All code used to generate the results seen in the manuscript "High spatial overlap but diverging age-related trajectories of cortical magnetic resonance imaging markers aiming to represent intracortical myelin and microstructure" published in Human Brain Mapping by Olivier Parent and colleagues (2023).

The article is available here: https://doi.org/10.1002/hbm.26259

Vertex-wise files are available on the Zenodo platform (https://zenodo.org/record/7729120#.ZA87d3bMK3A).

For questions/comments, please reach out to Olivier Parent ([email protected])

Dependencies

• minc-toolkit/1.9.17
• R/3.5.1
• RMINC/1.5.2.3
• Matlab/2020a

Directory structure

AHBA -> Analysis relating gene-expression-derived cell-type densities with cortical MRI markers

• data_paper -> Vertex-wise gene-expression-derived cell-type densities

AIC_age_effect -> Analysis determining optimal shape of age trajectories with the Akaike Information Criterion (AIC) of each cortical MRI marker

• data_paper -> Vertex-wise optimal shape of age trajectories
• AIC_age_effect.R -> Analysis code

BigBrain -> Analysis relating histology-derived cell density with cortical MRI markers

• data_paper -> Vertex-wise histology-derived cell density

R1_spatial_distribution_average -> Analysis relating average R1 relaxation rates with cortical MRI markers

• data_paper -> Vertex-wise average R1 values
• R1_spatial_distribution_average.R -> Analysis code

lin_age_effect -> Linear age effect of each cortical MRI marker

• data_paper -> Vertex-wise beta coefficients of linear age effect
• lin_age_effect.R -> Analysis code

quad_age_effect -> Quadratic age effect of each cortical MRI marker

• data_paper -> Vertex-wise beta coefficients of quadratic age effect
• quad_age_effect.R -> Analysis code

spatial_distribution_average -> Computing spatial distribution average for each cortical MRI marker

• data_paper -> Vertex-wise beta average values for each cortical MRI marker
• spatial_distribution_average.R -> Analysis code

spin_test -> Spatial correlations and p-values for each analysis above using the spin test

• data -> Masks of midline vertices
• scripts -> All scripts for the spin test. Run the files starting with run_

master_anon.csv -> CSV with demographic, cognitive, and quality control (QC) data for all included subjects

Steps to run code

1. Make sure all dependencies are loaded
2. Clone GitHub repository

git clone https://github.com/parent41/cortical_markers_paper

3. Download subject-wise vertex data for cortical MRI markers from the Canadian Open Neuroscience Platform (CONP) and on Zenodo. Put all files from the directory "vertex_files_20mm_anon" in a directory of the same name located in the main directory
4. Run R scripts to generate marker maps (for spatial distribution average, AIC of age effect shape, linear age effect, quadratic age effect)

# First set working directory to the specific analysis folder (e.g., spatial_distribution_average)
cd spatial_distribution_average
# Then run R script
Rscript spatial_distribution_average.R
cd ..

cd AIC_age_effect
Rscript AIC_age_effect.R
cd ..

cd lin_age_effect
Rscript lin_age_effect.R
cd ..

cd quad_age_effect
Rscript quad_age_effect.R
cd ..

cd R1_spatial_distribution_average
Rscript R1_spatial_distribution_average.R
cd ..

5. Run spin tests in Matlab (scripts to run begin with run_). Set working directory to ./spin_test/scripts/

matlab run_spatial_distribution_average.m
matlab run_R1_spatial_distribution_average.m
matlab run_lin_age_effect.m
matlab run_BigBrain.m
matlab run_AHBA.m