Identifying Individual Differences in Adolescent Neuropsychological Function Using the NIH Toolbox: An Application of Partially Ordered Classification Modeling

Objective: The Cognition Battery of the National Institutes of Heath Toolbox is a commonly utilized set of assessments of neuropsychological abilities, evaluating executive function, attention, working memory, processing speed, and episodic memory. We highlight the utility of an advanced statistical model in providing nuanced characterization of neurocognition in an adolescent population. We propose that partially ordered set (POSET) models are well suited to analyze polyfactorial tasks and identify distinct profiles of cognitive functioning. Method: Two models were considered using POSET classification. The first modeled 5 distinct cognitive functions and allowed for multiple functions to contribute to task performance. The second simpler model involved only 2 broader-based functions without polyfactorial task specifications. Existing performance data from 745 adolescents aged 14-17 years were analyzed. Posterior probabilities of classification performance and the discriminatory properties of the estimated response distributions indicated how well the modeling approaches fit the data. Results: The larger first model resulted in 8 profiles or states characterized by combinations of high or low functioning in 5 distinct functions. The simpler second model involved 2 broader-based functions that resulted in 4 states. Comparing model fit criteria, we believe that the finer-grained first model may better reflect the cognitive constructs associated with the tasks. Notably, POSET modeling did not always provide adequate classification of working memory because of the limited design of the Cognition Battery. Conclusions: We demonstrate that the use of POSET models is a feasible approach for detailed analysis of neurocognitive data that can extract information on cognitive functions, even when provided with limited task batteries. General Scientific Summary Our study highlights the utility and importance of advanced statistical modeling methods that systematically account for the multifactorial nature of neuropsychological tasks. Such modeling has the potential to reveal individual profiles of cognitive strengths and weaknesses that account for performance across tests. These profiles can then be examined in association with findings from neuroimaging studies or in guiding individually tailoring educational approaches. Importantly, this analysis illustrates how open-source resources such as the National Institutes of Heath Toolbox can enable application of neurops