4. Understanding junifer

Before you start, you should understand how junifer works. junifer is a tool conceived to extract features from neuroimaging data in an easy-to-use manner, with minimal coding and minimal user expertise in the internal aspects.

Unlike other tools like FSL, SPM, AFNI, etc., junifer is not a toolbox to pre-process data, but a toolbox to extract features from previously pre-processed data.

The main idea is that you have a set of images (e.g. a set of functional MRI, structural MRI, diffusion MRI, etc.) and you want to extract features to later use in statistical analyses or machine learning (for example, using julearn).

Important

junifer is not a toolbox to create pipelines, but a tool to configure the junifer pipeline, which is intended to be fixed and not to be changed. If you want to create a pipeline, you should use other tools like nipype.

Contents:

• 4.1. The junifer Pipeline
• 4.2. The Data Object
  • 4.2.1. Description
  • 4.2.2. Data Types
• 4.3. Data Grabber
  • 4.3.1. Description
  • 4.3.2. Base Classes
• 4.4. Data Reader
  • 4.4.1. Description
  • 4.4.2. File Formats
• 4.5. Preprocess
  • 4.5.1. Description
  • 4.5.2. Confound Removal
  • 4.5.3. Warping or Transformation to other spaces
• 4.6. Marker
  • 4.6.1. Description
• 4.7. Storage
  • 4.7.1. Description
  • 4.7.2. Storage Types
  • 4.7.3. Storage Interfaces