Pavlovian bias in Parkinson's disease: an objective marker of impulsivity that modulates with deep brain stimulation.
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ABSTRACT:
SUBMITTER: Eisinger RS
PROVIDER: S-EPMC7417529 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
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Pavlovian bias in Parkinson's disease: an objective marker of impulsivity that modulates with deep brain stimulation.
Eisinger Robert S RS Scott Bonnie M BM Le Anh A Ponce Elena M Torres EMT Lanese Joseph J Hundley Christopher C Nelson Brawn B Ravy Tasmeah T Lopes Janine J Thompson Sable S Sathish Sneha S O'Connell Rebecca L RL Okun Michael S MS Bowers Dawn D Gunduz Aysegul A
Scientific reports 20200810 1
Impulsivity is a common symptom in Parkinson's disease (PD). Adaptive behavior is influenced by prepotent action-reward and inaction-avoid loss Pavlovian biases. We aimed to assess the hypothesis that impulsivity in PD is associated with Pavlovian bias, and to assess whether dopaminergic medications and deep brain stimulation (DBS) influence Pavlovian bias. A PD DBS cohort (N = 37) completed a reward-based Go/No-Go task and bias measures were calculated. This DBS cohort completed the task under ...[more]
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Project description:Objective.Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD) but its success depends on a time-consuming process of trial-and-error to identify the optimal stimulation settings for each individual patient. Data-driven optimization algorithms have been proposed to efficiently find the stimulation setting that maximizes a quantitative biomarker of symptom relief. However, these algorithms cannot efficiently take into account stimulation settings that may control symptoms but also cause side effects. Here we demonstrate how multi-objective data-driven optimization can be used to find the optimal trade-off between maximizing symptom relief and minimizing side effects.Approach.Cortical and motor evoked potential data collected from PD patients during intraoperative stimulation of the subthalamic nucleus were used to construct a framework for designing and prototyping data-driven multi-objective optimization algorithms. Using this framework, we explored how these techniques can be applied clinically, and characterized the design features critical for solving this optimization problem. Our two optimization objectives were to maximize cortical evoked potentials, a putative biomarker of therapeutic benefit, and to minimize motor potentials, a biomarker of motor side effects.Main Results.Using thisin silicodesign framework, we demonstrated how the optimal trade-off between two objectives can substantially reduce the stimulation parameter space by 61 ± 19%. The best algorithm for identifying the optimal trade-off between the two objectives was a Bayesian optimization approach with an area under the receiver operating characteristic curve of up to 0.94 ± 0.02, which was possible with the use of a surrogate model and a well-tuned acquisition function to efficiently select which stimulation settings to sample.Significance.These findings show that multi-objective optimization is a promising approach for identifying the optimal trade-off between symptom relief and side effects in DBS. Moreover, these approaches can be readily extended to newly discovered biomarkers, adapted to DBS for disorders beyond PD, and can scale with the development of more complex DBS devices.
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Project description:Deep brain stimulation is an established therapy for Parkinson's disease; however, its effectiveness is hindered by limited understanding of therapeutic mechanisms and the lack of a robust feedback signal for tailoring stimulation. We recently reported that subthalamic nucleus deep brain stimulation evokes a neural response resembling a decaying high-frequency (200-500?Hz) oscillation that typically has a duration of at least 10?ms and is localizable to the dorsal sub-region. As the morphology of this response suggests a propensity for the underlying neural circuitry to oscillate at a particular frequency, we have named it evoked resonant neural activity. Here, we determine whether this evoked activity is modulated by therapeutic stimulation - a critical attribute of a feedback signal. Furthermore, we investigated whether any related changes occurred in spontaneous local field potentials. Evoked and spontaneous neural activity was intraoperatively recorded from 19 subthalamic nuclei in patients with Parkinson's disease. Recordings were obtained before therapeutic stimulation and during 130?Hz stimulation at increasing amplitudes (0.67-3.38?mA), 'washout' of therapeutic effects, and non-therapeutic 20?Hz stimulation. Therapeutic efficacy was assessed using clinical bradykinesia and rigidity scores. The frequency and amplitude of evoked resonant neural activity varied with the level of 130?Hz stimulation (p?<?.001). This modulation coincided with improvement in bradykinesia and rigidity (p?<?.001), and correlated with spontaneous beta band suppression (p?<?.001). Evoked neural activity occupied a similar frequency band to spontaneous high-frequency oscillations (200-400?Hz), both of which decreased to around twice the 130?Hz stimulation rate. Non-therapeutic stimulation at 20?Hz evoked, but did not modulate, resonant activity. These results indicate that therapeutic deep brain stimulation alters the frequency of evoked and spontaneous oscillations recorded in the subthalamic nucleus that are likely generated by loops within the cortico-basal ganglia-thalamo-cortical network. Evoked resonant neural activity therefore has potential as a tool for providing insight into brain network function and has key attributes of a dynamic feedback signal for optimizing therapy.
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