Population level inference for multivariate MEG analysis.

Multivariate analysis is a very general and powerful technique for analysing Magnetoencephalography (MEG) data. An outstanding problem however is how to make inferences that are consistent over a group of subjects as to whether there are condition-specific differences in data features, and what are...

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Autores principales:	Anna Jafarpour, Gareth Barnes, Lluis Fuentemilla, Emrah Duzel, Will D Penny
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bfff262a057e4505bb29101816605d02
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spelling	oai:doaj.org-article:bfff262a057e4505bb29101816605d022021-11-18T09:01:09ZPopulation level inference for multivariate MEG analysis.1932-620310.1371/journal.pone.0071305https://doaj.org/article/bfff262a057e4505bb29101816605d022013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23940738/?tool=EBIhttps://doaj.org/toc/1932-6203Multivariate analysis is a very general and powerful technique for analysing Magnetoencephalography (MEG) data. An outstanding problem however is how to make inferences that are consistent over a group of subjects as to whether there are condition-specific differences in data features, and what are those features that maximise these differences. Here we propose a solution based on Canonical Variates Analysis (CVA) model scoring at the subject level and random effects Bayesian model selection at the group level. We apply this approach to beamformer reconstructed MEG data in source space. CVA estimates those multivariate patterns of activation that correlate most highly with the experimental design; the order of a CVA model is then determined by the number of significant canonical vectors. Random effects Bayesian model comparison then provides machinery for inferring the optimal order over the group of subjects. Absence of a multivariate dependence is indicated by the null model being the most likely. This approach can also be applied to CVA models with a fixed number of canonical vectors but supplied with different feature sets. We illustrate the method by identifying feature sets based on variable-dimension MEG power spectra in the primary visual cortex and fusiform gyrus that are maximally discriminative of data epochs before versus after visual stimulation.Anna JafarpourGareth BarnesLluis FuentemillaEmrah DuzelWill D PennyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 8, p e71305 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anna Jafarpour Gareth Barnes Lluis Fuentemilla Emrah Duzel Will D Penny Population level inference for multivariate MEG analysis.
description	Multivariate analysis is a very general and powerful technique for analysing Magnetoencephalography (MEG) data. An outstanding problem however is how to make inferences that are consistent over a group of subjects as to whether there are condition-specific differences in data features, and what are those features that maximise these differences. Here we propose a solution based on Canonical Variates Analysis (CVA) model scoring at the subject level and random effects Bayesian model selection at the group level. We apply this approach to beamformer reconstructed MEG data in source space. CVA estimates those multivariate patterns of activation that correlate most highly with the experimental design; the order of a CVA model is then determined by the number of significant canonical vectors. Random effects Bayesian model comparison then provides machinery for inferring the optimal order over the group of subjects. Absence of a multivariate dependence is indicated by the null model being the most likely. This approach can also be applied to CVA models with a fixed number of canonical vectors but supplied with different feature sets. We illustrate the method by identifying feature sets based on variable-dimension MEG power spectra in the primary visual cortex and fusiform gyrus that are maximally discriminative of data epochs before versus after visual stimulation.
format	article
author	Anna Jafarpour Gareth Barnes Lluis Fuentemilla Emrah Duzel Will D Penny
author_facet	Anna Jafarpour Gareth Barnes Lluis Fuentemilla Emrah Duzel Will D Penny
author_sort	Anna Jafarpour
title	Population level inference for multivariate MEG analysis.
title_short	Population level inference for multivariate MEG analysis.
title_full	Population level inference for multivariate MEG analysis.
title_fullStr	Population level inference for multivariate MEG analysis.
title_full_unstemmed	Population level inference for multivariate MEG analysis.
title_sort	population level inference for multivariate meg analysis.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/bfff262a057e4505bb29101816605d02
work_keys_str_mv	AT annajafarpour populationlevelinferenceformultivariatemeganalysis AT garethbarnes populationlevelinferenceformultivariatemeganalysis AT lluisfuentemilla populationlevelinferenceformultivariatemeganalysis AT emrahduzel populationlevelinferenceformultivariatemeganalysis AT willdpenny populationlevelinferenceformultivariatemeganalysis
_version_	1718421047325229056

Population level inference for multivariate MEG analysis.

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