68 lines
2.1 KiB
Markdown
68 lines
2.1 KiB
Markdown
# JALE
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A Python package for conducting ALE (Activation Likelihood Estimation) meta-analyses, supporting a range of analysis workflows: standard ALE, probabilistic or cross-validated ALE, standard ALE contrast, and balanced ALE contrast.
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For more detailed information regarding installation, usage and features please visit the [Github Wiki](https://github.com/LenFrahm/JALE/wiki).
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## Table of Contents
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- [Installation](#installation)
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- [Usage](#usage)
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- [Features](#features)
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- [Background and References](#background-and-references)
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- [License](#license)
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## Installation
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To install the ALE Meta-Analysis Package, run:
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```bash
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pip install jale
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```
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## Usage
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Here’s how to use the project:
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JALE requires a project folder that contains 3 files:
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1. Experiment Data (Author, Subjects, Coordinates, Tags)
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2. Analysis Data (Type of ALE, Tags to be included)
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3. Yaml config file (specifying project folder path, filenames and ALE parameters)
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The format of these files is very important and you should check the wiki for instructions.
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For example files click [here](https://raw.githubusercontent.com/LenFrahm/JALE/master/JALE/assets/examples.zip).
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Running an ALE can be done in two ways:
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1. via CLI:
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```bash
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python -m jale /path/to/yaml/file
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```
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2. in Python:
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```python
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from jale.ale import run_ale
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run_ale(yaml_path='/path/to/yaml/file')
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```
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## Features
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- Standard ("Main Effect") ALE
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- Probabilistic ("Cross-Validated") ALE
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- Standard ALE Contrast
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- Balanced ALE Contrast
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## Background and References
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This project is based on research by
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- [Eickhoff et al., 2012](https://doi.org/10.1016/j.neuroimage.2011.09.017).
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- [Eickhoff et al., 2016](https://doi.org/10.1016/j.neuroimage.2016.04.072).
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- [Frahm et al., 2022](https://doi.org/10.1002/hbm.25898).
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- [Frahm et al., 2023](https://doi.org/10.1016/j.neuroimage.2023.120383).
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