If you use this pipeline on your work, please cite us:
Alfredo Lucas, & Kathryn A. Davis. (2023). ATLASpy: A python library for visualizing volumetric brain data. Zenodo. https://doi.org/10.5281/zenodo.8083548
To install:
pip install atlaspy
You should be able to use atlaspy after that.
Detailed examples of using ATLASpy can be found here: https://github.com/allucas/atlaspy/blob/main/examples/atlaspy_examples_v2.ipynb
ATLASpy is not restricted to the default atlases included, but it can use any atlas as long as STL files are available for it. See here for instructions on how to generate these STL files: https://github.com/allucas/atlaspy/tree/main/examples/custom_atlas_readme.md
import atlaspy.core as apy
apy.plot_raw_atlas_lrt('dkt', cmap='Set3')
This will plot the DKT atlas with random coloring for each ROI
We can repeat this process for the HarvardOxford atlas using ho:
apy.plot_rois_atlas_lrt('ho', df_values, cmap='Set3')
The Schaefer400 atlas using sch400:
apy.plot_rois_atlas_lrt('sch400', df_values, cmap='Set3')
For plotting values from a CSV file or a dataframe, the columns of the CSV must include atlas_index, which is the number assigned to the ROI of each atlas, and roi_value, which is the values that we would like to plot at that brain region. The atlases used are included in source_data/atlases/niftis and their region assignments in source_data/atlases/luts. An example input CSV is included in examples/cortical_thickness_dkt.csv. To visualize this example use the code below:
import atlaspy.core as apy
import pandas as pd
df_values = pd.read_csv('examples/cortical_thickness_dkt.csv')
apy.plot_rois_atlas_lrt('dkt', df_values, cmap='Set3')
This will generate something like this:
We can change the colormap to any Matplotlib colormap
apy.plot_rois_atlas_lrt('dkt', df_values, cmap='RdYlBu')
This will generate something like this:
