NiMARE's primary goal is to consolidate coordinate- and image-based meta-analysis methods with a simple, shared interface. This should reduce brand loyalty to any given algorithm, as it should be easy to employ the most appropriate algorithm for a given project.
A secondary goal of NiMARE is to implement some of the more cutting-edge methods for analyses built on meta-analytic data. There are many tools or algorithms that use meta-analytic data, including automated annotation, meta-analytic functional characterization analysis, and meta-analytic parcellation. Many of these methods are either tied to a specific meta-analysis package or never make it from publication to useable code.
Ultimately, we plan to support all (or most) of the methods listed below in NiMARE:
- 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.LDAModel)
- Generalized correspondence latent Dirichlet allocation (nimare.annotate.topic.GCLDAModel)
- Deep Boltzmann machines (nimare.annotate.topic.BoltzmannModel)
- Vector model-based annotation (nimare.annotate.vector)
- Global Vectors for Word Representation model (nimare.annotate.vector.Word2BrainModel)
- Text2Brain model (nimare.annotate.vector.Text2BrainModel)
- Database extraction (nimare.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)