Peptidergic neurons in error computations and behavioral flexibility
Peptidergic neurons in error computations and behavioral flexibility
批准号:
10721319
负责人:
EMILY L SYLWESTRAK
金额:
$175.76万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-01 至 2026-08-31
关键词:
AddressAffectAnimalsAnti-Anxiety AgentsAntidepressive AgentsAnxietyAreaAttention deficit hyperactivity disorderAutomobile DrivingAxonBehaviorBehavioralBrainCellsClinicalDataDecision MakingDevelopmentDiseaseElectrophysiology (science)ElementsEnvironmentExhibitsFailureFiberFutureGeneticGenetic MarkersGenetic TranscriptionGoalsHabenulaJointsLateralLeadLearningMapsMeasuresMental DepressionModelingMonitorMotivationMusNeural PathwaysNeuronsNeuropeptidesOrganismOutcomeOutcome MeasureOutputPathway interactionsPopulationProbabilityPsychological reinforcementRecording of previous eventsResearchReversal LearningRewardsRoleSchizophreniaSignal TransductionStreamStressStructureSubstance PSynapsesSynaptic TransmissionTAC1 geneTACR1 geneTestingTheoretical modelTherapeuticTypologyWorkantagonistcell typeexpectationexperimental studyflexibilityin vivoinsightmaladaptive behaviorneuralneuropsychiatric disordernew therapeutic targetoptogeneticsprogramsresponseside effecttargeted treatmenttooltranslational impacttransmission process
中文摘要
项目总结
英文摘要
PROJECT SUMMARY
Survival in dynamic environments demands that behaviors are flexible and adaptive. An organism must make
predictions about which actions lead to rewards, calculate how outcomes differ from those predictions (prediction
errors), and adapt a behavioral strategy accordingly. Neurons encoding prediction errors can be found
throughout many reward-related brain structures, with the highest densities in the VTA and the lateral habenula
(LHb). Transcriptional analyses have found the LHb contains a diverse set of intermingled cell types, but at
present, it is unknown how prediction error-encoding neurons map onto transcriptionally-defined habenular cell
types. Therefore, we lack a genetic cell type framework to identify and selectively manipulate them to better
understand their role in behavior. To address this need, this proposal aims to determine how Tac1LHb neurons
encode reward parameters, adapt their responses in dynamic environments, and impact behavioral flexibility.
Our central hypothesis is that prediction errors trigger activity in Tac1LHb neurons and promote flexible behavior.
In Aim 1, we will use population level and single unit recordings to determine how different reward parameters
(reward probability, reward history, reward interval, and reward size) modulate Tac1LHb activity at unexpected
outcomes. In Aim 2, we will determine the circuit- and synaptic-level mechanisms by which Tac1LHb activity
changes with reward history. In Aim 3, we propose to manipulate Tac1LHb activity in a reversal learning task to
determine if it is necessary for behavioral flexibility, and to test a theoretical model describing the computational
role of Tac1LHb activity in decision-making. The Tac1 gene encodes the neuropeptide Substance P and our
proposal will determine the role of peptidergic signaling in synaptic transmission in this circuit, as well as its
impact on behavior. Completion of the proposed research program will establish a cell-type resolved view of
error-encoding neurons and demonstrate a key role for Tac1LHb neurons in behavioral flexibility. Deficits in
behavioral flexibility are a hallmark of several neuropsychiatric disorders, including schizophrenia, ADHD, OCD,
and depression. Joint information on cell typology and function has the potential to advance new therapeutic
targets to treat maladaptive behavior in these disorders.
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