Open Access
19 December 2016 Language mapping in children using resting-state functional connectivity: comparison with a task-based approach
Anne Gallagher, Julie Tremblay, Phetsamone Vannasing
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Abstract
Patients with brain tumor or refractory epilepsy may be candidates for neurosurgery. Presurgical evaluation often includes language investigation to prevent or reduce the risk of postsurgical language deficits. Current techniques involve significant limitations with pediatric populations. Recently, near-infrared spectroscopy (NIRS) has been shown to be a valuable neuroimaging technique for language localization in children. However, it typically requires the child to perform a task (task-based NIRS), which may constitute a significant limitation. Resting-state functional connectivity NIRS (fcNIRS) is an approach that can be used to identify language networks at rest. This study aims to assess the utility of fcNIRS in children by comparing fcNIRS to more conventional task-based NIRS for language mapping in 33 healthy participants: 25 children (ages 3 to 16) and 8 adults. Data were acquired at rest and during a language task. Results show very good concordance between both approaches for language localization (Dice similarity coefficient=0.81±0.13) and hemispheric language dominance (kappa=0.86, p<0.006). The fcNIRS technique may be a valuable tool for language mapping in clinical populations, including children and patients with cognitive and behavioral impairments.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Anne Gallagher, Julie Tremblay, and Phetsamone Vannasing "Language mapping in children using resting-state functional connectivity: comparison with a task-based approach," Journal of Biomedical Optics 21(12), 125006 (19 December 2016). https://doi.org/10.1117/1.JBO.21.12.125006
Received: 26 July 2016; Accepted: 21 November 2016; Published: 19 December 2016
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CITATIONS
Cited by 16 scholarly publications.
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KEYWORDS
Near infrared spectroscopy

Brain mapping

Brain

Lithium

Data acquisition

Functional magnetic resonance imaging

Neuroimaging

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