Benchmarking spatial interpolation methods for brain maps

Authors: Yigu Zhou, Vincent Bazinet, Bratislav Misic

This repository contains scripts and functions to reproduce results in "Benchmarking spatial interpolation methods for brain maps".

Repository Structure

Notebooks that contains main analyses and figures are stored here. Each notebook calls scripts and functions inside code, and files inside data.

code

This folder contains scripted runs and wrappers for interpolation functions.

/interpmodules contains deterministic (deterministic.py) and spatially-informed stochastic (geospatial.py) interpolation functions, as well as benchmark metrics (metrics.py) and utility functions (helpers.py) that support them.

*config.py files specify global variables such as project directory, analysis parameters, transform matrices, etc.

getdata_*.py or .ipynb contain code for handling GRF, empirical surface, and empirical volume data.

run_*.py or .sh contain code to set up and run interpolation for all combinations of data modality/characteristics

res1_* notebooks contain code to generate figures from analyses with GRF

res2_* notebooks contain code to generate figures from analyses with empirical surface maps (Neuromaps)

usecase_* notebooks contain code to generate figures from analyses with empirical volumetric data (iEEG or microarray)

data

This folder contains GRF, empirical surface and volume maps (from Neuromaps), each stored as a Pyvista.PolyData object.

/sampling contains data matrixes that shuffle training and testing samples with the spherical or midthckness template surfaces.

/yee_transformed-points contains MNI coordinates from Yee et al., 2025 for the microarray data

Requirements

Environment. Python 3.11.5, GNU bash 5.1.16
Software. The experiments presented utilize a number of published and openly available packages for generation, processing, and analysis of spatial data.

GSTools
MGWR
PyKrige
Pyvista
Scikit-Gstat
Scikit-Image