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-networks03232nas a2200481 4500008004100000022001400041245011400055210006900169260001300238300001200251490000800263520183300271653001002104653000902114653001002123653002002133653001102153653001102164653001102175653002102186653003102207653000902238653001602247653002102263653001402284653000902298653002602307100002402333700002402357700001802381700002302399700001902422700003302441700001702474700001602491700001602507700001802523700002102541700002302562700002302585700002302608856011902631 2024 eng d a1573-737300aPredicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.0 aPredicting postsurgical functional status in highgrade glioma wi c2024 Aug a175-1850 v1693 a

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's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.

10aAdult10aAged10aBrain10aBrain Neoplasms10aFemale10aGlioma10aHumans10amachine learning10aMagnetic Resonance Imaging10aMale10aMiddle Aged10aNeoplasm Grading10aPrognosis10aRest10aRetrospective Studies1 aLuckett, Patrick, H1 aOlufawo, Michael, O1 aPark, Ki, Yun1 aLamichhane, Bidhan1 aDierker, Donna1 aVerastegui, Gabriel, Trevino1 aLee, John, J1 aYang, Peter1 aKim, Albert1 aButt, Omar, H1 aChheda, Milan, G1 aSnyder, Abraham, Z1 aShimony, Joshua, S1 aLeuthardt, Eric, C uhttps://neurotechcenter.org/publications/2024/predicting-post-surgical-functional-status-high-grade-glioma-resting02867nas a2200241 4500008004100000022001400041245010500055210006900160260001200229300000900241520204300250100002402293700001802317700001702335700001902352700003002371700001902401700002302420700001902443700002302462700002302485856011702508 2023 eng d a1933-069300aData-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.0 aDataefficient restingstate functional magnetic resonance imaging c04/2023 a1-123 a

OBJECTIVE: Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data.

METHODS: Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme.

RESULTS: The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations.

CONCLUSIONS: Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.

1 aLuckett, Patrick, H1 aPark, Ki, Yun1 aLee, John, J1 aLenze, Eric, J1 aWetherell, Julie, Loebach1 aEyler, Lisa, T1 aSnyder, Abraham, Z1 aAnces, Beau, M1 aShimony, Joshua, S1 aLeuthardt, Eric, C uhttps://neurotechcenter.org/publications/2023/data-efficient-resting-state-functional-magnetic-resonance-imaging02505nas a2200253 4500008004100000022001400041245007000055210006900125260001200194300001200206490000700218520170800225100002401933700001701957700001801974700001801992700002102010700002002031700002302051700001902074700002302093700002302116856011202139 2023 eng d a1664-229500aResting state network mapping in individuals using deep learning.0 aResting state network mapping in individuals using deep learning c01/2023 a10554370 v133 a

INTRODUCTION: Resting state functional MRI (RS-fMRI) is currently used in numerous clinical and research settings. The localization of resting state networks (RSNs) has been utilized in applications ranging from group analysis of neurodegenerative diseases to individual network mapping for pre-surgical planning of tumor resections. Reproducibility of these results has been shown to require a substantial amount of high-quality data, which is not often available in clinical or research settings.

METHODS: In this work, we report voxelwise mapping of a standard set of RSNs using a novel deep 3D convolutional neural network (3DCNN). The 3DCNN was trained on publicly available functional MRI data acquired in = 2010 healthy participants. After training, maps that represent the probability of a voxel belonging to a particular RSN were generated for each participant, and then used to calculate mean and standard deviation (STD) probability maps, which are made publicly available. Further, we compared our results to previously published resting state and task-based functional mappings.

RESULTS: Our results indicate this method can be applied in individual subjects and is highly resistant to both noisy data and fewer RS-fMRI time points than are typically acquired. Further, our results show core regions within each network that exhibit high average probability and low STD.

DISCUSSION: The 3DCNN algorithm can generate individual RSN localization maps, which are necessary for clinical applications. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.

1 aLuckett, Patrick, H1 aLee, John, J1 aPark, Ki, Yun1 aRaut, Ryan, V1 aMeeker, Karin, L1 aGordon, Evan, M1 aSnyder, Abraham, Z1 aAnces, Beau, M1 aLeuthardt, Eric, C1 aShimony, Joshua, S uhttps://neurotechcenter.org/publications/2023/resting-state-network-mapping-individuals-using-deep-learning