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 (olivier.parent@mail.mcgill.ca)

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

AICageeffect -> Analysis determining optimal shape of age trajectories with the Akaike Information Criterion (AIC) of each cortical MRI marker - datapaper -> Vertex-wise optimal shape of age trajectories - AICage_effect.R -> Analysis code

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

R1spatialdistributionaverage -> Analysis relating average R1 relaxation rates with cortical MRI markers - datapaper -> Vertex-wise average R1 values - R1spatialdistribution_average.R -> Analysis code

linageeffect -> Linear age effect of each cortical MRI marker - datapaper -> Vertex-wise beta coefficients of linear age effect - linage_effect.R -> Analysis code

quadageeffect -> Quadratic age effect of each cortical MRI marker - datapaper -> Vertex-wise beta coefficients of quadratic age effect - quadage_effect.R -> Analysis code

spatialdistributionaverage -> Computing spatial distribution average for each cortical MRI marker - datapaper -> Vertex-wise beta average values for each cortical MRI marker - spatialdistribution_average.R -> Analysis code

spintest -> 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 "vertexfiles20mm_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., spatialdistributionaverage) cd spatialdistributionaverage # Then run R script Rscript spatialdistributionaverage.R cd ..

cd AICageeffect Rscript AICageeffect.R cd ..

cd linageeffect Rscript linageeffect.R cd ..

cd quadageeffect Rscript quadageeffect.R cd ..

cd R1spatialdistributionaverage Rscript R1spatialdistributionaverage.R cd .. 5. Run spin tests in Matlab (scripts to run begin with run_). Set working directory to ./spin_test/scripts/ matlab runspatialdistributionaverage.m matlab runR1spatialdistributionaverage.m matlab runlinageeffect.m matlab runBigBrain.m matlab runAHBA.m ```