Resting-State Functional Brain Connectivity: Lessons from Functional Near-Infrared Spectroscopy

被引:69
|
作者
Niu, Haijing [1 ,2 ]
He, Yong [1 ,2 ]
机构
[1] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
[2] Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China
来源
NEUROSCIENTIST | 2014年 / 20卷 / 02期
基金
北京市自然科学基金;
关键词
connectome; connectomics; small-world; graph theory; network; functional connectivity; fNIRS; INDEPENDENT COMPONENT ANALYSIS; GRAPH-THEORETICAL ANALYSIS; DEFAULT-MODE NETWORK; PHYSIOLOGICAL NOISE; CYTOCHROME-OXIDASE; CONCURRENT FMRI; LIGHT; EEG; INFANTS; TISSUE;
D O I
10.1177/1073858413502707
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Resting-state functional near-infrared spectroscopy (R-fNIRS) is an active area of interest and is currently attracting considerable attention as a new imaging tool for the study of resting-state brain function. Using variations in hemodynamic concentration signals, R-fNIRS measures the brain's low-frequency spontaneous neural activity, combining the advantages of portability, low-cost, high temporal sampling rate and less physical burden to participants. The temporal synchronization of spontaneous neuronal activity in anatomically separated regions is referred to as resting-state functional connectivity (RSFC). In the past several years, an increasing body of R-fNIRS RSFC studies has led to many important findings about functional integration among local or whole-brain regions by measuring inter-regional temporal synchronization. Here, we summarize recent advances made in the R-fNIRS RSFC methodologies, from the detection of RSFC (e.g., seed-based correlation analysis, independent component analysis, whole-brain correlation analysis, and graph-theoretical topological analysis), to the assessment of RSFC performance (e.g., reliability, repeatability, and validity), to the application of RSFC in studying normal development and brain disorders. The literature reviewed here suggests that RSFC analyses based on R-fNIRS data are valid and reliable for the study of brain function in healthy and diseased populations, thus providing a promising imaging tool for cognitive science and clinics.
引用
收藏
页码:173 / 188
页数:16
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