Coordinate based meta-analysis of networks in neuroimaging studies

Tench, C.R.; Tanasescu, Radu; Constantinescu, C.S.; Cottam, W.J.; Auer, D.P.

Abstract

Meta-analysis of summary results from published neuroimaging studies independently testing a common hypothesis is performed using coordinate based meta-analysis (CBMA), which tests for consistent activation (in the case of functional MRI studies) of the same anatomical regions. Using just the reported coordinates it is also possible to meta-analyse coactivated regions to reveal a network-like structure of coordinate clusters (network nodes) distributed at the coactivated locations and a measure of the coactivation strength (network edges), which is determined by the presence/absence of reported activation.

Here a new coordinate-based method to estimate a network of coactivations is detailed, which utilises the Z score accompanying each reported. Coordinate based meta-analysis of networks (CBMAN) assumes that if the activation pattern reported by independent studies is truly consistent, then the relative magnitude of these Z scores might also be consistent. It is hypothesised that this is detectable as Z score covariance between coactivated regions provided the within study variances are small. Advantages of using the Z scores instead of coordinates to measure coactivation strength are that censoring by the significance thresholds can be considered, and that using a continuous measure rather than a dichotomous one can increase statistical power.

CBMAN uses maximum likelihood estimation to fit multivariate normal distributions to the standardised Z scores, and the covariances are considered as edges of a network of coactivated clusters (nodes). Here it is validated by numerical simulation and demonstrated on real data used previously to demonstrate CBMA. Software to perform CBMAN is freely available.

Citation

Tench, C., Tanasescu, R., Constantinescu, C., Cottam, W., & Auer, D. (2020). Coordinate based meta-analysis of networks in neuroimaging studies. NeuroImage, 205, 1-12. https://doi.org/10.1016/j.neuroimage.2019.116259

• Journal Article Type: Article
• Acceptance Date: Oct 7, 2019
• Online Publication Date: Oct 15, 2019
• Publication Date: Jan 15, 2020
• Deposit Date: Oct 16, 2019
• Publicly Available Date: Oct 16, 2020
• Journal: NeuroImage
• Print ISSN: 1053-8119
• Electronic ISSN: 1095-9572
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 205
• Article Number: 116259
• Pages: 1-12
• DOI: https://doi.org/10.1016/j.neuroimage.2019.116259
• Keywords: Cognitive Neuroscience; Neurology
• Public URL: https://nottingham-repository.worktribe.com/output/2848545
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S105381191930850X?via%3Dihub
• Contract Date: Oct 17, 2019