Manipulation brain volumes with nilearn

In this section, we detail the general tools to manipulate of brain images with nilearn.

• Input and output: neuroimaging data representation
  • Inputing data: file names or image objects
    • File names and objects, 3D and 4D images
    • File name matching: “globbing” and user path expansion
  • Fetching open datasets from Internet
  • Understanding neuroimaging data
    • Nifti and Analyze data
    • Niimg-like objects
    • Text files: phenotype or behavior
• Manipulating images: resampling, smoothing, masking, ROIs…
  • Functions for data preparation and image transformation
  • Resampling images
    • Resampling one image to match another one
    • Resampling to a specific target affine, shape, or resolution
  • Accessing individual volumes in 4D images
  • Computing and applying spatial masks
    • Extracting a brain mask
    • Masking data: from 4D Nifti images to 2D data arrays
  • Image operations: creating a ROI mask manually
• From neuroimaging volumes to data matrices: the masker objects
  • The concept of “masker” objects
  • NiftiMasker: applying a mask to load time-series
    • Custom data loading: loading only the first 100 time points
    • Controlling how the mask is computed from the data
      • Visualizing the computed mask
      • Different masking strategies
      • Extra mask parameters: opening, cutoff…
    • Common data preparation steps: smoothing, filtering, resampling
      • Smoothing
      • Temporal Filtering and confound removal
      • Resampling: resizing and changing resolutions of images
    • Inverse transform: unmasking data
  • Extraction of signals from regions: NiftiLabelsMasker, NiftiMapsMasker
    • Regions definition
    • NiftiLabelsMasker Usage
    • NiftiMapsMasker Usage
  • Extraction of signals from seeds: NiftiSpheresMasker