Performing meta-analyses

NiMARE implements a number of coordinate- and image-based meta-analysis algorithms in its meta module. The examples below use Studyset as the primary analysis input, with legacy Dataset objects appearing only in preprocessing steps where older APIs still require them.

For more information about the components that go into coordinate-based meta-analyses in NiMARE, see Coordinate-based meta-analysis in NiMARE, as well as Outputs of NiMARE.

Coordinate-based meta-analysis algorithms

Coordinate-based meta-analysis algorithms

Image-based meta-analysis algorithms

Image-based meta-analysis algorithms

KernelTransformers and CBMA

KernelTransformers and CBMA

The Estimator class

The Estimator class

The Corrector class

The Corrector class

Compare image and coordinate based meta-analyses

Compare image and coordinate based meta-analyses

Meta-analytic coactivation modeling analysis

Meta-analytic coactivation modeling analysis

Two-sample ALE meta-analysis

Two-sample ALE meta-analysis

Simulate data for coordinate based meta-analysis

Simulate data for coordinate based meta-analysis

Run a coordinate-based meta-analysis (CBMA) workflow

Run a coordinate-based meta-analysis (CBMA) workflow

Coordinate-based meta-regression algorithms

Coordinate-based meta-regression algorithms

Run an image-based meta-analysis (IBMA) workflow

Run an image-based meta-analysis (IBMA) workflow

Qualitative interpretation of ALE statistic maps

Qualitative interpretation of ALE statistic maps

Comparing ALE-based pairwise contrast strategies

Comparing ALE-based pairwise contrast strategies

Predictive ALE: fast FWE correction without Monte Carlo

Predictive ALE: fast FWE correction without Monte Carlo

Stability diagnostics: Jackknife vs. ResampledStability

Stability diagnostics: Jackknife vs. ResampledStability