Our Roadmap¶
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)
- Coordinate-based methods (
- 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
- Image-based methods (
- 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)
- Automated annotation (
- Database extraction (
nimare.extract
) NeuroVault
Neurosynth
Brainspell
PubMed abstract extraction
- Database 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)
- Functional characterization analysis (
- Meta-analytic parcellation (
nimare.parcellate
) Meta-analytic parcellation based on text (MAPBOT)
Coactivation-base parcellation (CBP)
Meta-analytic activation modeling-based parcellation (MAMP)
- Meta-analytic parcellation (
- Common workflows (
nimare.workflows
) Meta-analytic coactivation modeling (MACM)
Meta-analytic clustering analysis
Meta-analytic independent components analysis (metaICA)
- Common workflows (