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Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data

We discuss specific challenges and solutions in infant MEG, which is one of the most technically challenging areas of MEG studies. Our results can be generalized to a variety of challenging scenarios for MEG data acquisition, including clinical settings. We cover a wide range of steps in pre-process...

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Autores principales: Clarke, Maggie D., Larson, Eric, Peterson, Erica R., McCloy, Daniel R., Bosseler, Alexis N., Taulu, Samu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983818/
https://www.ncbi.nlm.nih.gov/pubmed/35401424
http://dx.doi.org/10.3389/fneur.2022.827529
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author Clarke, Maggie D.
Larson, Eric
Peterson, Erica R.
McCloy, Daniel R.
Bosseler, Alexis N.
Taulu, Samu
author_facet Clarke, Maggie D.
Larson, Eric
Peterson, Erica R.
McCloy, Daniel R.
Bosseler, Alexis N.
Taulu, Samu
author_sort Clarke, Maggie D.
collection PubMed
description We discuss specific challenges and solutions in infant MEG, which is one of the most technically challenging areas of MEG studies. Our results can be generalized to a variety of challenging scenarios for MEG data acquisition, including clinical settings. We cover a wide range of steps in pre-processing, including movement compensation, suppression of magnetic interference from sources inside and outside the magnetically shielded room, suppression of specific physiological artifact components such as cardiac artifacts. In the assessment of the outcome of the pre-processing algorithms, we focus on comparing signal representation before and after pre-processing and discuss the importance of the different components of the main processing steps. We discuss the importance of taking the noise covariance structure into account in inverse modeling and present the proper treatment of the noise covariance matrix to accurately reflect the processing that was applied to the data. Using example cases, we investigate the level of source localization error before and after processing. One of our main findings is that statistical metrics of source reconstruction may erroneously indicate that the results are reliable even in cases where the data are severely distorted by head movements. As a consequence, we stress the importance of proper signal processing in infant MEG.
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spelling pubmed-89838182022-04-07 Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data Clarke, Maggie D. Larson, Eric Peterson, Erica R. McCloy, Daniel R. Bosseler, Alexis N. Taulu, Samu Front Neurol Neurology We discuss specific challenges and solutions in infant MEG, which is one of the most technically challenging areas of MEG studies. Our results can be generalized to a variety of challenging scenarios for MEG data acquisition, including clinical settings. We cover a wide range of steps in pre-processing, including movement compensation, suppression of magnetic interference from sources inside and outside the magnetically shielded room, suppression of specific physiological artifact components such as cardiac artifacts. In the assessment of the outcome of the pre-processing algorithms, we focus on comparing signal representation before and after pre-processing and discuss the importance of the different components of the main processing steps. We discuss the importance of taking the noise covariance structure into account in inverse modeling and present the proper treatment of the noise covariance matrix to accurately reflect the processing that was applied to the data. Using example cases, we investigate the level of source localization error before and after processing. One of our main findings is that statistical metrics of source reconstruction may erroneously indicate that the results are reliable even in cases where the data are severely distorted by head movements. As a consequence, we stress the importance of proper signal processing in infant MEG. Frontiers Media S.A. 2022-03-23 /pmc/articles/PMC8983818/ /pubmed/35401424 http://dx.doi.org/10.3389/fneur.2022.827529 Text en Copyright © 2022 Clarke, Larson, Peterson, McCloy, Bosseler and Taulu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Clarke, Maggie D.
Larson, Eric
Peterson, Erica R.
McCloy, Daniel R.
Bosseler, Alexis N.
Taulu, Samu
Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data
title Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data
title_full Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data
title_fullStr Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data
title_full_unstemmed Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data
title_short Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data
title_sort improving localization accuracy of neural sources by pre-processing: demonstration with infant meg data
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983818/
https://www.ncbi.nlm.nih.gov/pubmed/35401424
http://dx.doi.org/10.3389/fneur.2022.827529
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