Multimodal classification of Parkinson's disease using delay differential analysis

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world. PD is known to lead to marked alterations in cortico-thalamo-basal ganglia activity and subsequent movements, which may provide a biomarker for PD diagnosis. Delay differential analysis (DDA) is a time domain analysis framework based on embedding theory in nonlinear dynamics. An embedding reveals the nonlinear invariant properties of an unknown dynamical system (here the brain) from a single time series electroencephalography (EEG) or behavioral signals. The DDA models serve as a low-dimensional nonlinear functional basis onto which the data are mapped. The combination of behavioral and neurological observations gives rise to a multimodal analysis framework that could improve our understanding and classification of PD using time series data from physical systems. We demonstrate how 750 ms of multimodal data can be used to improve DDA classification performance of PD, over clean EEG or behavioral time series data on their own, in two distinct virtual reach to grasp tasks in an uncertain and dynamic virtual reality environment. Thus, multimodal DDA may provide a tool for aiding the clinician in the diagnosis of PD and bolster classification performance through the combination of a wide array of neural or behavioral signals.