http://www.nitrc.org/forum/forum.php?forum_id=7840 <table border="0" width="100%"><tr><td align="left"/><td align="right"><a href="https://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&amp;cmd=Link&amp;LinkName=pubmed_pubmed&amp;from_uid=28927289">Related Articles</a></td></tr></table> <p><b>Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric fMRI Paradigms I: Revisiting Cluster-Based Inferences.</b></p> <p>Brain Connect. 2017 Sep 19;</p> <p>Authors: Gopinath K, Krishnamurthy V, Sathian K</p> <p>Abstract<br/> In a recent study Eklund et al. (Eklund et al., 2016) employing resting state functional magnetic resonance imaging (rsfMRI) data as a surrogate for null fMRI datasets posited that cluster-wise family-wise error (FWE) rate corrected inferences made using parametric statistical methods in fMRI studies over the past two decades may have been invalid, particularly for cluster defining thresholds (CDTs) less stringent than p &lt; 0.001; principally because the spatial autocorrelation functions (sACF) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggested otherwise (Eklund et al., 2016). Here we show that accounting for non-Gaussian signal components like those arising from resting state neural activity as well as physiological responses and motion artifacts in the null fMRI datasets yields first and second-level GLM analysis residuals with nearly uniform and Gaussian sACF. Further comparison with non-parametric permutation tests indicates that cluster-based FWE corrected inferences made with Gaussian spatial noise approximations are valid.<br/> </p><p>PMID: 28927289 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sat, 02 May 2026 16:25:00 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator