Adaptive deep brain stimulation for Parkinson's disease using motor cortex sensing

Abstract

Objective: Contemporary deep brain stimulation (DBS) for Parkinson's disease is delivered continuously, and adjustments based on patient's changing symptoms must be made manually by a trained clinician. Patients may be subjected to energy intensive settings at times when they are not needed, possibly resulting in stimulation-induced adverse effects, such as dyskinesia. One solution is 'adaptive' DBS, in which stimulation is modified in real time based on neural signals that co-vary with the severity of motor signs or of stimulation-induced adverse effects. Here we show the feasibility of adaptive DBS using a fully implanted neural prosthesis.

Approach: We demonstrate adaptive deep brain stimulation in two patients with Parkinson's disease using a fully implanted neural prosthesis that is enabled to utilize brain sensing to control stimulation amplitude (Activa PC + S). We used a cortical narrowband gamma (60-90 Hz) oscillation related to dyskinesia to decrease stimulation voltage when gamma oscillatory activity is high (indicating dyskinesia) and increase stimulation voltage when it is low.

Main results: We demonstrate the feasibility of 'adaptive deep brain stimulation' in two patients with Parkinson's disease. In short term in-clinic testing, energy savings were substantial (38%-45%), and therapeutic efficacy was maintained.

Significance: This is the first demonstration of adaptive DBS in Parkinson's disease using a fully implanted device and neural sensing. Our approach is distinct from other strategies utilizing basal ganglia signals for feedback control.

Figures

Figure 1

A. Example of power spectral densities from motor cortex recorded with and without dyskinesia, both during DBS. Inset shows raw motor cortex signal when DBS was off. Black arrow indicates narrowband gamma signal used for feedback (adapted from Swann et al. 2016). B. Schematic of closed loop algorithm. DBS voltage is decreased if narrowband gamma exceeds a threshold and increased when below the threshold. C. Example of the change in the peak frequency of the narrowband gamma with DBS. DBS caused the frequency to shift to half the stimulation frequency (adapted from Swann et. al 2016).

Figure 2

Schematics of current Open Loop (A) and Feedback Controlled (B) DBS. Note that in the Feedback Controlled version (B) the portion labeled “Nexus D/E” is external in the case of Nexus D and internal for Nexus E.

Figure 3

Adaptive DBS utilizing Activa PC+S under control of an external computer (Nexus D3), from patient 1. A. Schematic illustration of external control. B. 3-D reconstruction of patient’s brain and ECoG contacts used for sensing, derived from postoperative CT images computationally fused to a preoperative brain MRI. C. Neural data, classifier state, and stimulation state during adaptive DBS. Top panel: Spectrogram of time domain signal recorded from motor cortex for longest adaptive DBS session during which dyskinesia occurred. Second panel: classifier state (gold), DBS voltage (blue), and gamma power used as the control signal (red). The y-axis is in arbitrary units. Lower panels show a zoomed in view to demonstrate that classifier state transitions correspond appropriately to fluctuations in gamma band power.

Figure 4

Adaptive DBS utilizing Activa PC+S with fully embedded control (Nexus E), from Patient 1. A. Schematic of fully embedded control. B. Neural data, classifier state, and stimulation state during adaptive DBS. Panel format same as Figure 2. Note the more frequent transitions in the classifier compared to Nexus D3. This is likely because the Nexus E algorithm did not incorporate any smoothing of the signal, whereas Nexus D3 averaged the gamma signal over time before triggering a change in stimulation. This type of smoothing was not supported by Nexus E.

Figure 5

Examples of time series of the STN LFP and M1 ECoG potential, and their power spectra, both on and off DBS. The The gamma peak in the power spectrum is larger for cortical recordings. There is prominent stimulation artifact for both recording sites with DBS on. While the gamma peak in the power spectrum of the STN LFP is totally obscured during DBS on, the M1 gamma peak remains detectable, underscoring the utility of the M1 signal for feedback control. Note the differing scales of the y-axis for STN versus M1.