Functional connectomics from resting-state fMRI

被引:679
作者
Smith, Stephen M. [1 ]
Vidaurre, Diego [2 ]
Beckmann, Christian F. [1 ,3 ,4 ]
Glasser, Matthew F. [5 ]
Jenkinson, Mark [1 ]
Miller, Karla L. [1 ]
Nichols, Thomas E. [1 ,6 ,7 ]
Robinson, Emma C. [1 ]
Salimi-Khorshidi, Gholamreza [1 ]
Woolrich, Mark W. [1 ,2 ]
Barch, Deanna M. [5 ]
Ugurbil, Kamil [8 ]
Van Essen, David C. [5 ]
机构
[1] Univ Oxford, Oxford Ctr Funct MRI Brain FMRIB, Oxford, England
[2] Univ Oxford, Oxford Ctr Human Brain Act OHBA, Oxford, England
[3] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, NL-6525 ED Nijmegen, Netherlands
[4] Univ Twente, MIRA Inst Biomed Technol & Tech Med, NL-7500 AE Enschede, Netherlands
[5] Washington Univ, Sch Med, St Louis, MO USA
[6] Univ Warwick, Dept Stat, Coventry CV4 7AL, W Midlands, England
[7] Univ Warwick, WMG, Coventry CV4 7AL, W Midlands, England
[8] Univ Minnesota, Sch Med, Ctr Magnet Resonance Res, Minneapolis, MN 55455 USA
基金
英国惠康基金;
关键词
DIFFUSION MRI; INTRINSIC BRAIN; CONNECTIVITY; PARCELLATION; PROJECT; NETWORKS; CORTEX; INFERENCE; DYNAMICS; PROMISE;
D O I
10.1016/j.tics.2013.09.016
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI (rfMRI) for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image-processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project and highlight some upcoming challenges in functional connectomics.
引用
收藏
页码:666 / 682
页数:17
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