Usage

eigenstrapping aims to provide the user with a robust generative null model for null hypothesis testing of spatial brain maps.

These tutorials step through the basics of downloading pre-generated eigenmodes and loading brain maps (or as neuromaps calls them - annotations), generating your first surrogates, and plotting the variogram fits (to check the smoothness). These tutorials also show you how to generate eigenmodes on your own surfaces and surrogates using that surface, say if you have a pathological cohort that you expect has a different geometry to a healthy cohort. If you still have questions after going through this guide you can refer to the Reference API or ask a question on GitHub.

Table of Contents

• Tutorial 1: Generating surrogate maps on the cortex
  • Nulls with surface data
• Tutorial 2: Generating surrogate subcortical maps
• Tutorial 3: Generating surrogate maps on your own surfaces
  • Important
• Eigenstrapping general usage
  • Surface file formats
  • Volumetric file formats
  • General usage
• Meshes and mesh operations
  • Remeshing
  • Deriving eigenmodes from a mesh
  • Mesh distance calculations