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.
