Abstract Details

Title Predicting REM Sleep Behavior Disorder in Early Parkinson's Disease using Graph Theory-enhanced Resting-state fMRI Analysis

Topic Movement Disorders

Presentation(s) P11 - Poster Session 11 (11:45 AM-12:45 PM)

Poster/Presentation
Number 16-008

Objective Investigate whether machine learning analysis of resting-state functional MRI (rs-fMRI) can predict REM sleep behavior disorder (RBD) status in early Parkinson's disease (PD) patients and provide physiological insights into brain network differences underlying distinct phenotypes.

Background RBD predicts a malignant non-motor phenotype in PD that significantly influences disease progression and clinical outcomes. While previous studies have characterized non-motor phenotypes based on RBD presence or absence, the underlying physiological mechanisms distinguishing these phenotypes remain incompletely understood.

Design/Methods We analyzed rs-fMRI data from 271 early PD patients in the Parkinson's Progression Markers Initiative (PPMI) cohort. We used an 80/20 train-test split. Data preprocessing included MinMax scaling and principal component analysis (PCA), with five principal components selected via elbow method to explain maximum variance. rs-fMRI processing was performed using the CONN toolbox in MATLAB. We compared two feature sets: ROI-ROI connectivity features alone versus combined ROI-ROI and graph theory metrics, using the top 80 features for model training. Logistic regression was employed with 5-fold cross-validation (CV) and prediction performance on a test set for model validation.

Results The combined ROI-ROI and graph theory feature model achieved superior performance with balanced accuracy of 0.87, mean CV balanced accuracy of 0.85, and test set prediction 0.92. In contrast, using only ROI-ROI features yielded lower performance (balanced accuracy: 0.80, mean CV balanced accuracy: 0.78, test set balanced accuracy 0.79). Feature importance analysis identified key brain connectivity features contributing to RBD prediction.

Conclusions Our findings demonstrate that brain networks in early PD patients with and without RBD exhibit distinct physiological signatures detectable through rs-fMRI analysis. The enhanced predictive performance achieved by incorporating graph theory metrics suggests that network topology provides crucial information beyond simple connectivity patterns. This neuroimaging-based approach for RBD prediction offers potential clinical utility for stratifying patients and predicting long-term disease progression trajectories in early PD.

Authors/Disclosures
Alfonso Enrique Martinez Nunez, MD (University of Florida - Fixel Institute) PRESENTER	Dr. Martinez Nunez has nothing to disclose.
Joshua Wong, MD (University of Florida College of Medicine - Neurology)	The institution of Dr. Wong has received research support from NIH.