Título:
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Population level inference for multivariate MEG analysis
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Autor/a:
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Jafarpour, Anna; Barnes, Gareth; Fuentemilla Garriga, Lluís; Düzel, Emrah; Penny, Will
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Otros autores:
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Universitat de Barcelona |
Abstract:
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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. |
Materia(s):
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-Diagnòstic per la imatge -Anàlisi multivariable -Encèfal -Diagnostic imaging -Multivariate analysis -Encephalon |
Derechos:
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cc-by (c) Jafarpour, Anna et al., 2013
http://creativecommons.org/licenses/by/3.0/es |
Tipo de documento:
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Artículo Artículo - Versión publicada |
Editor:
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Public Library of Science (PLoS)
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