Tutorials 📚

These tutorials reproduce the figures found in the HippoMaps paper (DeKraker et al. 2024 ). They also serve as examples for common analyses, and to showcase ways of using HippoMaps tools.

Each tutorial is organized into the following sections:

• config includes things like input data directory, list of subjects, surface vertex density, etc. This is primarily what a user will edit when running this code on their own data.

• 0) Mapping of data into surfaces. If useCheckpoints is true, this will be skipped and pre-generated numpy arrays will be loaded instead.

• 1) First step of actual data analysis

• 2)+ Additional steps to break down and explain the analyses being done. These are specific to each tutorial, and include things like postprocessing, plotting, stats, etc.

Note also that the DimReduct tutorial on running dimensionality reduction and other analyses between all maps should be run AFTER all other tutorials (since it requires their output maps!)

Morphology:

• Morphology
  • 0) Map and load volumetric data to surfaces
  • 1) Average across subjects and plot

Histology-MRI-9p4T:

• Histology-MRI-9p4T
  • 0) Map and load volumetric data to surfaces
  • 1) Load, plot, and preprocess all surface data
  • 2) Group average and laminar microstructural profiles
  • 3) Summarize data according to primary gradients

MRI-7T-struct:

• MRI-7T-struct
  • 0) Map and load volumetric data to surfaces
  • 1) Load and plot all surface data
  • 2) Stability between subjects
  • 3) Summarize data according to primary gradients

MRI-3T-rsfMRI:

• MRI-3T-rsfMRI
  • 0) Map rsfMRI data to hippocampal surfaces
  • 1) Intrinsic Timescale of hippocampal rsfMRI
  • 2) Calculate Regional Homogeneity
  • 3) Functional connectivity of the hippocampus to neocortex
  • 4) Check consistency for all measures
  • 5) Gradients of differential hippocampal FC

iEEG:

• iEEG
  • 0) Map data to hippocampal surfaces
  • 1) Load the data and examine attributes
  • 2) Spatial extrapolation
  • 2) Summarize data according to primary gradients

DimReduct:

• Dimensionality-Reduction
  • 1) Load all maps and resample
  • 2) Check for correlations with AP, PD, or subfield organizations
  • 3) Gradient decomposition
  • 4) Other 2D projection methods

MRI-3T-taskfMRI:

• MRI-3T-taskfMRI
  • 0) Single-subject walkthrough
  • 0.1) Map data to hippocampal surface
  • 0.2) Create design matrix
  • 0.3) Define contrasts
  • 0.4) Fit the timeseries data
  • 1) Load and plot contrasts from ALL subjects
  • 2) Significance/reliability testing
  • 3) Consider neocortical results
  • 4) Compare to previously mapped features

TLE-HC:

• TLE-HC
  • 0) Map and load volumetric data to surfaces
  • 1) plot group-averages
  • 2) Examine inter-patient (ipsilateral) consistency
  • 3) Contextualize with other maps