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Hierarchical information-based clustering for connectivity-based cortex parcellation
One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area ("the functional fingerprint") is closely related to its anatomical connections...
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Published in: | Frontiers in neuroinformatics 2011-09, Vol.5, p.18-18 |
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creator | Gorbach, Nico S Schütte, Christoph Melzer, Corina Goldau, Mathias Sujazow, Olivia Jitsev, Jenia Douglas, Tania Tittgemeyer, Marc |
description | One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area ("the functional fingerprint") is closely related to its anatomical connections ("the connectional fingerprint") and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation and requires clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several non-trivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as premotoric areas and those associated with pre-frontal areas. |
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Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation and requires clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several non-trivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as premotoric areas and those associated with pre-frontal areas.</description><identifier>ISSN: 1662-5196</identifier><identifier>EISSN: 1662-5196</identifier><identifier>DOI: 10.3389/fninf.2011.00018</identifier><identifier>PMID: 21977015</identifier><language>eng</language><publisher>Switzerland: Frontiers Research Foundation</publisher><subject>Brain architecture ; Brain research ; Clustering ; cortex parcellation ; diffusion tractography ; Frontal gyrus ; hierarchical clustering ; Information Theory ; Localization ; Neural networks ; Neuroscience ; Precentral gyrus</subject><ispartof>Frontiers in neuroinformatics, 2011-09, Vol.5, p.18-18</ispartof><rights>2011. Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the associated terms available at https://www.frontiersin.org/articles/10.3389/fninf.2011.00018 .</rights><rights>Copyright © 2011 Gorbach, Schütte, Melzer, Goldau, Sujazow, Jitsev, Douglas and Tittgemeyer. 2011</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4748-b990de8b6aff2ed492a8de536c5fbeee7934abe7df03121499911b037435eaa43</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2295472890/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2295472890?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25731,27901,27902,36989,36990,44566,53766,53768,74869</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21977015$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gorbach, Nico S</creatorcontrib><creatorcontrib>Schütte, Christoph</creatorcontrib><creatorcontrib>Melzer, Corina</creatorcontrib><creatorcontrib>Goldau, Mathias</creatorcontrib><creatorcontrib>Sujazow, Olivia</creatorcontrib><creatorcontrib>Jitsev, Jenia</creatorcontrib><creatorcontrib>Douglas, Tania</creatorcontrib><creatorcontrib>Tittgemeyer, Marc</creatorcontrib><title>Hierarchical information-based clustering for connectivity-based cortex parcellation</title><title>Frontiers in neuroinformatics</title><addtitle>Front Neuroinform</addtitle><description>One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area ("the functional fingerprint") is closely related to its anatomical connections ("the connectional fingerprint") and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation and requires clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several non-trivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. 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subjects | Brain architecture Brain research Clustering cortex parcellation diffusion tractography Frontal gyrus hierarchical clustering Information Theory Localization Neural networks Neuroscience Precentral gyrus |
title | Hierarchical information-based clustering for connectivity-based cortex parcellation |
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