Neural Basis of Decision-Making Deficits in Traumatic Brain Injury
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|ClinicalTrials.gov Identifier: NCT02169310|
Recruitment Status : Recruiting
First Posted : June 23, 2014
Last Update Posted : June 18, 2020
- People with a traumatic brain injury (TBI) can have trouble making the best possible decisions. Researchers want to learn more about the parts of the brain that control decision making. They also want to know how these are different between people. This may help predict how people make decisions after TBI.
- To learn more about which parts of the brain are involved in making decisions and how decisions may be hurt after TBI.
- Adults age 18 to 60.
- Participants will be screened with medical history and physical exam. They will also take memory, attention, concentration, and thinking tests.
- Participants will do up to 2 experiments.
- For Experiment 1, participants may have 3 scans:
- PET: a chemical is injected through a thin tube into an arm vein. Participants lie on a bed that slides in and out of the scanner.
- MRI: a strong magnetic field and radio waves take pictures of the brain. Participants lie on a table that slides in and out of a metal cylinder. It makes loud knocking noises. Participants will get earplugs. They might be asked to do a task. A coil will be placed over the head.
- MEG: a cone with magnetic field detectors is lowered onto participants head.
- After the scans, participants will perform a decision-making task.
- For Experiment 2, participants will perform a decision-making task before and after receiving transcranial direct current stimulation (tDCS).
- tDCS: wet electrode sponges are placed over participants scalp and forehead. A current passes between the electrodes. It stimulating the brain.
- Participants will return 24-48 hours later to repeat the decision-making task.
|Condition or disease|
|Traumatic Brain Injury|
Deficits in decision-making are commonly found in individuals after traumatic brain injury (TBI) and can have a severe negative impact on quality of life. Converging evidence from both animal model and human studies suggest that decision-making deficits are linked with abnormal mesocorticolimbic network structure and function, and could potentially be mitigated through interventions that improve function within these neuronal circuits. The objectives of this protocol are to: (1) quantify differences in performance on a decision-making task between TBI patients and healthy volunteers; (2) determine whether baseline features of mesocorticolimbic network structure and function predict subsequent decision-making performance in both TBI patients and healthy volunteers; and (3) determine if facilitatory transcranial direct current stimulation (tDCS) applied over the dorsolateral prefrontal cortex (dlPFC), a mesocorticolimbic network region crucially involved in decision-making, improves decision-making after TBI.
Up to 40 healthy adult volunteers and 40 TBI patients will be recruited under the protocol to participate in up to two experiments.
We plan to accomplish the two objectives listed above within two experiments. Experiment 1 examines performance differences between TBI patients and healthy volunteers on a decision-making task, and explores whether baseline features of mesocorticolimbic network structure and function predict subsequent decision-making performance in TBI patients and healthy volunteers. Experiment 2, a proof-of-principle study, uses a sham-controlled, double-blind experimental design to examine whether facilitatory tDCS, a non-invasive intervention used recently to augment a range of cognitive functions, enhances decision-making performance in the same cohort of TBI patients and healthy volunteers.
The primary outcome measure for both Experiment 1 and 2 is performance in a computerized decision-making task. Secondary outcome measures will include quantitative estimates of structural and functional mesocorticolimbic network features, including MRI-based structural and functional connectivity, MEG-based functional dynamics, baseline dlPFC GABA concentration measured with magnetic resonance spectroscopy (MRS) imaging, dopamine D2 receptor binding potential within mesocorticolimbic subcortical nuclei measured with [11C]raclopride PET (Experiment 1). Multimodal data fusion modeling will be used to explore the predictive relationship between baseline mesocorticolimbic network states and decision-making task performance within a unified state-space framework (Experiment 1), as well as the ability of these network states to predict inter-individual differences in the effects of tDCS on decision-making task performance (Experiment 2).
|Study Type :||Observational|
|Estimated Enrollment :||80 participants|
|Official Title:||Neural Basis of Decision Making Deficits in Traumatic Brain Injury|
|Actual Study Start Date :||November 18, 2014|
|Estimated Primary Completion Date :||March 1, 2022|
|Estimated Study Completion Date :||March 1, 2022|
Healthy adult volunteers
- Performance in decision-making task [ Time Frame: Experiment 1 - The outcome measure is assessed after all neuroimaging procedures are completed. ]We fit an optimal Markov decision process model to the task and compare model parameters to the decision-making behavior observed in individual subjects.
- Performance in decision-making task for Experiment 2, time pt 1 [ Time Frame: Experiment 2 - The outcome measure is assessed immediately during application of real or sham tDCS on Visit 1, and 24-48 hours later on Visit 2. ]We fit an optimal Markov decision process model to the task and compare model parameters to the decisionmaking behavior observed in individual subjects.
- Performance in decision-making task for Experiment 2, time pt 2 [ Time Frame: Experiment 2 - The outcome measure is assessed prior application of real or sham tDCS on Visit 1, and 24-48 hours later on Visit 2. ]We fit an optimal Markov decision process model to the task and compare model parameters to the decisionmaking behavior observed in individual subjects.
- Performance in decision-making task for Experiment 2, time pt 3 [ Time Frame: Experiment 2 - The outcome measure is assessed after application of real or sham tDCS on Visit 1, and 24-48 hours later on Visit 2. ]We fit an optimal Markov decision process model to the task and compare model parameters to the decision-making behavior observed in individual subjects.
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT02169310
|Contact: Ethan Buch, Ph.D.||(301) email@example.com|
|Contact: Leonardo G Cohen, M.D.||(301) firstname.lastname@example.org|
|United States, Maryland|
|National Institutes of Health Clinical Center, 9000 Rockville Pike||Recruiting|
|Bethesda, Maryland, United States, 20892|
|Contact: For more information at the NIH Clinical Center contact Office of Patient Recruitment (OPR) 800-411-1222 ext TTY8664111010 email@example.com|
|Principal Investigator:||Leonardo G Cohen, M.D.||National Institute of Neurological Disorders and Stroke (NINDS)|