Coordinate based random effect size meta-analysis of neuroimaging studies
Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses repor...
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/41757/ |
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| author | Tench, Christopher R. Tanasescu, Radu Constantinescu, Cris S. Auer, Dorothee P. Cottam, William J. |
| author_facet | Tench, Christopher R. Tanasescu, Radu Constantinescu, Cris S. Auer, Dorothee P. Cottam, William J. |
| author_sort | Tench, Christopher R. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density.
Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is necessary to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely. |
| first_indexed | 2025-11-14T19:46:32Z |
| format | Article |
| id | nottingham-41757 |
| institution | University of Nottingham Malaysia Campus |
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| last_indexed | 2025-11-14T19:46:32Z |
| publishDate | 2017 |
| publisher | Elsevier |
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| spelling | nottingham-417572020-05-04T18:41:10Z https://eprints.nottingham.ac.uk/41757/ Coordinate based random effect size meta-analysis of neuroimaging studies Tench, Christopher R. Tanasescu, Radu Constantinescu, Cris S. Auer, Dorothee P. Cottam, William J. Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density. Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is necessary to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely. Elsevier 2017-04-05 Article PeerReviewed Tench, Christopher R., Tanasescu, Radu, Constantinescu, Cris S., Auer, Dorothee P. and Cottam, William J. (2017) Coordinate based random effect size meta-analysis of neuroimaging studies. NeuroImage, 153 . pp. 293-306. ISSN 1095-9572 Meta-analysis Neuroimaging Voxel based morphometry Functional MRI http://www.sciencedirect.com/science/article/pii/S1053811917302902 doi:10.1016/j.neuroimage.2017.04.002 doi:10.1016/j.neuroimage.2017.04.002 |
| spellingShingle | Meta-analysis Neuroimaging Voxel based morphometry Functional MRI Tench, Christopher R. Tanasescu, Radu Constantinescu, Cris S. Auer, Dorothee P. Cottam, William J. Coordinate based random effect size meta-analysis of neuroimaging studies |
| title | Coordinate based random effect size meta-analysis of neuroimaging studies |
| title_full | Coordinate based random effect size meta-analysis of neuroimaging studies |
| title_fullStr | Coordinate based random effect size meta-analysis of neuroimaging studies |
| title_full_unstemmed | Coordinate based random effect size meta-analysis of neuroimaging studies |
| title_short | Coordinate based random effect size meta-analysis of neuroimaging studies |
| title_sort | coordinate based random effect size meta-analysis of neuroimaging studies |
| topic | Meta-analysis Neuroimaging Voxel based morphometry Functional MRI |
| url | https://eprints.nottingham.ac.uk/41757/ https://eprints.nottingham.ac.uk/41757/ https://eprints.nottingham.ac.uk/41757/ |