Mapping Functional Language Areas with non-Functional Brain MRI

Omri Leshem, Atira Sara Bick, Nahum Kiryati, Netta Levin, Arnaldo Mayer
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:965-977, 2026.

Abstract

Mapping eloquent brain areas has become a standard of care in brain surgery. Current imaging-based techniques usually rely on functional MRI (fMRI), which measures neural activity via the blood oxygenation level-dependent signal. fMRI protocols are time-intensive, require active patient collaboration, and involve laborious manual post-processing and expertise, making them difficult to implement in some clinical scenarios. In this research, we propose a fully automated deep neural pipeline for the mapping of Broca and Wernicke functional language areas using multiple non-functional MRI modalities. The proposed method is evaluated on a cohort of 30 drug-resistant epilepsy patients, showing encouraging qualitative and quantitative results and suggesting its potential applicability as an effective and practical tool for neurosurgical planning and navigation. Implementation details can be found in our GitHub.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-leshem26a, title = {Mapping Functional Language Areas with non-Functional Brain MRI}, author = {Leshem, Omri and Bick, Atira Sara and Kiryati, Nahum and Levin, Netta and Mayer, Arnaldo}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {965--977}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/leshem26a/leshem26a.pdf}, url = {https://proceedings.mlr.press/v301/leshem26a.html}, abstract = {Mapping eloquent brain areas has become a standard of care in brain surgery. Current imaging-based techniques usually rely on functional MRI (fMRI), which measures neural activity via the blood oxygenation level-dependent signal. fMRI protocols are time-intensive, require active patient collaboration, and involve laborious manual post-processing and expertise, making them difficult to implement in some clinical scenarios. In this research, we propose a fully automated deep neural pipeline for the mapping of Broca and Wernicke functional language areas using multiple non-functional MRI modalities. The proposed method is evaluated on a cohort of 30 drug-resistant epilepsy patients, showing encouraging qualitative and quantitative results and suggesting its potential applicability as an effective and practical tool for neurosurgical planning and navigation. Implementation details can be found in our GitHub.} }
Endnote
%0 Conference Paper %T Mapping Functional Language Areas with non-Functional Brain MRI %A Omri Leshem %A Atira Sara Bick %A Nahum Kiryati %A Netta Levin %A Arnaldo Mayer %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-leshem26a %I PMLR %P 965--977 %U https://proceedings.mlr.press/v301/leshem26a.html %V 301 %X Mapping eloquent brain areas has become a standard of care in brain surgery. Current imaging-based techniques usually rely on functional MRI (fMRI), which measures neural activity via the blood oxygenation level-dependent signal. fMRI protocols are time-intensive, require active patient collaboration, and involve laborious manual post-processing and expertise, making them difficult to implement in some clinical scenarios. In this research, we propose a fully automated deep neural pipeline for the mapping of Broca and Wernicke functional language areas using multiple non-functional MRI modalities. The proposed method is evaluated on a cohort of 30 drug-resistant epilepsy patients, showing encouraging qualitative and quantitative results and suggesting its potential applicability as an effective and practical tool for neurosurgical planning and navigation. Implementation details can be found in our GitHub.
APA
Leshem, O., Bick, A.S., Kiryati, N., Levin, N. & Mayer, A.. (2026). Mapping Functional Language Areas with non-Functional Brain MRI. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:965-977 Available from https://proceedings.mlr.press/v301/leshem26a.html.

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