CHARGE_spatialWMH

Spatial WMH patterns

Description

This repository provides the code to perform analysis of spatial WMH distribution using the Bullseye segmentation.

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Installation

Use this python code and execute in an environment including FreeSurfer version 6.0.0p1 or above, nipype== OR use singularity container with pre-installed environment:

Usage

Own environments:

• have FreeSurfer version 7.3.1 (or at least>=6.0.0p1)
• activate python==3.9 environment with nipype==1.8.6, nibabel==? (beseg)
  • e.g. installation via conda: conda create -n below python nipype nibabel pip install pandas
• provide table of subjects to run and/or select them in ll. 83 ff in run_bullseye_WMH_segmentation.py
• run python run_bullseye_WMH_segmentation.py

Singularity container with all environments pre-installed

• type

Support

fbeyer@cbs.mpg.de

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General information

The pipeline includes the following steps:

1. the bullseye WM parcellation (bullseye_pipeline.py entirely taken from this github repositoy) It provides an (anatomy-independent) spatial localization based on an radial component (ie, lobes) and a depth component. It can be used to obtain region-specific quantification of white matter parameters (eg, a similar approach has been used to quantify regional white matter hyperintensity load in this and this papers).

The internals of the process are explained in this blog post.

• the bullseye parcellation is the intersection of a lobar parcellation and a depth parcellation
• the lobar parcellation consists of 4 lobes per hemisphere (frontal, parietal, temporal and occipital) + 1 consisting of the basal ganglia and thalamus as an additional region: (4*2) + 1 = 9 lobes
• the depth parcellation consists of 4 equidistant parcels spanning from the surface of the ventricles to the internal surface of the cortex

Required FreeSurfer input

However, not all the FreeSurfer output is required. The following is the mandatory portion of FreeSurfer data that is required by the pipeline:

• scansdir
  • subject-id1
    • mri
      • aseg.mgz
    • label
      • lh.aparc.annot
      • rh.aparc.annot
    • surf
      • lh.white
      • rh.white
      • lh.pial
      • rh.pial
  • subject-id2
    ...

2. the coregistration of WMH probability map to FreeSurfer space (create_flairreg_pipeline.py) old version: baseline only WMH probability maps are binarized at 0.1, coregistered to FreeSurfer space and binarized again at 0.1 for counting number of voxels in each parcel. Binary maps from longitudinal pipeline are also thresholded at 0.1 before extrating. latest version (11/23): WMH probability maps are coregistered to FreeSurfer space and summed up in the respective parcels.

3. the extraction of bullseye parcellated WML volumes (in utils.py) The bullseye parcellation is masked by the lesion map, a histogram is created and unassigned lesion volume + 36 WM parcellation volumes are extracted and saved in text file. Order:

   • undefined
   • 51, 52, 53, 54 : BG + depth
   • 111, 112, 113, 114: lh frontal + depth
   • 121, 122, 123, 124: lh occipital + depth
   • 131, 132, 133, 134: lh temporal + depth
   • 141, 142, 143, 144: lh parietal + depth
   • 211, 212, 213, 214: rh frontal + depth
   • 221, 222, 223, 224: rh occipital + depth
   • 231, 232, 233, 234: rh temporal + depth
   • 241, 242, 243, 244: rh parietal + depth

Output directory structure

After execution of the pipeline a directory output_dir is created with the following structure:

• output_dir
  • subject-id1
    • bullseye_wmparc.nii.gz
    • ples_lpa_mFLAIR_bl_thr0.1_bin_warped.nii.gz
    • ples_lpa_mFLAIR_bl_warped.nii.gz
    • res.txt
  • subject-id2
    ...

containing, respectively, the final bullseye parcellation, the WMH map (thresholded and binarized lesion map) and the results file (res.txt refers to thresholded WMH maps, res_HSB.txt to the sum of probabilities)