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A Rapid fMRI Paradigm for Localisation of the Language Network.

Accepted version
Peer-reviewed

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Abstract

Language assessment using imaging remains an arduous task due to the complexity of the language network. Given the complex neural instantiation of the language function, structural imaging is insufficient in determining the extensive network of relevant cortical and subcortical areas in patients with intrinsic brain tumours. Functional magnetic resonance imaging (fMRI) provides a non-invasive method for the localisation of language-dominant areas using task-specific brain activation maps. In this study, the authors provide the results of a rapid four-task-based fMRI paradigm that can be used to determine an individual's specific language network. Fifteen participants were recruited prospectively for this study. A 10-min pre-scan preparation was done with each participant prior to the scan on the various tasks they were expected to complete during the fMRI scan. The following four tasks were conducted: covert naming, overt naming, sentence completion and pyramids and palm trees test (PPTT). The average time taken for all four tasks was 25.9 ± 1.8 min (23-31 min). All tasks were successfully completed in less than 30 min for all but one volunteer (31 min). Subject specific task-based fMRI maps were generated for each task, providing overall insights into the bilaterally represented language network. Tasks were completed in a reasonable time duration, providing maximal information that can be translated into intraoperative testing during awake surgeries. Further research is needed to understand any limitations of this type of testing in patients with pre-existing neurological deficits and brain lesions.

Description

Journal Title

Eur J Neurosci

Conference Name

Journal ISSN

0953-816X
1460-9568

Volume Title

Publisher

Wiley

Rights and licensing

Except where otherwised noted, this item's license is described as Attribution 4.0 International
Sponsorship
NIHR Academy (CDF-2018-11-ST2-003)
National Institute for Health and Care Research (NIHR), Career Development Fellowship (CDF-2018-11-ST2-003).

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