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<feed xmlns="http://www.w3.org/2005/Atom"><title>UH Biocomputation Group - Network neuro</title><link href="http://biocomputation.herts.ac.uk/" rel="alternate"/><link href="http://biocomputation.herts.ac.uk/feeds/tags/network-neuro.atom.xml" rel="self"/><id>http://biocomputation.herts.ac.uk/</id><updated>2020-12-02T13:11:34+00:00</updated><entry><title>Cliques and cavities in human connectome</title><link href="http://biocomputation.herts.ac.uk/2020/12/02/cliques-and-cavities-in-human-connectome.html" rel="alternate"/><published>2020-12-02T13:11:34+00:00</published><updated>2020-12-02T13:11:34+00:00</updated><author><name>Emil Dmitruk</name></author><id>tag:biocomputation.herts.ac.uk,2020-12-02:/2020/12/02/cliques-and-cavities-in-human-connectome.html</id><summary type="html">&lt;p class="first last"&gt;Emil Dmitruk's Journal Club session where he will talk about a paper &amp;quot;Cliques and cavities in human connectome&amp;quot;.&lt;/p&gt;
</summary><content type="html">&lt;p&gt;This week on Journal Club session Emil Dmitruk will talk about a paper &amp;quot;Cliques and cavities in human connectome&amp;quot;.&lt;/p&gt;
&lt;hr class="docutils" /&gt;
&lt;p&gt;Encoding brain regions and their connections as a network of nodes and edges
captures many of the possible paths along which information can be transmitted
as humans order relations, concepts naturally expressed in the language of
algebraic topology. These tools can be used to study mesoscale network
structures that arise from the arrangement of densely connected substructures
called cliques in otherwise sparsely connected brain networks. We detect cliques
(all-to-all connected sets of brain regions) in the average structural
connectomes of 8 healthy adults scanned in triplicate and discover the presence
of more large cliques than expected in null networks constructed via wiring
minimization, providing architecture through which brain network can perform
rapid, local processing. We then locate topological cavities of different
dimensions, around which information may flow in either diverging or converging
patterns. These cavities exist consistently across subjects, differ from those
observed in null model networks, and - importantly - link regions of early and
late evolutionary origin in long loops, underscoring their unique role in
controlling brain function. These results offer a first demonstration that
techniques from algebraic topology offer a novel perspective on structural
connectomics, highlighting loop-like paths as crucial features in the human
brain’s structural architecture.&lt;/p&gt;
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&lt;div class="line"&gt;&lt;br /&gt;&lt;/div&gt;
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&lt;p&gt;Papers:&lt;/p&gt;
&lt;ul class="simple"&gt;
&lt;li&gt;Ann E. Sizemore, Chad Giusti, Ari Kahn, Jean M. Vettel, Richard F. Betzel1, Danielle S. Bassett &lt;a class="reference external" href="https://doi.org/10.1007/s10827-017-0672-6"&gt;&amp;quot;Cliques and cavities in human connectome&amp;quot;&lt;/a&gt; , J Comput Neurosci 44, 115–145 (2018).&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Date:&lt;/strong&gt; 04/12/2020 &lt;br /&gt;
&lt;strong&gt;Time:&lt;/strong&gt; 16:00 &lt;br /&gt;
&lt;strong&gt;Location&lt;/strong&gt;: online&lt;/p&gt;
</content><category term="Seminars"/><category term="Applied topology"/><category term="Persistent homology"/><category term="Network neuro"/><category term="applied topology"/><category term="network neuroscience"/><category term="persistent homology"/></entry></feed>