03066nas a2200301 4500008004100000022001400041245007000055210006700125260001600192300001100208490000700219520218100226653001002407653001002417653001502427653002502442653001202467653001102479653003102490653001402521653000902535100002102544700001702565700002302582700002402605700002302629856011202652 2024 eng d a1097-019300aComplexity organization of resting-state functional-MRI networks.0 aComplexity organization of restingstate functionalMRI networks c2024 Aug 15 ae268090 v453 a

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

10aAdult10aBrain10aConnectome10aDefault Mode Network10aEntropy10aHumans10aMagnetic Resonance Imaging10aNerve Net10aRest1 aTrevino, Gabriel1 aLee, John, J1 aShimony, Joshua, S1 aLuckett, Patrick, H1 aLeuthardt, Eric, C uhttps://neurotechcenter.org/publications/2024/complexity-organization-resting-state-functional-mri-networks02751nas 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