Determining the overlap between latent behavioral and neural changes in executive control in middle childhood

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Almost all forms of mental illness involve impairments in executive control (EC), the higher-level cognitive processes that support adaptive, self-regulated, goal-directed behavior. During middle childhood, both the cognitive EC system and the brain networks that support self-regulation undergo dramatic growth and reorganization. Sophisticated latent statistical modeling techniques show that children progressively draw on a wider array of specialized, proactive processes, including working memory and flexible attention, to perform executive tasks. At a neural level, children also show more clearly differentiated activity between task-positive central executive and dorsal attention networks, which support top-down, externally focused attention, and the task-negative default mode network, which supports internal self-awareness, episodic memory, and reflection. What remains unknown is whether observed changes in the structure of behaviorally-measured EC are reflective of these changes in neural network organization and whether measures of this specialization process may help to identify children at risk for psychopathology. To address these questions, the proposed study will capitalize on behavioral and resting state fMRI data from wave 1 of the Adolescent Brain and Cognitive Development (ABCD) Study, which includes over 11,000, US representative, 9 to 10 year-olds. First, the study will use sophisticated latent statistical modeling to determine whether a unique, specialized EC construct can be clearly segregated and differentiated from other cognitive skills (language, episodic memory) that also are developing rapidly during this age period. This modeling approach will produce a more refined measure of EC that is situated within the cognitive system as a whole. Second, this robust measure of latent, behavioral EC will be correlated with measures of functional neural network segregation to test the hypothesis that higher levels of EC are associated with higher levels of neural network specialization and flexibility. Specifically, analyses will determine whether children with higher latent EC, independent of other cognitive abilities, show more negative correlations between task-positive and task-negative neural networks. Finally, the study will examine whether lower gestational age, a well-established risk factor for atypical neural development, EC impairments, and psychopathology, is associated with lower levels of specialization and differentiation of EC and associated neural networks. By accomplishing these objectives, the study will provide a clearer picture of the nature of these critically important EC processes in this key period of transition to between childhood and adolescence. Through its unprecedented integration of complex latent statistical modeling at the behavioral level with network analysis at the level of the brain, the study will also clarify whether the process of EC specialization and segregation may be marker of risk for psychopathology, thereby aligning with NIMH’s mission to develop dimensional classification schemes and pinpoint promising targets for intervention.