# Neurocomputational mechanisms and connectivity dynamics underlying obsessive-compulsive disorder and phenotypical differentiations > **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $844,081 ## Abstract Project Summary/Abstract Obsessive-Compulsive Disorder (OCD) is a psychiatric disorder with a lifetime prevalence of 2-3% in the general population. It manifests in a variety of intrusive thoughts (obsessions), rigid decision-making and ritualistic behaviors (compulsions), with prolonged and disabling effects. However, the neural and computational mechanisms underlying the disorder or its differentiations remain unclear, so that misdiagnosis is frequent, and even when an appropriate treatment is established, about half the patients keep exhibiting disabling residual symptoms. In this project, we propose to use an innovative approach, relying on neurocomputational and connectivity analyses of fMRI data, jointly with multi-model-based computational analyses of choice behavior across decision-making tasks. With these analyses, the project aims to characterize OCD symptom severity and phenotype differentiation based on cortico-striatal and cortico-cortical circuit dynamics, and to establish a relation between network-based phenotypes and model-based parametrization of choice behavior across decision-making tasks. We will test the predictions of a newly published neurocomputational theory, and its leading hypothesis that rigidity in motor, planning and goal selections is caused by aberrant stability of transient dynamics in the dorsal, lateral and ventral cortico-striatal circuit, respectively. We will test this hypothesis in a population of 140 subjects, equally distributed across five categories defined on the YBOCS scale of OCD severity (subclinical, mild, moderate, severe and extreme). We will use Dynamic Causal Modeling and Dependency Network Analysis to estimate subject-specific cortico- striatal circuit dynamics (with planned redundancy to test convergence of results), and we will use computational models based on reinforcement learning and Bayesian inference algorithms for the analysis of choice behavior across decision-making tasks. OCD phenotypes are expected to show task-related motor, planning and goal selection rigidity, expressed both in terms of model-based parameters of choice behavior and effective connectivity and network measures responsible for aberrant circuit stability. If validated, this novel characterization of neurocomputational OCD phenotypes would provide a more comprehensive explanation of the heterogeneity in OCD symptomatology and treatment responses, helping the development of subject- specific treatment tools, such as, for instance, personalized neuromodulation targets in deep brain stimulation or transcranial magnetic stimulation. ## Key facts - **NIH application ID:** 10880025 - **Project number:** 1R01MH133625-01A1 - **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI - **Principal Investigator:** Martijn Figee - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $844,081 - **Award type:** 1 - **Project period:** 2024-06-10 → 2029-01-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10880025 ## Citation > US National Institutes of Health, RePORTER application 10880025, Neurocomputational mechanisms and connectivity dynamics underlying obsessive-compulsive disorder and phenotypical differentiations (1R01MH133625-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10880025. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*