Ergun Uc1, Fahim Anjum3, Soura Dasgupta2, Nandakumar Narayanan1
1Neurology, 2Electrical Engineering, University of Iowa, 3University of California San Francisco
Objective:
To develop and evaluate an EEG-based assay to predict cognitive functions in Parkinson’s disease (PD) using resting-state EEG data.
Background:
Cognitive dysfunction is a prominent feature of PD. PD related cognitive impairment is usually diagnosed by neuropsychological testing; however, these tests are complex, cannot capture cognitive fluctuations in PD patients, and are not readily compatible with repeated measurements, real-time feedback, or closed-loop neuromodulation therapies.
Design/Methods:
We recorded a few minutes of resting-state EEG data from a few electrodes. Our approach optimized a data-driven, machine-learning technique called ‘Linear predictive coding EEG Algorithms for Parkinson’s disease’ (LEAD-PD) to predict cognitive measures from 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal Cognitive Assessment (MOCA), as well as with different cognitive domains from NIH Toolbox, using multiple cross-validation schemes and randomization tests. We also investigated the relationship between them with multiple regression models, compared our approach with traditional EEG-based features and measured robustness with data truncation tests.
Results:
We found a strong correlation (rho = 0.69, p value < 0.001) between our proposed LEAD-PD cognitive index and the MOCA score using data from only 8 EEG electrodes. LEAD-PD index also strongly predicted other cognitive domains from the NIH toolbox (Dimensional Change Card Sort, Flanker Attention and Inhibitory Control, Picture Vocabulary Task, Pattern Completion Speed Processing, and the Picture Sequence Memory tests). Our approach was consistently more predictive than traditional EEG spectral analysis, yielded 80.44% accuracy (0.90 AUC, 79.52 % sensitivity, and 81.14% specificity) in detecting cognitive impairment, and extended to both PD and control participants. The relationship between our approach and the MOCA was nonlinear with an R2 of 0.46 for quadratic regression models.
Conclusions:
LEAD-PD approach using resting-state EEG is computationally-efficient and capable of providing continuous real-time control and feedback, facilitating the diagnosis and monitoring of cognitive impairment in PD.