Potential role for magnetoencephalography in distinguishing low-and high-grade gliomas: A preliminary study with histopathological confirmation

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Abstract

Gliomas are the most common form of tumor in the CNS and are exceptionally heterogeneous. Accurately characterizing gliomas, in terms of grade and type, is essential for predicting the rate of tumor progression. Histopathological grading and analysis based on biopsied tissue remains the gold standard, but non-and semi-invasive neuroimaging also plays a key role. Neuroimaging has been used to guide and optimize biopsies for several decades, but more recently molecular imaging and variants of MRI have shown promise in independently predicting glioma grade. Here we evaluated whether magnetoencephalographic (MEG) measurements of population-level physiology within the glioma space were predictive of the inherent grade of the tissue, based on definitive histopathological analyses. High-density MEG data were recorded from 11 patients who were undergoing functional mapping in preparation for resective surgery. The primary results indicated that glioma grade was positively correlated with the local amplitude of activity within the glioma space in the theta (4-7 Hz), alpha (8-14 Hz), and beta bands (14-30 Hz). Additionally, activity within the glioma was significantly elevated relative to the nonaffected homologue area in the same frequency bands. These results indicate that pathological levels of synchronization exist within the tumor space and that MEG may be a viable tool for noninvasively differentiating gliomas by their grade. Although these results should be considered preliminary and are only correlative in nature, these data suggest that MEG can potentially detect neurophysiological signatures or markers that predict the inherent grade of a glial tumor.

Original languageEnglish (US)
Pages (from-to)624-630
Number of pages7
JournalNeuro-Oncology
Volume14
Issue number5
DOIs
StatePublished - May 1 2012

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Magnetoencephalography
Glioma
Neuroimaging
Neoplasms
Molecular Imaging
Neuroglia
Biopsy

Keywords

  • Glioma grade
  • MEG
  • Theta
  • Tumor histology
  • Tumor physiology

ASJC Scopus subject areas

  • Oncology
  • Clinical Neurology
  • Cancer Research

Cite this

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title = "Potential role for magnetoencephalography in distinguishing low-and high-grade gliomas: A preliminary study with histopathological confirmation",
abstract = "Gliomas are the most common form of tumor in the CNS and are exceptionally heterogeneous. Accurately characterizing gliomas, in terms of grade and type, is essential for predicting the rate of tumor progression. Histopathological grading and analysis based on biopsied tissue remains the gold standard, but non-and semi-invasive neuroimaging also plays a key role. Neuroimaging has been used to guide and optimize biopsies for several decades, but more recently molecular imaging and variants of MRI have shown promise in independently predicting glioma grade. Here we evaluated whether magnetoencephalographic (MEG) measurements of population-level physiology within the glioma space were predictive of the inherent grade of the tissue, based on definitive histopathological analyses. High-density MEG data were recorded from 11 patients who were undergoing functional mapping in preparation for resective surgery. The primary results indicated that glioma grade was positively correlated with the local amplitude of activity within the glioma space in the theta (4-7 Hz), alpha (8-14 Hz), and beta bands (14-30 Hz). Additionally, activity within the glioma was significantly elevated relative to the nonaffected homologue area in the same frequency bands. These results indicate that pathological levels of synchronization exist within the tumor space and that MEG may be a viable tool for noninvasively differentiating gliomas by their grade. Although these results should be considered preliminary and are only correlative in nature, these data suggest that MEG can potentially detect neurophysiological signatures or markers that predict the inherent grade of a glial tumor.",
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AB - Gliomas are the most common form of tumor in the CNS and are exceptionally heterogeneous. Accurately characterizing gliomas, in terms of grade and type, is essential for predicting the rate of tumor progression. Histopathological grading and analysis based on biopsied tissue remains the gold standard, but non-and semi-invasive neuroimaging also plays a key role. Neuroimaging has been used to guide and optimize biopsies for several decades, but more recently molecular imaging and variants of MRI have shown promise in independently predicting glioma grade. Here we evaluated whether magnetoencephalographic (MEG) measurements of population-level physiology within the glioma space were predictive of the inherent grade of the tissue, based on definitive histopathological analyses. High-density MEG data were recorded from 11 patients who were undergoing functional mapping in preparation for resective surgery. The primary results indicated that glioma grade was positively correlated with the local amplitude of activity within the glioma space in the theta (4-7 Hz), alpha (8-14 Hz), and beta bands (14-30 Hz). Additionally, activity within the glioma was significantly elevated relative to the nonaffected homologue area in the same frequency bands. These results indicate that pathological levels of synchronization exist within the tumor space and that MEG may be a viable tool for noninvasively differentiating gliomas by their grade. Although these results should be considered preliminary and are only correlative in nature, these data suggest that MEG can potentially detect neurophysiological signatures or markers that predict the inherent grade of a glial tumor.

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