Executive functions, and in particular cognitive control functions, contribute to or are affected by numerous psychiatric and neurological conditions. Understanding how brain network dynamics support cognitive control function is crucial for clarifying the basis of resilience to injury and identifying opportunities for substantive advancements in intervention. While network science (e.g., graph theory) has led to enlightenment in the organization of the brain and basis of human cognition, elucidating translational implications requires an explicit focus. I propose to do so.
I aim to apply recent innovations in dynamic network analysis (recent extensions of graph theory) and network control theory in neuroimaging data to examine the basis of cognitive control function in health and dysfunction in stroke. The program integrates approaches from cognitive neuroscience, network science, and control theory. The goal is to produce a theoretical advance in the use of noninvasive brain stimulation treatments for cognitive dysfunction.
The specific aims for this project are to: 1) Quantify structural and dynamic brain network properties underlying cognitive control function in health and dysfunction following stroke 2) Use network control theory to intervene in brain networks that support cognitive control There are two main components of this project: (1) the analysis of network structure and function underlying adaptive cognitive control and (2) the use of network control theory applied to diffusion tractography data to (a) discriminate between network mechanisms of cognitive control and (b) facilitate cognitive control recovery in individuals that have suffered from stroke. This would provide a substantial advance in our knowledge of how cognitive control processes exert their influences across brain networks. While some research has begun to emerge in this area, I propose to use state of the art techniques within dynamic network analysis in conjunction with well-validated behavioral measures. This will serve as an important benchmark for work outside of the current application. It will also begin to characterize reference states underlying adaptive task performance that will be used to guide later control theory-based approaches to brain stimulation. Here, network control theory will be used to target noninvasive brain stimulation on an individual basis. This could lead to a substantive advance in our understanding of the variance in responsiveness to noninvasive brain stimulation and lead to a control theory based framework for intervention in cognitive control dysfunction. More broadly, the outcome this work will provide a step toward true integration between network neuroscience and systems engineering-based translation in neurological and psychiatric populations. These fields are developing rapidly, but an explicit focus on cognition and integration with the physical sciences will be required to conceptualize potent opportunities for intervention. This project offers the first opportunity to establish this intersection and promote a new interdisciplinary conversation between the fields represented.
Executive functions (here, cognitive control) are critical to a wide range of human behavior and are disrupted in a wide range of psychiatric and neurological disorders. However, the brain network mechanisms of cognitive control remain poorly elucidated. In addition, the implications of these mechanisms for translational efforts to remediate cognitive function have not been explored. In this application, I aim to use state of the art techniques in dynamic network neuroscience to examine the basis cognitive control in the human brain. I also aim to test a novel translational framework to remediate cognitive control dysfunction using network control theory.
|Medaglia, John D (2017) Graph Theoretic Analysis of Resting State Functional MR Imaging. Neuroimaging Clin N Am 27:593-607|
|Medaglia, John D (2017) Functional Neuroimaging in Traumatic Brain Injury: From Nodes to Networks. Front Neurol 8:407|
|Medaglia, John D; Huang, Weiyu; Segarra, Santiago et al. (2017) Brain network efficiency is influenced by the pathologic source of corticobasal syndrome. Neurology 89:1373-1381|
|Medaglia, John Dominic; Pasqualetti, Fabio; Hamilton, Roy H et al. (2017) Brain and cognitive reserve: Translation via network control theory. Neurosci Biobehav Rev 75:53-64|
|Betzel, Richard F; Gu, Shi; Medaglia, John D et al. (2016) Optimally controlling the human connectome: the role of network topology. Sci Rep 6:30770|