@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 {4492, title = {Methods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications.}, journal = {J Neural Eng}, volume = {20}, year = {2023}, month = {2023 Jul 21}, abstract = {

Surface electromyography measurements of the Hoffmann (H-) reflex are essential in a wide range of neuroscientific and clinical applications. One promising emerging therapeutic application is H-reflex operant conditioning, whereby a person is trained to modulate the H-reflex, with generalized beneficial effects on sensorimotor function in chronic neuromuscular disorders. Both traditional diagnostic and novel realtime therapeutic applications rely on accurate definitions of the H-reflex and M-wave temporal bounds, which currently depend on expert case-by-case judgment. The current study automates such judgments.Our novel wavelet-based algorithm automatically determines temporal extent and amplitude of the human soleus H-reflex and M-wave. In each of 20 participants, the algorithm was trained on data from a preliminary 3 or 4 min recruitment-curve measurement. Output was evaluated on parametric fits to subsequent sessions{\textquoteright} recruitment curves (92 curves across all participants) and on the conditioning protocol{\textquoteright}s subsequent baseline trials (\~{}1200 per participant) performed near. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later.Automatic bounds agreed well with manual estimates: 95\% lay within {\textpm}2.5 ms. The resulting H-reflex magnitude estimates showed excellent agreement (97.5\% average across participants) between automatic and retrospective bounds regarding which trials would be considered successful for operant conditioning. Recruitment-curve parameters also agreed well between automatic and manual methods: 95\% of the automatic estimates of the current required to elicitfell within{\textpm}1.4\%of the retrospective estimate; for the {\textquoteright}threshold{\textquoteright} current that produced an M-wave 10\% of maximum, this value was{\textpm}3.5\%.Such dependable automation of M-wave and H-reflex definition should make both established and emerging H-reflex protocols considerably less vulnerable to inter-personnel variability and human error, increasing translational potential.

}, keywords = {Electric Stimulation, Electromyography, H-Reflex, Humans, Muscle, Skeletal, Peripheral Nerves, Retrospective Studies}, issn = {1741-2552}, doi = {10.1088/1741-2552/ace6fb}, author = {McKinnon, Michael L and Hill, N Jeremy and Carp, Jonathan S and Dellenbach, Blair and Thompson, Aiko K} } @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} }