Defining data-driven subgroups of obsessive-compulsive disorder with different treatment responses based on...

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• Obsessive-compulsive disorder (OCD) exhibits significant heterogeneity in symptoms and treatment outcomes.
• Identifying homogeneous subgroups within OCD is crucial for understanding and managing the disorder.
• Resting-state functional connectivity (rsFC) offers a potential neurobiological basis for subgrouping.

Purpose of the Study:

• To identify subgroups of obsessive-compulsive disorder (OCD) patients based on resting-state functional connectivity (rsFC).
• To investigate differences in treatment response among these identified OCD subgroups.
• To explore the role of default mode network (DMN) connectivity in predicting treatment outcomes.

Main Methods:

• Utilized resting-state functional MRI data from 107 OCD patients and 110 healthy controls (HCs).
• Employed support vector machine (SVM) analysis to identify discriminating rsFC features between OCD patients and HCs.
• Applied hierarchical clustering to subdivide OCD patients into subgroups based on selected rsFC features.

Main Results:

• Identified 35 rsFC features with high sensitivity and specificity differentiating OCD from HCs.
• Subdivided OCD patients into two distinct subgroups with similar demographic and clinical profiles.
• One subgroup exhibited more impaired rsFC within and between the default mode network (DMN) and other brain networks.
• This subgroup also demonstrated significantly lower symptom improvement and a lower response rate to treatment over 16 weeks.

Conclusions:

• Aberrant rsFC within and between the DMN and other networks is associated with treatment response in OCD.
• These neurobiological alterations may serve as predictive biomarkers for treatment outcomes in OCD patients.
• Findings support the development of more personalized and optimal treatment strategies for OCD based on neuroimaging data.