CC BY 4.0 · Indian Journal of Neurosurgery 2023; 12(02): 116-131
DOI: 10.1055/s-0043-1771214
Review Article

Connectomic Networks and Their Impact on Clinical Outcomes in Glioma Treatment: A Review

1   Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, United States
Emma R. Earl
2   School of Medicine, University of Utah, Salt Lake City, Utah, United States
Jaden B. Brooks
2   School of Medicine, University of Utah, Salt Lake City, Utah, United States
3   School of Medicine, University of Nevada, Reno, Nevada, United States
4   School of Medicine, Rutgers Robert Wood Johnson, New Brunswick, New Jersey, United States
Michael E. Sughrue
5   Department of Neurosurgery, Prince of Wales Hospital, Randwick, New South Wales, Australia
1   Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, United States
6   Global Neurosciences Institute, Upland, Pennsylvania, United States
7   Department of Neurosurgery, Drexel University College of Medicine, Philadelphia, Pennsylvania, United States
› Author Affiliations


The emerging field of connectomics has provided an improved understanding of the structural and functional organization of the human brain into large-scale brain networks. Recent studies have helped define the canonical neurological networks and outline how considering their presence may aid in surgical decision-making in brain tumor patients. Gliomas represent one of the most common types of brain tumor and often involve displacement and/or infiltration of neurological pathways, suggesting an opportunity to use connectomic maps to improve patient morbidity and mortality based on oncofunctional goals. This review aims to provide a working knowledge of important neurological networks, examine the use of networks in surgical planning, and describe the current literature discussing the impact of these networks on clinical outcomes in glioma resection.

Publication History

Article published online:
24 July 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (

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