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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

基本信息

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
项目类别：
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

项目摘要

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