Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA).
Current functional Magnetic Resonance Imaging technology is able to resolve billions of individual functional connections characterizing the human connectome. Classical statistical inferential procedures attempting to make valid inferences across this many measures from a reduced set of observations...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010634 |
| _version_ | 1811205202977488896 |
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| author | Alfonso Nieto-Castanon |
| author_facet | Alfonso Nieto-Castanon |
| author_sort | Alfonso Nieto-Castanon |
| collection | DOAJ |
| description | Current functional Magnetic Resonance Imaging technology is able to resolve billions of individual functional connections characterizing the human connectome. Classical statistical inferential procedures attempting to make valid inferences across this many measures from a reduced set of observations and from a limited number of subjects can be severely underpowered for any but the largest effect sizes. This manuscript discusses fc-MVPA (functional connectivity Multivariate Pattern Analysis), a novel method using multivariate pattern analysis techniques in the context of brain-wide connectome inferences. The theory behind fc-MVPA is presented, and several of its key concepts are illustrated through examples from a publicly available resting state dataset, including an analysis of gender differences across the entire functional connectome. Finally, Monte Carlo simulations are used to demonstrate the validity and sensitivity of this method. In addition to offering powerful whole-brain inferences, fc-MVPA also provides a meaningful characterization of the heterogeneity in functional connectivity across subjects. |
| first_indexed | 2024-04-12T03:27:52Z |
| format | Article |
| id | doaj.art-f994ae4d64c341d49043da67f9daff76 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T03:27:52Z |
| publishDate | 2022-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-f994ae4d64c341d49043da67f9daff762022-12-22T03:49:40ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-11-011811e101063410.1371/journal.pcbi.1010634Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA).Alfonso Nieto-CastanonCurrent functional Magnetic Resonance Imaging technology is able to resolve billions of individual functional connections characterizing the human connectome. Classical statistical inferential procedures attempting to make valid inferences across this many measures from a reduced set of observations and from a limited number of subjects can be severely underpowered for any but the largest effect sizes. This manuscript discusses fc-MVPA (functional connectivity Multivariate Pattern Analysis), a novel method using multivariate pattern analysis techniques in the context of brain-wide connectome inferences. The theory behind fc-MVPA is presented, and several of its key concepts are illustrated through examples from a publicly available resting state dataset, including an analysis of gender differences across the entire functional connectome. Finally, Monte Carlo simulations are used to demonstrate the validity and sensitivity of this method. In addition to offering powerful whole-brain inferences, fc-MVPA also provides a meaningful characterization of the heterogeneity in functional connectivity across subjects.https://doi.org/10.1371/journal.pcbi.1010634 |
| spellingShingle | Alfonso Nieto-Castanon Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA). PLoS Computational Biology |
| title | Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA). |
| title_full | Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA). |
| title_fullStr | Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA). |
| title_full_unstemmed | Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA). |
| title_short | Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA). |
| title_sort | brain wide connectome inferences using functional connectivity multivariate pattern analyses fc mvpa |
| url | https://doi.org/10.1371/journal.pcbi.1010634 |
| work_keys_str_mv | AT alfonsonietocastanon brainwideconnectomeinferencesusingfunctionalconnectivitymultivariatepatternanalysesfcmvpa |