Evidence map›Paper›PMID 39400782›Full record

ArticleBrain topography2024

Validation of On-Head OPM MEG for Language Laterality Assessment.

Lindsey Power, Timothy Bardouille, Kristin M Ikeda, Antonina Omisade

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Brain topography, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lindsey PowerSchool of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada.
Timothy BardouilleDepartment of Physics & Atmospheric Science, Dalhousie University, Halifax, NS, Canada. tim.bardouille@dal.ca.
Kristin M IkedaDivision of Neurology, Department of Medicine, Dalhousie University, Halifax, NS, Canada.
Antonina OmisadeAcquired Brain Injury (Epilepsy Program), Nova Scotia Health, Halifax, NS, Canada.

Funding

University Internal Medicine Research Foundation 1025662
6 · The paper itself

Abstract

Pre-surgical localization of language function in the brain is critical for patients with medically intractable epilepsy. MEG has emerged as a valuable clinical tool for localizing language areas in clinical populations, however, it is limited for widespread application due to the low availability of the system. Recent advances in optically pumped magnetometer (OPM) systems account for some of the limitations of traditional MEG and have been shown to have a similar signal-to-noise ratio. However, the novelty of these systems means that they have only been tested for limited sensory and motor applications. In this work, we aim to validate a novel on-head OPM MEG procedure for lateralizing language processes. OPM recordings, using a soft cap with flexible sensor placement, were collected from 19 healthy, right-handed controls during an auditory word recognition task. The resulting evoked fields were assessed for hemispheric laterality of the response. Principal component analysis (PCA) of the grand average language response indicated that the first two principal components were lateralized to the left hemisphere. The PCA also revealed that all participants had evoked topographies that closely resembled the average left-lateralized response. Left-lateralized responses were consistent with what is expected for a group of healthy right-handed individuals. These findings demonstrate that language-related evoked fields can be elucidated from on-head OPM MEG recordings in a group of healthy adult participants. In the future, on-head OPM MEG and the associated lateralization methods should be validated in patient populations as they may have utility in the pre-surgical mapping of language functions in patients with epilepsy.

Indexed as

Functional LateralityLanguageMagnetoencephalographyAcoustic StimulationAdultBrainBrain MappingFemaleHumansMaleMiddle AgedPrincipal Component AnalysisYoung AdultLanguageMagnetoencephalographyMethodsOPMPre-surgical mapping

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.