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Building normative models of Reinforcement Learning Decision Making Behavior

Award Number: R21MH131881
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 12/01/2022
PERIOD OF PERFORMANCE END DATE: 05/31/2026

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Building normative models of Reinforcement Learning Decision Making Behavior - The main objective in the field of psychiatry is to be able to treat patients at an individual level. To reach this goal of precision medicine, large scale initiatives such as the RDoC have been developed to find new ways to parse heterogeneity in psychiatric disorders. Their success has been slow partly due to a slow transition away from case-control studies based on diagnoses and limitations due to small sample size. Therefore, there is a critical need to find alternative solutions at an affordable cost. One strategy is to identify a complex behavior such as Reinforcement Learning based decision making (RLDM) that is impaired across various psychiatric disorders and adopt a computational framework to explain heterogeneity at an individual level. RLDM is a multifaceted construct involving several sub-processes ranging from estimating values of different options in the environment (valuation), accumulating evidence for these options (sequential sampling), choosing the best option (explore- exploit behavior), estimating the outcome value (salience attribution) and lastly integrating relevant information about outcomes and updating the value of stimuli (learning rate). These sub-processes can be quantified by utilizing computational models. However, prior to building normative models of these RLDM constructs that can be potentially utilized in clinical practice, it is critical to assess the reliability of these RLDM model-derived parameters to avoid translational failures. Therefore, our main goals of this proposal are to (1) parse RLDM sub- processes into mathematically-defined parameters in a large sample using a diverse set of tasks; (2) assess test-retest reliability of these parameters; and finally (3) build normative models of the parameters and chart the heterogeneity at the level of the individual. To achieve this goal, we will acquire behavioral data from six RL tasks, including delay and effort discounting, probabilistic learning, virtual patch foraging, Pavlovian instrumental transfer and approach-avoid conflict tasks. These tasks are each optimized to measure at least two of the RL sub-processes separately. We will collect behavioral data and self-report assessments from a community sample from 1000 participants (aged 18-85). To estimate test-retest reliability of RLDM parameters, we will invite 500 healthy participants from this sample to complete the session again in one week’s time. First, we will apply start- of-art computational models to quantify RLDM behavior in each subject. Second, we will calculate the test-retest reliability of these parameters. Third, we will build normative models to link each of the RLDM construct with age and calculate each subject’s deviation from the norm. Lastly, we will conduct soft clustering on these deviations to identify clusters and investigate their differences in psychopathology and general functioning.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $0 )
2026	2024	THE MCLEAN HOSPITAL CORPORATION	115 MILL ST	BELMONT	MA	02478	MIDDLESEX	USA	Mental Health Research Grants	000	2	12/24/2025	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2024 ( Subtotal = $205,000 )
2024	2024	THE MCLEAN HOSPITAL CORPORATION	115 MILL ST	BELMONT	MA	02478	MIDDLESEX	USA	Mental Health Research Grants	000	2	11/17/2023	NON-COMPETING CONTINUATION	$184,500
2024	2024	THE MCLEAN HOSPITAL CORPORATION	115 MILL ST	BELMONT	MA	02478	MIDDLESEX	USA	Mental Health Research Grants	001	2	6/4/2024	NON-COMPETING CONTINUATION	$20,500
														Subtotal = $205,000

Issue Date FY: 2023 ( Subtotal = $491,800 )
2023	2023	MCLEAN HOSPITAL CORPORATION, THE	115 MILL ST	BELMONT	MA	02478	MIDDLESEX	USA	Mental Health Research Grants	001	1	8/31/2023	SUPPLEMENT FOR EXPANSION	$245,800
2023	2023	MCLEAN HOSPITAL CORPORATION, THE	115 MILL ST	BELMONT	MA	02478	MIDDLESEX	USA	Mental Health Research Grants	000	1	11/29/2022	NEW	$246,000
														Subtotal = $491,800

Grand Total All Awards = $696,800

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Building normative models of Reinforcement Learning Decision Making Behavior

Award Number: R21MH131881

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 12/01/2022

PERIOD OF PERFORMANCE END DATE: 05/31/2026

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