Coordinate based meta-analysis of functional neuroimaging data: false discovery control and diagnostics
Coordinate based meta-analysis (CBMA) is widely used to find regions of consistent activation across fMRI studies that have been selected for their functional relevance to a given hypothesis. Only reported coordinates (foci), and a model of their spatial uncertainty, are used in the analysis. Result...
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Public Library of Science
2013
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| Online Access: | https://eprints.nottingham.ac.uk/2447/ |
| _version_ | 1848790787925475328 |
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| author | Tench, Christopher R. Tanasescu, Radu Auer, Dorothee P. Constantinescu, Cris S. |
| author_facet | Tench, Christopher R. Tanasescu, Radu Auer, Dorothee P. Constantinescu, Cris S. |
| author_sort | Tench, Christopher R. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Coordinate based meta-analysis (CBMA) is widely used to find regions of consistent activation across fMRI studies that have been selected for their functional relevance to a given hypothesis. Only reported coordinates (foci), and a model of their spatial uncertainty, are used in the analysis. Results are clusters of foci where multiple studies have reported in the same spatial region, indicating functional relevance. There are several published methods that perform the analysis in a voxel-wise manner, resulting in around 105 statistical tests, and considerable emphasis placed on controlling the risk of type 1 statistical error. Here we address this issue by dramatically reducing the number of tests, and by introducing a new false discovery rate control: the false cluster discovery rate (FCDR). FCDR is particularly interpretable and relevant to the results of CBMA, controlling the type 1 error by limiting the proportion of clusters that are expected under the null hypothesis. We also introduce a data diagnostic scheme to help ensure quality of the analysis, and demonstrate its use in the example studies. We show that we control the false clusters better than the widely used ALE method by performing numerical experiments, and that our clustering scheme results in more complete reporting of structures relevant to the functional task. |
| first_indexed | 2025-11-14T18:18:10Z |
| format | Article |
| id | nottingham-2447 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:18:10Z |
| publishDate | 2013 |
| publisher | Public Library of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-24472020-05-04T16:37:44Z https://eprints.nottingham.ac.uk/2447/ Coordinate based meta-analysis of functional neuroimaging data: false discovery control and diagnostics Tench, Christopher R. Tanasescu, Radu Auer, Dorothee P. Constantinescu, Cris S. Coordinate based meta-analysis (CBMA) is widely used to find regions of consistent activation across fMRI studies that have been selected for their functional relevance to a given hypothesis. Only reported coordinates (foci), and a model of their spatial uncertainty, are used in the analysis. Results are clusters of foci where multiple studies have reported in the same spatial region, indicating functional relevance. There are several published methods that perform the analysis in a voxel-wise manner, resulting in around 105 statistical tests, and considerable emphasis placed on controlling the risk of type 1 statistical error. Here we address this issue by dramatically reducing the number of tests, and by introducing a new false discovery rate control: the false cluster discovery rate (FCDR). FCDR is particularly interpretable and relevant to the results of CBMA, controlling the type 1 error by limiting the proportion of clusters that are expected under the null hypothesis. We also introduce a data diagnostic scheme to help ensure quality of the analysis, and demonstrate its use in the example studies. We show that we control the false clusters better than the widely used ALE method by performing numerical experiments, and that our clustering scheme results in more complete reporting of structures relevant to the functional task. Public Library of Science 2013-07-29 Article PeerReviewed Tench, Christopher R., Tanasescu, Radu, Auer, Dorothee P. and Constantinescu, Cris S. (2013) Coordinate based meta-analysis of functional neuroimaging data: false discovery control and diagnostics. PLoS ONE, 8 (7). e70143/1-e70143/12. ISSN 1932-6203 http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0070143 doi:10.1371/journal.pone.0070143 doi:10.1371/journal.pone.0070143 |
| spellingShingle | Tench, Christopher R. Tanasescu, Radu Auer, Dorothee P. Constantinescu, Cris S. Coordinate based meta-analysis of functional neuroimaging data: false discovery control and diagnostics |
| title | Coordinate based meta-analysis of functional neuroimaging data: false discovery control and diagnostics |
| title_full | Coordinate based meta-analysis of functional neuroimaging data: false discovery control and diagnostics |
| title_fullStr | Coordinate based meta-analysis of functional neuroimaging data: false discovery control and diagnostics |
| title_full_unstemmed | Coordinate based meta-analysis of functional neuroimaging data: false discovery control and diagnostics |
| title_short | Coordinate based meta-analysis of functional neuroimaging data: false discovery control and diagnostics |
| title_sort | coordinate based meta-analysis of functional neuroimaging data: false discovery control and diagnostics |
| url | https://eprints.nottingham.ac.uk/2447/ https://eprints.nottingham.ac.uk/2447/ https://eprints.nottingham.ac.uk/2447/ |