Resting-state EEG distinguishes depression in Parkinson’s disease

ABSTRACT Introduction Depression is a non-motor symptom of Parkinson’s disease (PD). PD-related depression is hard 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. Methods We recruited 18 PD patients, 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. Result 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 (30-80 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 declines gracefully as data are truncated. Conclusions 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 neurophysiologically-based biomarkers for non-motor symptoms of PD. HIGHLIGHTS We used EEG to analyze depression in Parkinson’s disease. Depressed Parkinson’s patients had distinct spectral EEG features. Machine-learning algorithms could accurately distinguish depression in Parkinson’s disease.