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.

项目摘要 青少年是通过增加适应不良决策的风险来实现的，该概况承认了风险 心理病理学。先前的工作表明，记忆和决策系统在 然而，青少年发展的研究传统上已经孤立地测试了这些认知过程。 结果，关于记忆的发展和相应中性的发展知之甚少 过程影响青年的适应性和适应不良行为。拟议的项目将测试小说 指定神经发育途径的概念模型，记忆指导决策 - 在青少年期间制作。这个统一的框架正式化了基础的神经计算机制 记忆引导决策制定的两个维度：特异性和概括。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种概念模型，包括人造 神经网络，多元神经影像学方法和机器学习方法进行预测建模。 AIM 1（K99阶段）检验了以下假设，即增强了海马和纹状体之间的相互作用 支持使用记忆特异性来指导决策。 AIM 2（K99阶段）检验了增强的假设 海马和VMPFC之间的相互作用支持使用内存概括来指导决策。 AIM 3（R00阶段）测试了基本假设，即青少年依靠特异性来指导决策 由于海马 - 纹状体回路的早期成熟，而在此期间出现了概括 过渡到成年，与海马和腹膜之间的增强联系 前额叶皮层（VMPFC）。此外，AIM 3将测试降低海马 - 纹状体的假设 相互作用可以预测青少年期间抑郁症状的风险。这项工作直接建立在 候选人早期的博士后作品表征了记忆引导的行为轨迹 青少年期间的决策。通过检查计算基础和神经发育 拟议的研究轨迹记忆引导的决策制定，将揭示学习和决策的方式 - 使过程随着青少年的年龄而完善。这项工作有可能阐明为什么青少年 是适应不良决策的风险增加的时期，拟议的研究将确定如何确定 异常决策赋予了抑郁症的风险。该奖项将为候选人提供强大的候选人 发育认知神经科学的背景，通过神经元进行计算建模的批判性培训 网络，多元fMRI方法，机器学习和高级统计技术，以促进 成功过渡到独立研究职业。拟议的研究和培训计划将有助于 候选人实现了领导独立研究实验室的最终目标，她将在其中实施 计算和翻译的方法来研究适应性和适应不良的神经发育 青少年期间的行为。