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.

Introduction: For those with weak cognitive skills, certain mathematical problem-solving strategies may be more difficult than others. Tailored mathematical instruction that recognizes cognitive load and incorporates ways to reduce this may help to overcome mathematical difficulties. To investigate the effects of different strategies and cognitive load we explored brain hemodynamic responses associated with the use of different strategies to solve subtraction of fractions. We focused on those born extremely preterm (EPT; less than 28 weeks gestation) as they are known to have cognitive challenges and struggle with mathematics in particular. We also included a group of full-term (FT) peers for comparison. Methods: Functional MRI was acquired while the participants mentally solved fraction equations using either an improper fractions strategy, where whole numbers are converted to improper fractions before solving, or a mixed fractions strategy, where computations are carried out separately for the whole numbers and fraction components. Different fraction item types were given, which affected respective required cognitive loads per strategy. Diffusion and T1-weighted structural images were also acquired. All imaging modalities were compared between the two groups. Results: The EPT and FT groups differed in terms of task-related hemodynamic responses, regional volumes, and white matter connectivity. Functional group differences varied with strategy and item type. These differences were greatest when the mixed fractions strategy was prompted for an item type that required relatively greater cognitive load and involved borrowing. Other findings showed reduced white and grey matter volume and reduced white matter connectivity in widespread areas in the EPT group compared to the FT group. Conclusion: Changes in the brain related to preterm birth are complex. The understanding of function and structure presented here may help inform pedagogical practices by allowing for tailoring of mathematical education through identifying suitable strategy adoption that depends on problem type, to circumvent weaknesses in cognitive skills.