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Neurosurgical interventions including deep brain stimulation (DBS) and capsulotomy have been demonstrated effective for refractory obsessive-compulsive disorder (OCD), although treatment-shared/-specific network mechanisms remain largely unclear. We retrospectively analyzed resting-state fMRI data from three cohorts: a cross-sectional dataset of 186 subjects (104 OCD and 82 healthy controls), and two longitudinal datasets of refractory patients receiving ventral capsule/ventral striatum DBS (14 OCD) and anterior capsulotomy (27 OCD). We developed a machine learning model predictive of OCD symptoms (indexed by the Yale–Brown Obsessive Compulsive Scale, Y-BOCS) based on functional connectivity profiles and used graphic measures of network communication to characterize treatment-induced profile changes. We applied a linear model on 2 levels treatments (DBS or capsulotomy) and outcome to identify whether pre-surgical network communication was associated with differential treatment outcomes. We identified 54 functional connectivities within fronto-subcortical networks significantly predictive of Y-BOCS score in patients across 3 independent cohorts, and observed a coexisting pattern of downregulated cortico-subcortical and upregulated cortico-cortical network communication commonly shared by DBS and capsulotomy. Furthermore, increased cortico-cortical communication at ventrolateral and centrolateral prefrontal cortices induced by DBS and capsulotomy contributed to improvement of mood and anxiety symptoms, respectively (p < 0.05). Importantly, pretreatment communication of ventrolateral and centrolateral prefrontal cortices were differentially predictive of mood and anxiety improvements by DBS and capsulotomy (effect sizes = 0.45 and 0.41, respectively). These findings unravel treatment-shared and treatment-specific network characteristics induced by DBS and capsulotomy, which may facilitate the search of potential evidence-based markers for optimally selecting among treatment options for a patient.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls” (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

Deep brain stimulation (DBS) is effective for patients with treatment-refractory obsessive-compulsive disorder. DBS of the ventral anterior limb of the internal capsule (vALIC) rapidly improves mood and anxiety with optimal stimulation parameters. To understand these rapid effects of vALIC-DBS, we studied functional interactions within the affective amygdala circuit. We compared resting state functional magnetic resonance imaging data during chronic stimulation versus one week of stimulation discontinuation in patients, and obtained two resting state scans from matched healthy volunteers to account for test-retest effects. Imaging data were analyzed using functional connectivity analysis and dynamic causal modelling. Improvement in mood and anxiety following DBS was associated with reduced amygdala-insula functional connectivity. Directional connectivity analysis revealed that DBS increased the impact of the ventromedial prefrontal cortex on the amygdala, and decreased the impact of the amygdala on the insula. These results highlight the importance of the amygdala circuit in the pathophysiology of OCD, and suggest a neural systems model through which negative mood and anxiety are modulated by vALIC-DBS for OCD and possibly other psychiatric disorders.