Christopher R. Tench

This paper introduces CVCA, a method for contrast analysis in coordinate-based meta-analysis that avoids voxel-wise significance testing and provides effect estimates with reduced uncertainty.

This paper uses neuroimaging meta-analyses to identify the insular and midcingulate cortex as key regions of the central autonomic system, showing task-independent and task-specific roles in autonomic signal processing.

The paper introduces a new coordinate-based meta-analysis method for neuroimaging studies that aims to be more interpretable by reducing empirical assumptions and linking statistical thresholds to effect replication.

This paper performs a coordinate-based meta-analysis to identify consistent grey matter loss in MS and CIS patients, finding significant clusters and correlations with disease duration.

The paper introduces a new clustering algorithm for coordinate based meta-analysis that supports region of interest studies and uses false discovery rate error control.

This paper introduces CBMAN, a method that extends coordinate-based meta-analysis to estimate networks of correlated clusters in neuroimaging data.

This paper performs a coordinate-based meta-analysis of whole-brain voxel-based morphometry studies in PTSD and finds no consistent grey matter changes specific to PTSD.

The paper introduces ClusterZ, a method for coordinate-based random effect size meta-analysis and meta-regression in neuroimaging, which improves statistical significance determination by using false cluster discovery rate control.