<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Park, Ki Yun</style></author><author><style face="normal" font="default" size="100%">Shimony, Joshua S</style></author><author><style face="normal" font="default" size="100%">Chakrabarty, Satrajit</style></author><author><style face="normal" font="default" size="100%">Tanenbaum, Aaron B</style></author><author><style face="normal" font="default" size="100%">Hacker, Carl D</style></author><author><style face="normal" font="default" size="100%">Donovan, Kara M</style></author><author><style face="normal" font="default" size="100%">Luckett, Patrick H</style></author><author><style face="normal" font="default" size="100%">Milchenko, Mikhail</style></author><author><style face="normal" font="default" size="100%">Sotiras, Aristeidis</style></author><author><style face="normal" font="default" size="100%">Marcus, Daniel S</style></author><author><style face="normal" font="default" size="100%">Leuthardt, Eric C</style></author><author><style face="normal" font="default" size="100%">Snyder, Abraham Z</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal approaches to analyzing functional MRI data in glioma patients.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neurosci Methods</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Neurosci Methods</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Connectome</style></keyword><keyword><style  face="normal" font="default" size="100%">Glioma</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnetic Resonance Imaging</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">402</style></volume><pages><style face="normal" font="default" size="100%">110011</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Resting-state fMRI is increasingly used to study the effects of gliomas on the functional organization of the brain. A variety of preprocessing techniques and functional connectivity analyses are represented in the literature. However, there so far has been no systematic comparison of how alternative methods impact observed results.&lt;/p&gt;&lt;p&gt;&lt;b&gt;NEW METHOD: &lt;/b&gt;We first surveyed current literature and identified alternative analytical approaches commonly used in the field. Following, we systematically compared alternative approaches to atlas registration, parcellation scheme, and choice of graph-theoretical measure as regards differentiating glioma patients (N = 59) from age-matched reference subjects (N = 163).&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Our results suggest that non-linear, as opposed to affine registration, improves structural match to an atlas, as well as measures of functional connectivity. Functionally- as opposed to anatomically-derived parcellation schemes maximized the contrast between glioma patients and reference subjects. We also demonstrate that graph-theoretic measures strongly depend on parcellation granularity, parcellation scheme, and graph density.&lt;/p&gt;&lt;p&gt;&lt;b&gt;COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: &lt;/b&gt;Our current work primarily focuses on technical optimization of rs-fMRI analysis in glioma patients and, therefore, is fundamentally different from the bulk of papers discussing glioma-induced functional network changes. We report that the evaluation of glioma-induced alterations in the functional connectome strongly depends on analytical approaches including atlas registration, choice of parcellation scheme, and graph-theoretical measures.&lt;/p&gt;</style></abstract></record></records></xml>