A pilot study of machine learning of resting-state EEG and depression in Parkinson's disease. (2022)

• Record Type: Journal Article
• Authors: Espinoza, Arturo I.; May, Patrick; Anjum, Md Fahim; Singh, Arun; Cole, Rachel C.; Trapp, Nicholas; Dasgupta, Soura; Narayanan, Nandakumar S.
• Is Part Of: Clinical parkinsonism & related disorders. Volume 7(2023)
• Publication Date: 2022
• Subjects: Parkinson's disease -- Depression -- Scalp electroencephalography (EEG) -- Machine learning algorithm -- Classification; Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; 616.833005
• DOI: 10.1016/j.prdoa.2022.100166
• Languages: English
• ISSNs: 2590-1125

Abstract

Highlights: We used EEG to analyze depression in Parkinson's disease. Depressed PD patients have different spectral EEG compared to non-depressed PD and depressed patients without PD. Spectral EEG can contribute to the understanding of the neurophysiological basis depression in PD. Machine learning algorithms can help distinguish depression in Parkinson's disease. Abstract: Introduction: Depression is a non-motor symptom of Parkinson's disease (PD). PD-related depression is difficult to diagnose, and the neurophysiological basis is poorly understood. Depression can markedly affect cortical function, which suggests that scalp electroencephalography (EEG) may be able to distinguish depression in PD. We conducted a pilot study of depression and resting-state EEG in PD. Methods: We recruited 18 PD patients without depression, 18 PD patients with depression, and 12 demographically similar non-PD patients with clinical depression. All patients were on their usual medications. We collected resting-state EEG in all patients and compared cortical brain signal features between patients with and without depression. We used a machine learning algorithm that harnesses the entire power spectrum (linear predictive coding of EEG Algorithm for PD: LEAPD) to distinguish between groups. Results: We found differences between PD patients with and without depression in the alpha band (8–13 Hz) globally and in the beta (13–30 Hz) and gamma (30–50 Hz) bands in the central electrodes. From two minutes of resting-state EEG, we found that LEAPD-based machine learning could robustly distinguish between PD patients with and without depression with 97 % accuracy and between PD patients with depression and non-PD patients with depression with 100 % accuracy. We verified the robustness of our finding by confirming that the classification accuracy gracefully declines as data are randomly truncated. Conclusions: Our results suggest that resting-state EEG power spectral analysis has the potential to distinguish depression in PD accurately. We demonstrated the efficacy of the LEAPD algorithm in identifying PD patients with depression from PD patients without depression and controls with depression. Our data provide insight into cortical mechanisms of depression and could lead to novel neurophysiological markers for non-motor symptoms of PD.