Characterizing cognitive control networks using a precision neuroscience approach

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Impairments in cognitive control are central to many mental health disorders (McTeague et al., 2017). In parallel, there is mounting evidence from a range of neuroimaging studies implicating impairments of network computations in disorders of mental health (Fornito et al., 2015). A crucial ‘missing piece’ bridging these two aspects of brain function is a relatively poor understanding of the way in which the network-level computations of the brain relate to cognitive control processes, and the precise ways in which these relationships fluctuate and unfold over weeks and months in each individual. Before we can understand fluctuations in the trajectories of mental illnesses, we need to first understand the temporal variability of healthy individuals over time. “Recent ‘dense-scanning’ datasets that acquire substantially more data per subject provide a potential solution to this challenge, but these studies have lacked width (they include few subjects, e.g., 4-10) and breadth (they focus on individual tasks/states, often the ‘resting state’). We will overcome these shortcoming with a dataset scanning 55 subjects each for a total 12 hours over the course of 6 months on 8 unique tasks that span multiple constructs of cognitive control (working memory, attention, set shifting, inhibition, and performance monitoring). The resultant dataset will be wide (i.e. multiple subjects per task), broad (e.g. multiple tasks per construct) and deep (e.g. multiple repetitions of each task over time). This precision neuroscience approach allows us to identify global and local changes in neural networks that are necessary both (a) in preparation for fast, effective controlled performance, and (b) to support flexible post-error and post-conflict control adjustments to improve subsequent performance. Once we have identified these behavioral and neural network signatures of cognitive control that are reproducible across task, construct, session, we will leverage this information in a novel ‘targeted network attack’ procedure to engineer breakdowns in the network architecture by precision challenges to the cognitive system. Tailored combinations of tasks that rely on overlapping network architectures will be combined to identify specific network features that are ripe for failure in healthy subjects, and as such, represent likely nodes for subsequent failure in disease. Together, this work will uncover novel links between cognitive control and functional brain network architecture across tasks, constructs, and sessions (Aim 1) that are essential for effective and flexible behavior (Aim 2) and are likely to fail across diverse disease states (Aim 3). Our precision neuroscience approach relates closely to the precision medicine initiative at the NIH, as our deep-scanning procedure allows us to identify subject-level network features necessary for effective cognitive control. In addition, by making the data openly accessible to other researchers, we expect these data sets will become an incomparably rich source of information for those studying the essential link between cognitive control and network-level computations.