R01MH123687
Project Grant
Overview
Grant Description
Neural Mechanisms of Learning Relevance in Multidimensional Environments - Project Summary / Abstract
This proposal investigates in the nonhuman primate how attentional load changes the behavioral and neural strategies for flexibly learning object relevance. High attentional load characterizes real-world learning scenarios with multiple, multidimensional objects. Evidence suggests that the neural mechanisms underlying learning during high attentional load fundamentally differs from neural mechanisms used to learn under low load.
Our proposal elucidates how learning at increasing attentional load (1) changes the cognitive subcomponent processes used to succeed learning, (2) changes which brain areas are used to flexibly learn, and (3) recruits additional neural circuit mechanisms to realize fast adjustments.
First, we will address the specific behavioral subcomponent processes used for learning the relevance of objects in environments with increasing number of visual feature dimensions reflecting increasing attentional load. Simple learning can be achieved efficiently with a hybrid mechanism that uses working memory (WM) of recently rewarded objects to guide future choices together with slower reinforcement learning (RL) for updating longer-term value expectations. When attentional load increases, working memory breaks down, and efficient learners flexibly adjust their exploration rates and attentional prioritization to speed up reinforcement learning. Our proposal quantifies these changing learning strategies with multi-component WM-RL modeling.
Second, while subjects learn with varying strategies which features to use for making a decision, we will test the causal role of three brain regions implicated to realize the respective learning mechanisms. We use transcranial focused ultrasound stimulation to induce transient, fully reversible lesions allowing to functionally disrupt confined neuronal ensembles. With this tool, we elucidate the hypothesized contributions of ventrolateral prefrontal cortex to learning using fast working memory of rewarded objects, the contribution of the anterior cingulate cortex in adjusting exploration strategies, and the contribution of the anterior striatum for attentional biasing of slower reinforcement learning of the highest reward-value object within a complex, multidimensional feature space.
Third, our project elucidates how the local circuits in each of the three brain areas contribute to successful learning with varying strategies. We use massively parallel recordings of single neuron activity in ventrolateral prefrontal cortex, anterior cingulate cortex, and anterior striatum to extract those cell classes whose firing encodes the key learning variables. We expect that subclasses of interneurons maximally correlate their firing only during those periods when the area-specific learning strategy is realized. This approach pinpoints the cell classes that maximally correlate with choice probabilities, prediction errors, working memory, and exploration rates when subjects adjust their learning strategies to successfully learn the relevance of objects with real-world complexity.
This proposal investigates in the nonhuman primate how attentional load changes the behavioral and neural strategies for flexibly learning object relevance. High attentional load characterizes real-world learning scenarios with multiple, multidimensional objects. Evidence suggests that the neural mechanisms underlying learning during high attentional load fundamentally differs from neural mechanisms used to learn under low load.
Our proposal elucidates how learning at increasing attentional load (1) changes the cognitive subcomponent processes used to succeed learning, (2) changes which brain areas are used to flexibly learn, and (3) recruits additional neural circuit mechanisms to realize fast adjustments.
First, we will address the specific behavioral subcomponent processes used for learning the relevance of objects in environments with increasing number of visual feature dimensions reflecting increasing attentional load. Simple learning can be achieved efficiently with a hybrid mechanism that uses working memory (WM) of recently rewarded objects to guide future choices together with slower reinforcement learning (RL) for updating longer-term value expectations. When attentional load increases, working memory breaks down, and efficient learners flexibly adjust their exploration rates and attentional prioritization to speed up reinforcement learning. Our proposal quantifies these changing learning strategies with multi-component WM-RL modeling.
Second, while subjects learn with varying strategies which features to use for making a decision, we will test the causal role of three brain regions implicated to realize the respective learning mechanisms. We use transcranial focused ultrasound stimulation to induce transient, fully reversible lesions allowing to functionally disrupt confined neuronal ensembles. With this tool, we elucidate the hypothesized contributions of ventrolateral prefrontal cortex to learning using fast working memory of rewarded objects, the contribution of the anterior cingulate cortex in adjusting exploration strategies, and the contribution of the anterior striatum for attentional biasing of slower reinforcement learning of the highest reward-value object within a complex, multidimensional feature space.
Third, our project elucidates how the local circuits in each of the three brain areas contribute to successful learning with varying strategies. We use massively parallel recordings of single neuron activity in ventrolateral prefrontal cortex, anterior cingulate cortex, and anterior striatum to extract those cell classes whose firing encodes the key learning variables. We expect that subclasses of interneurons maximally correlate their firing only during those periods when the area-specific learning strategy is realized. This approach pinpoints the cell classes that maximally correlate with choice probabilities, prediction errors, working memory, and exploration rates when subjects adjust their learning strategies to successfully learn the relevance of objects with real-world complexity.
