• Doris D Wang, Coralie de Hemptinne, Salman Qasim, Svjetlana Miocinovic, Jill L Ostrem, and Philip A Starr.
• Neurosurgery. 2015 Aug 1;62 Suppl 1:233.

IntroductionProminent beta band oscillations have been observed in Parkinson disease (PD) in the subthalamic nucleus (STN) and globus pallidus interna (GPi), and proposed as closed-loop signal controls for deep brain stimulation therapy. The GPi is a less studied target than the STN, and human basal ganglia physiology has rarely been compared across different disease states. We test the hypothesis that resting-state local field potential (LFP) characteristics distinguish the parkinsonian state from segmental and generalized dystonia.MethodsResting-state GPi LFPs were recorded intraoperatively from DBS electrodes in 14 PD, 3 craniocervical dystonia (c-dys), and 4 generalized dystonia (g-dys) patients in the awake state. Alpha-beta power characteristics were calculated characterizing the peak power band at those frequencies. Phase-amplitude interactions (PAC) in both cortex and STN are increasingly recognized as potential disease biomarkers, we quantified the interaction between the amplitude of high-frequency activity and alpha-beta phase as previously described.ResultsGPi LFPs demonstrated similar frequencies for the alpha-beta spectral peak, at 19.0 ± 5.8 Hz for PD, 20.5 ± 6.8 Hz for c-dys, and 17.6 ± 8.3 Hz for g-dys patients (P = .79). The amplitude of the log spectral peak power was not significantly different (P = .38). Eight PD, 2 c-dys, and 3 g-dys patients showed coupling between alpha-beta phase and the amplitude of a narrowband high-frequency oscillation (280-360 Hz). Coupling of beta phase to broadband power, as observed in the motor cortex in PD, was not found in GPi.ConclusionDirect comparison of GPi LFPs in PD, craniocervical, and generalized dystonia shows that all patients have peak spectral power in the beta band, casting doubt on the view that excessive beta-band power is a specific biomarker of the parkinsonian state. Cross-frequency interactions are also found in GPi and are not specific for disease state. The study reveals potential similarities between abnormal neural network activity and synchronization in PD and dystonia.

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