Keywords: White Matter, Diffusion Tensor ImagingIn this study, we compared the classification performance of different machine learning models for discriminating OCD patients based on DTI tractography. Firstly, we extracted DTI metrics and tract volumes as features. Following feature selection, four machine learning models were performed for classification. Finally, a novel SHapley Additive exPlanations (SHAP) analysis was used to intepret the value of importance for each feature. We found that XGBoost exhibited the best classification performance among the four models. The model explanation by SHAP suggested that the volume of callosal orbital frontal tract was the most important factor in differentiating OCD from healthy controls.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords