Add like
Add dislike
Add to saved papers

Classification of obsessive-compulsive disorder from resting-state fMRI.

Obssesive-compulsive disorder (OCD) is a serious mental illness that affects the overall quality of the patients' daily lives. Accurate diagnosis of this disorder is a primary step towards effective treatment. Diagnosing OCD is a lengthy procedure that involves interviews, symptom rating scales and behavioral observation as well as the experience of a clinician. Discovering signal processing and network based biomarkers from functional magnetic resonance scans of patients may greatly assist the clinicians in their diagnostic assessments. In this paper, we explore the use of Pearson's correlation scores and network based features to predict if a subject has OCD. We extracted mean time series from 112 brain regions and decomposed them to 5-frequency bands. The mean time courses were used to calculate the Pearson's correlation matrix and network based features for each band. Minimum redundancy maximum relevance feature selection method is applied to the Pearson's correlation matrix and network based features from each frequency band to select the best features for the final predictor. A leave-one-out cross validation method is used for the final predictor performance. Our proposed methodology achieves 80% accuracy (23 out of 29 subjects classified correctly) with 81% sensitivity(13 out of 16 OCD subjects identified correctly) and 77% specificity (10 out of 13 controls identified correctly) using leave-one-out with in-fold feature ranking and selection. The most discriminating feature bands are 0.06-0.11 Hz for Pearson's correlation and 0.03-0.06 Hz for network based features. The high classification accuracy indicates the predictive power of the network features as well as carefully chosen Pearson's correlation values.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app