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PLoS Comput Biol. 2018 Oct 17;14(10):e1006487. doi: 10.1371/journal.pcbi.1006487. eCollection 2018 Oct.

Data-driven brain network models differentiate variability across language tasks.

Bansal K1,2,3, Medaglia JD4,5, Bassett DS6,7,5,8, Vettel JM2,6,9, Muldoon SF1,10.

Author information

1
Department of Mathematics, University at Buffalo - SUNY, Buffalo, New York, United States of America.
2
Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland, United States of America.
3
Department of Biomedical Engineering, Columbia University, New York, New York, United States of America.
4
Department of Psychology, Drexel University, Philadelphia, Pennsylvania, United States of America.
5
Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
6
Department of Biomedical Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
7
Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
8
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
9
Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America.
10
Computational and Data-Enabled Science and Engineering Program, University at Buffalo - SUNY, Buffalo, New York, United States of America.

Abstract

The relationship between brain structure and function has been probed using a variety of approaches, but how the underlying structural connectivity of the human brain drives behavior is far from understood. To investigate the effect of anatomical brain organization on human task performance, we use a data-driven computational modeling approach and explore the functional effects of naturally occurring structural differences in brain networks. We construct personalized brain network models by combining anatomical connectivity estimated from diffusion spectrum imaging of individual subjects with a nonlinear model of brain dynamics. By performing computational experiments in which we measure the excitability of the global brain network and spread of synchronization following a targeted computational stimulation, we quantify how individual variation in the underlying connectivity impacts both local and global brain dynamics. We further relate the computational results to individual variability in the subjects' performance of three language-demanding tasks both before and after transcranial magnetic stimulation to the left-inferior frontal gyrus. Our results show that task performance correlates with either local or global measures of functional activity, depending on the complexity of the task. By emphasizing differences in the underlying structural connectivity, our model serves as a powerful tool to assess individual differences in task performances, to dissociate the effect of targeted stimulation in tasks that differ in cognitive demand, and to pave the way for the development of personalized therapeutics.

PMID:
30332401
PMCID:
PMC6192563
DOI:
10.1371/journal.pcbi.1006487
[Indexed for MEDLINE]
Free PMC Article

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