Neurocomputational Mechanisms of Affective Semantic Memory Development

情感语义记忆发展的神经计算机制

• 批准号: 10755053
• 负责人: Anna Vannucci
• 金额: $ 4.83万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10755053
• 关键词: Adolescent; Adult; Affect; Affective; Age; Anxiety; Award; Bayesian Prediction; Behavior; Brain; Caregivers; Child; Child Behavior; Childhood; Clinical; Code; Cognitive; Computer Models; Computing Methodologies; Coupling; Data; Development; Ecological momentary assessment; Emotional; Emotions; Environment; Event; Faculty; Foundations; Functional Magnetic Resonance Imaging; Funding; Future; Goals; Human; Human Development; Individual Differences; Intervention; Investigation; Knowledge; Lead; Learning; Link; Literature; Machine Learning; Medial; Memory; Mental Depression; Mental Health; Mental disorders; Mentorship; Methodology; Methods; Modeling; National Institute of Mental Health; Nature; Neurobiology; Neurosciences; Outcome; Parents; Pattern; Phase; Positioning Attribute; Postdoctoral Fellow; Principal Investigator; Process; Psychopathology; Research; Risk; Role; Sampling; Science; Semantic memory; Semantics; Shapes; Statistical Models; Surveys; System; Techniques; Testing; Time; Training; career; caregiving; cognitive neuroscience; computational neuroscience; design; early adolescence; early experience; early life adversity; emotional behavior; experience; experimental study; high risk; human model; indexing; innovation; neglect; neural; neurodevelopment; neuroimaging; novel strategies; post-doctoral training; pre-doctoral; resilience; response; skills; social; tenure track; theories

PROJECT SUMMARY / ABSTRACT My career goal is to lead an interdisciplinary team that investigates the developmental neurocomputational mechanisms that link early-life adversity to mental health. To reach this goal, I require intensive training in human neuroimaging methods, cognitive computational neuroscience, and dyadic methodologies. My training to date has provided a strong foundation of skills in developmental neuroscience, early-life adversity, statistical modeling, experience sampling, and machine learning. I identified the best possible mentorship team and scientific environment to support me in expanding this skill set with essential training in emotional memory, fMRI analysis, computational modeling, real-world dyadic (parent-child) behavior methods, and professional skills. With this protected training time, I will be poised to successfully transition to a competitive postdoctoral position and ultimately obtain a tenure-track faculty position at the forefront of developmental neuroscience. Research: More than one-third of children experience early caregiving adversity such as abuse, neglect, and parental abandonment/separation, which accounts for the onset of at least 30-45% of mental health disorders world-wide. Clinical association studies show that this increased risk for psychopathology is linked to perturbations in midline cortico-subcortical circuitry, comprised of the midcingulo-insular “salience” network and posterior-medial “default mode” network. At the same time, experimental studies in cognitive neuroscience find that this circuitry has a broader functional role in semantic social and affective knowledge. Might the neural alterations that give rise to psychopathology risk following early caregiving adversity be one and the same as the neurobiology found to represent learned affective semantic memories? In line with my preliminary data, I will test the hypothesis that alterations in midline cortico-subcortical circuitry following early adversity represent the affective semantic knowledge learned during early caregiving experiences. During the F99-phase, I will identify the midline cortico-subcortical activity patterns associated with children’s affective semantic memories during fMRI in a developmental sample enriched for early caregiving adversity (Aim 1.1), and then examine the links between affective semantic neural representations and real-world parent-child emotional behaviors using dyadic ecological momentary assessments (Aim 1.2). In the K00-phase, I will determine whether Bayesian predictive coding in midline cortico-subcortical circuitry explains the neural computations underlying affective semantic memory in childhood and examine how these neurocomputational mechanisms differ as a function of age, socioemotional context, and early adversity exposure (Aim 2). This research will result in more powerful, ecologically-sensitive computational models of human development through the integration of cutting-edge approaches to neurocomputation, real-time parent-child behavior, and affective semantic memory. Such advances have the potential to significantly extend neurodevelopmental models of psychopathology, which is essential for developing interventions tailored to age, early experience, and neurocomputational mechanism.

项目摘要 /摘要 我的职业目标是领导一个研究发展性神经计算的跨学科团队 将早期广告与心理健康联系起来的机制。要达到这个目标，我需要深入培训 人类神经影像学方法，认知计算神经科学和二元方法。我的训练 迄今为止，为发育神经科学，早期广告，统计的技能提供了强大的基础 建模，经验采样和机器学习。我确定了最好的Mentalship团队， 科学环境支持我通过情感记忆中的基本培训来扩展这种技能， fMRI分析，计算建模，现实世界二元（亲子）行为方法和专业 技能。有了这个受保护的训练时间，我将被中毒，成功过渡到竞争性博士后 位置并最终在发育神经科学的最前沿获得终身任职的职位。 研究：超过三分之一的儿童经历了早期的护理冒险，例如虐待，忽视和 父母遗弃/分离，占至少30-45％的心理健康障碍的发作 全世界。临床协会研究表明，这种心理病理学风险增加与 中线皮质毛皮电路的扰动，由中林ul中的“显着性”网络组成 后中部“默认模式”网络。同时，认知神经科学的实验研究发现 该电路在语义社会和情感知识中具有更广泛的功能作用。可能是中立的 早期护理冒险后引起精神病理学风险的改变与 神经生物学发现代表了学到的情感语义记忆？根据我的初步数据一致 将测试以下假设：早期广告后，中线皮质毛皮层的改变 在早期护理经验中学到的情感语义知识。在F99期间，我会 确定与儿童的情感语义记忆相关的中线皮质毛皮活动模式 在fMRI期间，在富含早期照顾广告的发展样本中（AIM 1.1），然后检查 情感语义神经元表示与现实世界亲子情感行为之间的联系 二元生态瞬时评估（AIM 1.2）。在k00相，我将确定贝叶斯是否是否 中线皮质毛皮电路中的预测编码解释了情感的神经计算 童年时期的语义记忆和检查这些神经计算机制如何与 年龄，社会情感环境和早期广告曝光（AIM 2）。这项研究将导致更强大的 人类发展的生态敏感计算模型通过尖端的整合 神经成绩，实时亲子行为和情感语义记忆的方法。这样的 进步有可能显着扩展心理病理的神经发育模型 对于制定针对年龄，早期经验和神经计算机制量身定制的干预措施至关重要的。