Understanding Motivation in Parkinson's Patients Through Neurophysiology

Overview

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

• Other Stimulation on
  Stimulation from Percept DBS will be on while the patient is playing a decision-making game on a computer-based application.
• Other Stimulation off
  Stimulation from Percept DBS will be off while the patient is playing a decision-making game on a computer-based application.
• Behavioral Decision Making Task
  Patients will be playing a decision making task through a computer-based application.

Primary outcome measures

• Percent of Risky Decisions made with Percept DBS stimulation on for Parkinson's Disease Patients [Time frame: 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 [Time frame: 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 [Time frame: 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 [Time frame: 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 [Time frame: 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 [Time frame: 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 [Time frame: 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 [Time frame: 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 [Time frame: 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 [Time frame: The values will be collected starting from admission in clinic. Data collection and analysis of said values can take up to three years.]

Secondary outcome measures (6)

• Interaction Between Neural Low Frequency Power in the LFP with Reaction Time (seconds) [Time frame: 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 Success Rate (percentage) [Time frame: 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) [Time frame: 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) [Time frame: 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) [Time frame: 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) [Time frame: Analysis will occur continuously throughout the study period (up to three years) based on collected behavioral and neural data.]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

Non-randomized

Model

Crossover

Masking

Open label

Primary purpose

Basic science

Study locations

United States · 1 center

• University of California San Francisco — San Francisco

Publications

• 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. PMID 25092308
• 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. PMID 26156984
• 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. PMID 26545853
• 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. PMID 33200989
• 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. PMID 39746820
• 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.

Identifiers

NCT: NCT05065151 · 20-31239

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