To characterize and quantify the presence of narrow-band gamma (NBG) oscillations across deep brain stimulation (DBS) targets in patients with movement disorders.
Electrophysiology guided DBS programming is emerging as a more efficient alternative to conventional “trial-and-error” programming. Although substantial progress has been made in identifying signals corresponding to disease states, such as beta-band power with parkinsonism, many aspects of local field potentials (LFPs) recorded via DBS remain incompletely characterized. One such signal, excessive synchronization of NBG oscillations, is observable in Parkinson’s Disease (PD) patients following levodopa administration or DBS. Clarifying the link between NBG and clinical outcomes could establish NBG as a biomarker for electrophysiology-guided DBS and inform personalized neuromodulation.
A retrospective review of patients who underwent DBS implantation with a sensing-enabled DBS system (Medtronic Percept) was conducted. LFPs were recorded during clinical programming visits with patients at rest. NBG peaks were identified by removing the aperiodic components then detecting frequencies whose power exceed the 95% confidence interval of baseline gamma power (55-95Hz). Descriptive statistics were used to identify group-level difference between diagnoses and surgical targets.
283 patients (Dystonia n=14, Essential Tremor n=37, PD n=232) corresponding to 397 DBS leads (GPi n=263, STN n=93, VIM n=41) were analyzed. NBG was detected in 21% of dystonia patients, 32% of ET patients, and 23% of PD patients, with no significant association between diagnoses and NBG presence (Fischer’s Exact, p=0.298). Within the PD cohort, ANOVA revealed significant differences in mean NBG power across targets (p<0.001). Post-hoc comparisons showed higher NBG power in STN (16.3 ± 13.2 µV²/Hz) than GPi (6.7 ± 5.4 µV²/Hz; p=0.049), and markedly increased power in VIM (69.9 µV²/Hz; p<0.001 vs both STN and GPi).
NBG oscillations are identifiable across multiple movement disorders and DBS targets, supporting their potential as electrophysiological biomarkers for disease state and treatment response.