Hierarchical topographic factor analysis

Jeremy R. Manning, Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, David M. Blei
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sj3yvqxaz5d3hi5ckwqyl2p3ja" style="color: black;">2014 International Workshop on Pattern Recognition in Neuroimaging</a> </i> &nbsp;
Recent work has revealed that cognitive processes are often reflected in patterns of functional connectivity throughout the brain (for review see [16] ). However, examining functional connectivity patterns using traditional methods carries a substantial computational burden (of computing time and memory). Here we present a technique, termed Hierarchical Topographic Factor Analysis, for efficiently discovering brain networks in large multisubject neuroimaging datasets.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/prni.2014.6858530">doi:10.1109/prni.2014.6858530</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/prni/ManningRKTCNB14.html">dblp:conf/prni/ManningRKTCNB14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/caxnzat47baqxghvgtqu7iyzue">fatcat:caxnzat47baqxghvgtqu7iyzue</a> </span>
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