Morphology

Overview

Morphological measures of thickness, gyrification, and curvature are calculated within HippUnfold. Here we examine these to ensure they are consistent with previous work, and that they don’t differ drastically between datasets.

import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib
import hippomaps as hm

Histology data

source_dir = '../../publication-hippomaps/sourcedata/BIDS_HISTO/'
hippunfold_dir = '../../publication-hippomaps/hippunfold/HISTO_v1.3.0_100um/hippunfold/'

subs = ['bbhist', 'bbhist', 'pli3d', '122017', '122017', '152017', '152017']
ses = ''
hemis = ['L','R','L','R','L','L','R','L','R']
labels = 'hipp'
den='unfoldiso'

# get expected number of vertices and their indices
nV,iV = hm.config.get_nVertices(labels,den)

features = ['thickness', 'gyrification', 'curvature']
morph_dat_hist = np.zeros([nV,len(subs),len(features)])*np.nan
d=[]
for f,feature in enumerate(features):
    for s,sub in enumerate(subs):
        d = nib.load(f'{hippunfold_dir}/sub-{sub}/surf/sub-{sub}_hemi-{hemis[s]}_space-corobl'\
                     f'_den-{den}_label-{labels}_{feature}.shape.gii')
        morph_dat_hist[:,s,f] = d.darrays[0].data
        if feature=='curvature':
            morph_dat_hist[:,s,f] = np.tanh(morph_dat_hist[:,s,f])
            if hemis[s]=="R":
                morph_dat_hist[:,s,f] = -morph_dat_hist[:,s,f]

hm.plotting.surfplot_canonical_foldunfold(np.mean(morph_dat_hist,axis=1), labels=labels, hemis=['L'], unfoldAPrescale=True, den=den, color_bar='right', share='row', tighten_cwindow=True, embed_nb=True)

/export03/data/opt/venv/lib/python3.8/site-packages/brainspace/plotting/base.py:287: UserWarning: Interactive mode requires 'panel'. Setting 'interactive=False'
  warnings.warn("Interactive mode requires 'panel'. "

Ultra-high field MRI data at 7T

hippunfold_dir = '../../publication-hippomaps/hippunfold/PNI_v1.3.0_super/hippunfold'

subs = ['PNC002', 'PNC003', 'PNC006', 'PNC007', 'PNC009', 'PNC010', 'PNC015', 'PNC016', 'PNC018', 'PNC019']
ses = ''
hemis = ['L','R']
labels = ['hipp']#,'dentate']
den = '0p5mm'

# get expected number of vertices and their indices
nV,iV = hm.config.get_nVertices(labels,den)

morph_dat_MRI7T = np.zeros([nV,2,len(subs),len(features)])*np.nan
d=[]
for h,hemi in enumerate(hemis):
    for l,label in enumerate(labels):
        for f,feature in enumerate(features):
            for s,sub in enumerate(subs):
                try:
                    d = nib.load(f'{hippunfold_dir}/sub-{sub}/surf/sub-{sub}_hemi-{hemi}_space-T1w'\
                                 f'_den-{den}_label-{label}_{feature}.shape.gii')
                    morph_dat_MRI7T[iV[l],h,s,f] = d.darrays[0].data
                except:
                    print(f'{sub} {feature} {label} not found')
morph_dat_MRI7T[:,0,:,2] = -morph_dat_MRI7T[:,0,:,2]

hm.plotting.surfplot_canonical_foldunfold(np.nanmean(morph_dat_MRI7T,axis=(1,2)), hemis=['L'], labels=labels, unfoldAPrescale=True, den=den, color_bar='right', share='row', tighten_cwindow=True, embed_nb=True)