Awardee
Funding Goals
THE MISSION OF THE NATIONAL INSTITUTE OF MENTAL HEALTH (NIMH) IS TO TRANSFORM THE UNDERSTANDING AND TREATMENT OF MENTAL ILLNESSES THROUGH BASIC AND CLINICAL RESEARCH, PAVING THE WAY FOR PREVENTION, RECOVERY, AND CURE. IN MAY 2020, NIMH RELEASED ITS NEW STRATEGIC PLAN FOR RESEARCH. THE NEW STRATEGIC PLAN BUILDS ON THE SUCCESSES OF PREVIOUS NIMH STRATEGIC PLANS BY PROVIDING A FRAMEWORK FOR SCIENTIFIC RESEARCH AND EXPLORATION, AND ADDRESSING NEW CHALLENGES IN MENTAL HEALTH. THE NEW STRATEGIC PLAN OUTLINES FOUR HIGH-LEVEL GOALS: GOAL 1: DEFINE THE BRAIN MECHANISMS UNDERLYING COMPLEX BEHAVIORS GOAL 2: EXAMINE MENTAL ILLNESS TRAJECTORIES ACROSS THE LIFESPAN GOAL 3: STRIVE FOR PREVENTION AND CURES GOAL 4: STRENGTHEN THE PUBLIC HEALTH IMPACT OF NIMH-SUPPORTED RESEARCH THESE FOUR GOALS FORM A BROAD ROADMAP FOR THE INSTITUTE'S RESEARCH PRIORITIES OVER THE NEXT FIVE YEARS, BEGINNING WITH THE FUNDAMENTAL SCIENCE OF THE BRAIN AND BEHAVIOR, AND EXTENDING THROUGH EVIDENCE-BASED SERVICES THAT IMPROVE PUBLIC HEALTH OUTCOMES. THE INSTITUTE'S OVERALL FUNDING STRATEGY IS TO SUPPORT A BROAD SPECTRUM OF INVESTIGATOR-INITIATED RESEARCH IN FUNDAMENTAL SCIENCE, WITH INCREASING USE OF INSTITUTE-SOLICITED INITIATIVES FOR APPLIED RESEARCH WHERE PUBLIC HEALTH IMPACT IS A SHORT-TERM MEASURE OF SUCCESS. THE NEW STRATEGIC PLAN ALSO ADDRESSES A NUMBER OF CROSS-CUTTING THEMES THAT ARE RELEVANT TO ALL RESEARCH SUPPORTED BY NIMH, THESE THEMES HIGHLIGHT AREAS WHERE NIMH-FUNDED SCIENCE MAY HAVE THE GREATEST IMPACT, BRIDGE GAPS, AND OFFER NOVEL APPROACHES TO ACCELERATE ADVANCES IN MENTAL HEALTH RESEARCH. FOR EXAMPLE, NIMH VALUES A COMPREHENSIVE RESEARCH AGENDA THAT TAKES AN INCLUSIVE APPROACH THAT ENSURES RESEARCH INTERESTS ARE VARIED, MAINTAIN DIVERSE PARTICIPATION AND PARTNERSHIPS, AND ACHIEVE RESEARCH GOALS ACROSS MULTIPLE TIMEFRAMES. THIS INCLUDES DIVERSE METHODOLOGIES, TOOLS, AND MODELS, RESEARCH ADDRESSING COMPLEX BASIC, TRANSLATIONAL, AND APPLIED QUESTIONS, RESEARCH INCLUDING BOTH SEXES AND, AS APPROPRIATE, GENETIC BACKGROUND, AND, PARTICIPANTS FROM DIVERSE RACIAL AND ETHNIC BACKGROUNDS, AND ACROSS GENDER IDENTITIES, GEOGRAPHICAL CONTEXT, SOCIOECONOMIC STATUS, NEUROTYPE, AND AGE OFFERING THE BEST POSSIBLE REPRESENTATION, FOR THE BROADEST NUMBER OF INDIVIDUALS WHO MAY ULTIMATELY BENEFIT FROM THESE SCIENTIFIC ADVANCES. TO ACCOMPLISH THE GOALS OUTLINED IN THE NEW STRATEGIC PLAN, NIMH WILL SUPPORT RESEARCH THAT AIMS: TO CHARACTERIZE THE GENOMIC, MOLECULAR, CELLULAR, AND CIRCUIT COMPONENTS CONTRIBUTING TO BRAIN ORGANIZATION AND FUNCTION, TO IDENTIFY THE DEVELOPMENTAL, FUNCTIONAL, AND REGULATORY MECHANISMS RELEVANT TO COGNITIVE, AFFECTIVE, AND SOCIAL DOMAINS, ACROSS UNITS OF ANALYSIS, AND, TO GENERATE AND VALIDATE NOVEL TOOLS, TECHNIQUES, AND MEASURES TO QUANTIFY CHANGES IN THE ACTIVITY OF MOLECULES, CELLS, CIRCUITS, AND CONNECTOMES. TO DISCOVER GENE VARIANTS AND OTHER GENOMIC