (i.e., left inferior temporal gyrus, right supplementary motor area and right lingual gyrus) and increased ALFF value (i.e., right orbitofrontal cortex), whereas subtype 2 exhibited increased GMVs (i.e., left cuneus, right precentral gyrus, left postcentral gyrus and left hippocampus) and decreased ALFF value (i.e., right caudate nucleus). Furthermore, the altered GMVs was negatively correlated with abnormal ALFF values in both subtype 1 and 2. This study requires further validation via a larger, independent dataset and should consider the potential influences of psychotropic medication on OCD patients' brain activities. Results revealed two reproducible subtypes of OCD based on underlying multimodal neuroimaging and provided new perspectives on the classification of OCD. • Data-driven subtype analysis of OCD patients rather than previous symptom-based analysis • The multimodal fusion method fuses the magnetic resonance images of two modalities. • Semi-supervised machine learning approach for subtype analysis of obsessive-compulsive disorder [ABSTRACT FROM AUTHOR]