Individual differences in sleep-related neural dynamics in sign trackers vs goal trackers
Individual differences in sleep-related neural dynamics in sign trackers vs goal trackers
批准号:
10220524
负责人:
Omar Jamil Ahmed
金额:
$39.0万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-20 至 2023-08-31
关键词:
AcuteAddictive BehaviorAffectAlcohol abuseAlcohol consumptionAlcoholsAreaAttentionBehaviorBehavioralBrain regionClinicalConsumptionCorpus striatum structureCuesDataData SetDiseaseDopamineElectroencephalographyElectrophysiology (science)EnvironmentExposure toFeelingFemaleFoodFutureGlobus PallidusGoalsHippocampus (Brain)HumanImpulsivityIndividualIndividual DifferencesInterventionLeadLearningLinkMedialMethodsModelingMotivationNeurobiologyNeuronsNucleus AccumbensPerformancePharmaceutical PreparationsPhenotypePopulationPopulation ControlPre-Clinical ModelPrecision HealthPrefrontal CortexProceduresRattusRegulationRelapseRewardsRiskRisk FactorsRodentRodent ModelRoleSiteSleepSleep ArchitectureSleep DeprivationSleep disturbancesStimulusSubstance Use DisorderSubstance abuse problemSystemTestingTranslatingWorkaddictionalcohol riskattentional controlbasechicken eggcholinergiccohortcravingdrug cravingdrug seeking behaviorexperienceexperimental studyimprovedincentive salienceindividual variationinnovationinsightinterestlearned behaviormalemotivated behaviorneural circuitneuroregulationnovelpre-clinicalpsychologicreceptorrelating to nervous systemresponsesleep regulationtrait
中文摘要
项目摘要/摘要
英文摘要
PROJECT SUMMARY / ABSTRACT
In humans, altered sleep is associated with an increased risk of alcohol and substance abuse disorders in
some select individuals. This presents a chicken and the egg problem that is often difficult to answer in clinical
populations: do the sleep differences in these individuals represent an underlying sleep architecture that
promotes addictive behavior, or are the sleep disturbances caused by the consumption of drugs or alcohol?
Here, we use and extend a preclinical model to understand the relationship between sleep deprivation and
reward-seeking behaviors. There are individual differences in the motivational states that are triggered by
reward-related cues, with some individuals experiencing stronger feelings of desire and craving (i.e. incentive
salience) when exposed to a cue than others. These individual differences can be modeled in rodents using a
Pavlovian Conditioned Approach (PCA) procedure, in which a discrete and localizable cue is paired with food
reward. When the cue is available, some rats will approach and interact with it (“sign trackers”, STs); whereas
other rats will approach the site of food delivery (“goal trackers”, GTs). The differences between STs and GTs
represent stable behavioral traits that are largely genetically determined, and are associated with differences in
other psychological (e.g. impulsivity) and neurobiological (e.g. altered dopaminergic activity) features.
Our central hypothesis is that sleep deprivation differentially impacts reward-related learning in different
individuals. In Aim 1, we will causally test this hypothesis by sleep-depriving a very large number of male and
female rodents and asking how the disturbed sleep alters their reward-seeking behaviors in the PCA task. Our
preliminary data show that sleep deprivation causes a remarkably robust increase in sign-tracking behavior
with a clear loss of more nuanced, intermediate behaviors. In Aim 2, we will carry out massive-scale task- and
sleep-related electrophysiological recordings simultaneously from multiple reward-related brain regions,
including the nucleus accumbens, ventral pallidum, medial prefrontal cortex and hippocampus. Many of these
areas are implicated in both motivational control and sleep regulation, making them ideal candidates to reveal
correlated individual differences in incentive salience and sleep-related dynamics. The significance of this
project is two-fold: we will establish the first preclinical model that relates sleep deprivation to individual
differences in learning and reward-seeking; and secondly, by identifying neural signatures that differ across
individuals with distinct reward-seeking phenotypes, this work can be translated into using sleep EEG to
identify and help individuals who are prone to drug-seeking behavior or relapse.
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