http://www.nitrc.org/forum/forum.php?forum_id=7157 <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=28214528">Related Articles</a></td></tr></table> <p><b>Adaptive Independent Vector Analysis for Multi-Subject Complex-Valued fMRI Data.</b></p> <p>J Neurosci Methods. 2017 Feb 15;:</p> <p>Authors: Kuang LD, Lin QH, Gong XF, Cong F, Calhoun VD</p> <p>Abstract<br/> BACKGROUND: Complex-valued fMRI data can provide additional insights beyond magnitude-only data. However, independent vector analysis (IVA), which has exhibited great potential for group analysis of magnitude-only fMRI data, has rarely been applied to complex-valued fMRI data. The main challenges in this application include the extremely noisy nature and large variability of the source component vector (SCV) distribution.<br/> NEW METHOD: To address these challenges, we propose an adaptive fixed-point IVA algorithm for analyzing multiple-subject complex-valued fMRI data. We exploited a multivariate generalized Gaussian distribution (MGGD)- based nonlinear function to match varying SCV distributions in which the MGGD shape parameter was estimated using maximum likelihood estimation. To achieve our de-noising goal, we updated the MGGD-based nonlinearity in the dominant SCV subspace, and employed a post-IVA de-noising strategy based on phase information in the IVA estimates. We also incorporated the pseudo-covariance matrix of fMRI data into the algorithm to emphasize the noncircularity of complex-valued fMRI sources.<br/> RESULTS: from simulated and experimental fMRI data demonstrated the efficacy of our method. Comparison with Existing Method(s) Our approach exhibited significant improvements over typical complex-valued IVA algorithms, especially during higher noise levels and larger spatial and temporal changes. As expected, the proposed complex-valued IVA algorithm detected more contiguous and reasonable activations than the magnitude-only method for task-related (393%) and default mode (301%) spatial maps.<br/> CONCLUSIONS: The proposed approach is suitable for decomposing multi-subject complex-valued fMRI data, and has great potential for capturing additional subject variability.<br/> </p><p>PMID: 28214528 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Wed, 06 May 2026 13:31:16 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator