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Functional MRI in Neuro-Oncology: State of the Art and Future Directions

Overview
Journal Radiology
Specialty Radiology
Date 2023 Sep 5
PMID 37668519
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Abstract

Since its discovery in the early 1990s, functional MRI (fMRI) has been used to study human brain function. One well-established application of fMRI in the clinical setting is the neurosurgical planning of patients with brain tumors near eloquent cortical areas. Clinical fMRI aims to preoperatively identify eloquent cortices that serve essential functions in daily life, such as hand movement and language. The primary goal of neurosurgery is to maximize tumor resection while sparing eloquent cortices adjacent to the tumor. When a lesion presents in the vicinity of an eloquent cortex, surgeons may use fMRI to plan their best surgical approach by determining the proximity of the lesion to regions of activation, providing guidance for awake brain surgery and intraoperative brain mapping. The acquisition of fMRI requires patient preparation prior to imaging, determination of functional paradigms, monitoring of patient performance, and both processing and analysis of images. Interpretation of fMRI maps requires a strong understanding of functional neuroanatomy and familiarity with the technical limitations frequently present in brain tumor imaging, including neurovascular uncoupling, patient compliance, and data analysis. This review discusses clinical fMRI in neuro-oncology, relevant ongoing research topics, and prospective future developments in this exciting discipline.

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References
1.
Lee J, Luckett P, Fakhri M, Leuthardt E, Shimony J . Resting State Functional MR Imaging of Language Function. Neuroimaging Clin N Am. 2020; 31(1):69-79. PMC: 8592270. DOI: 10.1016/j.nic.2020.09.005. View

2.
Morone F, Makse H . Influence maximization in complex networks through optimal percolation. Nature. 2015; 524(7563):65-8. DOI: 10.1038/nature14604. View

3.
Roland J, Hacker C, Snyder A, Shimony J, Zempel J, Limbrick D . A comparison of resting state functional magnetic resonance imaging to invasive electrocortical stimulation for sensorimotor mapping in pediatric patients. Neuroimage Clin. 2019; 23:101850. PMC: 6514367. DOI: 10.1016/j.nicl.2019.101850. View

4.
Niu C, Wang Y, Cohen A, Liu X, Li H, Lin P . Machine learning may predict individual hand motor activation from resting-state fMRI in patients with brain tumors in perirolandic cortex. Eur Radiol. 2021; 31(7):5253-5262. DOI: 10.1007/s00330-021-07825-w. View

5.
Herbet G, Duffau H . Revisiting the Functional Anatomy of the Human Brain: Toward a Meta-Networking Theory of Cerebral Functions. Physiol Rev. 2020; 100(3):1181-1228. DOI: 10.1152/physrev.00033.2019. View