Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks.

ABSTRACT: Localizing the eloquent cortex is a crucial part of presurgical planning. While invasive mapping is the gold standard, there is increasing interest in using noninvasive fMRI to shorten and improve the process. However, many surgical patients cannot adequately perform task-based fMRI protocols. Resting-state fMRI has emerged as an alternative modality, but automated eloquent cortex localization remains an open challenge. In this paper, we develop a novel deep learning architecture to simultaneously identify language and primary motor cortex from rs-fMRI connectivity. Our approach uses the representational power of convolutional neural networks alongside the generalization power of multi-task learning to find a shared representation between the eloquent subnetworks. We validate our method on

SUBMITTER: Nandakumar N 

PROVIDER: S-EPMC9245684 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature