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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23940738/?tool=EBI |
| _version_ | 1818337444632723456 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T14:55:19Z |
| format | Article |
| id | doaj.art-bfff262a057e4505bb29101816605d02 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T14:55:19Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-bfff262a057e4505bb29101816605d022022-12-21T23:41:14ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0188e7130510.1371/journal.pone.0071305Population level inference for multivariate MEG analysis.Anna JafarpourGareth BarnesLluis FuentemillaEmrah DuzelWill D PennyMultivariate 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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23940738/?tool=EBI |
| spellingShingle | Anna Jafarpour Gareth Barnes Lluis Fuentemilla Emrah Duzel Will D Penny Population level inference for multivariate MEG analysis. PLoS ONE |
| title | 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_short | Population level inference for multivariate MEG analysis. |
| title_sort | population level inference for multivariate meg analysis |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23940738/?tool=EBI |
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