Data-driven brain network models predict individual variability in behavior

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 predict individual differences in task performances, to dissociate the effect of targeted stimulation in tasks that differ in cognitive complexity, and to pave the way for the development of personalized therapeutics.

arXiv comment26 pages, 6 figures, 3 tables

References in corpus (6)

• Dynamic reconfiguration of human brain networks during learning
• Controllability of Brain Networks
• The onset of synchronization in large networks of coupled oscillators
• Optimally controlling the human connectome: the role of network topology
• Network inference - with confidence - from multivariate time series
• Personalized brain network models for assessing structure-function relationships

Cited by in corpus (6)

• Cognitive chimera states in human brain networks
• Models of communication and control for brain networks: distinctions, convergence, and future outlook
• Relations between large scale brain connectivity and effects of regional stimulation depend on collective dynamical state
• Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks
• Global excitability and network structure in the human brain
• Network analysis of the human structural connectome including the brainstem