Investigation of long-term reproducibility of intrinsic connectivity network mapping: a resting-state fMRI study.
Investigation of long-term reproducibility of intrinsic connectivity network mapping: a resting-state fMRI study.
复制标题
内在连通性网络映射的长期可重复性的研究：一项静止状态fMRI研究。
DOI：
10.3174/ajnr.a2894
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发表时间：
2012-05
期刊：
影响因子：
--
通讯作者：
Chen NK
中科院分区：
文献类型：
--
作者：
Chou YH;Panych LP;Dickey CC;Petrella JR;Chen NK
Connectivity mapping based on resting-state fMRI is rapidly developing and this methodology has great potential for clinical applications. However, before resting-state fMRI can be applied for diagnosis, prognosis, and monitoring treatment for an individual patient with neurologic or psychiatric diseases, it is essential to assess its long-term reproducibility and between-subject variations among healthy individuals. The purpose of the study is to (1) quantify the long-term test-retest reproducibility of intrinsic connectivity network (ICN) measures derived from resting-state fMRI, and (2) assess the between-subject variation of ICN measures across the whole brain. Longitudinal resting-state fMRI data of six healthy volunteers were acquired from nine scan sessions over a period of more than one year. The within-subject reproducibility and between-subject variation of ICN measures, across 1) the whole brain and 2) major nodes of the default mode network, were quantified with intraclass correlation coefficient (ICC) and coefficient of variance (COV). Our data show that the long-term test-retest reproducibility of ICN measures is outstanding, with over 70% of the connectivity networks showing an ICC greater than 0.60. COV across six healthy volunteers in this sample was greater than 0.2, suggesting significant between-subject variation. Our data indicate that resting-state ICN measures (e.g., the correlation coefficients between fMRI signal profiles from two different brain regions) are potentially suitable as biomarkers for monitoring disease progression and treatment effects in clinical trials and individual patients. Because between-subject variation is significant, it may be difficult to use quantitative ICN measures, in their current state, as a diagnostic tool.
