http://www.nitrc.org/forum/forum.php?forum_id=8733 <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=29948907">Related Articles</a></td></tr></table> <p><b>Encoding the local connectivity patterns of fMRI for cognitive task and state classification.</b></p> <p>Brain Imaging Behav. 2018 Jun 15;:</p> <p>Authors: Onal Ertugrul I, Ozay M, Yarman Vural FT</p> <p>Abstract<br/> In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.<br/> </p><p>PMID: 29948907 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Mon, 27 Apr 2026 1:18:40 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator