Functional brain hubs and their test-retest reliability: A multiband resting-state functional MRI study

被引:156
|
作者
Liao, Xu-Hong [1 ,2 ]
Xia, Ming-Rui [3 ,4 ]
Xu, Ting [5 ]
Dai, Zheng-Jia [3 ,4 ]
Cao, Xiao-Yan [1 ,2 ]
Niu, Hai-Jing [3 ,4 ]
Zuo, Xi-Nian [5 ]
Zang, Yu-Feng [1 ,2 ]
He, Yong [3 ,4 ]
机构
[1] Hangzhou Normal Univ, Ctr Cognit & Brain Disorders, Hangzhou 310015, Zhejiang, Peoples R China
[2] Zhejiang Key Lab Res Assessment Cognit Impairment, Hangzhou, Zhejiang, Peoples R China
[3] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
[4] Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China
[5] Chinese Acad Sci, Inst Psychol, Magnet Resonance Imaging Res Ctr, Key Lab Behav Sci,Lab Funct Connectome & Dev, Beijing 100101, Peoples R China
基金
北京市自然科学基金; 中国国家自然科学基金;
关键词
Functional connectivity; Connectome; Graph theory; Default-mode; Global signals; fMRI; CONNECTIVITY MRI; CEREBRAL-CORTEX; GLOBAL SIGNAL; LOW-FREQUENCY; NETWORK; FLUCTUATIONS; ORGANIZATION; ARCHITECTURE; CENTRALITY; REGRESSION;
D O I
10.1016/j.neuroimage.2013.07.058
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Resting-state functional MRI (R-fMRI) has emerged as a promising neuroimaging technique used to identify global hubs of the human brain functional connectome. However, most R-fMRI studies on functional hubs mainly utilize traditional R-fMRI data with relatively low sampling rates (e.g., repetition time [TR] = 2 s). R-fMRI data scanned with higher sampling rates are important for the characterization of reliable functional connectomes because they can provide temporally complementary information about functional integration among brain regions and simultaneously reduce the effects of high frequency physiological noise. Here, we employed a publicly available multiband R-fMRI dataset with a sub-second sampling rate (TR = 645 ms) to identify global hubs in the human voxel-wise functional networks, and further examined their test-retest (TRT) reliability over scanning time. We showed that the functional hubs of human brain networks were mainly located at the default-mode regions (e.g., medial prefrontal and parietal cortex as well as the lateral parietal and temporal cortex) and the sensorimotor and visual cortex. These hub regions were highly anatomically distance-dependent, where short-range and long-range hubs were primarily located at the primary cortex and the multimodal association cortex, respectively. We found that most functional hubs exhibited fair to good TRT reliability using intraclass correlation coefficients. Interestingly, our analysis suggested that a 6-minute scan duration was able to reliably detect these functional hubs. Further comparison analysis revealed that these results were approximately consistent with those obtained using traditional R-fMRI scans of the same subjects with TR = 2500 ms, but several regions (e.g., lateral frontal cortex, paracentral lobule and anterior temporal lobe) exhibited different TRT reliability. Finally, we showed that several regions (including the medial/lateral prefrontal cortex and lateral temporal cortex) were identified as brain hubs in a high frequency band (02-03 Hz), which is beyond the frequency scope of traditional R-fMRI scans. Our results demonstrated the validity of multiband R4MRI data to reliably detect functional hubs in the voxel-wise whole-brain networks, which motivated the acquisition of high temporal resolution R-fMRI data for the studies of human brain functional connectomes in healthy and diseased conditions. (C) 2013 Elsevier Inc. All rights reserved.
引用
下载
收藏
页码:969 / 982
页数:14
相关论文
共 50 条
  • [21] Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective
    Zuo, Xi-Nian
    Xing, Xiu-Xia
    NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, 2014, 45 : 100 - 118
  • [22] Test-retest reliability of time-varying patterns of brain activity across single band and multiband resting-state functional magnetic resonance imaging in healthy older adults
    Cahart, Marie-Stephanie
    Dell'Acqua, Flavio
    Giampietro, Vincent
    Cabral, Joana
    Timmers, Maarten
    Streffer, Johannes
    Einstein, Steven
    Zelaya, Fernando
    Williams, Steven C. R.
    O'Daly, Owen
    FRONTIERS IN HUMAN NEUROSCIENCE, 2022, 16
  • [23] Frequency-Resolved Connectome Hubs and Their Test-Retest Reliability in the Resting Human Brain
    Wang, Lei
    Chen, Xiaodan
    Xu, Yuehua
    Cao, Miao
    Liao, Xuhong
    He, Yong
    NEUROSCIENCE BULLETIN, 2022, 38 (05) : 519 - 532
  • [24] Frequency-Resolved Connectome Hubs and Their Test-Retest Reliability in the Resting Human Brain
    Lei Wang
    Xiaodan Chen
    Yuehua Xu
    Miao Cao
    Xuhong Liao
    Yong He
    Neuroscience Bulletin, 2022, 38 : 519 - 532
  • [25] Test-retest reliability of functional MRI memory laterality indices
    Harrington, GS
    Farias, S
    Alsaadi, T
    ARCHIVES OF CLINICAL NEUROPSYCHOLOGY, 2004, 19 (07) : 912 - 912
  • [26] Test-retest reliability of white matter structural brain networks: a multiband diffusion MRI study
    Zhao, Tengda
    Duan, Fei
    Liao, Xuhong
    Dai, Zhengjia
    Cao, Miao
    He, Yong
    Shu, Ni
    FRONTIERS IN HUMAN NEUROSCIENCE, 2015, 9
  • [27] Test-Retest Reliability of Resting-State Magnetoencephalography Power in Sensor and Source Space
    Martin-Buro, Maria Carmen
    Garces, Pilar
    Maestu, Fernando
    HUMAN BRAIN MAPPING, 2016, 37 (01) : 179 - 190
  • [28] A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity
    Huang, Lijie
    Huang, Taicheng
    Zhen, Zonglei
    Liu, Jia
    SCIENTIFIC DATA, 2016, 3
  • [29] A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity
    Lijie Huang
    Taicheng Huang
    Zonglei Zhen
    Jia Liu
    Scientific Data, 3
  • [30] Test-Retest Reliability of Graph Metrics in Functional Brain Network
    Galazzo, Ilaria Boscolo
    Zumerle, Francesco
    Paolini, Edoardo
    Endrizzi, Walter
    Menegaz, Gloria
    Storti, Silvia F.
    2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 2021, : 343 - 346