This repository contains scripts and functions to reproduce the results presented in Homophily-informed generative models of brain maps
main_script.py contains a script that allows anyone to replicate the analyses described in the paper. The script uses function stored in functions.py to run the analyses and plot the figures presented in the paper.
The data folder contains the data that was used for the analyses described in the paper. See the Methods section of the paper for more information on how this data was generated. Before using this data, please refer to the relevant publications listed in the references section below for more information about the data terms associated with each dataset.
The results folder contains the main results of the experiments presented in the paper.
The figures folder contains the main elements of the figures presented in the manuscript. As mentionned above, the code to re-generate each element is stored in main_script.py.
The experiments presented in this repository make use of a certain number of python packages that will be necessary to run the main script. These packages are:
Brain maps
The brain maps were fetched from neuromaps. Please cite the relevant publication associated with each brain map (listed in neuromaps, and in Table 1 of the manuscript)
neuromaps: Markello, R. D., Hansen, J. Y., Liu, Z. Q., Bazinet, V., Shafiei, G., Suárez, L. E., ... & Misic, B. (2022). Neuromaps: structural and functional interpretation of brain maps. Nature methods, 19(11), 1472-1479.
Connectivity (structural and functional)
The connectivity data is from the Human Connectome Project (HCP)
data acquisition: Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., & Wu-Minn HCP Consortium. (2013). The WU-Minn human connectome project: an overview. Neuroimage, 80, 62-79.
data processing: Park, B. Y., de Wael, R. V., Paquola, C., Larivière, S., Benkarim, O., Royer, J., ... & Bernhardt, B. C. (2021). Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function. Neuroimage, 224, 117429.
Laminar similarity
The laminar similarity matrix was constructed using data from the BigBrain Atlas. The data, registered to the fsaverage surface was obtained from the BigBrainWrap toolbox
BigBrain atlas: Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M. É., ... & Evans, A. C. (2013). BigBrain: an ultrahigh-resolution 3D human brain model. science, 340(6139), 1472-1475.
BigBrainWrap: Paquola, C., Royer, J., Lewis, L. B., Lepage, C., Glatard, T., Wagstyl, K., ... & Bernhardt, B. (2021). The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging. Elife, 10, e70119.
Receptor similarity
The receptor similarity matrix relies on data from an atlas of 19 PET-derived brain maps of neurotransmitter receptors and transporters. This atlas is available on neuromaps. Before using this data, please refer to the relevant references listed in neuromaps associated to each PET-derived brain map
PET-atlas: Hansen, J. Y., Shafiei, G., Markello, R. D., Smart, K., Cox, S. M., Nørgaard, M., ... & Misic, B. (2022). Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nature neuroscience, 25(11), 1569-1581.
neuromaps: Markello, R. D., Hansen, J. Y., Liu, Z. Q., Bazinet, V., Shafiei, G., Suárez, L. E., ... & Misic, B. (2022). Neuromaps: structural and functional interpretation of brain maps. Nature methods, 19(11), 1472-1479.
Transcriptional similarity
The transcriptional similarity matrix was constructed using data from the Allen Human Brain Atlas. This data was pre-processed using abagen.
Allen Human Brain Atlas: Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., Shen, E. H., Ng, L., Miller, J. A., ... & Jones, A. R. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416), 391-399.
Abagen: Markello, R. D., Arnatkeviciute, A., Poline, J. B., Fulcher, B. D., Fornito, A., & Misic, B. (2021). Standardizing workflows in imaging transcriptomics with the abagen toolbox. elife, 10, e72129.