title: Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson's disease from healthy individuals.
abstract: The pathophysiology of Parkinson's disease (PD) is known to involve altered patterns of neuronal firing and synchronization in cortical-basal ganglia circuits. One window into the nature of the aberrant temporal dynamics in the cerebral cortex of PD patients can come from analysis of the patients electroencephalography (EEG). Rather than using spectral-based methods, we used data models based on delay differential equations (DDE) as non-linear time-domain classification tools to analyze EEG recordings from PD patients on and off dopaminergic therapy and healthy individuals. Two sets of 50 1-s segments of 64-channel EEG activity were recorded from nine PD patients on and off medication and nine age-matched controls. The 64 EEG channels were grouped into 10 clusters covering frontal, central, parietal, and occipital brain regions for analysis. DDE models were fitted to individual trials, and model coefficients and error were used as features for classification. The best models were selected using repeated random sub-sampling validation and classification performance was measured using the area under the ROC curve A'. In a companion paper, we show that DDEs can uncover hidden dynamical structure from short segments of simulated time series of known dynamical systems in high noise regimes. Using the same method for finding the best models, we found here that even short segments of EEG data in PD patients and controls contained dynamical structure, and moreover, that PD patients exhibited a greater dynamic range than controls. DDE model output on the means from one set of 50 trials provided nearly complete separation of PD patients off medication from controls: across brain regions, the area under the receiver-operating characteristic curves, A', varied from 0.95 to 1.0. For distinguishing PD patients on vs. off medication, classification performance A' ranged from 0.86 to 1.0 across brain regions. Moreover, the generalizability of the model to the second set of 50 trials was excellent, with A' ranging from 0.81 to 0.94 across brain regions for controls vs. PD off medication, and from 0.62 to 0.82 for PD on medication vs. off. Finally, model features significantly predicted individual patients' motor severity, as assessed with standard clinical rating scales.
authors.affiliation: Institute for Neural Computation, University of California San Diego , La Jolla, CA , USA ; Computational Neurobiology Laboratory, Howard Hughes Medical Institute, Salk Institute for Biological Studies , La Jolla, CA , USA.
authors.forename: Claudia
authors.initials: C
authors.lastname: Lainscsek
authors.affiliation: Institute for Neural Computation, University of California San Diego , La Jolla, CA , USA.
authors.forename: Manuel E
authors.initials: ME
authors.lastname: Hernandez
authors.affiliation: Computational Neurobiology Laboratory, Howard Hughes Medical Institute, Salk Institute for Biological Studies , La Jolla, CA , USA ; School of Medicine, Indiana University , Indianapolis, IN , USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Weyhenmeyer
authors.affiliation: Institute for Neural Computation, University of California San Diego , La Jolla, CA , USA ; Computational Neurobiology Laboratory, Howard Hughes Medical Institute, Salk Institute for Biological Studies , La Jolla, CA , USA.
authors.forename: Terrence J
authors.initials: TJ
authors.lastname: Sejnowski
authors.affiliation: Institute for Neural Computation, University of California San Diego , La Jolla, CA , USA ; Graduate Program in Neurosciences, University of California San Diego , La Jolla, CA , USA.
authors.forename: Howard
authors.initials: H
authors.lastname: Poizner
keywords: Parkinson’s disease; classification; dopamine-replacement; electroencephalography; non-linear dynamical analysis