Controllability of structural brain networks

被引:551
|
作者
Gu, Shi [1 ,2 ]
Pasqualetti, Fabio [3 ]
Cieslak, Matthew [4 ]
Telesford, Qawi K. [2 ,5 ]
Yu, Alfred B. [5 ]
Kahn, Ari E. [2 ]
Medaglia, John D. [2 ]
Vettel, Jean M. [4 ,5 ]
Miller, Michael B. [4 ]
Grafton, Scott T. [4 ]
Bassett, Danielle S. [2 ,6 ]
机构
[1] Univ Penn, Dept Appl Math & Computat Sci, Philadelphia, PA 19104 USA
[2] Univ Penn, Dept Bioengn, Philadelphia, PA 19104 USA
[3] Univ Calif Riverside, Dept Mech Engn, Riverside, CA 92521 USA
[4] Univ Calif Santa Barbara, Dept Psychol & Brain Sci, Santa Barbara, CA 93106 USA
[5] Army Res Lab, Translat Neurosci Branch, Aberdeen, MD 20783 USA
[6] Univ Penn, Dept Elect & Syst Engn, Philadelphia, PA 19104 USA
来源
NATURE COMMUNICATIONS | 2015年 / 6卷
基金
美国国家科学基金会;
关键词
RICH-CLUB ORGANIZATION; STATE FUNCTIONAL CONNECTIVITY; RESTING-STATE; COGNITIVE CONTROL; HUMAN CONNECTOME; HUBS; ARCHITECTURE; ATTENTION;
D O I
10.1038/ncomms9414
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.
引用
收藏
页数:10
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