CRCNS: Functional Brain Networks with Tensioned Stability for Optimal Processing

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Understanding the brain processes underlying alcohol use and misuse are essential for the development of effective treatments for alcohol use disorder or AUD. Human brain imaging has greatly contributed to our current understanding of AUD, but much more remains to be understood. Most recently, human neuroscience has been transformed by the integration of network science and neuroimaging (now coined network neuroscience). Functional brain imaging is used to generate networks to examine interconnected groups of synchronized brain regions. The overarching hypothesis of this project is that brain synchronization is only half of the brain network story. This work asserts that functional brain networks actually have two critical sublayers. The first layer is the well-established network of synchronization that is identified using correlation methods, called the cooperative functional network (cFN). The second layer is a proposed network that resists synchronization and is called the impervious functional network (iFN). The iFN is essential for shifting synchronization within and between subnetworks to support shifts in cognitive demands. The two layers coexist in tension, each playing their own role to support stable, yet flexible, brain function. This project combines mathematical modeling and simulation with application of the methods to predict alcohol drinking behavior in National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) data set. Success of this project will demonstrate that both cFN and iFN are essential components of a more complete understanding of normal and abnormal brain function. There is a desperate need for a deeper understanding of alcohol use/misuse and for better diagnosis and treatment of AUD. Unfortunately, potential biomarkers and treatment trials targeting these conditions have continually failed. This project could have transformative potential as it brings forth a previously undiscovered organizational principle of the brain, and could lead to new diagnostic and therapeutic strategies designs based on the combined knowledge of the cFN and iFN. In addition to transforming our understanding of AUD, this work has the potential to revolutionize clinical studies and the care of patients with a range of brain disorders.