Computational and neurodevelopmental mechanisms of memory-guided decision-making

记忆引导决策的计算和神经发育机制

• 批准号: 10723314
• 负责人: Catherine Insel
• 金额: $ 13.08万
• 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: 2023-07-03 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723314
• 关键词: Acceleration; Adaptive Behaviors; Adolescence; Adolescent; Adult; Age; Award; Behavior; Behavioral; Categories; Clinical; Computer Models; Corpus striatum structure; Data; Data Set; Decision Making; Development; Eating; Episodic memory; Event; Failure; Food; Fostering; Foundations; Functional Magnetic Resonance Imaging; Goals; Hippocampus; Individual; Knowledge; Learning; Link; Longevity; Machine Learning; Measures; Mediating; Memory; Mental Depression; Mental Health; Methods; Modeling; Outcome; Pathway interactions; Phase; Postdoctoral Fellow; Prefrontal Cortex; Process; Psychopathology; Research; Research Training; Rewards; Risk; Sampling; Sesame - dietary; Shapes; Specific qualifier value; Specificity; Support System; System; Techniques; Testing; Theoretical model; Training; Work; Youth; adolescent brain development; artificial neural network; career; cognitive neuroscience; cognitive process; cognitive testing; computational neuroscience; data de-identification; depressive symptoms; experience; longitudinal design; maladaptive behavior; neural; neural network; neurobiological mechanism; neurodevelopment; neuroimaging; novel; predictive modeling; research study; risk prediction; translational approach; two-dimensional

PROJECT SUMMARY Adolescence is accompanied by enhanced risk for maladaptive decision-making, a profile that confers risk for psychopathology. Prior work has shown that memory and decision-making systems continue to mature during adolescence, however, developmental studies have traditionally tested these cognitive processes in isolation. As a result, remarkably little is known about how the development of memory and the corresponding neural processes influence adaptive and maladaptive behaviors in youth. The proposed project will test a novel conceptual model that specifies the neurodevelopmental pathways through which memory guides decision- making during adolescence. This unifying framework formalizes the neurocomputational mechanisms underlying two dimensions of memory-guided decision making: specificity and generalization. The proposed research will test this conceptual model by leveraging recent advances from computational neuroscience, including artificial neural networks, multivariate neuroimaging methods, and machine learning approaches to predictive modeling. Aim 1 (K99 phase) tests the hypothesis that enhanced interactions between the hippocampus and striatum support the use of memory specificity to guide decisions. Aim 2 (K99 phase) tests the hypothesis that enhanced interactions between the hippocampus and vmPFC support the use of memory generalization to guide decisions. Aim 3 (R00 phase) tests the overarching hypothesis that adolescents rely on specificity to guide decision-making due to the early maturation of hippocampal-striatal circuitry, whereas generalization emerges during the transition to adulthood, in tandem with strengthening connections between the hippocampus and ventromedial prefrontal cortex (vmPFC). Further, Aim 3 will test the hypothesis that decreased hippocampal-striatal interactions predict risk for depressive symptoms during adolescence. This work directly builds on the candidate’s earlier postdoctoral work that has characterized the behavioral trajectories of memory-guided decision-making during adolescence. By examining the computational foundations and neurodevelopmental trajectories of memory-guided decision-making, the proposed research will reveal how learning and decision- making processes refine with age during adolescence. This work has the potential to illuminate why adolescence is a period of heightened risk for maladaptive decision-making, and the proposed research will identify how aberrant decision-making confers risk for depression. This award will provide the candidate, who has a strong background in developmental cognitive neuroscience, with critical training in computational modeling with neural networks, multivariate fMRI methods, machine learning, and advanced statistical techniques to promote a successful transition to an independent research career. The proposed research study and training plan will help the candidate achieve her ultimate goal of leading an independent research lab, where she will implement computational and translational approaches to study the neurodevelopment of adaptive and maladaptive behaviors during adolescence.

项目总结 青春期伴随着做出不适应决策的风险增加，这一特征增加了 精神变态学。先前的研究表明，记忆和决策系统在 然而，青春期的发展研究传统上是孤立地测试这些认知过程。 其结果是，人们对记忆和相应神经的发育过程知之甚少 过程会影响青年的适应和不适应行为。拟议中的项目将测试一部小说 概念模型，指定记忆引导决策的神经发育路径- 在青春期制造的。这个统一的框架使潜在的神经计算机制正式化 记忆引导决策的两个维度：专一性和概括性。拟议的研究将 通过利用计算神经科学的最新进展来测试这个概念模型，包括人工智能 用于预测建模的神经网络、多变量神经成像方法和机器学习方法。 目标1(K99阶段)测试了增强海马体和纹状体之间的相互作用的假设 支持使用记忆专一性来指导决策。目标2(K99阶段)测试增强的假设 海马体和vmPFC之间的相互作用支持使用记忆泛化来指导决策。 目标3(R00阶段)测试了主要的假设，即青少年依赖特异性来指导决策 由于海马纹状体回路的早期成熟，而泛化则出现在 过渡到成年，同时加强海马体和腹内侧部之间的联系 前额叶皮质(VmPFC)。此外，目标3将检验海马纹状体减少的假设 交互作用预测青春期出现抑郁症状的风险。这项工作直接建立在 候选人早期的博士后工作，描述了记忆引导的行为轨迹 青春期的决策。通过研究计算基础和神经发育 记忆引导决策的轨迹，这项拟议的研究将揭示学习和决策是如何-- 在青春期，制作过程会随着年龄的增长而细化。这项工作有可能解释为什么青春期 是做出不适应决策的高风险时期，拟议中的研究将确定 不正常的决策会增加患抑郁症的风险。这个奖项将为候选人提供一个强有力的 具有发展认知神经科学背景，接受过神经计算建模方面的重要培训 网络、多变量功能磁共振方法、机器学习和高级统计技术，以促进 成功地过渡到独立的研究生涯。拟议的研究性学习和培训计划将有所帮助 候选人实现了她领导一个独立研究实验室的最终目标，她将在那里实施 研究适应性和非适应性神经发育的计算和转换方法 青春期的行为。