Citation

Shen H, Li Z, Qin J, Liu Q, Lubin W, Zeng LL, Li H, Hu D. Neuroimage 2015; 124(Pt A): 367-378.

Affiliation

College of Mechatronics and Automation, National University of Defense Technology Changsha, Hunan 410073, China. Electronic address: dwhu@nudt.edu.cn.

Copyright

(Copyright © 2015, Elsevier Publishing)

DOI

10.1016/j.neuroimage.2015.09.010

PMID

26363345

Abstract

An increasing number of neuroimaging studies have suggested that the fluctuations of low-frequency resting-state functional connectivity (FC) are not noise but are instead linked to the shift between distinct cognitive states. However, there is very limited knowledge about whether and how the fluctuations of FC at rest are influenced by long-term training and experience. Here, we investigated how the dynamics of resting-state FC are linked to driving behavior by comparing 20 licensed taxi drivers with 20 healthy non-drivers using a sliding window approach. We found that the driving experience could be effectively decoded with 90% (p<0.001) accuracy by the amplitude of low-frequency fluctuations in some specific connections, based on a multivariate pattern analysis technique. Interestingly, the majority of these connections fell within a set of distributed regions named "the vigilance network". Moreover, the decreased amplitude of the FC fluctuations within the vigilance network in the drivers was negatively correlated with the number of years that they had driven a taxi. Furthermore, temporally quasi-stable functional connectivity segmentation revealed significant differences between the drivers and non-drivers in the dwell time of specific vigilance-related transient brain states, although the brain's repertoire of functional states was preserved. Overall, these results suggested a significant link between the changes in the time-dependent aspects of resting-state FC within the vigilance network and long-term driving experiences. The results not only improve our understanding of how the brain supports driving behavior but also shed new light on the relationship between the dynamics of functional brain networks and individual behaviors.

Language: en