02751nas a2200337 4500008004100000022001400041245007600055210006900131260001300200300001100213490000800224520171700232653001001949653001501959653001101974653001101985653003101996100001802027700002302045700002602068700002402094700002002118700002102138700002402159700002302183700002402206700002202230700002302252700002302275856011502298 2024 eng d a1872-678X00aOptimal approaches to analyzing functional MRI data in glioma patients.0 aOptimal approaches to analyzing functional MRI data in glioma pa c2024 Feb a1100110 v4023 a

BACKGROUND: 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.

NEW METHOD: 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).

RESULTS: 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.

COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: 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.

10aBrain10aConnectome10aGlioma10aHumans10aMagnetic Resonance Imaging1 aPark, Ki, Yun1 aShimony, Joshua, S1 aChakrabarty, Satrajit1 aTanenbaum, Aaron, B1 aHacker, Carl, D1 aDonovan, Kara, M1 aLuckett, Patrick, H1 aMilchenko, Mikhail1 aSotiras, Aristeidis1 aMarcus, Daniel, S1 aLeuthardt, Eric, C1 aSnyder, Abraham, Z uhttps://neurotechcenter.org/publications/2024/optimal-approaches-analyzing-functional-mri-data-glioma-patients