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
相关论文
共 50 条
  • [41] Optimizing Driver Nodes for Structural Controllability of Temporal Networks
    Srighakollapu, Manikya Valli
    Kalaimani, Rachel Kalpana
    Pasumarthy, Ramkrishna
    IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2022, 9 (01): : 380 - 389
  • [42] Towards a graphic tool of structural controllability of temporal networks
    Pan, Yujian
    Li, Xiang
    2014 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2014, : 1784 - 1787
  • [43] Structural Controllability of Linear Dynamical Networks with Homogeneous Subsystems
    Xue, Mengran
    Roy, Sandip
    IFAC PAPERSONLINE, 2019, 52 (03): : 25 - 30
  • [44] Structural Controllability of Temporal Networks with a Single Switching Controller
    Yao, Peng
    Hou, Bao-Yu
    Pan, Yu-Jian
    Li, Xiang
    PLOS ONE, 2017, 12 (01):
  • [45] On the structural output controllability and functional observability of undirected networks
    Zhang, Yuan
    Cheng, Ranbo
    Xia, Yuanqing
    AUTOMATICA, 2025, 173
  • [46] On the Priority Maximum Matching of Structural Controllability of Temporal Networks
    Pan Yujian
    Li Xiang
    Zhan Jingyuan
    2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 1164 - 1169
  • [47] Structural controllability and reducibility of RLC networks with bipolar transistor
    Feng, XY
    Lu, KS
    Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9, 2005, : 1015 - 1020
  • [48] Towards structural controllability of local-world networks
    Sun, Shiwen
    Ma, Yilin
    Wu, Yafang
    Wang, Li
    Xia, Chengyi
    PHYSICS LETTERS A, 2016, 380 (22-23) : 1912 - 1917
  • [49] Effects of Edge Directions on the Structural Controllability of Complex Networks
    Xiao, Yandong
    Lao, Songyang
    Hou, Lvlin
    Small, Michael
    Bai, Liang
    PLOS ONE, 2015, 10 (08):
  • [50] Effects Of Symmetry On The Structural Controllability Of Neural Networks: A Perspective
    Whalen, Andrew J.
    Brennan, Sean N.
    Sauer, Timothy D.
    Schiff, Steven J.
    2016 AMERICAN CONTROL CONFERENCE (ACC), 2016, : 5785 - 5790