NiMARE is a Python package for performing meta-analyses, and derivative analyses using meta-analytic data, of the neuroimaging literature. While meta-analytic packages exist which implement one or two algorithms each, NiMARE provides a standard syntax for performing a wide range of analyses and for interacting with databases of coordinates and images from fMRI studies (e.g., brainspell, Neurosynth, and NeuroVault).
NiMARE joins a growing Python ecosystem for neuroimaging research, which includes such tools as `Nipype`_, `Nibabel`_, and `Nilearn`_. As with these other tools, NiMARE is open source, collaboratively developed, and built with ease of use in mind.
This page outlines NiMARE's purpose and its role in a proposed meta-analytic ecosystem.
NiMARE's primary goal is to consolidate coordinate- and image-based meta-analysis methods with a simple, shared and comprehensive 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. NiMARE also provides an environment where comparisons between methods are easier to perform.
A secondary goal of NiMARE is to implement some of the more cutting-edge methods for analyses built on meta-analytic neuroimaging 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 (i.e., documented and tested) code.
Important
For more up-to-date information and for information about other elements in the ecosystem, please see neurostuff.github.io.
NiMARE aims to fill a gap in a burgeoning meta-analytic ecosystem. The goal of NiMARE is to collect a wide range of meta-analytic tools in one Python library. Currently, those methods are spread out across a range of programming languages and user interfaces, or are never even translated from the original papers into useable tools. NiMARE operates on NIMADS-format datasets, which users will be able to compile by searching the NeuroStore database with the pyNIMADS library. A number of other services in the ecosystem will then use NiMARE functions to perform meta-analyses, including Neurosynth 2.0 and `NeuroVault`_.
Outside of the shared ecosystem detailed above, there are a number of tools.
`BrainMap`_: The `BrainMap`_ suite includes applications for the ALE CBMA algorithm (via the `GingerALE`_ app) and interacting with the BrainMap database (via the `Sleuth`_ and `Scribe`_ apps).
The `MKDA Toolbox`_: This toolbox implements the MKDA algorithm, as well as a range derivative analyses.
The `SDM`_ Toolbox: This toolbox contains the hybrid coordinate/image-based seed-based d-mapping algorithm.
`NeuRoi Toolbox`_: This toolbox contains an implementation of the `Analysis of Brain Coordinates`_ (ABC) CBMA algorithm.
`IBMA SPM extension`_: This SPM extension implements a number of image-based meta-analysis algorithms.
`ERPscanr`_: A resource for semi-automated, large-scale meta-analyses of ERP data.