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| Name | Class |
|---|---|
| Yale University | OTHER |
| Rune Labs | UNKNOWN |
| University of Birmingham | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Stimulation | Experimental | Patients will be getting standard clinically acceptable stimulation within already safety validated stimulation ranges through their Medtronic Percept device. |
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| No Stimulation | Experimental | Patients will have stimulation turned off through their Medtronic Percept device. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Stimulation on | Other | Stimulation from Percept DBS will be on while the patient is playing a decision-making game on a computer-based application. |
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| Measure | Description | Time Frame |
|---|---|---|
| Percent of Risky Decisions made with Percept DBS stimulation on for Parkinson's Disease Patients | 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. | 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 |
| Percent of Risky Decisions made with Percept DBS stimulation off for Parkinson's Disease Patients | 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. | 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 |
| Reaction Time During Decision-Making Task | The investigators will measure the time it takes for patients to make each decision on the value-based decision-making task in the clinic. | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. |
| Task Success Rate | The investigators will calculate the percentage of trials in which patients complete the task successfully according to predefined task performance criteria. | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. |
| Acceptance Rate of Risky Versus Safe Options | The proportion of trials where patients accept risky versus safe options will be calculated and compared across stimulation ON and OFF conditions. |
| Measure | Description | Time Frame |
|---|---|---|
| Interaction Between Neural Low Frequency Power in the LFP with Reaction Time (seconds) | 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. | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Sarah Wang, PhD | Contact | 415-353-7885 | Sarah.Wang@ucsf.edu |
| Name | Affiliation | Role |
|---|---|---|
| Simon J Little, MBBS, PhD | University of California, San Francisco | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of California San Francisco | Recruiting | San Francisco | California | 94158 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 25092308 | Background | Rutledge RB, Skandali N, Dayan P, Dolan RJ. A computational and neural model of momentary subjective well-being. Proc Natl Acad Sci U S A. 2014 Aug 19;111(33):12252-7. doi: 10.1073/pnas.1407535111. Epub 2014 Aug 4. | |
| 26156984 | Background | Rutledge RB, Skandali N, Dayan P, Dolan RJ. Dopaminergic Modulation of Decision Making and Subjective Well-Being. J Neurosci. 2015 Jul 8;35(27):9811-22. doi: 10.1523/JNEUROSCI.0702-15.2015. |
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Future data sharing will be covered by UCSF data sharing compliance rules.
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| ID | Term |
|---|---|
| D010300 | Parkinson Disease |
| ID | Term |
|---|---|
| D020734 | Parkinsonian Disorders |
| D001480 | Basal Ganglia Diseases |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
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Within subject crossover counterbalanced study.
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| Stimulation off | Other | Stimulation from Percept DBS will be off while the patient is playing a decision-making game on a computer-based application. |
|
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| Decision Making Task | Behavioral | Patients will be playing a decision making task through a computer-based application. |
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| The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. |
| Force Exertion During Motor Responses | Force data will be recorded during motor responses to evaluate physical engagement and motor control during task performance. | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. |
| Low frequency local field potentials | 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. | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. |
| Electroencephalogram (EEG) theta band power | 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. | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. |
| Electroencephalogram (EEG) beta band power | 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. | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. |
| Electroencephalogram (EEG) gamma band power | 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. | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. |
| Electroencephalogram (EEG) alpha band power | 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. | The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years. |
| Interaction Between Neural Low Frequency Power in the LFP with Success Rate (percentage) | 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. | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. |
| Interaction Between Neural Low Frequency Power in the LFP with Force Exerted (arbitrary units) | 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. | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. |
| Interaction Between Neural Low Frequency Power in the EEG with Reaction Time (seconds) | 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. | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. |
| Interaction Between Neural Low Frequency Power in the EEG with Success Rate (percentage) | 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. | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. |
| Interaction Between Neural Low Frequency Power in the EEG with Force Exerted (arbitrary units) | 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. | Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data. |
| 26545853 | Background | Eldar E, Rutledge RB, Dolan RJ, Niv Y. Mood as Representation of Momentum. Trends Cogn Sci. 2016 Jan;20(1):15-24. doi: 10.1016/j.tics.2015.07.010. Epub 2015 Nov 3. |
| 33200989 | Background | Blain B, Rutledge RB. Momentary subjective well-being depends on learning and not reward. Elife. 2020 Nov 17;9:e57977. doi: 10.7554/eLife.57977. |
| 39746820 | Background | Talbot J, Cutler J, Tamm M, Little SJ, Harmer CJ, Husain M, Lockwood PL, Apps MAJ. Dopamine Boosts Motivation for Prosocial Effort in Parkinson's Disease. J Neurosci. 2026 Feb 25;46(8):e1593242024. doi: 10.1523/JNEUROSCI.1593-24.2024. |
| Background | Pisauro MA, Pollicino D, Fisher L, Apps MAJ. Neural and computational mechanisms of effort under the pressure of a deadline. bioRxiv. 2024. doi:10.1101/2024.04.17.589910. |
| D009422 | Nervous System Diseases |
| D009069 | Movement Disorders |
| D000080874 | Synucleinopathies |
| D019636 | Neurodegenerative Diseases |