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Artificial intelligence in preoperative brain mapping for functional neurosurgery: A narrative review of current evidence and clinical limitations

Kimia Kazemzadeh*
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1 Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
Advanced Neurology, 025480120 https://doi.org/10.36922/AN025480120
Received: 27 November 2025 | Revised: 9 May 2026 | Accepted: 2 June 2026 | Published online: 12 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Preoperative brain mapping localizes eloquent regions before tumor surgery. The goal is to maximize resection while avoiding permanent deficits. Conventional techniques—navigated transcranial magnetic stimulation (nTMS), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG)—have well-known limitations: patient cooperation requirements, acquisition variability, and interpretation challenges. nTMS combined with tractography predicts motor deficits better than fMRI in most comparative studies, because fMRI is prone to false signals from neurovascular uncoupling and co-activation of non-essential areas. MEG offers good spatial accuracy but is expensive and technically complex. Artificial intelligence (AI) can automate segmentation, integrate multimodal data, and improve functional localization. Early studies report increased mapping precision. Real-time AI-assisted neuronavigation, robotics integration, augmented/virtual reality, and explainable AI models are under development. But challenges remain: data heterogeneity, lack of external validation, and unresolved ethical and legal issues. Large-scale multicenter studies and prospective AI-driven trials are missing. Most published evidence is retrospective and single-center. This review synthesizes current AI applications in preoperative brain mapping, identifies what works and what does not, and argues that rigorous validation—not hype—will determine whether AI truly advances precision neurosurgery.

Keywords
Brain mapping
Artificial intelligence
Neurosurgery
Deep learning
Preoperative planning
Navigated transcranial magnetic stimulation
Functional magnetic resonance imaging
Funding
None.
Conflict of interest
The author declares no conflicts of interest.
References
  1. Rosazza C, Zacà D, Bruzzone MG. Pre-surgical brain mapping: to rest or not to rest? Front Neurol. 2018;9:520. doi: 10.3389/fneur.2018.00520

 

  1. Silva MA, See AP, Essayed WI, Golby AJ, Tie Y. Challenges and techniques for presurgical brain mapping with functional MRI. Neuroimage Clin. 2018;17:794–803. doi: 10.1016/j.nicl.2017.12.008

 

  1. Leone A, Carbone F, Spetzger U, et al. Preoperative mapping techniques for brain tumor surgery: a systematic review. Front Oncol. 2025;14:1481430. doi: 10.3389/fonc.2024.1481430

 

  1. Sharifi G, Rahmanian A, Bemana G, Kalani N, Kazeminezhad A. Brain mapping in neurosurgery. Iran J Neurosurg. 2022;8:0–0. doi: 10.32598/irjns.Specialissue.E4

 

  1. Kazemzadeh K, Akhlaghdoust M, Zali A. Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery. Front Surg. 2023;10:1241923.doi: 10.3389/fsurg.2023.1241923

 

  1. Muir MT, Noll K, Prinsloo S, et al. Preoperative brain mapping predicts language outcomes after eloquent tumor resection. Hum Brain Mapp. 2025;46(15):e70340. doi: 10.1002/hbm.70340

 

  1. Mhanna HYA, Omar AF, Radzi YM, et al. Systematic review of functional magnetic resonance imaging (fMRI) applications in the preoperative planning and treatment assessment of brain tumors. Heliyon. 2025;11(3):e42464. doi: 10.1016/j.heliyon.2025.e42464

 

  1. Krieg SM, Sabih J, Bulubasova L, et al. Preoperative motor mapping by navigated transcranial magnetic brain stimulation improves outcome for motor eloquent lesions. Neuro-Oncol. 2014;16(9):1274–1282. doi: 10.1093/neuonc/nou007

 

  1. De Tiège X, Lundqvist D, Beniczky S, Seri S, Paetau R. Current clinical magnetoencephalography practice across Europe: Are we closer to use MEG as an established clinical tool? Seizure. 2017;50:53–59. doi: 10.1016/j.seizure.2017.06.002

 

  1. Bocanegra-Becerra JE, Neves Ferreira JS, Simoni G, et al. Machine Learning Algorithms for Neurosurgical Preoperative Planning: A Scoping Review. World Neurosurg. 2025;194:123465. doi: 10.1016/j.wneu.2024.11.048

 

  1. Tangsrivimol JA, Schonfeld E, Zhang M, et al. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future. Diagnostics. 2023;13(14)doi:10.3390/ diagnostics13142429

 

  1. Yangi K, Hong J, Gholami AS, et al. Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties. Front Neurol. 2025;16. doi: 10.3389/fneur.2025.1532398

 

  1. Seghier ML. 7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery. Eur Radiol Exp. 2024;8(1):73. doi: 10.1186/s41747-024-00472-y

 

