Mapping of the Language Network With Deep Learning

• Main Authors: Patrick Luckett, John J. Lee, Ki Yun Park, Donna Dierker, Andy G. S. Daniel, Benjamin A. Seitzman, Carl D. Hacker, Beau M. Ances, Eric C. Leuthardt, Abraham Z. Snyder, Joshua S. Shimony
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-08-01
• Series: Frontiers in Neurology
• Subjects: functional MRI; language; deep learning; resting state network; convolutional neural network
• Online Access: https://www.frontiersin.org/article/10.3389/fneur.2020.00819/full

Background: Pre-surgical functional localization of eloquent cortex with task-based functional MRI (T-fMRI) is part of the current standard of care prior to resection of brain tumors. Resting state fMRI (RS-fMRI) is an alternative method currently under investigation. Here, we compare group level language localization using T-fMRI vs. RS-fMRI analyzed with 3D deep convolutional neural networks (3DCNN).Methods: We analyzed data obtained in 35 patients with brain tumors that had both language T-fMRI and RS-MRI scans during pre-surgical evaluation. The T-fMRI data were analyzed using conventional techniques. The language associated resting state network was mapped using a 3DCNN previously trained with data acquired in >2,700 normal subjects. Group level results obtained by both methods were evaluated using receiver operator characteristic analysis of probability maps of language associated regions, taking as ground truth meta-analytic maps of language T-fMRI responses generated on the Neurosynth platform.Results: Both fMRI methods localized major components of the language system (areas of Broca and Wernicke). Word-stem completion T-fMRI strongly activated Broca's area but also several task-general areas not specific to language. RS-fMRI provided a more specific representation of the language system.Conclusion: 3DCNN was able to accurately localize the language network. Additionally, 3DCNN performance was remarkably tolerant of a limited quantity of RS-fMRI data.