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.

项目概要/摘要 我的职业目标是领导一个跨学科团队，研究发育神经计算 为了实现这一目标，我需要在以下方面进行强化培训。 人类神经影像方法、认知计算神经科学和二元方法论。 迄今为止，已为发展神经科学、早年逆境、统计学等方面的技能奠定了坚实的基础。 我确定了最好的指导团队并进行了建模、经验采样和机器学习。 科学环境支持我通过情感记忆的基本训练来扩展这一技能， 功能磁共振成像分析、计算建模、现实世界二元（亲子）行为方法和专业 通过这段受保护的培训时间，我将成功过渡到有竞争力的博士后。 职位并最终获得发展神经科学前沿的终身教授职位。 研究：超过三分之一的儿童经历过早期的照顾逆境，例如虐待、忽视和 父母遗弃/分居，至少 30-45% 的精神健康障碍是由父母遗弃/分居造成的 世界范围内的临床关联研究表明，这种精神病理学风险的增加与以下因素有关。 中线皮质-皮质下回路的扰动，由中扣带-岛叶“显着”网络组成 与此同时，认知神经科学的实验研究发现后内侧“默认模式”网络。 该电路在语义社会和情感知识中具有更广泛的功能作用。 早期护理逆境后引起精神病理学风险的改变与以下相同： 神经生物学发现代表习得的情感语义记忆？根据我的初步数据，我 将检验早期逆境后中线皮质-皮质下回路的变化代表的假设 在 F99 阶段，我将在早期护理经历中学到的情感语义知识。 识别与儿童情感语义记忆相关的中线皮质-皮质下活动模式 在功能磁共振成像期间，在富含早期护理逆境的发育样本中进行（目标 1.1），然后检查 使用情感语义神经表征和现实世界的亲子情感行为之间的联系 二元生态瞬时评估（目标1.2）在K00阶段，我将确定是否贝叶斯。 中线皮质-皮质下电路的预测编码解释了情感背后的神经计算 儿童时期的语义记忆，并检查这些神经计算机制如何根据 年龄、社会情感背景和早期逆境经历（目标 2）。 通过整合尖端技术，建立人类发展的生态敏感计算模型 神经计算、实时亲子行为和情感语义记忆的方法。 进步有可能显着扩展精神病理学的神经发育模型，即 对于制定适合年龄、早期经历和神经计算机制的干预措施至关重要。