Version 1
: Received: 9 May 2024 / Approved: 11 May 2024 / Online: 13 May 2024 (07:47:23 CEST)
Version 2
: Received: 22 October 2024 / Approved: 24 October 2024 / Online: 24 October 2024 (11:51:07 CEST)
How to cite:
Ellis, D. G.; Warren, D.; Garlinghouse, M.; Aizenberg, M. R. Alterations in Graph Network Connectivity Predict Neurocognitive Changes in Patients Undergoing Brain Tumor Surgery. Preprints2024, 2024050739. https://doi.org/10.20944/preprints202405.0739.v1
Ellis, D. G.; Warren, D.; Garlinghouse, M.; Aizenberg, M. R. Alterations in Graph Network Connectivity Predict Neurocognitive Changes in Patients Undergoing Brain Tumor Surgery. Preprints 2024, 2024050739. https://doi.org/10.20944/preprints202405.0739.v1
Ellis, D. G.; Warren, D.; Garlinghouse, M.; Aizenberg, M. R. Alterations in Graph Network Connectivity Predict Neurocognitive Changes in Patients Undergoing Brain Tumor Surgery. Preprints2024, 2024050739. https://doi.org/10.20944/preprints202405.0739.v1
APA Style
Ellis, D. G., Warren, D., Garlinghouse, M., & Aizenberg, M. R. (2024). Alterations in Graph Network Connectivity Predict Neurocognitive Changes in Patients Undergoing Brain Tumor Surgery. Preprints. https://doi.org/10.20944/preprints202405.0739.v1
Chicago/Turabian Style
Ellis, D. G., Matthew Garlinghouse and Michele R Aizenberg. 2024 "Alterations in Graph Network Connectivity Predict Neurocognitive Changes in Patients Undergoing Brain Tumor Surgery" Preprints. https://doi.org/10.20944/preprints202405.0739.v1
Abstract
Background: Patients undergoing brain tumor resection experience changes to their neurocognitive abilities, many of which can be difficult to predict. We hypothesized that changes in brain connectivity could predict changes in neurocognitive functioning, demonstrating the potential for brain connectivity aware surgical planning to provide enhanced outcomes for patients.
Methods: Patients underwent functional and diffusion MR scanning and neuropsychological testing before tumor resection and two weeks post-resection. Using this functional and diffusion imaging, we measured changes in the topology of the functional and structural graph networks, respectively. From the neuropsychological testing scores, we derived a composite score that describes a patient’s overall level of neurocognitive functioning. We then used a multiple linear regression model to test if structural and functional connectivity measures could predict changes in composite scores.
Results: Twenty-one subjects completed imaging and neuropsychological evaluation both before and after surgery. The multiple linear regression model showed that changes in functional local efficiency were inversely correlated with changes in composite score (p
Medicine and Pharmacology, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.