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PLoS Comput Biol. 2016 Sep 9;12(9):e1005076. doi: 10.1371/journal.pcbi.1005076. eCollection 2016 Sep.

Stimulation-Based Control of Dynamic Brain Networks.

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Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
US Army Research Laboratory, Aberdeen Proving Ground, Maryland, United States of America.
Department of Mathematics and Computational and Data-Enabled Science and Engineering Program, University at Buffalo, SUNY, Buffalo, New York, United States of America.
Department of Mechanical Engineering, University of California, Riverside, Riverside, California, United States of America.
Applied Mathematics and Computational Science Graduate Program, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America.
Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.


The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement.

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