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Regional Quantification with NiLearn

Author: Ellyn Butler

Updated: May 7, 2021

Replicator: TBD

Goal: Quantify regional values using NiLearn

Input data: Two 3D niftis in the same space, one a labeled image, and the other a modality that needs to be quantified

Output: A numpy array of regional values in ascending order according to their index as defined in the labeled image

This tutorial demonstrates how to quantify regional values in an atlas. You will need a labeled image in the same space as the modality you want to quantify regionally. Here, I use a cortical thickness image as an example.

This tutorial is based on the quantification portion of the antslongct pipeline.

  1. Load required libraries
    import sys
    import nibabel as nib
    import pandas as pd
    import numpy as np
    import nilearn
    from nilearn.input_data import NiftiLabelsMasker
    
  2. Declare dummy subject and session labels
    # You should provide as command line inputs for your own script
    sub = 'sub-101'
    ses = 'ses-STUDY1'
    
  3. Load the labels in the subject’s T1w space
    atlas = nib.load(sub+'_'+ses+'_DKTlabels.nii.gz')
    
  4. Load the image that needs regional quantification (cortical thickness here)
    cort = nib.load(sub+'_'+ses+'_CorticalThickness.nii.gz')
    
  5. Load the index to region name mapping
    dkt_df = pd.read_csv('mindboggleCorticalLabels.csv')
    
  6. Rename columns to be python-friendly
    dkt_df = dkt_df.rename(columns={"Label.ID": "LabelID", "Label.Name": "LabelName"})
    
  7. Get the integer values that correspond to each region in the DKT atlas
    ints = dkt_df.LabelID.values
    
  8. Get the names of the regions
    names = dkt_df.LabelName.to_numpy()
    names = [name.replace('.', '_') for name in names]
    
  9. Load the labeled image and fit the masker object
    masker = NiftiLabelsMasker(sub+'_'+ses+'_DKTIntersection.nii.gz')
    masker.fit()
    
  10. Quantify regional values!!!
    cortvals = masker.transform(sub+'_'+ses+'_CorticalThickness.nii.gz')
    
  11. Create column names
    cort_names = ['mprage_jlf_ct_'+name for name in names]
    colnames = ['sublabel', 'seslabel']
    colnames.extend(cort_names)
    
  12. Create a vector of values for the session’s row in the csv
    vals = [sub, ses]
    vals.extend(cortvals.tolist()[0])
    
  13. Output data as a csv
    out_df = pd.DataFrame(data=[vals], columns=colnames)
    out_df.to_csv(sub+'_'+ses+'_struc.csv', index=False)