Deep Brain Stimulation, Motivation, Parkinson Disease
Conditions
Keywords
Parkinson's, Medtronic, Percept, DBS, Deep Brain Stimulation, Parkinson's Disease, Parkinson Disease, PD, Reward-based decision making, Task
Brief summary
The study aims to better understand motivation and value-based decision-making in Parkinson's patients through neurophysiology using Medtronic's Percept DBS device. By combining behavioral tasks with neural recordings, the study seeks to uncover how DBS affects motivation, particularly in relation to effort, reward, and timing.
Detailed description
Participants will perform reward-based decision-making tasks designed to assess both self-benefitting and prosocial motivation. The tasks will evaluate how effort and reward influence decision-making, as well as how proximity to a deadline impacts choices. These tasks will be conducted in both clinic and home settings. Throughout the study, participants will remain on their regular dopaminergic medications. Each participant will complete sessions under two stimulation conditions: their usual DBS settings and with DBS turned off. Neural activity will be recorded using the Percept device, which enables real-time and chronic at-home data streaming. Additionally, participants will wear a device that captures movement, sleep, heart rate variability, and self-reported measures. The primary outcomes are behavioral: changes in reaction time, acceptance rate, and success rate across different DBS conditions. The secondary outcomes focus on identifying neural oscillatory biomarkers time-locked to specific decision-making events. By linking brain activity to motivational behavior, this study aims to advance our understanding of non-motor symptoms in PD and inform the development of adaptive DBS algorithms targeting these symptoms.
Interventions
OTHERStimulation on
Stimulation from Percept DBS will be on while the patient is playing a decision-making game on a computer-based application.
OTHERStimulation off
Stimulation from Percept DBS will be off while the patient is playing a decision-making game on a computer-based application.
BEHAVIORALDecision Making Task
Patients will be playing a decision making task through a computer-based application.
Sponsors
University of California, San Francisco
Yale University
Rune Labs
University of Birmingham
Study design
Allocation
NON_RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
BASIC_SCIENCE
Masking
NONE
Intervention model description
Within subject crossover counterbalanced study.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Has Parkinson's Disease or Dystonia * Has Medtronic Percept or RC+S DBS device implanted in either GPI or STN * Has DBS device implanted either bilaterally or unilaterally * Male or female * More than 1 month post-DBS surgery
Exclusion criteria
* Severe cognitive impairments * Has MOCA score below 20 * Pregnancy * Age less than 18 years old
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Percent of Risky Decisions made with Percept DBS stimulation on for Parkinson's Disease Patients | The values will be collected starting from admission in clinic and the at-home paradigm. Data collection and analysis of said values can take up to three years | Patients' responses on the tablet will be recorded in-clinic and at home. The investigators will tally their choices from the value-based decision making game (risky versus safe decisions) and report an average of risky responses. |
| Percent of Risky Decisions made with Percept DBS stimulation off for Parkinson's Disease Patients | The values will be collected starting from admission in clinic and the at-home paradigm. Data collection and analysis of said values can take up to three years | Patients' responses on the tablet will be recorded in-clinic and at home. The investigators will tally their choices from the value-based decision making game (risky versus safe decisions) and report an average of risky responses. |
| Reaction Time During Decision-Making Task | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. | The investigators will measure the time it takes for patients to make each decision on the value-based decision-making task in the clinic. |
| Task Success Rate | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. | The investigators will calculate the percentage of trials in which patients complete the task successfully according to predefined task performance criteria. |
| Acceptance Rate of Risky Versus Safe Options | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. | The proportion of trials where patients accept risky versus safe options will be calculated and compared across stimulation ON and OFF conditions. |
| Force Exertion During Motor Responses | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. | Force data will be recorded during motor responses to evaluate physical engagement and motor control during task performance. |
| Low frequency local field potentials | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. | Local field potentials (LFPs) will be recorded from the implanted Percept device to evaluate neural activity during decision making under different stimulation conditions. Low frequency power will be calculated during specific time intervals during the task. Cardiac artifact will be removed with electrocardiogram (ECG) measurements recorded during the same task. |
| Electroencephalogram (EEG) theta band power | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. | Scalp EEG will be recorded concurrently with task performance to assess cortical activity patterns during decision-making. Average theta band power will be calculated in specific time intervals during the task. |
| Electroencephalogram (EEG) beta band power | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. | Scalp EEG will be recorded concurrently with task performance to assess cortical activity patterns during decision-making. Average beta band power will be calculated in specific time intervals during the task. |
| Electroencephalogram (EEG) gamma band power | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. | Scalp EEG will be recorded concurrently with task performance to assess cortical activity patterns during decision-making. Average gamma band power will be calculated in specific time intervals during the task. |
| Electroencephalogram (EEG) alpha band power | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. | Scalp EEG will be recorded concurrently with task performance to assess cortical activity patterns during decision-making. Average alpha band power will be calculated in specific time intervals during the task. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Interaction Between Neural Low Frequency Power in the LFP with Reaction Time (seconds) | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. | We will assess the effect of reaction time on low frequency power in the LFP data (theta, alpha, beta power) recorded from the Percept device with statistical modeling. |
| Interaction Between Neural Low Frequency Power in the LFP with Success Rate (percentage) | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. | We will assess the effect of success rate on low frequency power in the LFP data (theta, alpha, beta power) recorded from the Percept device with statistical modeling. |
| Interaction Between Neural Low Frequency Power in the LFP with Force Exerted (arbitrary units) | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. | We will assess the effect of force exerted on low frequency power in the LFP data (theta, alpha, beta power) recorded from the Percept device with statistical modeling. |
| Interaction Between Neural Low Frequency Power in the EEG with Reaction Time (seconds) | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. | We will assess the effect of reaction time on low frequency power in the EEG data (theta, alpha, beta power) recorded from the Percept device with statistical modeling. |
| Interaction Between Neural Low Frequency Power in the EEG with Success Rate (percentage) | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. | We will assess the effect of success rate on low frequency power in the EEG data (theta, alpha, beta power) recorded from the Percept device with statistical modeling. |
| Interaction Between Neural Low Frequency Power in the EEG with Force Exerted (arbitrary units) | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. | We will assess the effect of force exerted on low frequency power in the EEG data (theta, alpha, beta power) recorded from the Percept device with statistical modeling. |
Countries
United States
Contacts
CONTACTSarah Wang, PhD
PRINCIPAL_INVESTIGATORSimon J Little, MBBS, PhD
University of California, San Francisco
Outcome results
None listed