@article {4570, title = {Complexity organization of resting-state functional-MRI networks.}, journal = {Hum Brain Mapp}, volume = {45}, year = {2024}, month = {2024 Aug 15}, pages = {e26809}, abstract = {

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{\textquoteright}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.

}, keywords = {Adult, Brain, Connectome, Default Mode Network, Entropy, Humans, Magnetic Resonance Imaging, Nerve Net, Rest}, issn = {1097-0193}, doi = {10.1002/hbm.26809}, author = {Trevino, Gabriel and Lee, John J and Shimony, Joshua S and Luckett, Patrick H and Leuthardt, Eric C} } @article {4573, title = {Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.}, journal = {J Neurooncol}, volume = {169}, year = {2024}, month = {2024 Aug}, pages = {175-185}, abstract = {

PURPOSE: 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.

METHODS: 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 < 70, KPS >= 70).

RESULTS: 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.

CONCLUSION: 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{\textquoteright}s location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.

}, keywords = {Adult, Aged, Brain, Brain Neoplasms, Female, Glioma, Humans, machine learning, Magnetic Resonance Imaging, Male, Middle Aged, Neoplasm Grading, Prognosis, Rest, Retrospective Studies}, issn = {1573-7373}, doi = {10.1007/s11060-024-04715-1}, author = {Luckett, Patrick H and Olufawo, Michael O and Park, Ki Yun and Lamichhane, Bidhan and Dierker, Donna and Verastegui, Gabriel Trevino and Lee, John J and Yang, Peter and Kim, Albert and Butt, Omar H and Chheda, Milan G and Snyder, Abraham Z and Shimony, Joshua S and Leuthardt, Eric C} }