usage: __main__.py [-h] --metrics METRICS [METRICS ...] --list_ids FILE
[--not_only_common] [--input_connectoflow]
[-v [{DEBUG,INFO,WARNING}]] [-f]
in_folder out_folder
Script to compute PCA analysis on diffusion metrics. Output returned is all
significant principal components (e.g. presenting eigenvalues > 1) in a
connectivity matrix format. This script can take into account all edges from
every subject in a population or only non-zero edges across all subjects.
The script can take directly as input a connectoflow output folder. Simply use
the --input_connectoflow flag. For other type of folder input, the script
expects a single folder containing all matrices for all subjects.
Example:
[in_folder]
|--- sub-01_ad.npy
|--- sub-01_md.npy
|--- sub-02_ad.npy
|--- sub-02_md.npy
|--- ...
The plots, tables and principal components matrices will be outputted in the
designated folder from the <out_folder> argument. If you want to move back your
principal components matrices in your connectoflow output, you can use a
similar bash command for all principal components:
for sub in `cat list_id.txt`;
do
cp out_folder/${sub}_PC1.npy connectoflow_output/$sub/Compute_Connectivity/
done
Interpretation of resulting principal components can be done by evaluating the
loadings values for each metrics. A value near 0 means that this metric doesn't
contribute to this specific component whereas high positive or negative values
mean a larger contribution. Components can then be labeled based on which
metric contributes the highest. For example, a principal component showing a
high loading for afd_fixel and near 0 loading for all other metrics can be
interpreted as axonal density (see Gagnon et al. 2022 for this specific example
or ref [3] for an introduction to PCA).
EXAMPLE USAGE:
scil_connectivity_compute_pca.py input_folder/ output_folder/
--metrics ad fa md rd [...] --list_ids list_ids.txt
positional arguments:
in_folder Path to the input folder. See explanation above for its expected organization.
out_folder Path to the output folder to export graphs, tables and principal
components matrices.
options:
-h, --help show this help message and exit
--metrics METRICS [METRICS ...]
Suffixes of all metrics to include in PCA analysis (ex: ad md fa rd).
They must be immediately followed by the .npy extension.
--list_ids FILE Path to a .txt file containing a list of all ids.
--not_only_common If true, will include all edges from all subjects and not only
common edges (Not recommended)
--input_connectoflow If true, script will assume the input folder is a Connectoflow output.
-v [{DEBUG,INFO,WARNING}]
Produces verbose output depending on the provided level.
Default level is warning, default when using -v is info.
-f Force overwriting of the output files.
[1] Chamberland M, Raven EP, Genc S, Duffy K, Descoteaux M, Parker GD, Tax CMW,
Jones DK. Dimensionality reduction of diffusion MRI measures for improved
tractometry of the human brain. Neuroimage. 2019 Oct 15;200:89-100.
doi: 10.1016/j.neuroimage.2019.06.020. Epub 2019 Jun 20. PMID: 31228638;
PMCID: PMC6711466.
[2] Gagnon A., Grenier G., Bocti C., Gillet V., Lepage J.-F., Baccarelli A. A.,
Posner J., Descoteaux M., Takser L. (2022). White matter microstructural
variability linked to differential attentional skills and impulsive behavior
in a pediatric population. Cerebral Cortex.
https://doi.org/10.1093/cercor/bhac180
[3] https://towardsdatascience.com/what-are-pca-loadings-and-biplots-9a7897f2e559