Journal
Frontiers in Neurology
Publication Date
1-1-2021
Volume
12
First Page
642241
Document Type
Open Access Publication
DOI
10.3389/fneur.2021.642241
Rights and Permissions
Lamichhane B, Daniel AGS, Lee JJ, Marcus DS, Shimony JS and Leuthardt EC (2021) Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients. Front. Neurol. 12:642241. doi: 10.3389/fneur.2021.642241 © 2021 Lamichhane, Daniel, Lee, Marcus, Shimony and Leuthardt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Recommended Citation
Lamichhane, Bidhan; Daniel, Andy G S; Lee, John J; Marcus, Daniel S; Shimony, Joshua S; and Leuthardt, Eric C, "Machine learning analytics of resting-state functional connectivity predicts survival outcomes of glioblastoma multiforme patients." Frontiers in Neurology. 12, 642241 (2021).
https://digitalcommons.wustl.edu/open_access_pubs/10297
