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Modeling Group-Level Repeated Measurements of Neuroimaging Data Using the Univariate General Linear Model

Group-level repeated measurements are common in neuroimaging, yet their analysis remains complex. Although a variety of specialized tools now exist, it is surprising that to-date there has been no clear discussion of how repeated-measurements can be analyzed appropriately using the standard general...

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Autor principal: McFarquhar, Martyn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478886/
https://www.ncbi.nlm.nih.gov/pubmed/31057352
http://dx.doi.org/10.3389/fnins.2019.00352
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author McFarquhar, Martyn
author_facet McFarquhar, Martyn
author_sort McFarquhar, Martyn
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description Group-level repeated measurements are common in neuroimaging, yet their analysis remains complex. Although a variety of specialized tools now exist, it is surprising that to-date there has been no clear discussion of how repeated-measurements can be analyzed appropriately using the standard general linear model approach, as implemented in software such as SPM and FSL. This is particularly surprising given that these implementations necessitate the use of multiple models, even for seemingly conventional analyses, and that without care it is very easy to specify contrasts that do not correctly test the effects of interest. Despite this, interest in fitting these types of models using conventional tools has been growing in the neuroimaging community. As such it has become even more important to elucidate the correct means of doing so. To begin, this paper will discuss the key concept of the expected mean squares (EMS) for defining suitable F-ratios for testing hypotheses. Once this is understood, the logic of specifying correct repeated measurements models in the GLM should be clear. The ancillary issue of specifying suitable contrast weights in these designs will also be discussed, providing a complimentary perspective on the EMS. A set of steps will then be given alongside an example of specifying a 3-way repeated-measures ANOVA in SPM. Equivalency of the results compared to other statistical software will be demonstrated. Additional issues, such as the inclusion of continuous covariates and the assumption of sphericity, will also be discussed. The hope is that this paper will provide some clarity on this confusing topic, giving researchers the confidence to correctly specify these forms of models within traditional neuroimaging analysis tools.
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spelling pubmed-64788862019-05-03 Modeling Group-Level Repeated Measurements of Neuroimaging Data Using the Univariate General Linear Model McFarquhar, Martyn Front Neurosci Neuroscience Group-level repeated measurements are common in neuroimaging, yet their analysis remains complex. Although a variety of specialized tools now exist, it is surprising that to-date there has been no clear discussion of how repeated-measurements can be analyzed appropriately using the standard general linear model approach, as implemented in software such as SPM and FSL. This is particularly surprising given that these implementations necessitate the use of multiple models, even for seemingly conventional analyses, and that without care it is very easy to specify contrasts that do not correctly test the effects of interest. Despite this, interest in fitting these types of models using conventional tools has been growing in the neuroimaging community. As such it has become even more important to elucidate the correct means of doing so. To begin, this paper will discuss the key concept of the expected mean squares (EMS) for defining suitable F-ratios for testing hypotheses. Once this is understood, the logic of specifying correct repeated measurements models in the GLM should be clear. The ancillary issue of specifying suitable contrast weights in these designs will also be discussed, providing a complimentary perspective on the EMS. A set of steps will then be given alongside an example of specifying a 3-way repeated-measures ANOVA in SPM. Equivalency of the results compared to other statistical software will be demonstrated. Additional issues, such as the inclusion of continuous covariates and the assumption of sphericity, will also be discussed. The hope is that this paper will provide some clarity on this confusing topic, giving researchers the confidence to correctly specify these forms of models within traditional neuroimaging analysis tools. Frontiers Media S.A. 2019-04-17 /pmc/articles/PMC6478886/ /pubmed/31057352 http://dx.doi.org/10.3389/fnins.2019.00352 Text en Copyright © 2019 McFarquhar. http://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 Neuroscience
McFarquhar, Martyn
Modeling Group-Level Repeated Measurements of Neuroimaging Data Using the Univariate General Linear Model
title Modeling Group-Level Repeated Measurements of Neuroimaging Data Using the Univariate General Linear Model
title_full Modeling Group-Level Repeated Measurements of Neuroimaging Data Using the Univariate General Linear Model
title_fullStr Modeling Group-Level Repeated Measurements of Neuroimaging Data Using the Univariate General Linear Model
title_full_unstemmed Modeling Group-Level Repeated Measurements of Neuroimaging Data Using the Univariate General Linear Model
title_short Modeling Group-Level Repeated Measurements of Neuroimaging Data Using the Univariate General Linear Model
title_sort modeling group-level repeated measurements of neuroimaging data using the univariate general linear model
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478886/
https://www.ncbi.nlm.nih.gov/pubmed/31057352
http://dx.doi.org/10.3389/fnins.2019.00352
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