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0.0.10

Contents:

  • About NiMARE
  • Installation
  • API
  • Examples
    • Working with datasets
    • Performing meta-analyses
    • Automated annotation
    • Functional characterization analysis
  • Contributing to NiMARE
  • NiMARE Developer Guide
  • Our Roadmap
  • Command Line Interface
  • Outputs of NiMARE
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  • NiMARE: Neuroimaging Meta-Analysis Research Environment
  • Edit on GitHub

Examples

Working with datasets

Using Neurovault Statistical Maps in NiMARE

Using Neurovault Statistical Maps in NiMARE

Load and work with a Dataset

Load and work with a Dataset

Download the Neurosynth or NeuroQuery databases

Download the Neurosynth or NeuroQuery databases

Transform images into coordinates

Transform images into coordinates

Performing meta-analyses

Generate modeled activation maps with peaks2maps

Generate modeled activation maps with peaks2maps

Compare image and coordinate based meta-analyses on 21 pain studies

Compare image and coordinate based meta-analyses on 21 pain studies

Run a meta-analytic coactivation modeling analysis

Run a meta-analytic coactivation modeling analysis

Generate modeled activation maps

Generate modeled activation maps

Run coordinate-based subtraction and conjunction analyses

Run coordinate-based subtraction and conjunction analyses

Run image-based meta-analyses on 21 pain studies

Run image-based meta-analyses on 21 pain studies

Run coordinate-based meta-analyses on 21 pain studies

Run coordinate-based meta-analyses on 21 pain studies

Simulate data for coordinate based meta-analysis (CBMA)

Simulate data for coordinate based meta-analysis (CBMA)

Test combinations of kernels and estimators for coordinate-based meta-analyses.

Test combinations of kernels and estimators for coordinate-based meta-analyses.

Automated annotation

Train an LDA model and use it

Train an LDA model and use it

Train a GCLDA model and use it

Train a GCLDA model and use it

Work with the Cognitive Atlas

Work with the Cognitive Atlas

Functional characterization analysis

Decode regions of interest and subsets of Datasets

Decode regions of interest and subsets of Datasets

Download all examples in Python source code: auto_examples_python.zip

Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

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