NCS-FO: Dynamic computational phenotyping of human cognition and brain function

认知神经科学的一个长期目标是了解认知的哪些方面是个人共有的，哪些是个人独有的。对后者的研究通常关注的是随着时间的推移相对稳定的特征，构成了所谓的静态表型。表型分析已被证明是预测跨时间和跨任务的行为的一种强大方法。例如，在4岁时延迟满足的能力的个体差异预测了青春期的学习、语言和社会情感能力。但是，这种静态的表型方法的可预测性的一个主要限制是，它们不能捕捉到个体内的差异。静态表型源于在特定时间和背景下对任务的表现，而我们知道认知表现(以及对认知表现的大脑测量)在相对较短的时间框架内会有所不同，这取决于睡眠、压力、情绪、警觉性和动机等因素。要预测一个人在一段时间内的认知表现，需要了解这个人的认知状态是如何变化的，以及是什么推动了这些变化。这项由哈佛大学研究人员开展的研究项目将通过随着时间的推移反复收集个人数据来填补这一空白。通过将计算模型与数据相匹配，研究人员将提取每个个体的动态“计算表型”。他们假设，变化将被一组相对较小的动力学参数通过计算捕捉到，并将找到一小组大脑网络映射到这些参数上。如果这一假设是正确的，那么该项目将有可能打开有针对性的、精确的和针对个人的培训干预的大门，以提高认知表现。这个项目是由理解神经和认知系统的综合策略(NCS)资助的，这是一个多学科项目，由计算机和信息科学与工程(CEISE)、教育和人力资源(EHR)、工程(ENG)和社会、行为和经济科学(SBE)的主管部门联合支持。计算表型最近已经成为表征个体之间差异的一种强大的技术。通过将计算认知模型与行为数据相匹配，研究人员可以使用结果参数估计作为个人的认知“指纹”。与其他类型的表型(例如，基于调查的表型)相比，计算表型的优势在于与潜在的认知和神经机制更紧密地联系在一起。研究表明，计算表型在预测个体水平的结果、设计干预措施和提供传统诊断标准的替代方面具有实用价值。这种方法的一个关键局限性是，它通常将表型概念化为一种特征--个体的静态描述符。在第一个目标中，研究人员将对计算表型的动态概念化进行形式化和实验验证。为了实现这一目标，研究人员将让参与者在三个月内每周完成一系列行为任务，并建立相应的计算模型。这项纵向研究的数据将被用来估计每个参与者的计算表型如何随着时间的推移而独特地变化，研究人员将使用统计方法来提取表型中的低维结构。在第二个目标中，研究人员将使用纵向神经成像结合行为电池来识别大脑中跟踪低维表型结构的网络，使他们能够精确定位个体内变异的神经轨迹。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

A long-term goal of cognitive neuroscience is to understand which aspects of cognition are shared across individuals and which are unique to an individual. Studies of the latter are typically concerned with traits that are relatively stable over time, constituting what is referred to as a static phenotype. Phenotyping has proven to be a powerful approach for predicting behavior across time and tasks. For example, individual differences in the ability to delay gratification at age 4 years predict academic, verbal, and socioemotional competence in adolescence. But a major limitation to the predictability of such static approaches to phenotyping is that they do not capture within-individual variation. Static phenotypes are derived from performances on tasks measured at a specific time and context, whereas we know that cognitive performances (and brain measures of it) vary within individuals in relatively short time frames depending on such factors as sleep, stress, mood, alertness, and motivation. To predict an individual's cognitive performance across time, one needs to understand how the individual's cognitive state changes and what drives those changes. This research project, conducted by investigators at Harvard University, will fill this gap by collecting individual data repeatedly over time. By fitting computational models to the data, the researchers will extract a dynamic "computational phenotype” of each individual. They hypothesize that changes will be captured computationally by a relatively small set of dynamical parameters and that a small set of brain networks will be found to map onto those parameters. If this hypothesis is correct, then the project will have the potential to open the door to targeted, precise, and individual-specific training interventions to improve cognitive performance. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).Computational phenotyping has recently emerged as a powerful technique for characterizing variation between individuals. By fitting computational cognitive models to behavioral data, investigators can use the resulting parameter estimates as a cognitive “fingerprint” for an individual. Computational phenotypes have the advantage over other kinds of phenotypes (e.g., those based on surveys) of being more closely linked to underlying cognitive and neural mechanisms. Research has shown the utility of computational phenotyping in predicting individual-level outcomes, designing interventions, and providing an alternative to traditional diagnostic criteria. A critical limitation of this approach is that it has typically conceptualized the phenotype as a trait—a static descriptor of an individual. In the first aim, the investigators will formalize and experimentally validate a dynamic conceptualization of the computational phenotype. To accomplish this aim, the investigators will have participants complete a battery of behavioral tasks– weekly over three months – for which established computational models exist. Data from this longitudinal study will be used to estimate how each participant’s computational phenotype uniquely changes over time, and the investigators will employ statistical methods to extract low-dimensional structure in the phenotype. In the second aim, the investigators will use longitudinal neuroimaging in conjunction with the behavioral battery to identify networks in the brain that track the low-dimensional phenotype structure, allowing them to pinpoint the neural locus of intra-individual variation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.