ELEMENTS THAT CONTRIBUTE TO THE DEVELOPMENT OF MENTAL ILLNESSES IN DIVERSE POPULATIONS, TO ADVANCE OUR UNDERSTANDING OF THE COMPLEX ETIOLOGY OF MENTAL ILLNESSES USING MOLECULAR EPIDEMIOLOGIC APPROACHES THAT INCORPORATE INDIVIDUAL GENETIC INFORMATION IN LARGE COHORTS, TO ELUCIDATE HOW HUMAN GENETIC VARIATION AFFECTS THE COORDINATION OF MOLECULAR, CELLULAR, AND PHYSIOLOGICAL NETWORKS SUPPORTING HIGHER-ORDER FUNCTIONS AND EMERGENT PROPERTIES OF NEUROBIOLOGICAL SYSTEMS, AND, TO DEVELOP NOVEL TOOLS AND TECHNIQUES FOR THE ANALYSIS OF LARGE-SCALE GENETIC, MULTI-OMIC DATA AS IT APPLIES TO MENTAL HEALTH. TO UTILIZE CONNECTOMIC APPROACHES TO IDENTIFY BRAIN NETWORKS AND CIRCUIT COMPONENTS THAT CONTRIBUTE TO VARIOUS ASPECTS OF MENTAL FUNCTION AND DYSFUNCTION, TO DETERMINE THROUGH BRAIN-WIDE ANALYSIS HOW CHANGES IN THE PHYSIOLOGICAL PROPERTIES OF MOLECULES, CELLS, AND CIRCUITS CONTRIBUTE TO MENTAL ILLNESSES, TO DEVELOP MOLECULAR, CELLULAR, AND CIRCUIT-LEVEL BIOMARKERS OF IMPAIRED NEURAL FUNCTION IN HUMANS, AND, TO DEVELOP INNOVATIVE TECHNOLOGIES, INCLUDING NEW IMAGING, COMPUTATIONAL, PHARMACOLOGICAL, AND GENETIC TOOLS TO INTERROGATE AND MODULATE CIRCUIT ACTIVITY AND STRUCTURE ALTERED IN MENTAL ILLNESSES. TO ELUCIDATE THE MECHANISMS CONTRIBUTING TO THE TRAJECTORIES OF BRAIN DEVELOPMENT AND BEHAVIOR, AND, TO CHARACTERIZE THE EMERGENCE AND PROGRESSION OF MENTAL ILLNESSES, AND IDENTIFYING SENSITIVE PERIODS FOR OPTIMAL INTERVENTION. TO DETERMINE EARLY RISK AND PROTECTIVE FACTORS, AND RELATED MECHANISMS, TO SERVE AS NOVEL INTERVENTION GROUPS, AND, TO DEVELOP RELIABLE AND ROBUST BIOMARKERS AND ASSESSMENT TOOLS TO PREDICT ILLNESS ONSET, COURSE, AND ACROSS DIVERSE POPULATIONS. TO DEVELOP NOVEL INTERVENTIONS USING A MECHANISM-INFORMED, EXPERIMENTAL THERAPEUTICS APPROACH, AND, TO DEVELOP AND IMPLEMENT MEASUREMENT STRATEGIES TO FACILITATE MECHANISM-BASED INTERVENTION DEVELOPMENT AND TESTING. TO INVESTIGATE PERSONALIZED INTERVENTION STRATEGIES ACROSS DISEASE PROGRESSION AND DEVELOPMENT, AND, TO DEVELOP AND REFINE COMPUTATIONAL APPROACHES AND RESEARCH DESIGNS THAT CAN BE USED TO INFORM AND TEST PERSONALIZED INTERVENTIONS. TO DEVELOP AND TEST APPROACHES FOR ADAPTING, COMBINING, AND SEQUENCING INTERVENTIONS TO ACHIEVE THE GREATEST IMPACT ON THE LIVES AND FUNCTIONING OF PERSONS SEEKING CARE, TO CONDUCT EFFICIENT PRAGMATIC TRIALS THAT EMPLOY NEW TOOLS TO RAPIDLY IDENTIFY, ENGAGE, ASSESS, AND FOLLOW PARTICIPANTS IN THE CONTEXT OF ROUTINE CARE, AND, TO ENHANCE THE PRACTICAL RELEVANCE OF EFFECTIVENESS RESEARCH VIA DEPLOYMENT-FOCUSED, HYBRID, EFFECTIVENESS-IMPLEMENTATION STUDIES. TO EMPLOY ASSESSMENT PLATFORMS WITHIN HEALTHCARE SYSTEMS TO ACCURATELY ASSESS THE DISTRIBUTION AND DETERMINANTS OF MENTAL ILLNESSES AND TO INFORM STRATEGIES FOR IMPROVED SERVICES, TO OPTIMIZE REAL-WORLD DATA COLLECTION SYSTEMS TO IDENTIFY STRATEGIES FOR IMPROVING ACCESS, QUALITY, EFFECTIVENESS, AND CONTINUITY OF MENTAL