Prediction Error and Affective Salience Abnormalities in Aging and Late-Life Depression

衰老和晚年抑郁症的预测误差和情感显着性异常

• 批准号: 10527361
• 负责人: Lindsay Victoria
• 金额: $ 17.71万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-20 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10527361
• 关键词: Affective; Age; Aging; Amygdaloid structure; Anhedonia; Anterior; Association Learning; Attention; Behavior; Behavior Therapy; Behavioral; Brain; Cognition; Cognitive; Cognitive deficits; Cognitive remediation; Complement; Computer Models; Computing Methodologies; Data; Depressed mood; Development; Dorsal; Elderly; Event; Foundations; Functional Magnetic Resonance Imaging; Functional disorder; Future; Generations; Goals; Impairment; Individual; Instruction; Insula of Reil; Intervention; K-Series Research Career Programs; Knowledge; Learning; Maintenance; Major Depressive Disorder; Mental Depression; Mentored Research Scientist Development Award; Mentors; Methodology; Mood Disorders; National Institute of Mental Health; Neurobiology; Nucleus Accumbens; Outcome; Participant; Patient Self-Report; Performance; Positive Valence; Predisposition; Process; Psychological reinforcement; Psychopathology; Punishment; Recording of previous events; Research; Research Design; Research Domain Criteria; Research Personnel; Reversal Learning; Rewards; Role; Scientist; Series; Severities; Statistical Models; Stimulus; Structure; Study models; System; Task Performances; Technical Expertise; Thalamic structure; Time; Training; Ventral Striatum; Weight; Work; age effect; aged; behavior measurement; career; cognitive task; computational neuroscience; depressive symptoms; design; discount; experience; geriatric depression; interest; network models; neural; neural network; neurobiological mechanism; neuroimaging; normal aging; outcome prediction; personalized medicine; research study; reward expectancy; reward processing; skills; symptomatology

PROJECT SUMMARY The goal of this K01 Mentored Research Scientist Career Development Award is to provide the candidate with the conceptual knowledge and technical skills needed to pursue an independent research career as a computational cognitive neuroscientist focused on aging and late-life mood disorders. The candidate has a strong background in cognitive paradigm design and functional neuroimaging methodology, and an evolving interest in computational modeling of cognitive and affective dysfunctions in depressed older adults. The research study will use computational approaches to clarify disrupted mechanisms of reward and salience processes and may lead to findings that can serve as data-driven targets for future personalized treatments. The proposed training consists of formal courses, structured tutorials, and hands-on methodological instruction intended to strengthen the candidate's understanding and development of computational models for the study of Positive Valence Systems (PVS) abnormalities in aging and late-life depression. Her mentoring team consists of accomplished investigators who will provide guidance and training in neurobiological and computational approaches for the study of reward and salience abnormalities. The research study complements the candidate's training plan, as it focuses on PVS dysfunctions that may contribute to reward processing abnormalities in normal aging and late-life depression. It is based on the prediction that aging-related abnormalities in reward circuits interacting with neurobiological abnormalities of depression may alter reward expectancy and reward responsiveness, leading depressed older adults to assign greater affective salience to negative stimuli. Accordingly, the study proposes to investigate the impact of the effects of aging and depressive symptoms on prediction error encoding of affectively salient stimuli at three levels of analysis: circuits, behavior, and self-report. The participants will be older adults aged 60-85 years with major depression (N = 34, stratified into two levels of severity) or no history or presence of psychopathology (N = 34). The proposed study will use task-based fMRI and computational modeling to examine how the dynamic interaction of age and late-life depressive symptomatology influences neural network functions and the resulting reward and salience processing behaviors. This study and the studies to follow promise to identify personalized behavioral and neurobiological targets for much-needed interventions. This work is timely, as advances in cognitive remediation, brain stimulation, and targeted behavioral interventions are becoming increasingly capable of influencing selective neurobiological functions and associated behaviors.

项目摘要 这个K 01指导研究科学家职业发展奖的目标是为候选人提供 概念知识和技术技能需要追求独立的研究生涯作为一个 计算认知神经科学家专注于衰老和晚年情绪障碍。候选人有一个 在认知范式设计和功能神经成像方法学方面有很强的背景， 对老年抑郁症患者认知和情感功能障碍的计算机建模感兴趣。的 一项研究将使用计算方法来阐明奖励和突出性的破坏机制 过程，并可能导致可以作为未来个性化治疗的数据驱动目标的发现。 拟议的培训包括正式课程、结构化教程和实践方法指导 旨在加强候选人对研究计算模型的理解和发展 正价系统（PVS）异常的老龄化和晚年抑郁症。她的指导团队 由有成就的研究人员组成，他们将提供神经生物学方面的指导和培训， 奖励和显著性异常研究的计算方法。 该研究补充了候选人的培训计划，因为它侧重于PVS功能障碍， 导致正常衰老和晚年抑郁症中的奖励处理异常。它是基于 预测与年龄相关的奖励回路异常与神经生物学异常相互作用， 抑郁可能会改变奖励期望和奖励反应，导致抑郁的老年人分配 对消极刺激的情感突显性更强。因此，本研究建议调查 年龄和抑郁症状对三种情绪显著性刺激的预测误差编码的影响 分析层次：电路、行为和自我报告。参与者将是60-85岁的老年人， 重度抑郁症（N = 34，分为两个严重程度）或无精神病理学病史或存在精神病理学（N = 34）。 这项研究将使用基于任务的功能磁共振成像和计算模型来研究动态 年龄和晚年抑郁症的相互作用影响神经网络功能， 从而产生奖励和显著性处理行为。本研究和后续研究的承诺，以确定 个性化的行为和神经生物学目标，以进行急需的干预。这项工作是及时的，因为 认知补救、脑刺激和有针对性的行为干预的进展正在成为 越来越能够影响选择性神经生物学功能和相关行为。