http://www.nitrc.org/forum/forum.php?forum_id=7907 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes.</b></p> <p>Hum Brain Mapp. 2017 Oct 11;:</p> <p>Authors: Chen X, Lu B, Yan CG</p> <p>Abstract<br/> Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability/replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability &lt; 0.3 for between-subject sex differences, &lt; 0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., &lt; 80 [40 per group]) not only minimized power (sensitivity &lt; 2%), but also decreased the likelihood that significant results reflect "true" effects (PPV &lt; 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility. Hum Brain Mapp 00:000-000, 2017. © 2017 Wiley Periodicals, Inc.<br/> </p><p>PMID: 29024299 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sat, 02 May 2026 7:41:51 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator