http://www.nitrc.org/forum/forum.php?forum_id=2708 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Enhancing reproducibility of fMRI statistical maps using generalized canonical correlation analysis in NPAIRS framework.</b></p> <p>Neuroimage. 2012 Feb 14;</p> <p>Authors: Afshin-Pour B, Hossein-Zadeh GA, Strother SC, Soltanian-Zadeh H</p> <p>Abstract<br/> Common fMRI data processing techniques usually minimize a temporal cost function or fit a temporal model to extract an activity map. Here, we focus on extracting a highly, spatially reproducible statistical parametric map (SPM) from fMRI data using a cost function that does not depend on a model of the subjects' temporal response. Based on a generalized version of canonical correlation analysis (gCCA), we propose a method to extract a highly reproducible map by maximizing the sum of pair-wise correlations between some maps. In a group analysis, each map is calculated from a linear combination of fMRI scans of a subset of subjects under study. The proposed method is applied to BOLD fMRI datasets without any spatial smoothing from 10 subjects performing a simple reaction time (RT) task. Using the NPAIRS split-half resampling framework with a reproducibility measure based on SPM correlations, we compare the proposed approach with canonical variate analysis (CVA) and a simple general linear model (GLM). gCCA provides statistical parametric maps with higher reproducibility than CVA and GLM with correlation reproducibilities across independent split-half SPMs of 0.78, 0.46, and 0.41, respectively. Our results show that gCCA is an efficient approach for extracting the default mode network, assessing brain connectivity, and processing event-related and resting-state datasets in which the temporal BOLD signal varies from subject to subject.<br/> </p><p>PMID: 22366080 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sun, 26 Apr 2026 9:02:24 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator