<?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><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%">Luckett, Patrick H</style></author><author><style face="normal" font="default" size="100%">Olufawo, Michael O</style></author><author><style face="normal" font="default" size="100%">Park, Ki Yun</style></author><author><style face="normal" font="default" size="100%">Lamichhane, Bidhan</style></author><author><style face="normal" font="default" size="100%">Dierker, Donna</style></author><author><style face="normal" font="default" size="100%">Verastegui, Gabriel Trevino</style></author><author><style face="normal" font="default" size="100%">Lee, John J</style></author><author><style face="normal" font="default" size="100%">Yang, Peter</style></author><author><style face="normal" font="default" size="100%">Kim, Albert</style></author><author><style face="normal" font="default" size="100%">Butt, Omar H</style></author><author><style face="normal" font="default" size="100%">Chheda, Milan G</style></author><author><style face="normal" font="default" size="100%">Snyder, Abraham Z</style></author><author><style face="normal" font="default" size="100%">Shimony, Joshua S</style></author><author><style face="normal" font="default" size="100%">Leuthardt, Eric C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neurooncol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Neurooncol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</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%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnetic Resonance Imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasm Grading</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Rest</style></keyword><keyword><style  face="normal" font="default" size="100%">Retrospective Studies</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 Aug</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">169</style></volume><pages><style face="normal" font="default" size="100%">175-185</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;PURPOSE: &lt;/b&gt;High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Adult HGG patients (N = 102) from the neurosurgery brain tumor service at Washington University Medical Center were retrospectively recruited. All patients completed structural neuroimaging and resting state functional MRI prior to surgery. Demographics, measures of resting state network connectivity (FC), tumor location, and tumor volume were used to train a random forest classifier to predict functional outcomes based on Karnofsky Performance Status (KPS &lt; 70, KPS ≥ 70).&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The models achieved a nested cross-validation accuracy of 94.1% and an AUC of 0.97 in classifying KPS. The strongest predictors identified by the model included FC between somatomotor, visual, auditory, and reward networks. Based on location, the relation of the tumor to dorsal attention, cingulo-opercular, and basal ganglia networks were strong predictors of KPS. Age was also a strong predictor. However, tumor volume was only a moderate predictor.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record></records></xml>