Characterization of attentional event-related potential from REM sleep behavior disorder patients based on explainable machine learning

Abstract

Background and objectiveIdiopathic rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of neurodegeneration and is associated with cortical dysfunction. The purpose of this study was to investigate the spatiotemporal characteristics of cortical activities underlying impaired visuospatial attention in iRBD patients using an explainable machine-learning approach. MethodsAn algorithm based on a convolutional neural network (CNN) was devised to discriminate cortical current source activities of iRBD patients due to single-trial event-related potentials (ERPs), from those of normal controls. The ERPs from 16 iRBD patients and 19 age- and sex-matched normal controls were recorded while the subjects were performing visuospatial attentional task, and converted to two-dimensional images representing current source densities on flattened cortical surface. The CNN classifier was trained based on overall data, and then, a transfer learning approach was applied for the fine-tuning to each patient. ResultsThe trained classifier yielded high classification accuracy. The critical features for the classification were determined by layer-wise relevance propagation, so that the spatiotemporal characteristics of cortical activities that were most relevant to cognitive impairment in iRBD were revealed. ConclusionsThese results suggest that the recognized dysfunction in visuospatial attention of iRBD patients originates from neural activity impairment in relevant cortical regions and may contribute to the development of useful iRBD biomarkers based on neural activity.