  1. Nam SM, Byun YH, Dho Y-S, Park C-K. Envisioning the Future of the Neurosurgical Operating Room with the Concept of the Medical Metaverse. J Korean Neurosurg Soc. 2025;68(2):137–149. doi: 10.3340/jkns.2024.0160

 

  1. Mirza AB, Vastani A, Suvarna R, et al. Preoperative and intraoperative neuromonitoring and mapping techniques impact oncological and functional outcomes in supratentorial function-eloquent brain tumours: a systematic review and meta-analysis. eClinicalMedicine. 2025;80:103055. doi: 10.1016/j.eclinm.2024.103055

 

  1. Paiva WS, Fonoff ET, Marcolin MA, Cabrera HN, Teixeira MJ. Cortical mapping with navigated transcranial magnetic stimulation in low-grade glioma surgery. Neuropsychiatr Dis Treat. 2012:197–201. doi: 10.2147/NDT.S30151

 

  1. Romero JR, Ramirez DM, Aglio LS, Gugino LD. Brain mapping using transcranial magnetic stimulation. Neurosurg Clin. 2011;22(2):141–152. doi: 10.1016/j.nec.2010.11.002

 

  1. Krings T, Chiappa KH, Foltys H, Reinges MH, Cosgrove RG, Thron A. Introducing navigated transcranial magnetic stimulation as a refined brain mapping methodology. Neurosurg Rev. 2001;24(4):171–179. doi: 10.1007/s101430100151

 

  1. Paiva WS, Fonoff ET, dos Santos Silva RP, et al. Preoperative cortical mapping for brain tumor surgery using navigated transcranial stimulation: Analysis of accuracy. Brain Sci. 2024;14(9):867. doi: 10.3390/brainsci14090867

 

  1. Gao C, Wu X, Cheng X, et al. Individualized brain mapping for navigated neuromodulation. Chin Med J. 2024;137(05):508–523. doi: 10.1097/CM9.0000000000002979

 

  1. Soto AM, Stenroos M, Matsuda RH, et al. Real-time electric-field neuronavigation on realistic head models for conventional and multi-locus TMS. Brain Stimul. 2026;19(3):103113. doi: 10.1016/j.brs.2026.103113

 

  1. Zhao L. Advances in functional magnetic resonance imaging-based brain function mapping: a deep learning perspective. Psychoradiology. 2025;5:kkaf007. doi: 10.1093/psyrad/kkaf007

 

  1. Bi Y, Abrol A, Fu Z, Calhoun VD. A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data. Hum Brain Mapp. 2024;45(17):e26783. doi: 10.1002/hbm.26783

 

  1. Gong C, Jing C, Chen X, et al. Generative AI for brain image computing and brain network computing: a review. Front Neurosci. 2023;17:1203104. doi: 10.3389/fnins.2023.1203104

 

  1. Singh SP. Magnetoencephalography: basic principles. Ann Indian Acad Neurol. 2014;17(5):107. doi: 10.4103/0972-2327.128676

 

  1. Kim JA, Davis KD. Magnetoencephalography: Physics, techniques, and applications in the basic and clinical neurosciences. J Neurophysiol. 2021;125(3):938–956. doi: 10.1152/jn.00530.2020

 

  1. Xu H, Zheng L, Liao P, Lyu B, Gao J-H. DeepReducer: A linear transformer-based model for MEG denoising. NeuroImage. 2025;308:121080. doi: 10.1016/j.neuroimage.2025.121080

 

  1. Yang Y, Luo S, Wang W, Gao X, Yao X, Wu T. From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications. NeuroImage Clin. 2024;42:103608. doi: 10.1016/j.nicl.2024.103608

 

  1. Kim H-S, Lee J, Jeong C-H, Koo YS. Clinical utility of magnetoencephalography in epilepsy evaluation: a qualitative systematic review. J Epilepsy Res. 2025;15(2):83. doi: 10.14581/jer.25010

 

  1. Guggisberg AG, Siebner HR, Lundell H, et al. Emergent technologies in clinical neurophysiology to study the central nervous system: IFCN handbook chapter. Clin Neurophysiol. 2025:2110942. doi: 10.1016/j.clinph.2025.2110942

 

  1. Shimamoto T, Sano Y, Yoshimitsu K, Masamune K, Muragaki Y. Precise brain-shift prediction by new combination of W-Net deep learning for neurosurgical navigation. Neurol Med-Chir. 2023;63(7):295–303. doi: 10.2176/jns-nmc.2022-0350

 

  1. Boaro A, Kaczmarzyk JR, Kavouridis VK, et al. Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice. Sci Rep. 2022;12(1):15462. doi: 10.1038/s41598-022-19356-5

 

