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Coordinate based random effect size meta-analysis of neuroimaging studies.

C R Tench¹, Radu Tanasescu², C S Constantinescu¹

¹Division of Clinical Neurosciences, Clinical Neurology, University of Nottingham, Queen's Medical Centre, Nottingham, UK.

|April 9, 2017

Summary

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Coordinate-based meta-analysis (CBMA) improves neuroimaging study power. The new ClusterZ algorithm performs random effects meta-analysis and meta-regression on coordinates and effect sizes, enhancing statistical rigor and controlling errors.

Area of Science:

Neuroimaging
Neuroscience
Biostatistics

Background:

Low statistical power in neuroimaging studies hinders interpretation.
Coordinate-based meta-analysis (CBMA) aggregates findings from published studies.
Existing CBMA methods rely on coordinate density for significance.

Purpose of the Study:

Introduce a novel coordinate-based random effects meta-analysis and meta-regression method.
Develop an algorithm (ClusterZ) to analyze coordinates and effect sizes.
Improve statistical rigor in neuroimaging meta-analyses.

Main Methods:

The ClusterZ algorithm analyzes coordinates and reported t/Z scores, standardized by subject number.
Statistical significance is determined by random effects meta-analysis of reported effects, accounting for data censoring.

Keywords:

Functional MRI Meta-analysis Neuroimaging Voxel based morphometry

Related Experiment Videos

Type 1 error is controlled using the false cluster discovery rate (FCDR).

Main Results:

ClusterZ was validated using simulated data and real neuroimaging datasets.
Demonstrated effectiveness on grey matter loss in multiple sclerosis and pain perception studies.
The method provides robust statistical significance determination beyond coordinate density.

Conclusions:

ClusterZ offers a statistically rigorous approach to coordinate-based meta-analysis.
The method effectively mitigates issues associated with low power in neuroimaging.
Software implementation is freely available for broader research application.