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RT Journal Article
SR Electronic
T1 Investigation of Long-Term Reproducibility of Intrinsic Connectivity Network Mapping: A Resting-State fMRI Study
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
DO 10.3174/ajnr.A2894
A1 Chou, Y.-h.
A1 Panych, L.P.
A1 Dickey, C.C.
A1 Petrella, J.R.
A1 Chen, N.-k.
YR 2012
UL http://www.ajnr.org/content/early/2012/01/19/ajnr.A2894.abstract
AB BACKGROUND AND PURPOSE: 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 was to quantify the long-term test-retest reproducibility of ICN measures derived from resting-state fMRI and to assess the between-subject variation of ICN measures across the whole brain. MATERIALS AND METHODS: Longitudinal resting-state fMRI data of 6 healthy volunteers were acquired from 9 scan sessions during &gt;1 year. The within-subject reproducibility and between-subject variation of ICN measures, across the whole brain and major nodes of the DMN, were quantified with the ICC and COV. RESULTS: Our data show that the long-term test-retest reproducibility of ICN measures is outstanding, with &gt;70% of the connectivity networks showing an ICC &gt; 0.60. The COV across 6 healthy volunteers in this sample was &gt;0.2, suggesting significant between-subject variation. CONCLUSIONS: Our data indicate that resting-state ICN measures (eg, the correlation coefficients between fMRI signal-intensity profiles from 2 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. Abbreviations COVcoefficient of varianceDMNdefault mode networkICCintraclass correlation coefficientICNintrinsic connectivity networkIPCinferior parietal cortexITCinferior temporal cortexMPFCmedial prefrontal cortexMTGmiddle temporal gyrusPCCposterior cingulate cortexPHCparahippocampal cortexSFCsuperior frontal cortexVACCventral anterior cingulate cortex