Subthalamic spatio-spectral-connectivity of psychiatric symptoms in Parkinson's disease.

Abstract

Psychiatric symptoms in Parkinson's disease (PD) are highly prevalent and challenging to treat. This study maps oscillatory neural activity to diverse psychiatric symptoms in PD, using resting-state subthalamic nucleus (STN) local field potentials (LFPs) and frontal EEG in 55 PD patients undergoing deep brain stimulation (DBS). We tested whether 1) distinct psychiatric symptoms are associated with frequency-specific neural signatures using power spectral analyses and machine learning, across both eyes-open and eyes-closed sensory-attentional states. 2) symptom encoding is spatially segregated within the STN, with electrophysiological (defined by peak spectral power) and anatomical (defined by STN boundaries) mappings providing complementary information. 3) these regions exhibit distinct structural connectivity profiles, assessed using STN-seeded tractography from the UK Biobank normative connectome. Our analysis revealed spectral, spatial, and connectivity segregation. Depression was associated with increased alpha power, primarily detected by anatomical mapping, whereas apathy (increased high beta) and trait impulsivity (reduced low gamma) were detected with both anatomical and electrophysiological STN mapping. UK Biobank analyses further showed that STN-based alpha clusters (depression-related) preferentially connected with prefrontal, orbitofrontal, and cingulate cortices, while peak low-beta clusters (motor-related) connected with SMA and premotor areas. High-beta and low-gamma bands showed convergent connectivity across peak and STN-based clusters despite ventral-dorsal differences. These findings disentangle neurophysiological substrates of PD psychiatry, identifying symptom-specific biomarkers and informing targeted neuromodulation strategies.