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Journal article
Data-driven brain network models differentiate variability across language tasks
PLoS computational biology, v 14(10), pp e1006487-e1006487
01 Oct 2018
PMID: 30332401
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Data-driven brain network models differentiate variability across language tasks
- Creators
- Kanika Bansal - University at Buffalo, State University of New YorkJohn D. Medaglia - Drexel UniversityDanielle S. Bassett - University of PennsylvaniaJean M. Vettel - United States Army Research LaboratorySarah F. Muldoon - University at Buffalo, State University of New York
- Publication Details
- PLoS computational biology, v 14(10), pp e1006487-e1006487
- Publisher
- Public Library Science
- Number of pages
- 25
- Grant note
- Alfred P. Sloan Foundation MacArthur Foundation 1-DP5 OD021352-01 / National Institute of Health through the National Institute of Mental Health
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000450712400024
- Scopus ID
- 2-s2.0-85055170094
- Other Identifier
- 991019168210804721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biochemical Research Methods
- Mathematical & Computational Biology