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体共有的，哪些方面是个体独有的。对后者的研究通常涉及随时间相对稳定的性状，构成所谓的静态表型。表型分析已被证明是预测跨时间和任务行为的强大方法。例如，4岁时延迟满足能力的个体差异可以预测青春期的学术，语言和社会情感能力。但是，这种静态表型分析方法的可预测性的一个主要限制是，它们不能捕获个体内的变异。静态表型来自于在特定时间和环境下测量的任务表现，而我们知道认知表现（以及大脑对其的测量）在相对较短的时间内因睡眠、压力、情绪、警觉性和动机等因素而异。为了预测一个人在一段时间内的认知表现，人们需要了解个人的认知状态如何变化以及是什么驱动了这些变化。这项由哈佛大学的研究人员进行的研究项目将通过随着时间的推移反复收集个体数据来填补这一空白。通过将计算模型与数据拟合，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态参数计算捕获，并且将发现一组较小的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为有针对性的、精确的和针对个人的训练干预打开大门，以提高认知能力。该项目由理解神经和认知系统的综合策略（NCS）资助，NCS是一个由计算机和信息科学与工程（CISE），教育和人力资源（EHR），工程（ENG）和社会，行为和经济科学（SBE）董事会联合支持的多学科计划。通过将计算认知模型拟合到行为数据，研究人员可以使用所得的参数估计值作为个体的认知“指纹”。计算表型具有优于其他种类表型的优势（例如，那些基于调查的研究）与潜在的认知和神经机制有更密切的联系。研究表明，计算表型在预测个人水平的结果，设计干预措施，并提供替代传统的诊断标准的效用。这种方法的一个关键局限是，它通常将表型概念化为一种特质，即个体的静态描述符。在第一个目标中，研究人员将正式和实验验证计算表型的动态概念化。为了实现这一目标，研究人员将让参与者完成一系列行为任务-在三个月内每周一次-已有既定的计算模型。这项纵向研究的数据将用于估计每个参与者的计算表型如何随时间发生独特的变化，研究人员将采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位点。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值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.