Neuropsychological and social predictors of participation in a deep brain stimulation study of Parkinson's disease and dystonia.

Objectives: Participation is essential to DBS research, yet circumstances that affect diverse participation remain unclear. Here we evaluate factors impacting participation in an adaptive DBS study of Parkinson's disease (PD) and dystonia. Methods: Twenty participants were implanted with a sensing-enabled DBS device (Medtronic Summit RC+S) that allows neural data streaming in naturalistic settings and encouraged to stream as much as possible for the first five months after surgery. Using standardized baseline data obtained through neuropsychological evaluation, we compared neuropsychological and social variables to streaming hours. Results: Marital status and irritability significantly impacted streaming hours (estimate=136.7, bootstrapped ( b ) CI b =45.0 to 249.0, p b =0.016, and estimate=-95.1, CI b =-159.9 to -49.2, p b =0.027, respectively). These variables remained significant after multivariable analysis. Composite scores on verbal memory evaluations predicted the number of hours of data streamed (R 2 =0.284, estimate=67.7, CI b =20.1 to 119.9, p b =0.019). Discussion: Verbal memory impairment, irritability, and lack of a caregiver may be associated with decreased participation. Further study of factors that impact research participation is critical to the sustained inclusion of diverse participants.

Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson's disease.

In Parkinson's disease, imbalances between 'antikinetic' and 'prokinetic' patterns of neuronal oscillatory activity are related to motor dysfunction. Invasive brain recordings from the motor network have suggested that medical or surgical therapy can promote a prokinetic state by inducing narrowband gamma rhythms (65-90 Hz). Excessive narrowband gamma in the motor cortex promotes dyskinesia in rodent models, but the relationship between narrowband gamma and dyskinesia in humans has not been well established. To assess this relationship, we used a sensing-enabled deep brain stimulator system, attached to both motor cortex and basal ganglia (subthalamic or pallidal) leads, paired with wearable devices that continuously tracked motor signs in the contralateral upper limbs. We recorded 984 h of multisite field potentials in 30 hemispheres of 16 subjects with Parkinson's disease (2/16 female, mean age 57 ± 12 years) while at home on usual antiparkinsonian medications. Recordings were done 2-4 weeks after implantation, prior to starting therapeutic stimulation. Narrowband gamma was detected in the precentral gyrus, subthalamic nucleus or both structures on at least one side of 92% of subjects with a clinical history of dyskinesia. Narrowband gamma was not detected in the globus pallidus. Narrowband gamma spectral power in both structures co-fluctuated similarly with contralateral wearable dyskinesia scores (mean correlation coefficient of ρ = 0.48 with a range of 0.12-0.82 for cortex, ρ = 0.53 with a range of 0.5-0.77 for subthalamic nucleus). Stratification analysis showed the correlations were not driven by outlier values, and narrowband gamma could distinguish 'on' periods with dyskinesia from 'on' periods without dyskinesia. Time lag comparisons confirmed that gamma oscillations herald dyskinesia onset without a time lag in either structure when using 2-min epochs. A linear model incorporating the three oscillatory bands (beta, theta/alpha and narrowband gamma) increased the predictive power of dyskinesia for several subject hemispheres. We further identified spectrally distinct oscillations in the low gamma range (40-60 Hz) in three subjects, but the relationship of low gamma oscillations to dyskinesia was variable. Our findings support the hypothesis that excessive oscillatory activity at 65-90 Hz in the motor network tracks with dyskinesia similarly across both structures, without a detectable time lag. This rhythm may serve as a promising control signal for closed-loop deep brain stimulation using either cortical or subthalamic detection.

Personalized chronic adaptive deep brain stimulation outperforms conventional stimulation in Parkinson's disease.

Deep brain stimulation is a widely used therapy for Parkinson's disease (PD) but currently lacks dynamic responsiveness to changing clinical and neural states. Feedback control has the potential to improve therapeutic effectiveness, but optimal control strategy and additional benefits of "adaptive" neurostimulation are unclear. We implemented adaptive subthalamic nucleus stimulation, controlled by subthalamic or cortical signals, in three PD patients (five hemispheres) during normal daily life. We identified neurophysiological biomarkers of residual motor fluctuations using data-driven analyses of field potentials over a wide frequency range and varying stimulation amplitudes. Narrowband gamma oscillations (65-70 Hz) at either site emerged as the best control signal for sensing during stimulation. A blinded, randomized trial demonstrated improved motor symptoms and quality of life compared to clinically optimized standard stimulation. Our approach highlights the promise of personalized adaptive neurostimulation based on data-driven selection of control signals and may be applied to other neurological disorders.