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Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis
Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative v...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2994831/ https://www.ncbi.nlm.nih.gov/pubmed/21152081 http://dx.doi.org/10.1371/journal.pone.0015065 |
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author | Xu, Rui Zhen, Zonglei Liu, Jia |
author_facet | Xu, Rui Zhen, Zonglei Liu, Jia |
author_sort | Xu, Rui |
collection | PubMed |
description | Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. |
format | Text |
id | pubmed-2994831 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-29948312010-12-10 Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis Xu, Rui Zhen, Zonglei Liu, Jia PLoS One Research Article Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. Public Library of Science 2010-11-30 /pmc/articles/PMC2994831/ /pubmed/21152081 http://dx.doi.org/10.1371/journal.pone.0015065 Text en Xu et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Xu, Rui Zhen, Zonglei Liu, Jia Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis |
title | Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis |
title_full | Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis |
title_fullStr | Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis |
title_full_unstemmed | Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis |
title_short | Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis |
title_sort | mapping informative clusters in a hierarchial framework of fmri multivariate analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2994831/ https://www.ncbi.nlm.nih.gov/pubmed/21152081 http://dx.doi.org/10.1371/journal.pone.0015065 |
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