meta-neuro 2.1.1

Medial Tractography Analysis (MeTA)

MeTA is a workflow implemented to minimize microstructural heterogeneity in diffusion MRI (dMRI) metrics by extracting and parcellating the core volume along the bundle length in the voxel-space directly while effectively preserving bundle shape and efficiently capturing the regional variation within and along white matter (WM) bundles.

Contact: Iyad Ba Gari iyad.bagari@usc.edu

If you use MeTA code, please cite the following publication:

• Ba Gari, I., et al.: Heritability and Genetic Correlations Along the Corticospinal Tract. International Workshop on Computational Diffusion MRI. Cham: Springer Nature Morocco, 2024
• Ba Gari, I., et al.: Along-tract parameterization of white matter microstructure using medial tractography analysis (MeTA). In: The 19th International Symposium on Medical Information Processing and Analysis (2023)

Installation

MeTA supports Python version >=3.11 and <3.14. Using pip:

pip install meta-neuro

Using Bioconda:

conda config --add channels bioconda
conda install bioconda::meta-neuro

Usage

## Using DSI Studio transforms:
# Compute density map and convert streamlines to a binary image:
density_map --tractogram subject_CST.tt.gz --reference subject_FA.nii.gz \
            --output "output_dir/subjectID_CST.nii.gz"

# Medial Tractography Analysis (MeTA):
meta --subject "subjectID_12345" --bundle "CST" \
    --mbundle "model_CST.tt.gz" --sbundle "subject_CST.tt.gz" \
    --mask "output_dir/subjectID_CST.nii.gz" \
    --warp "subjectID.1InverseWarp.nii.gz" --warp_source "dsi_studio" \
    --seg_method "hyperplane" --num_segments 15 --output "output_dir"

# Extract Voxel-based Bundle Profile: Compute volumetric profile for DTI maps e.g., FA, MD, RD, AD, etc. Output two files: 1) *_segments_average.csv file with the average profile along the bundle length, and 2) *_segments_voxelwise.h5 (with option `--voxelwise `): the profile for each voxel in the bundle.
volumetric_profile --subject "subjectID_12345" --bundle "CST" --mask CST_local_all.nii.gz --map subject_FA.nii.gz --output "output_dir"

# Extract Streamline-based Profile: Compute streamline profile based on tractography and DTI maps e.g., FA, MD, RD, AD, etc. output two files: 1) *_streamlines_average.csv file with the average profile along the bundle length, and 2) *_streamlines_pointwise.h5 (with option `--pointwise `): the profile for each point of streamline.

streamlines_profile --subject "subjectID_12345" --bundle "CST" --tractogram "subject_CST.tt.gz" --mask CST_local_all.nii.gz --map subject_FA.nii.gz --output "output_dir"

# Extract Bundle Shape Features: Bundle shape features implemented based on Yeh et al., 2020. The following features are extracted: Total number of streamlines, Average streamlines length, Span, Curl, Volume, Surface area, Diameter, Elongation, Irregularity


shape_metrics --subject "subjectID_12345" --bundle "CST" --mask CST_local_all.nii.gz --tractogram "subject_CST.tt.gz" --output CST_streamlines_metrics.csv