AUTHOR=Luckett Patrick , Lee John J. , Park Ki Yun , Dierker Donna , Daniel Andy G. S. , Seitzman Benjamin A. , Hacker Carl D. , Ances Beau M. , Leuthardt Eric C. , Snyder Abraham Z. , Shimony Joshua S.
TITLE=Mapping of the Language Network With Deep Learning
JOURNAL=Frontiers in Neurology
VOLUME=11
YEAR=2020
URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2020.00819
DOI=10.3389/fneur.2020.00819
ISSN=1664-2295
ABSTRACT=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.