Advanced usage: manual pipelines and scaling up

In this section of the nilearn documentation, we describe the building blocks that can be assembled to create a machine-learning data analysis pipeline. This section is slightly more advanced and abstract than the other. It provides knowledge that is useful to go off the beaten path in terms of data processing.

• Building your own neuroimaging machine-learning pipeline
  • Data loading and preprocessing
    • Downloading the data
    • Loading non image data: experiment description
    • Masking the data: from 4D image to 2D array
      • Applying a mask
      • Automatically computing a mask
  • Applying a scikit-learn machine learning method
  • Unmasking (inverse_transform)
  • Visualizing results
  • Going further
• Downloading statistical maps from the Neurovault repository
  • Specific images or collections
  • Selection filters
  • Output
  • Neurosynth annotations
  • Examples using Neurovault
  • References