http://www.nitrc.org/forum/forum.php?forum_id=2499 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Investigation of Long-Term Reproducibility of Intrinsic Connectivity Network Mapping: A Resting-State fMRI Study.</b></p> <p>AJNR Am J Neuroradiol. 2012 Jan 19;</p> <p>Authors: Chou YH, Panych LP, Dickey CC, Petrella JR, Chen NK</p> <p>Abstract<br/> 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.<br/> </p><p>PMID: 22268094 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sun, 26 Apr 2026 1:31:55 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator