Abstract
We develop a novel method to localize the language and motor areas of the eloquent cortex in brain tumor patients based on resting-state fMRI (rs-fMRI) connectivity. Our method leverages the representation power of convolutional neural networks through specialized filters that act topologically on the rs-fMRI connectivity data. This Graph Neural Network (GNN) classifies each parcel in the brain into eloquent cortex, tumor, or background gray matter, thus accommodating varying tumor characteristics across patients. Our loss function also reflects the large class-imbalance present in our data. We evaluate our GNN on rs-fMRI data from 60 brain tumor patients with different tumor sizes and locations. We use motor and language task fMRI for validation. Our model achieves better localization than linear SVM, random forest, and a multilayer perceptron architecture. Our GNN is able to correctly identify bilateral language areas in the brain even when trained on patients whose language network is lateralized to the left hemisphere.
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Acknowledgements
This work was supported by the National Science Foundation CAREER award 1845430 (PI: Venkataraman) and the Research & Education Foundation Carestream Health RSNA Research Scholar Grant RSCH1420.
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Nandakumar, N., Manzoor, K., Pillai, J.J., Gujar, S.K., Sair, H.I., Venkataraman, A. (2019). A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity. In: Schirmer, M., Venkataraman, A., Rekik, I., Kim, M., Chung, A. (eds) Connectomics in NeuroImaging. CNI 2019. Lecture Notes in Computer Science(), vol 11848. Springer, Cham. https://doi.org/10.1007/978-3-030-32391-2_2
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DOI: https://doi.org/10.1007/978-3-030-32391-2_2
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