In Vivo Tumour Mapping Using Electrocorticography Alterations During Awake Brain Surgery: A Pilot Study

During awake brain surgery for tumour resection, in situ EEG recording (ECoG) is used to identify eloquent areas surrounding the tumour. We used the ECoG setup to record the electrical activity of cortical and subcortical tumours and then performed frequency and connectivity analyses in order to ide...

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Bibliographic Details
Published in:Brain topography Vol. 29; no. 5; pp. 766 - 782
Main Authors: Boussen, Salah, Velly, Lionel, Benar, Christian, Metellus, Philippe, Bruder, Nicolas, Trébuchon, Agnès
Format: Journal Article
Language:English
Published: New York Springer US 01.09.2016
Springer Nature B.V
Springer Verlag
Subjects:
Adult
Aged
Biomedical and Life Sciences
Biomedicine
Brain - physiology
Brain Mapping - methods
Brain Neoplasms - surgery
Cognitive science
Databases, Factual
Electrocorticography - methods
Female
Glioma - surgery
Humans
Intraoperative Care - methods
Male
Middle Aged
Neurology
Neuroscience
Neurosciences
Neurosurgical Procedures
Original Paper
Pilot Projects
Psychiatry
Wakefulness
Young Adult
Connectivity
Cerebral Tumour
Awake Brain Surgery
ECoG
EEG
ISSN:0896-0267, 1573-6792, 1573-6792
Online Access:Get full text
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Summary:During awake brain surgery for tumour resection, in situ EEG recording (ECoG) is used to identify eloquent areas surrounding the tumour. We used the ECoG setup to record the electrical activity of cortical and subcortical tumours and then performed frequency and connectivity analyses in order to identify ECoG impairments and map tumours. We selected 16 patients with cortical (8) and subcortical (8) tumours undergoing awake brain surgery. For each patient, we computed the spectral content of tumoural and healthy areas in each frequency band. We computed connectivity of each electrode using connectivity markers (linear and non-linear correlations, phase-locking and coherence). We performed comparisons between healthy and tumour electrodes. The ECoG alterations were used to implement automated classification of the electrodes using clustering or neural network algorithms. ECoG alterations were used to image cortical tumours.Cortical tumours were found to profoundly alter all frequency contents (normalized and absolute power), with an increase in the δ activity and a decreases for the other bands (P < 0.05). Cortical tumour electrodes showed high level of connectivity compared to surrounding electrodes (all markers, P < 0.05). For subcortical tumours, a relative decrease in the γ1 band and in the alpha band in absolute amplitude (P < 0.05) were the only abnormalities. The neural network algorithm classification had a good performance: 93.6 % of the electrodes were classified adequately on a test subject. We found significant spectral and connectivity ECoG changes for cortical tumours, which allowed tumour recognition. Artificial neural algorithm pattern recognition seems promising for electrode classification in awake tumour surgery.
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ISSN:0896-0267
1573-6792
1573-6792
DOI:10.1007/s10548-016-0502-6