Graph-based inter-subject pattern analysis of FMRI data.
In brain imaging, solving learning problems in multi-subjects settings is difficult because of the differences that exist across individuals. Here we introduce a novel classification framework based on group-invariant graphical representations, allowing to overcome the inter-subject variability pres...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2014-01-01
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Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC4134217?pdf=render |
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author | Sylvain Takerkart Guillaume Auzias Bertrand Thirion Liva Ralaivola |
author_facet | Sylvain Takerkart Guillaume Auzias Bertrand Thirion Liva Ralaivola |
author_sort | Sylvain Takerkart |
collection | DOAJ |
description | In brain imaging, solving learning problems in multi-subjects settings is difficult because of the differences that exist across individuals. Here we introduce a novel classification framework based on group-invariant graphical representations, allowing to overcome the inter-subject variability present in functional magnetic resonance imaging (fMRI) data and to perform multivariate pattern analysis across subjects. Our contribution is twofold: first, we propose an unsupervised representation learning scheme that encodes all relevant characteristics of distributed fMRI patterns into attributed graphs; second, we introduce a custom-designed graph kernel that exploits all these characteristics and makes it possible to perform supervised learning (here, classification) directly in graph space. The well-foundedness of our technique and the robustness of the performance to the parameter setting are demonstrated through inter-subject classification experiments conducted on both artificial data and a real fMRI experiment aimed at characterizing local cortical representations. Our results show that our framework produces accurate inter-subject predictions and that it outperforms a wide range of state-of-the-art vector- and parcel-based classification methods. Moreover, the genericity of our method makes it is easily adaptable to a wide range of potential applications. The dataset used in this study and an implementation of our framework are available at http://dx.doi.org/10.6084/m9.figshare.1086317. |
first_indexed | 2024-12-13T13:57:49Z |
format | Article |
id | doaj.art-2bec3e643d3e4fb1bd701b94f9021f70 |
institution | Directory Open Access Journal |
issn | 1932-6203 |
language | English |
last_indexed | 2024-12-13T13:57:49Z |
publishDate | 2014-01-01 |
publisher | Public Library of Science (PLoS) |
record_format | Article |
series | PLoS ONE |
spelling | doaj.art-2bec3e643d3e4fb1bd701b94f9021f702022-12-21T23:42:50ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0198e10458610.1371/journal.pone.0104586Graph-based inter-subject pattern analysis of FMRI data.Sylvain TakerkartGuillaume AuziasBertrand ThirionLiva RalaivolaIn brain imaging, solving learning problems in multi-subjects settings is difficult because of the differences that exist across individuals. Here we introduce a novel classification framework based on group-invariant graphical representations, allowing to overcome the inter-subject variability present in functional magnetic resonance imaging (fMRI) data and to perform multivariate pattern analysis across subjects. Our contribution is twofold: first, we propose an unsupervised representation learning scheme that encodes all relevant characteristics of distributed fMRI patterns into attributed graphs; second, we introduce a custom-designed graph kernel that exploits all these characteristics and makes it possible to perform supervised learning (here, classification) directly in graph space. The well-foundedness of our technique and the robustness of the performance to the parameter setting are demonstrated through inter-subject classification experiments conducted on both artificial data and a real fMRI experiment aimed at characterizing local cortical representations. Our results show that our framework produces accurate inter-subject predictions and that it outperforms a wide range of state-of-the-art vector- and parcel-based classification methods. Moreover, the genericity of our method makes it is easily adaptable to a wide range of potential applications. The dataset used in this study and an implementation of our framework are available at http://dx.doi.org/10.6084/m9.figshare.1086317.http://europepmc.org/articles/PMC4134217?pdf=render |
spellingShingle | Sylvain Takerkart Guillaume Auzias Bertrand Thirion Liva Ralaivola Graph-based inter-subject pattern analysis of FMRI data. PLoS ONE |
title | Graph-based inter-subject pattern analysis of FMRI data. |
title_full | Graph-based inter-subject pattern analysis of FMRI data. |
title_fullStr | Graph-based inter-subject pattern analysis of FMRI data. |
title_full_unstemmed | Graph-based inter-subject pattern analysis of FMRI data. |
title_short | Graph-based inter-subject pattern analysis of FMRI data. |
title_sort | graph based inter subject pattern analysis of fmri data |
url | http://europepmc.org/articles/PMC4134217?pdf=render |
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