Analyzing fMRI using GLMs

Nilearn’s stats module uses Generalized Linear Model to analyse functional MRI data. Nilearn offers functions to set up first- and second-level models, perform t-tests, multiple-comparisons correction and thresholding, and allows for the generation of html reports that detail the analyses performed and present thresholded results.

• An introduction to GLMs in fMRI statistical analysis
  • A primer on BOLD-fMRI data analysis
    • What is fMRI ?
    • fMRI data modelling
    • fMRI statistical analysis
    • Multiple Comparisons
• First level models
  • HRF models
  • Design matrix: event-based and time series-based
    • Event-based
    • Time series-based
  • Fitting a first level model
    • Computing contrasts
  • Extracting predicted time series and residuals
  • Surface-based analysis
• Second level models
  • Fitting a second level model
  • Thresholding statistical maps
  • Multiple comparisons correction
  • Voxel based morphometry