Coordinate- and Image-based meta-analysis in Python
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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

  • Automated annotation (nimare.annotate)
    • Tf-idf vectorization of text (nimare.annotate.tfidf)
    • Ontology-based annotation (nimare.annotate.ontology)
      • Cognitive Paradigm Ontology (nimare.annotate.ontology.cogpo)
      • Cognitive Atlas (nimare.annotate.ontology.cogat)
    • Topic model-based annotation (nimare.annotate.topic)
      • Latent Dirichlet allocation (nimare.annotate.topic.lda)
      • Generalized correspondence latent Dirichlet allocation (nimare.annotate.topic.gclda)
      • Deep Boltzmann machines (nimare.annotate.topic.boltzmann)
    • Vector model-based annotation (nimare.annotate.vector)
      • Global Vectors for Word Representation model (nimare.annotate.vector.word2brain)
      • Text2Brain model (nimare.annotate.vector.text2brain)
  • Database extraction (nimare.dataset.extract)
    • NeuroVault
    • Neurosynth
    • Brainspell
    • PubMed abstract extraction
  • Functional characterization analysis (nimare.decode)
    • BrainMap decoding
    • Neurosynth correlation-based decoding
    • Neurosynth MKDA-based decoding
    • BrainMap decoding
    • Text2brain encoding
    • Generalized correspondence latent Dirichlet allocation (GCLDA)
  • Meta-analytic parcellation (nimare.parcellate)
    • Meta-analytic parcellation based on text (MAPBOT)
    • Coactivation-base parcellation (CBP)
    • Meta-analytic activation modeling-based parcellation (MAMP)
  • Common workflows (nimare.workflows)
    • Meta-analytic coactivation modeling (MACM)
    • Meta-analytic clustering analysis
    • Meta-analytic independent components analysis (metaICA)


Local installation (development version)

pip install git+[peaks2maps-cpu]

If you have TensorFlow configured to take advantage of your local GPU use

pip install git+[peaks2maps-gpu]

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/ develop


Please see our contributing guidelines for more information on contributing to NiMARE.

We ask that all contributions to NiMARE respect our code of conduct.