http://www.nitrc.org/forum/forum.php?forum_id=5951 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Evaluation of Second-Level Inference in fMRI Analysis.</b></p> <p>Comput Intell Neurosci. 2016;2016:1068434</p> <p>Authors: Roels SP, Loeys T, Moerkerke B</p> <p>Abstract<br/> We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference. <br/> </p><p>PMID: 26819578 [PubMed - in process]</p> en-us Copyright 2000-2026 NITRC OSI Mon, 04 May 2026 8:12:04 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator