Biology subjects

Stiso, J.

Publications and source records attributed to Stiso, J..

2 recordsLinked to original sources

bioRxiv2018

Optimization of Energy State Transition Trajectory Supports the Development of Executive Function During Youth

Executive function develops rapidly during adolescence, and failures of executive function are associated with both risk-taking behaviors and psychopathology. However, it remains relatively unknown how structural brain networks mature during this critical period to facilitate energetically demanding transitions to activate the frontoparietal system, which is critical for executive function. In a sample of 946 human youths (ages 8-23 yr) who completed diffusion imaging as part of the Philadelphia Neurodevelopment Cohort, we capitalized upon recent advances in network control theory in order to calculate the control energy necessary to activate the frontoparietal system given the existing structural network topology. We found that the control energy required to activate the frontoparietal system declined with development. Moreover, we found that this control energy pattern contains sufficient information to make accurate predictions about individuals brain maturity. Finally, the control energy costs of the cingulate cortex were negatively correlated with executive performance, and partially mediated the development of executive performance with age. These results could not be explained by changes in general network control properties or in network modularity. Taken together, our results reveal a mechanism by which structural networks develop during adolescence to facilitate the instantiation of activation states necessary for executive function.\n\nSIGNIFICANCE STATEMENTExecutive function undergoes protracted development during youth, but it is unknown how structural brain networks mature to facilitate the activation of the frontoparietal cortex that is critical for executive processes. Here, we leverage recent advances in network control theory to establish that structural brain networks evolve in adolescence to lower the energetic cost of activating the frontoparietal system. Our results suggest a new mechanistic framework for understanding how brain network maturation supports cognition, with clear implications for disorders marked by executive dysfunction, such as ADHD and psychosis.

neuroscience↗

bioRxiv2018

White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions

Electrical brain stimulation is currently being investigated as a potential therapy for neurological disease. However, opportunities to optimize and personalize such therapies are challenged by the fact that the beneficial impact (and potential side effects) of focal stimulation on both neighboring and distant regions is not well understood. Here, we use network control theory to build a formal model of brain network function that makes explicit predictions about how stimulation spreads through the brains white matter network and influences large-scale dynamics. We test these predictions using combined electrocorticography (ECoG) and diffusion weighted imaging (DWI) data from patients with medically refractory epilepsy undergoing evaluation for resective surgery, and who volunteered to participate in an extensive stimulation regimen. We posit a specific model-based manner in which white matter tracts constrain stimulation, defining its capacity to drive the brain to new states, including states associated with successful memory encoding. In a first validation of our model, we find that the true pattern of white matter tracts can be used to more accurately predict the state transitions induced by direct electrical stimulation than the artificial patterns of a topological or spatial network null model. We then use a targeted optimal control framework to solve for the optimal energy required to drive the brain to a given state. We show that, intuitively, our model predicts larger energy requirements when starting from states that are farther away from a target memory state. We then suggest testable hypotheses about which structural properties will lead to efficient stimulation for improving memory based on energy requirements. We show that the strength and homogeneity of edges between controlled and uncontrolled nodes, as well as the persistent modal controllability of the stimulated region, predict energy requirements. Our work demonstrates that individual white matter architecture plays a vital role in guiding the dynamics of direct electrical stimulation, more generally offering empirical support for the utility of network control theoretic models of brain response to stimulation.

neuroscience↗