This award supports fundamental research to examine how activity within brain networks allows humans to adapt their behavior in order to achieve goals and complete mental tasks. Such processes within the brain, referred to as cognitive control, are thought to differentiate individuals in terms of mental abilities that are critical for successful navigation in activities of daily life, such as planning, problem solving and reasoning. Current brain imaging methods enable examination of the activity and interactions among brain networks as individuals perform various tasks, thus providing a window into the mechanisms of cognitive control. However, research efforts to date have mostly used imaging data to generate snapshots of brain activity that are averaged across groups of individuals and many different events while performing a task. In this research program, the investigators develop a new form of analysis to characterize the moment-to-moment fluctuations in brain activity, within each individual, as they transition from rest to cognitively demanding task conditions. In particular, efforts will be directed towards the development of a computational model that can predict how brain networks coordinate activity over seconds-level timescales in response to changing task conditions. A key aspect of the effort will be to develop unique models for each individual, drawing from a large database of previously obtained neuroimaging data. In these data, individuals perform a range of tasks requiring different cognitive control strategies, some proactive (sustained) versus others reactive (transient). Thus, application of the model will reveal how the brains of these individuals differentially respond to various types of cognitive demand. The development of this model also provides a unique opportunity for education and outreach; specific efforts will be directed toward the development of a software platform through which members of the public can work with demonstration models to probe and learn about how different patterns of brain activity relate to cognitive function.
Functional neuroimaging has allowed for detailed spatial and temporal characterizations of brain network activation in an effort to elucidate the neural underpinnings of cognitive control. However, such analyses usually rely on static snapshots of neural activation patterns in individual brain regions and/or correlational indices of inter-regional co-activation (i.e., functional connectivity). Further progress in understanding distinctions between cognitive states and cognitive control strategies requires more precise descriptions of the brain dynamics that govern how patterns of neural activity (trajectories) evolve across time. Leveraging recent advancements in optimization theory that allow for reliable high-dimensional parameter estimation, this award will support the validation and parameterization of single-subject dynamical models using high-resolution, long-duration resting-state fMRI data from the Human Connectome Project, which contains data from over 1000 individuals. Subsequent model analysis will characterize individual differences in terms of brain network dynamics, focusing on quantitative metrics of the ruggedness of the attractor landscape (which indicates the diversity of achievable trajectories) and the consequent energetic costs incurred by shifting between cognitive states and strategies. Hypothesis testing will be conducted with a unique follow-up dataset, consisting of a subset of HCP participants and monozygotic (identical) twins (over 100 in total) tracked in multiple neuroimaging sessions, under conditions that systematically manipulate cognitive control strategies.
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.
