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Brain response pattern identification of fMRI data using a particle swarm optimization-based approach
Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4999570/ https://www.ncbi.nlm.nih.gov/pubmed/27747594 http://dx.doi.org/10.1007/s40708-016-0049-z |
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author | Ma, Xinpei Chou, Chun-An Sayama, Hiroki Chaovalitwongse, Wanpracha Art |
author_facet | Ma, Xinpei Chou, Chun-An Sayama, Hiroki Chaovalitwongse, Wanpracha Art |
author_sort | Ma, Xinpei |
collection | PubMed |
description | Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be considered as a multi-objective pattern classification problem with the aim to optimize response patterns, in which informative voxels interacting with each other are selected, achieving high classification accuracy associated with cognitive stimulus conditions. To solve the problem, we propose a feature interaction detection framework, integrating hierarchical heterogeneous particle swarm optimization and support vector machines, for voxel selection in MVPA. In the proposed approach, we first select the most informative voxels and then identify a response pattern based on the connectivity of the selected voxels. The effectiveness of the proposed approach was examined for the Haxby’s dataset of object-level representations. The computational results demonstrated higher classification accuracy by the extracted response patterns, compared to state-of-the-art feature selection algorithms, such as forward selection and backward selection. |
format | Online Article Text |
id | pubmed-4999570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-49995702016-08-31 Brain response pattern identification of fMRI data using a particle swarm optimization-based approach Ma, Xinpei Chou, Chun-An Sayama, Hiroki Chaovalitwongse, Wanpracha Art Brain Inform Article Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be considered as a multi-objective pattern classification problem with the aim to optimize response patterns, in which informative voxels interacting with each other are selected, achieving high classification accuracy associated with cognitive stimulus conditions. To solve the problem, we propose a feature interaction detection framework, integrating hierarchical heterogeneous particle swarm optimization and support vector machines, for voxel selection in MVPA. In the proposed approach, we first select the most informative voxels and then identify a response pattern based on the connectivity of the selected voxels. The effectiveness of the proposed approach was examined for the Haxby’s dataset of object-level representations. The computational results demonstrated higher classification accuracy by the extracted response patterns, compared to state-of-the-art feature selection algorithms, such as forward selection and backward selection. Springer Berlin Heidelberg 2016-04-07 /pmc/articles/PMC4999570/ /pubmed/27747594 http://dx.doi.org/10.1007/s40708-016-0049-z Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Ma, Xinpei Chou, Chun-An Sayama, Hiroki Chaovalitwongse, Wanpracha Art Brain response pattern identification of fMRI data using a particle swarm optimization-based approach |
title | Brain response pattern identification of fMRI data using a particle swarm optimization-based approach |
title_full | Brain response pattern identification of fMRI data using a particle swarm optimization-based approach |
title_fullStr | Brain response pattern identification of fMRI data using a particle swarm optimization-based approach |
title_full_unstemmed | Brain response pattern identification of fMRI data using a particle swarm optimization-based approach |
title_short | Brain response pattern identification of fMRI data using a particle swarm optimization-based approach |
title_sort | brain response pattern identification of fmri data using a particle swarm optimization-based approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4999570/ https://www.ncbi.nlm.nih.gov/pubmed/27747594 http://dx.doi.org/10.1007/s40708-016-0049-z |
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