Predicting obsessive-compulsive disorder severity combining neuroimaging and machine learning methods.

BACKGROUND

Recently, machine learning methods have been used to discriminate, on an individual basis, patients from healthy controls through brain structural magnetic resonance imaging (MRI). However, the application of these methods to predict the severity of psychiatric symptoms is less common.

METHODS

Herein, support vector regression (SVR) was employed to evaluate whether gray matter volumes encompassing cortical-subcortical loops contain discriminative information to predict obsessive-compulsive disorder (OCD) symptom severity in 37 treatment-naïve adult OCD patients.

RESULTS

The Pearson correlation coefficient between predicted and observed symptom severity scores was 0.49 (p=0.002) for total Dimensional Yale-Brown Obsessive-Compulsive Scale (DY-BOCS) and 0.44 (p=0.006) for total Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). The regions that contained the most discriminative information were the left medial orbitofrontal cortex and the left putamen for both scales.

LIMITATIONS

Our sample is relatively small and our results must be replicated with independent and larger samples.

CONCLUSIONS

These results indicate that machine learning methods such as SVR analysis may identify neurobiological markers to predict OCD symptom severity based on individual structural MRI datasets.