http://www.nitrc.org/forum/forum.php?forum_id=5249 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Anatomically-adapted Graph Wavelets for Improved Group-level fMRI Activation Mapping.</b></p> <p>Neuroimage. 2015 Jun 6;</p> <p>Authors: Behjat H, Leonardi N, Sörnmo L, Van De Ville D</p> <p>Abstract<br/> A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar grey matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries of GM are then constructed as a means to implement a GM-based spatial transformation on fMRI data. The proposed approach is evaluated using real as well as semi-synthetic multi-subject data. Compared to SPM and WSPM using classical wavelets, the proposed approach shows superior type-I error control. The results on real data suggest a higher detection sensitivity as well as the capability to capture subtle, connected patterns of brain activity.<br/> </p><p>PMID: 26057594 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sat, 02 May 2026 12:21:12 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator