A pilot study of machine learning of resting-state EEG and depression in Parkinson’s disease

Arturo I. Espinoza; Patrick May; Md Fahim Anjum; Arun Singh; Rachel C. Cole; Nicholas Trapp; Soura Dasgupta; Nandakumar S. Narayanan

doi:10.1016/j.prdoa.2022.100166

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A pilot study of machine learning of resting-state EEG and depression in Parkinson’s disease

Journal article

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A pilot study of machine learning of resting-state EEG and depression in Parkinson’s disease

Arturo I. Espinoza, Patrick May, Md Fahim Anjum, Arun Singh, Rachel C. Cole, Nicholas Trapp, Soura Dasgupta and Nandakumar S. Narayanan

Clinical parkinsonism & related disorders, Vol.7, 100166

09/29/2022

DOI: 10.1016/j.prdoa.2022.100166

PMCID: PMC9529981

PMID: 36203748

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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.

Classification

Depression

Machine learning algorithm

Parkinson’s disease

Scalp electroencephalography (EEG)

Details

Title: Subtitle: A pilot study of machine learning of resting-state EEG and depression in Parkinson’s disease
Creators: Arturo I. Espinoza - University of Iowa
Patrick May - Department of Electrical and Computer Engineering, University of Iowa, United States
Md Fahim Anjum - Department of Electrical and Computer Engineering, University of Iowa, United States
Arun Singh - University of South Dakota
Rachel C. Cole - University of Iowa
Nicholas Trapp - University of Iowa
Soura Dasgupta - University of Iowa
Nandakumar S. Narayanan
Resource Type: Journal article
Publication Details: Clinical parkinsonism & related disorders, Vol.7, 100166
DOI: 10.1016/j.prdoa.2022.100166
PMID: 36203748
PMCID: PMC9529981
NLM abbreviation: Clin Park Relat Disord
ISSN: 2590-1125
eISSN: 2590-1125
Publisher: Elsevier
Grant note: DOI: 10.13039/100000002, name: National Institutes of Health, award: P20NS123151, R01NS100849
Language: English
Date published: 09/29/2022
Academic Unit: Neurology; Electrical and Computer Engineering; Psychiatry; Iowa Neuroscience Institute
Record Identifier: 9984303288302771

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