{"id":3457,"date":"2023-09-06T11:38:57","date_gmt":"2023-09-06T16:38:57","guid":{"rendered":"https:\/\/my.dev.vanderbilt.edu\/masi\/?p=3457"},"modified":"2023-09-06T11:44:36","modified_gmt":"2023-09-06T16:44:36","slug":"characterizing-streamline-count-invariant-graph-measures-of-structural-connectomes","status":"publish","type":"post","link":"https:\/\/my.dev.vanderbilt.edu\/masi\/2023\/09\/characterizing-streamline-count-invariant-graph-measures-of-structural-connectomes\/","title":{"rendered":"Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes"},"content":{"rendered":"

Nancy R. Newlin, Francois Rheault, Kurt G. Schilling,\u00a0Bennett A.\u00a0Landman<\/span>. “Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes” Journal of Magnetic Resonance Imaging. January 2023.<\/p>\n

Full Text<\/a><\/p>\n

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Background<\/h3>\n

While graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is a lack of community consensus regarding the number of streamlines needed to generate connectomes.<\/p>\n<\/section>\n

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Purpose<\/h3>\n

The purpose was to define the relationship between streamline count and graph-measure value, reproducibility, and repeatability.<\/p>\n

\"Figure1_revised\"<\/p>\n<\/section>\n

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Study Type<\/h3>\n

Retrospective analysis of previously prospective study.<\/p>\n<\/section>\n

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Population<\/h3>\n

Ten healthy subjects, 70% female, aged 25.3\u2009\u00b1\u20095.9\u2009years.<\/p>\n<\/section>\n

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Field Strength\/Sequence<\/h3>\n

A 3-T, T1-weighted sequences and diffusion-weighted imaging (DWI) with two gradient strengths (b<\/i>-values\u00a0=\u00a01200 and 3000\u2009sec\/mm2<\/sup>, echo time [TE]\u00a0=\u00a068\u2009msec, repetition time [TR]\u00a0=\u00a05.4\u00a0seconds, 120 slices, field of view\u00a0=\u00a0188\u2009mm2<\/sup>).<\/p>\n<\/section>\n

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Assessment<\/h3>\n

A total of 13 graph-theory measures were derived for each subject by generating probabilistic whole-brain tractography from DWI and mapping the structural connectivity to connectomes. The streamline count invariance from changes in mean, repeatability, and reproducibility were derived.<\/p>\n<\/section>\n

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Statistical Tests<\/h3>\n

Paired\u00a0t<\/i>-test with\u00a0P<\/i>\u00a0value <0.05 was used to compare graph-measure means with a reference, intraclass correlation coefficient (ICC) to measure repeatability, and concordance correlation coefficient (CCC) to measure reproducibility.<\/p>\n<\/section>\n

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Results<\/h3>\n

Modularity and global efficiency converged to their reference mean with ICC\u2009>\u20090.90 and CCC\u2009>\u20090.99. Edge count, small-worldness, randomness, and average betweenness centrality converged to the reference mean, with ICC\u2009>\u20090.90 and CCC\u2009>\u20090.95. Assortativity and average participation coefficient converged with ICC\u2009>\u20090.75 and CCC\u2009>\u20090.90. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient did not converge, though had ICC\u2009>\u20090.90 and CCC\u2009>\u20090.99. For these measures, alternate definitions that converge a reference mean are provided.<\/p>\n<\/section>\n

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Data Conclusion<\/h3>\n

Modularity and global efficiency are streamline count invariant for greater than 6 million and 100,000 streamlines, respectively. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient were strongly dependent on streamline count.<\/p>\n<\/section>\n

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\"FigureX2_revised\"<\/p>\n

\"Figure5_revised\"<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Nancy R. Newlin, Francois Rheault, Kurt G. Schilling,\u00a0Bennett A.\u00a0Landman. “Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes” Journal of Magnetic Resonance Imaging. January 2023. Full Text Background While graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is…<\/p>\n","protected":false},"author":9288,"featured_media":3458,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[196,195,132,82,1,40,49],"tags":[],"class_list":["post-3457","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-complex-network-analysis","category-connectomics","category-harmonization","category-magnetic-resonance-imaging","category-news","category-reproducibility","category-tractography"],"_links":{"self":[{"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/3457","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/users\/9288"}],"replies":[{"embeddable":true,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/comments?post=3457"}],"version-history":[{"count":6,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/3457\/revisions"}],"predecessor-version":[{"id":3468,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/3457\/revisions\/3468"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media\/3458"}],"wp:attachment":[{"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media?parent=3457"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/categories?post=3457"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/my.dev.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/tags?post=3457"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}