rois = json.load(f)
roi_colors = {'EAC': rois['EAC']['A1'], 'V1': rois['EVC']['V1']}
rois['V1'] = {'V1': 1}
rois.pop('EVC')
# Create separate masks
masks = {}
for roi in rois:
mask = np.zeros(mmp.shape)
for area in rois[roi]:
mask[mmp == rois[roi][area]] = 1
write_gifti(
mask,
join(tpl_dir, f'tpl-fsaverage6_hemi-{hemi}_desc-{roi}_mask.label.gii'),
join(tpl_dir, f'tpl-fsaverage6_hemi-{hemi}_desc-MMP_dseg.label.gii'))
masks[roi] = mask.astype(bool)
n_voxels = np.sum(mask)
np.save(join(tpl_dir, f'tpl-fsaverage6_hemi-{hemi}_desc-{roi}_mask.npy'),
mask)
print(f"Created {hemi} {roi} mask containing " f"{n_voxels} vertices")
# Create single parcellation map
mask_map = np.zeros(mmp.shape)
for i, mask_name in enumerate(masks):
mask_map[masks[mask_name]] = i + 1
write_gifti(
mask_map,
# Compute ISCs
print(f"Started ISC analysis for {subtask} ({hemi})")
iscs = isc(data)
print(f"Finished ISC analysis for {subtask} ({hemi})")
# Split ISCs into subject-/run-specific GIfTI files
assert len(subject_list) == len(run_list) == len(iscs)
for s, fn, r in zip(subject_list, run_list, iscs):
isc_fn = join(
afni_dir, s, 'func',
fn.replace('_desc-clean.func.gii',
'_isc.gii').replace(f'task-{task}',
f'task-{subtask}'))
template_fn = join(afni_dir, s, 'func', fn)
write_gifti(r, isc_fn, template_fn)
print(f"Saved {subtask} {s} ({hemi}) ISC")
# Custom mean estimator with Fisher z transformation for correlations
def fisher_mean(correlation, axis=None):
return np.tanh(np.nanmean(np.arctanh(correlation), axis=axis))
# Compute mean ISC maps per task
for hemi in ['L', 'R']:
global_isc = []
for task in task_meta:
# Skip 'schema' task for simplicity
# Compute mean across all input images
chdir(afni_dir)
for hemi in ['L', 'R']:
input_fns = join(afni_dir, 'sub-*', 'func',
f'sub-*_task-*_space-{space}_'
f'hemi-{hemi}_desc-tsnr.func.gii')
mean_fn = join(afni_dir, f'group_space-{space}_hemi-{hemi}_'
'desc-mean_tsnr.gii')
run(f'3dMean -verbose -prefix {mean_fn} {input_fns}', shell=True)
print(f"Finished computing mean tSNR for {hemi} hemisphere")
# Compute median across all input images
for hemi in ['L', 'R']:
input_fns = glob(join(afni_dir, 'sub-*', 'func',
f'sub-*_task-*_space-{space}_'
f'hemi-{hemi}_desc-tsnr.func.gii'))
tsnr_stack = []
for input_fn in input_fns:
tsnr_stack.append(read_gifti(input_fn))
tsnr_stack = np.vstack(tsnr_stack)
print(f"Loaded all {tsnr_stack.shape[0]} tSNR maps")
tsnr_median = np.median(tsnr_stack, axis=0)
median_fn = join(afni_dir, f'group_space-{space}_hemi-{hemi}_'
'desc-median_tsnr.gii')
write_gifti(tsnr_median, median_fn, input_fns[0])
print(f"Finished computing median tSNR for {hemi} hemisphere")
# Pull in any subject's FreeSurfer parcellation
# All subjects in fsaverage6 space have the same medial NaNs
subject = 'sub-001'
task = 'tunnel'
# Check against known number of medial wall vertices
n_medial = {'L': 3486, 'R': 3491}
for hemi in ['L', 'R']:
fs6_fn = join(deriv_dir, 'fmriprep', subject, 'func',
f'{subject}_task-{task}_space-{space}_hemi-{hemi}.func.gii')
fs6 = read_gifti(fs6_fn)[0]
# Get the medial wall NaNs output by fMRIPrep
medial_vs = np.isnan(fs6)
assert np.sum(medial_vs) == n_medial[hemi]
print(f"Excluding {np.sum(medial_vs)} {hemi} medial wall vertices")
# Negate to get a cortex-only mask
cortical_vs = (~medial_vs).astype(float)
mask_fn = join(mask_dir, f'tpl-{space}_hemi-{hemi}_desc-cortex_mask.gii')
write_gifti(cortical_vs, mask_fn, fs6_fn)
# Make a 1D file for SUMA's SurfSmooth -c_mask
mask_1D = mask_fn.replace('.gii', '.1D')
with open(mask_1D, 'w') as f:
f.write('\n'.join(cortical_vs.astype(int).astype(str).tolist()))
print(f"Created {hemi} {space} cortex mask")
roi_colors = {
'EAC': rois['EAC']['A1'],
'AAC': rois['AAC']['STSdp'],
'PCC': rois['PCC']['POS2'],
'PMC': rois['PCC']['POS2'],
'TPOJ': rois['TPOJ']['TPOJ1']
}
# Create separate masks
masks = {}
for roi in rois:
mask = np.zeros(mmp.shape)
for area in rois[roi]:
mask[mmp == rois[roi][area]] = 1
write_gifti(mask, f'data/MMP_{roi}_fsaverage6.{hemi}.gii',
f'data/MMP_fsaverage6.{hemi}.gii')
masks[roi] = mask.astype(bool)
n_voxels = np.sum(mask)
np.save(f'data/{roi}_mask_{hemi}.npy', mask)
print(f"Created {hemi} {roi} mask containing " f"{n_voxels} voxels")
# Create single parcellation map
mask_map = np.zeros(mmp.shape)
for i, mask_name in enumerate(masks):
mask_map[masks[mask_name]] = i + 1
write_gifti(mask_map, f'data/MMP_ROIs_fsaverage6.{hemi}.gii',
f'data/MMP_fsaverage6.{hemi}.gii')