Biotypes of OCD
Project Overview
This research project aims to identify distinct biological subtypes (biotypes) of Obsessive-Compulsive Disorder (OCD) using advanced machine learning techniques and neuroimaging data. By understanding these different subtypes, we can develop more targeted and effective treatments for individuals with OCD.
Key Research Questions
- How can we identify distinct neurobiological subtypes of OCD using multimodal brain imaging data?
- What are the relationships between different symptom dimensions and brain network patterns in OCD?
- How do these biotypes relate to treatment response and clinical outcomes?
- Can machine learning algorithms help predict which treatments will be most effective for different OCD biotypes?
Methodology
Our approach combines:
- Advanced neuroimaging techniques (fMRI, DTI, structural MRI)
- Machine learning and artificial intelligence algorithms
- Clinical assessments and symptom profiling
- Network neuroscience approaches
- Longitudinal treatment outcome data
Current Status
We are currently:
- Collecting and analyzing neuroimaging data from individuals with OCD
- Developing and validating machine learning models for biotype classification
- Investigating the relationship between identified biotypes and treatment outcomes
- Collaborating with clinical partners to translate findings into practical treatment recommendations