Detection of functional brain network reconfiguration during task-driven cognitive states

被引:117
|
作者
Telesford, Qawi K. [1 ,2 ]
Lynall, Mary-Ellen [3 ,4 ]
Vettel, Jean [2 ,4 ]
Miller, Michael B. [4 ]
Grafton, Scott T. [4 ]
Bassett, Danielle S. [1 ,5 ]
机构
[1] Univ Penn, Dept Bioengn, 210 S 33rd St,240, Philadelphia, PA 19104 USA
[2] US Army, Res Lab, Aberdeen Proving Ground, MD 21001 USA
[3] Univ Cambridge, Dept Psychiat, Cambridge, England
[4] Univ Calif Santa Barbara, Dept Psychol & Brain Sci, Santa Barbara, CA 93106 USA
[5] Univ Penn, Dept Elect & Syst Engn, Philadelphia, PA 19104 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
RESTING-STATE; DYNAMIC RECONFIGURATION; COMMUNITY STRUCTURE; CONNECTIVITY; TRACKING; CORTEX; PATTERNS; AMYGDALA;
D O I
10.1016/j.neuroimage.2016.05.078
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Network science offers computational tools to elucidate the complex patterns of interactions evident in neuroimaging data. Recently, these tools have been used to detect dynamic changes in network connectivity that may occur at short time scales. The dynamics of fMRI connectivity, and how they differ across time scales, are far from understood. A simple way to interrogate dynamics at different time scales is to alter the size of the time window used to extract sequential (or rolling) measures of functional connectivity. Here, in n = 82 participants performing three distinct cognitive visual tasks in recognition memory and strategic attention, we subdivided regional BOLD time series into variable sized time windows and determined the impact of time window size on observed dynamics. Specifically, we applied a multilayer community detection algorithm to identify temporal communities and we calculated network flexibility to quantify changes in these communities over time. Within our frequency band of interest, large and small windows were associated with a narrow range of network flexibility values across the brain, while medium time windows were associated with a broad range of network flexibility values. Using medium time windows of size 75-100 s, we uncovered brain regions with low flexibility (considered core regions, and observed in visual and attention areas) and brain regions with high flexibility (considered periphery regions, and observed in subcortical and temporal lobe regions) via comparison to appropriate dynamic network null models. Generally, this work demonstrates the impact of time window length on observed network dynamics during task performance, offering pragmatic considerations in the choice of time window in dynamic network analysis. More broadly, this work reveals organizational principles of brain functional connectivity that are not accessible with static network approaches. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:188 / 200
页数:13
相关论文
共 50 条
  • [1] The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance
    Shine, James M.
    Bissett, Patrick G.
    Bell, Peter T.
    Koyejo, Oluwasanmi
    Balsters, Joshua H.
    Gorgolewski, Krzysztof J.
    Moodie, Craig A.
    Poldrack, Russell A.
    NEURON, 2016, 92 (02) : 544 - 554
  • [2] Reconfiguration of the Brain Functional Network Associated with Visual Task Demands
    Wen, Xue
    Zhang, Delong
    Liang, Bishan
    Zhang, Ruibin
    Wang, Zengjian
    Wang, Junjing
    Liu, Ming
    Huang, Ruiwang
    PLOS ONE, 2015, 10 (07):
  • [3] Task-Driven Saliency Detection on Music Video
    Numano, Shunsuke
    Enami, Naoko
    Ariki, Yasuo
    COMPUTER VISION - ACCV 2014 WORKSHOPS, PT II, 2015, 9009 : 658 - 671
  • [4] Towards a task-driven framework for multimodal fatigue analysis during physical and cognitive tasks
    Tsiakas, Konstantinos
    Papakostas, Michalis
    Ford, James C.
    Makedon, Fillia
    5TH INTERNATIONAL WORKSHOP ON SENSOR-BASED ACTIVITY RECOGNITION AND INTERACTION (IWOAR 2018), 2018,
  • [5] Multiple Task-driven Face Detection Based on Super-resolution Pyramid Network
    Li, Jianjun
    Wang, Juxian
    Chen, Xingchen
    Luo, Zhenxing
    Song, Zhugang
    JOURNAL OF INTERNET TECHNOLOGY, 2019, 20 (04): : 1263 - 1272
  • [6] Functional brain network reconfiguration during learning in a dynamic environment
    Chang-Hao Kao
    Ankit N. Khambhati
    Danielle S. Bassett
    Matthew R. Nassar
    Joseph T. McGuire
    Joshua I. Gold
    Joseph W. Kable
    Nature Communications, 11
  • [7] Functional brain network reconfiguration during learning in a dynamic environment
    Kao, Chang-Hao
    Khambhati, Ankit N.
    Bassett, Danielle S.
    Nassar, Matthew R.
    McGuire, Joseph T.
    Gold, Joshua, I
    Kable, Joseph W.
    NATURE COMMUNICATIONS, 2020, 11 (01)
  • [8] A task-driven network for mesh classification and semantic part segmentation
    Dong, Qiujie
    Gong, Xiaoran
    Xu, Rui
    Wang, Zixiong
    Gao, Junjie
    Chen, Shuangmin
    Xin, Shiqing
    Tu, Changhe
    Wang, Wenping
    COMPUTER AIDED GEOMETRIC DESIGN, 2024, 111
  • [9] Spotlight Modeling proprioception with task-driven neural network models
    Scherberger, Hansjorg
    NEURON, 2024, 112 (09) : 1384 - 1386
  • [10] Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states
    Zhang, Heming
    Meng, Chun
    Di, Xin
    Wu, Xiao
    Biswal, Bharat
    NETWORK NEUROSCIENCE, 2023, 7 (03) : 1034 - 1050