@article {4587, title = {Optimal approaches to analyzing functional MRI data in glioma patients.}, journal = {J Neurosci Methods}, volume = {402}, year = {2024}, month = {2024 Feb}, pages = {110011}, abstract = {

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.

}, keywords = {Brain, Connectome, Glioma, Humans, Magnetic Resonance Imaging}, issn = {1872-678X}, doi = {10.1016/j.jneumeth.2023.110011}, author = {Park, Ki Yun and Shimony, Joshua S and Chakrabarty, Satrajit and Tanenbaum, Aaron B and Hacker, Carl D and Donovan, Kara M and Luckett, Patrick H and Milchenko, Mikhail and Sotiras, Aristeidis and Marcus, Daniel S and Leuthardt, Eric C and Snyder, Abraham Z} } @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} } @article {4343, title = {Potential differences between monolingual and bilingual patients in approach and outcome after awake brain surgery.}, journal = {J Neurooncol}, volume = {148}, year = {2020}, month = {07/2020}, pages = {587-598}, abstract = {

INTRODUCTION: 20.8\% of the United States population and 67\% of the European population speak two or more languages. Intraoperative different languages, mapping, and localization are crucial. This investigation aims to address three questions between BL and ML patients: (1) Are there differences in complications (i.e. seizures) and DECS techniques during intra-operative brain mapping? (2) Is EOR different? and (3) Are there differences in the recovery pattern post-surgery?

METHODS: Data from 56 patients that underwent left-sided awake craniotomy for tumors infiltrating possible dominant hemisphere language areas from September 2016 to June 2019 were identified and analyzed in this study; 14 BL and 42 ML control patients. Patient demographics, education level, and the age of language acquisition were documented and evaluated. fMRI was performed on all participants.

RESULTS: 0 (0\%) BL and 3 (7\%) ML experienced intraoperative seizures (P = 0.73). BL patients received a higher direct DECS current in comparison to the ML patients (average = 4.7, 3.8, respectively, P = 0.03). The extent of resection was higher in ML patients in comparison to the BL patients (80.9 vs. 64.8, respectively, P = 0.04). The post-operative KPS scores were higher in BL patients in comparison to ML patients (84.3, 77.4, respectively, P = 0.03). BL showed lower drop in post-operative KPS in comparison to ML patients (- 4.3, - 8.7, respectively, P = 0.03).

CONCLUSION: We show that BL patients have a lower incidence of intra-operative seizures, lower EOR, higher post-operative KPS and tolerate higher DECS current, in comparison to ML patients.

}, keywords = {Brain Mapping, Brain Neoplasms, Craniotomy, Female, Follow-Up Studies, Glioma, Humans, Incidence, Language, Male, Middle Aged, Monitoring, Intraoperative, Prognosis, Retrospective Studies, Seizures, United States, Wakefulness}, issn = {1573-7373}, doi = {10.1007/s11060-020-03554-0}, author = {ReFaey, Karim and Tripathi, Shashwat and Bhargav, Adip G and Grewal, Sanjeet S and Middlebrooks, Erik H and Sabsevitz, David S and Jentoft, Mark and Peter Brunner and Wu, Adela and Tatum, William O and Ritaccio, Anthony and Chaichana, Kaisorn L and Quinones-Hinojosa, Alfredo} }