Volumetric graynet

For the volumetric stream of processing, graynet relies on outputs from CAT12 toolbox within the SPM ecosystem. We plan to support various other VBM toolboxes soon - make your suggestions by opening an issue.

The following steps should help you get started and going quickly:

  • Install CAT12 toolbox and read the manual

  • Ensure you produce the outputs in normalized atlas space, by selecting the output option to be either Modulated normalized or normalized depending on your analyses plan. Correct processing would produce images of size 121 x 145 x 121, which must be the same as the atlases included in the CAT12 toolbox

  • Run the CAT12 processing and ensure it is complete

    • Perform the QA as suggested in their manual e.g. “SECOND MODULE: DISPLAY ONE SLICE FOR ALL IMAGES”.

    • For a more comprehensive QC of segmentation accuracy, you can employ visualqc

  • If everything went well, you would have an mri folder created in the output folder you chose. This folder would have several files, for each subject you processed e.g. mwp1SUBJECTID.nii contains the voxel-wise gray matter densities for SUBJECTID in the modulated (prefix m ) normalized (w ) atlas space. Similarly mwp2*.nii files would contain voxel-wise white matter densities.

If graynet is installed without error, you should have graynet command in your path. Just type graynet and you should see it display options and usage instructions, as presented in Command line interface page.

Suppose say

  • you already have CAT12 processing done at /work/project/cat12_vbm

  • for 5 subjects with identifiers are stored in subject_ids.txt (one per line)

  • you want to analyze network features based on GM density values (identified in graynet by spm_cat_gmdensity ), which have been resampled to the space of AAL atlas (identified in graynet by cat_aal ) provided in CAT12 (dimensions: 121 x 145 x 121, with voxel sizes: 1.5000, 1.5000, 1.5000)

  • use the manhattan method to compute histogram distance between two cortical ROIs (assuming all subjects resampled to same atlas)

  • and store the networks extracted in /work/project/cat12_vbm/graynet_volumetric

You could achieve it all with a single command:

cd /work/project
graynet edges -s subject_ids.txt -f spm_cat_gmdensity -i /work/project/cat12_vbm -w manhattan -a cat_aal -o /work/project/cat12_vbm/graynet_volumetric

That’s it! By the time you can get your coffee or stretch your legs, you should have graynet processing done.

Suppose, you prefer to analyze ROIs as defined by other atlases, graynet also supports the IBSR and LPBA40 as supplied by the CAT12 toolbox. If you are also curious to try multiple weight metrics - chebyshev and cosine, you can achieve it via the following command:

cd /work/project
graynet edges -s subject_ids.txt -f spm_cat_gmdensity -i /work/project/cat12_vbm -w chebyshev cosine manhattan -a cat_LPBA40 -o /work/project/cat12_vbm/graynet_volumetric

You could also study other features by simply adding more features to the -f, such as spm_cat_wmdensity (white matter density).

However, if you have 100s of subjects or your atlas has a large number of ROIs (like the AAL atlas which has 120), this computation can take a long time. It is advisable to move the processing to a larger server or computer cluster, by delegating few subjects to a single job. You could leverage this script to process any large dataset at once (produce jobs for processing on a cluster).

  • If you have access to a computer cluster, you can submit the generated jobs to the cluster.

  • If you don’t have access to a cluster, you could still use the generated job/script files to run them locally - few at a time.

After processing is done, graynet 2.0 writes one run directory per parameter combination. Each run directory contains canonical outputs such as run_metadata.json and edges_raw.parquet. If you need older GraphML or CSV exports, use the graynet export subcommands described in Command line interface.

The output folder will be graynet within proc_dir you choose in the script - determined by this statement in the above script:

out_dir = pjoin(proc_dir, 'graynet', '{}_{}_range{}_{}_nbins{}'.format(base_feature, atlas, edge_range[0], edge_range[1], num_bins))

which after expansion looks something like based on your choices: /your_proc_dir/graynet/spm_cat_gmdensity_cat_aal_range_0_1_nbins25/

Computing ROI-wise statistics

When available, graynet will help compute ROI-wise statistics (individual, not pair-wise) for visualization (e.g. mean and variance of GM density within amygdala), as well as to serve as a baseline for network-level features. Use the roi-stats subcommand to do this. Only one type of processing (ROI stats, or network-level features) can be done at a time.