HEALTH SERVICES, AND, TO COMPARE ALTERNATIVE FINANCING MODELS TO PROMOTE EFFECTIVE AND EFFICIENT CARE FOR INDIVIDUALS WITH SERIOUS EMOTIONAL DISTURBANCES AND SERIOUS MENTAL ILLNESSES. TO STRENGTHEN PARTNERSHIPS WITH KEY STAKEHOLDERS TO DEVELOP AND VALIDATE STRATEGIES FOR IMPLEMENTING, SUSTAINING, AND CONTINUOUSLY IMPROVE EVIDENCE-BASED PRACTICES, TO BUILD MODELS TO SCALE-UP EVIDENCE-BASED PRACTICES FOR USE IN PUBLIC AND PRIVATE PRIMARY CARE, SPECIALTY CARE AND OTHER SETTINGS, AND, TO DEVELOP DECISION-SUPPORT TOOLS AND TECHNOLOGIES THAT INCREASE THE EFFECTIVENESS AND CONTINUOUS IMPROVEMENT OF MENTAL HEALTH INTERVENTIONS IN PUBLIC AND PRIVATE PRIMARY CARE, SPECIALTY CARE, AND OTHER SETTINGS. TO ADAPT, VALIDATE, AND SCALE-UP PROGRAMS CURRENTLY IN USE THAT IMPROVE MENTAL HEALTH SERVICES FOR UNDERSERVED POPULATIONS, TO DEVELOP AND VALIDATE SERVICE DELIVERY MODELS THAT PROVIDE EVIDENCE-BASED CARE FOR INDIVIDUALS THROUGHOUT THE COURSE OF MENTAL ILLNESS, TO DEVELOP AND VALIDATE SYSTEMS-LEVEL STRATEGIES USING TECHNOLOGY AND OTHER APPROACHES, TO IDENTIFY, SUPPORT, AND MONITOR THE EFFECTIVENESS OF EVIDENCE-BASED CARE THROUGHOUT THE COURSE OF ILLNESS, AND, TO DEVELOP AND VALIDATE DECISION-MAKING MODELS THAT BRIDGE MENTAL HEALTH, MEDICAL, AND OTHER CARE SETTINGS TO INTEGRATE THE APPROPRIATE CARE FOR PEOPLE WITH SERIOUS MENTAL ILLNESSES AND COMORBID MEDICAL CONDITIONS.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Nashville,
Tennessee
37203
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 366% from $683,341 to $3,185,696.
Vanderbilt University was awarded
Neural Learning Mechanisms in Multidimensional Environments
Project Grant R01MH123687
worth $3,185,696
from the National Institute of Mental Health in April 2021 with work to be completed primarily in Nashville Tennessee United States.
The grant
has a duration of 4 years 9 months and
was awarded through assistance program 93.242 Mental Health Research Grants.
The Project Grant was awarded through grant opportunity NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 3/20/25
Period of Performance
4/1/21
Start Date
1/31/26
End Date
Funding Split
$3.2M
Federal Obligation
$0.0
Non-Federal Obligation
$3.2M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R01MH123687
Transaction History
Modifications to R01MH123687
Additional Detail
Award ID FAIN
R01MH123687
SAI Number
R01MH123687-1873317951
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75N700 NIH National Institute of Mental Health
Funding Office
75N700 NIH National Institute of Mental Health
Awardee UEI
GTNBNWXJ12D5
Awardee CAGE
5E694
Performance District
TN-05
Senators
Marsha Blackburn
Bill Hagerty
Bill Hagerty
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
National Institute of Mental Health, National Institutes of Health, Health and Human Services (075-0892) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,224,144 | 100% |
Modified: 3/20/25