  1. Awuah WA, Adebusoye FT, Wellington J, et al. Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery. World Neurosurg X. 2024;23:100301. doi: 10.1016/j.wnsx.2024.100301

 

  1. Khan DZ, Luengo I, Barbarisi S, et al. Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0). J Neurosurg. 2021;137(1):51–58. doi: 10.3171/2021.6.JNS21923

 

  1. Buyck F, Vandemeulebroucke J, Ceranka J, et al. Computer-vision based analysis of the neurosurgical scene–A systematic review. Brain Spine. 2023;3:102706. doi: 10.1016/j.bas.2023.102706

 

  1. Huang J, Shlobin NA, DeCuypere M, Lam SK. Deep learning for outcome prediction in neurosurgery: a systematic review of design, reporting, and reproducibility. Neurosurgery. 2022;90(1):16–38. doi: 10.1227/NEU.0000000000001736

 

  1. Danilov G, Kotik K, Shifrin M, Strunina U, Pronkina T, Potapov A. Prediction of Postoperative Hospital Stay with Deep Learning Based on 101 654 Operative Reports in Neurosurgery. In: Studies in Health Technology and Informatics. IOS Press; 2019. doi: 10.3233/978-1-61499-959-1-125

 

  1. Pangal DJ, Kugener G, Zhu Y, et al. Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video. Sci Rep. 2022;12(1):8137. doi: 10.1038/s41598-022-11549-2

 

  1. Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nat Med. 2022;28(9):1773–1784. doi: 10.1038/s41591-022-01981-2

 

  1. Lipkova J, Chen RJ, Chen B, et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 2022;40(10):1095–1110. doi: 10.1016/j.ccell.2022.09.012

 

  1. Simon BD, Ozyoruk KB, Gelikman DG, Harmon SA, Türkbey B. The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review. Diagn Interv Radiol. 2025;31(4):303. doi: 10.4274/dir.2024.242631

 

  1. Nam Y, Kim J, Jung S-H, et al. Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine. Annu Rev Biomed Data Sci. 2024;7(1):225–250. doi: 10.1146/annurev-biodatasci-102523-103801

 

  1. Li W, Gumera A, Surya S, Edwards A, Basiri F, Eves C. The role of artificial intelligence in diagnostic neurosurgery: a systematic review. Neurosurg Rev. 2025;48(1):393. doi: 10.1007/s10143-025-03512-2

 

  1. Satushe V, Vyas V, Metkar S, Singh DP. AI in MRI brain tumor diagnosis: A systematic review of machine learning and deep learning advances (2010–2025). Chemom Intell Lab Syst. 2025;263:105414. doi: 10.1016/j.chemolab.2025.105414

 

  1. Khan MM, Scalia G, Shah N, Umana GE, Chavda V, Chaurasia B. Ethical concerns of AI in neurosurgery: a systematic review. Brain Behav. 2025;15(2):e70333. doi: 10.1002/brb3.70333

 

  1. Bayer S, Maier A, Ostermeier M, Fahrig R. Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery. Int J Biomed Imaging. 2017;2017:6028645. doi: 10.1155/2017/6028645

 

  1. Nimsky C, Ganslandt O, Cerny S, Hastreiter P, Greiner G, Fahlbusch R. Quantification of, visualization of, and compensation for brain shift using intraoperative magnetic resonance imaging. Neurosurgery. 2000;47(5):1070–9; discussion 1079–80. doi: 10.1097/00006123-200011000-00008

 

  1. Kim JT, Di L, Etame AB, Olson S, Vogelbaum MA, Tran ND. Use of virtual magnetic resonance imaging to compensate for brain shift during image-guided surgery: illustrative case. J Neurosurg Case Lessons. Jun 13 2022;3(24):Case21683. doi: 10.3171/case21683

 

  1. Skrinjar O, Nabavi A, Duncan J. Model-driven brain shift compensation. Med Image Anal. 2002;6(4):361–73. doi: 10.1016/s1361-8415(02)00062-2

 

  1. Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, Abedalthagafi M. Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ Precis Oncol. 2024;8(1):80. doi: 10.1038/s41698-024-00575-0

 

  1. Kashif M, Muthana A, Al-Qudah AM, Hoz SS. The influence of artificial intelligence on neurological surgery and patient outcome. Surg Neurol Int. 2024;15:211. doi: 10.25259/SNI_321_2024

 

  1. Brugnara G, Jayachandran Preetha C, Deike K, et al. Addressing the generalizability of AI in radiology using a novel data augmentation framework with synthetic patient image data: proof-of-concept and external validation for classification tasks in multiple sclerosis.Radiol Artif Intell. 2024;6(6):e230514. doi: 10.1148/ryai.230514
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Advanced Neurology, Electronic ISSN: 2810-9619 Print ISSN: 3060-8589, Published by AccScience Publishing
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