Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA
Multivariate pattern analysis (MVPA) has become vastly popular for analyzing functional neuroimaging data. At the group level, two main strategies are used in the literature. The standard one is hierarchical, combining the outcomes of within-subject decoding results in a second-level analysis. The a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811919307967 |
| _version_ | 1818042912537051136 |
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| author | Qi Wang Bastien Cagna Thierry Chaminade Sylvain Takerkart |
| author_facet | Qi Wang Bastien Cagna Thierry Chaminade Sylvain Takerkart |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | Multivariate pattern analysis (MVPA) has become vastly popular for analyzing functional neuroimaging data. At the group level, two main strategies are used in the literature. The standard one is hierarchical, combining the outcomes of within-subject decoding results in a second-level analysis. The alternative one, inter-subject pattern analysis, directly works at the group-level by using, e.g. a leave-one-subject-out cross-validation. This study provides a thorough comparison of these two group-level decoding schemes, using both a large number of artificial datasets where the size of the multivariate effect and the amount of inter-individual variability are parametrically controlled, as well as two real fMRI datasets comprising 15 and 39 subjects, respectively. We show that these two strategies uncover distinct significant regions with partial overlap, and that inter-subject pattern analysis is able to detect smaller effects and to facilitate the interpretation. The core source code and data are openly available, allowing to fully reproduce most of these results. |
| first_indexed | 2024-12-10T08:53:51Z |
| format | Article |
| id | doaj.art-5a8522b4a2324fceb81607ce886b29ed |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-10T08:53:51Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-5a8522b4a2324fceb81607ce886b29ed2022-12-22T01:55:30ZengElsevierNeuroImage1095-95722020-01-01204116205Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPAQi Wang0Bastien Cagna1Thierry Chaminade2Sylvain Takerkart3Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS - Faculté de Médecine, 27 Boulevard Jean Moulin, Marseille, 13005, France; Laboratoire d’Informatique et Systèmes UMR 7020, Aix-Marseille Université, CNRS, Ecole Centrale de Marseille - Faculté des Sciences, 163 Avenue de Luminy, Case 901, Marseille, 13009, FranceInstitut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS - Faculté de Médecine, 27 Boulevard Jean Moulin, Marseille, 13005, FranceInstitut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS - Faculté de Médecine, 27 Boulevard Jean Moulin, Marseille, 13005, FranceInstitut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS - Faculté de Médecine, 27 Boulevard Jean Moulin, Marseille, 13005, France; Corresponding author.Multivariate pattern analysis (MVPA) has become vastly popular for analyzing functional neuroimaging data. At the group level, two main strategies are used in the literature. The standard one is hierarchical, combining the outcomes of within-subject decoding results in a second-level analysis. The alternative one, inter-subject pattern analysis, directly works at the group-level by using, e.g. a leave-one-subject-out cross-validation. This study provides a thorough comparison of these two group-level decoding schemes, using both a large number of artificial datasets where the size of the multivariate effect and the amount of inter-individual variability are parametrically controlled, as well as two real fMRI datasets comprising 15 and 39 subjects, respectively. We show that these two strategies uncover distinct significant regions with partial overlap, and that inter-subject pattern analysis is able to detect smaller effects and to facilitate the interpretation. The core source code and data are openly available, allowing to fully reproduce most of these results.http://www.sciencedirect.com/science/article/pii/S1053811919307967fMRIMVPAGroup analysis |
| spellingShingle | Qi Wang Bastien Cagna Thierry Chaminade Sylvain Takerkart Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA NeuroImage fMRI MVPA Group analysis |
| title | Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA |
| title_full | Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA |
| title_fullStr | Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA |
| title_full_unstemmed | Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA |
| title_short | Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA |
| title_sort | inter subject pattern analysis a straightforward and powerful scheme for group level mvpa |
| topic | fMRI MVPA Group analysis |
| url | http://www.sciencedirect.com/science/article/pii/S1053811919307967 |
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