# Neuroimaging and Machine Learning in OCD: Advances in Diagnostic and Therapeutic Insights

## TL;DR

This review explores how combining neuroimaging and machine learning can improve OCD diagnosis and treatment by identifying brain abnormalities and enabling personalized strategies.

## Contribution

The paper highlights the novel integration of transfer learning with neuroimaging to address dataset limitations and improve diagnostic accuracy for OCD.

## Key findings

- ML algorithms achieved over 80% accuracy in diagnosing OCD by identifying CSTC circuit abnormalities.
- Transfer learning enhances predictive modeling and supports personalized treatment in resource-limited settings.
- Abnormalities in the cortico-striato-thalamo-cortical circuit are consistently linked to OCD.

## Abstract

Background/Objectives: Obsessive–Compulsive Disorder (OCD) is a chronic mental health condition characterized by intrusive thoughts and repetitive behaviors. Traditional diagnostic methods rely on subjective clinical assessments, delaying effective intervention. This review examines how advanced neuroimaging techniques, such as Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI), integrated with machine learning (ML), can improve OCD diagnostics by identifying structural and functional brain abnormalities, particularly in the cortico-striato-thalamo-cortical (CSTC) circuit. Methods: Findings from studies using MRI and DTI to identify OCD-related neurobiological markers are synthesized. Machine learning algorithms like Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) are evaluated for their ability to analyze neuroimaging data. The role of transfer learning in overcoming dataset limitations and heterogeneity is also explored. Results: ML algorithms have achieved diagnostic accuracies exceeding 80%, revealing subtle neurobiological markers linked to OCD. Abnormalities in the CSTC circuit are consistently identified. Transfer learning shows promise in enhancing predictive modeling and enabling personalized treatment strategies, especially in resource-constrained settings. Conclusions: The integration of neuroimaging and ML represents a transformative approach to OCD diagnostics, offering improved accuracy and biologically informed insights. Future research should focus on optimizing multimodal imaging techniques, increasing data generalizability, and addressing interpretability challenges to enhance clinical applicability. These innovations have the potential to advance precision diagnostics and support more targeted therapeutic interventions, ultimately improving outcomes for individuals with OCD.