Toward Developing Robust Myotonic Dystrophy Brain Biomarkers using White Matter Tract Profiles Sub-Band Energy and A Framework of Ensemble Predictive Learning

The myotonic dystrophies (DM1 and DM2) are dominantly inherited disorders that cause pathological changes throughout the body and the brain. DM patients have difficulties with memory, attention, executive functioning, social cognition, and visuospatial function. Quantifying and understanding diffusion measures along main brain white matter fiber tracts offer a unique opportunity to reveal new insights into DM development and characterization. In this work, a novel supervised system is proposed, which is based on Tract Profiles sub-band energy information. The proposed system utilizes a Bayesian stacked random forest to diagnose, characterize, and predict DM clinical outcomes. The evaluation data consists of fractional anisotropies calculated for twelve major white matter tracts of 96 healthy controls and 62 DM patients. The proposed system discriminates DM vs. control with 86% accuracy, which is significantly higher than previous works. Additionally, it discovered DM brain biomarkers that are accurate and robust and will be helpful in planning clinical trials and monitoring clinical performance.