Functional Data and Long Short-Term Memory Networks for Diagnosis of Parkinson's Disease

摘要

Computer-aided diagnostic tools for neurodegenerative and psychiatric disease and disorders have many practical clinical applications. In this work, we propose a two-component neural network based on Long Short-Term Memory (LSTM) for the automatic diagnosis of Parkinson's disease (PD) using whole brain resting-state functional magnetic resonance data. Given the recent findings on structural and functional asymmetry that could be observed in PDs, our proposed architecture consists of two LSTM networks that were designed to facilitate independent mining of patterns that may differ between the left and right hemispheres. Under a cross-validation framework, our proposed model achieved an F1-score of 0.701 ±0.055, which is competitive against an F1-score of 0.677 ± 0.033 achieved by a single LSTM model.