NCS-FO: Collaborative Research: Understanding Individual Differences in Cognitive Performance: Joint Hierarchical Bayesian Modeling of Behavioral and Neuroimaging Data

了解个人健康和福祉的复杂决定因素对于促进和维持健康的世界人口至关重要。 幸福不仅可以理解为没有身体和精神疾病，还可以理解为个人的生活质量和最佳功能。众所周知，个体在认知能力和倾向方面存在巨大差异，这可以从高阶认知任务的表现，决策偏好和情感能力中看出。然而，人们对这种差异的神经基础知之甚少：目前还不清楚大脑结构和功能在任务和过程中的个体差异如何与能力和能力联系起来。该项目探索了一个数学和计算框架，用于调查大样本神经成像和行为数据集，以提高我们对认知表现个体差异的理解。该项目的最终目标是根据观察到的（过去的）行为和神经成像数据预测新的真实世界情况下的个人认知表现，并有助于理解个人的认知健康和福祉。该项目还将为下一代科学家提供许多培训机会。技术方法将建立在认知科学，神经科学，统计学和机器学习的最新进展之上并将其整合。统计模型将整合来自大脑成像和行为测试的数据，以生成预测，否则可能无法使用单一数据源。这项研究将超越建立和解释个体差异，预测个人在各种任务中的认知表现。

Understanding the complex determinants of individual health and wellbeing is critical for the promotion and maintenance of a healthy world population. Wellbeing may be understood not only as the absence of physical and mental illness but also as the quality of life and optimal functioning of individuals. It is well known that individuals vary tremendously in terms of cognitive abilities and dispositions, as seen from performance on high-order cognitive tasks, decision-making preferences, and emotional competencies. However, the neural underpinnings of much of this variability are poorly understood: It is unclear how individual differences in brain structure and function across tasks and processes are linked to abilities and competencies. This project explores a mathematical and computational framework for investigating a large-sample neuroimaging and behavioral dataset in order to improve our understanding of individual differences in cognitive performance. An ultimate goal of the project is to predict individual cognitive performance in novel, real-world situations based on observed (past) behavioral and neuroimaging data and contribute to the understanding of cognitive health and wellbeing of individuals. The project will also offer many training opportunities for the next generation of scientists. The technical approach will build on and integrate recent advances in cognitive science, neuroscience, statistics, and machine learning. Statistical models will integrate data from both brain imaging and behavioral tests to generate predictions that otherwise may not be possible with a single source of data. The research will go beyond establishing and explaining individual differences to predicting individual cognitive performance in a variety of tasks.