http://www.nitrc.org/forum/forum.php?forum_id=4913 <table border="0" width="100%"><tr><td align="left"><a href="http://linkinghub.elsevier.com/retrieve/pii/S1053-8119(15)00153-6"><img src="//www.ncbi.nlm.nih.gov/corehtml/query/egifs/http:--linkinghub.elsevier.com-ihub-images-PubMedLink.gif" border="0"/></a> </td><td align="right"><a href="http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&amp;cmd=Link&amp;LinkName=pubmed_pubmed&amp;from_uid=25743045">Related Articles</a></td></tr></table> <p><b>Enhanced identification of BOLD-like components with multi-echo simultaneous multi-slice (MESMS) fMRI and multi-echo ICA.</b></p> <p>Neuroimage. 2015 Mar 2;</p> <p>Authors: Olafsson V, Kundu P, Wong EC, Bandettini PA, Liu TT</p> <p>Abstract<br/> The recent introduction of simultaneous multi-slice (SMS) acquisitions has enabled the acquisition of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data with significantly higher temporal sampling rates. In a parallel development, the use of multi-echo fMRI acquisitions in conjunction with a multi-echo independent component analysis (ME-ICA) approach has been introduced as a means to automatically distinguish functionally-related BOLD signal components from signal artifacts, with significant gains in sensitivity, statistical power, and specificity. In this work, we examine the gains that can be achieved with a combined approach in which data obtained with a multi-echo simultaneous multi-slice (MESMS) acquisition are analyzed with ME-ICA. We find that ME-ICA identifies significantly more BOLD-like components in the MESMS data as compared to data acquired with a conventional multi-echo single-slice acquisition. We demonstrate that the improved performance of MESMS derives from both an increase in the number of temporal samples and the enhanced ability to filter out high-frequency artifacts.<br/> </p><p>PMID: 25743045 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sat, 02 May 2026 2:38:32 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator