Journal
Journal of neuro-oncology
Publication Date
8-1-2024
Volume
169
Issue
1
First Page
175
Last Page
185
Document Type
Open Access Publication
DOI
10.1007/s11060-024-04715-1
Rights and Permissions
Luckett, P.H., Olufawo, M.O., Park, K.Y. et al. Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning. J Neurooncol 169, 175–185 (2024). https://doi.org/10.1007/s11060-024-04715-1 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Recommended Citation
Luckett, Patrick H; Olufawo, Michael O; Park, Ki Yun; Lamichhane, Bidhan; Dierker, Donna; Verastegui, Gabriel Trevino; Lee, John J; Yang, Peter; Kim, Albert; Butt, Omar H; Chheda, Milan G; Snyder, Abraham Z; Shimony, Joshua S; and Leuthardt, Eric C, "Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning." Journal of neuro-oncology. 169, 1. 175 - 185. (2024).
https://digitalcommons.wustl.edu/oa_4/5912
Department
ICTS (Institute of Clinical and Translational Sciences)
Additional Links
Supplemental material is available for this article at publisher site.
