z-yxin/nimare

Coordinate- and Image-based meta-analysis in Python

NiMARE: Neuroimaging Meta-Analysis Research Environment

A Python library for coordinate- and image-based meta-analysis.

Supported meta-analytic methods (nimare.meta)

• Coordinate-based methods (nimare.meta.cbma)
  • Kernel-based methods
    • Activation likelihood estimation (ALE)
    • Specific coactivation likelihood estimation (SCALE)
    • Multilevel kernel density analysis (MKDA)
    • Kernel density analysis (KDA)
  • Model-based methods (nimare.meta.cbma.model)
    • Bayesian hierarchical cluster process model (BHICP)
    • Hierarchical Poisson/Gamma random field model (HPGRF)
    • Spatial Bayesian latent factor regression (SBLFR)
    • Spatial binary regression (SBR)
• Image-based methods (nimare.meta.ibma)
  • Mixed effects general linear model (MFX-GLM)
  • Random effects general linear model (RFX-GLM)
  • Fixed effects general linear model (FFX-GLM)
  • Stouffer's meta-analysis
  • Random effects Stouffer's meta-analysis
  • Weighted Stouffer's meta-analysis
  • Fisher's meta-analysis

Additional functionality

• Functional characterization analysis (nimare.decode)
  • Generalized correspondence latent Dirichlet allocation (GCLDA)
  • Neurosynth correlation-based decoding
  • Neurosynth MKDA-based decoding
  • BrainMap decoding

Installation

Local installation

python setup.py install

Installation with Docker

To build the Docker image:

docker build -t test/nimare .

To run the Docker container:

docker run -it -v `pwd`:/home/neuro/code/NiMARE -p8888:8888 test/nimare bash

Once inside the container, you can install NiMARE:

python /home/neuro/code/NiMARE/setup.py develop