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

• 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

• 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)

pip install git+https://github.com/neurostuff/NiMARE.git#egg=nimare[peaks2maps-cpu]

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

pip install git+https://github.com/neurostuff/NiMARE.git#egg=nimare[peaks2maps-gpu]

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

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

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