High-field MRI data at 3T

hippunfold_dir = '../../publication-hippomaps/hippunfold/MICs_v1.3.0/hippunfold'

subs = ['HC048', 'HC043', 'HC087', 'HC037', 'HC055', 'HC100', 'HC036', 'HC017', 'HC088', 'HC040', 'HC058', 'HC076', 'HC090', 'HC059', 'HC101', 'HC063', 'HC094', 'HC024', 'HC050',
 'HC080', 'HC013', 'HC026', 'HC001', 'HC084', 'HC105', 'HC083', 'HC042', 'HC014', 'HC033', 'HC081', 'HC106', 'HC108', 'HC095', 'HC002', 'HC102', 'HC028', 'HC020', 'HC049',
 'HC007', 'HC023', 'HC065', 'HC025', 'HC056', 'HC003', 'HC015', 'HC077', 'HC067', 'HC072', 'HC109', 'HC086', 'HC089', 'HC091', 'HC031', 'HC039', 'HC112', 'HC068', 'HC034',
 'HC032', 'HC060', 'HC047', 'HC103', 'HC046', 'HC009', 'HC097', 'HC116', 'HC053', 'HC079', 'HC029', 'HC075', 'HC078', 'HC057', 'HC018', 'HC074', 'HC064',
 'HC096', 'HC010', 'HC038', 'HC093', 'HC082', 'HC092', 'HC027', 'HC019', 'HC005', 'HC008', 'HC011', 'HC044', 'HC030', 'HC035', 'HC085', 'HC069', 'HC041', 'HC012', 'HC054', 'HC022', 'HC016', 'HC099',
 'HC073', 'HC052', 'HC045']
ses = 'ses-01'
hemis = ['L','R']
labels = ['hipp']#,'dentate']
den = '0p5mm'

# get expected number of vertices and their indices
nV,iV = hm.config.get_nVertices(labels,den)

morph_dat_MRI3T = np.zeros([nV,2,len(subs),len(features)])*np.nan
d=[]
for h,hemi in enumerate(hemis):
    for l,label in enumerate(labels):
        for f,feature in enumerate(features):
            for s,sub in enumerate(subs):
                try:
                    d = nib.load(f'{hippunfold_dir}/sub-{sub}/{ses}/surf/sub-{sub}_{ses}_hemi-{hemi}_space-T1w'\
                                 f'_den-{den}_label-{label}_{feature}.shape.gii')
                    morph_dat_MRI3T[iV[l],h,s,f] = d.darrays[0].data
                except:
                    print(f'{sub} {feature} {label} not found')
morph_dat_MRI3T[:,0,:,2] = -morph_dat_MRI3T[:,0,:,2]

hm.plotting.surfplot_canonical_foldunfold(np.nanmean(morph_dat_MRI3T,axis=(1,2)), hemis=['L'], labels=labels, unfoldAPrescale=True, den=den, color_bar='right', share='row', tighten_cwindow=True, embed_nb=True)

save

# save 2D map
for f,feature in enumerate(features):
    for h,hemi in enumerate(hemis):
        cdat = np.nanmean(morph_dat_MRI3T[iV[0],h,:,f],axis=1).flatten()
        data_array = nib.gifti.GiftiDataArray(data=cdat)
        image = nib.gifti.GiftiImage()
        image.add_gifti_data_array(data_array)
        nib.save(image, f'../maps/HippoMaps-initializationMaps/Dataset-MICs/MRI-3T-{feature}_average-99_hemi-{hemi}_den-0p5mm_label-hipp.shape.gii')

# save 2D map
for f,feature in enumerate(features):
    cdat = np.nanmean(morph_dat_MRI7T[iV[0],:,:,f],axis=(1,2)).flatten()
    data_array = nib.gifti.GiftiDataArray(data=cdat)
    image = nib.gifti.GiftiImage()
    image.add_gifti_data_array(data_array)
    nib.save(image, f'../maps/HippoMaps-initializationMaps/Dataset-PNI/MRI-7T-{feature}_average-20_hemi-mix_den-0p5mm_label-hipp.shape.gii')

for f,feature in enumerate(features):
    cdat = np.nanmean(morph_dat_hist[:,:,f],axis=1).flatten()
    data_array = nib.gifti.GiftiDataArray(data=cdat)
    image = nib.gifti.GiftiImage()
    image.add_gifti_data_array(data_array)
    nib.save(image, f'../maps/HippoMaps-initializationMaps/Dataset-Mixed/histology-{feature}_average-7_hemi-mix_den-unfoldiso_label-hipp.shape.gii')