

In [5]:

    
import numpy as np
import pandas as pd
import nibabel as nib
from nilearn import input_data, image

Pre-Modafinil

Define paths to input data (taken from an fmriprep run on OpenNeuro).



In [19]:

    
bold_T1w_preproc = '/Users/dlurie/Dropbox/Projects/avalanche/avalanche/data/ds000133/sub-01/pre/sub-01_ses-pre_task-rest_run-01_bold_space-MNI152NLin2009cAsym_preproc.nii.gz'



In [20]:

    
bold_T1w_brainmask = '/Users/dlurie/Dropbox/Projects/avalanche/avalanche/data/ds000133/sub-01/pre/sub-01_ses-pre_task-rest_run-01_bold_space-MNI152NLin2009cAsym_brainmask.nii.gz'



In [8]:

    
bold_confounds = '/Users/dlurie/Dropbox/Projects/avalanche/avalanche/data/ds000133/sub-01/pre/sub-01_ses-pre_task-rest_run-01_bold_confounds.tsv'

Load confounds.



In [9]:

    
all_confounds = pd.read_csv(bold_confounds, sep='\t')



In [10]:

    
all_confounds.columns









    Out[10]:





Index(['WhiteMatter', 'GlobalSignal', 'stdDVARS', 'non-stdDVARS',
       'vx-wisestdDVARS', 'FramewiseDisplacement', 'tCompCor00', 'tCompCor01',
       'tCompCor02', 'tCompCor03', 'tCompCor04', 'tCompCor05', 'aCompCor00',
       'aCompCor01', 'aCompCor02', 'aCompCor03', 'aCompCor04', 'aCompCor05',
       'Cosine00', 'Cosine01', 'NonSteadyStateOutlier00', 'X', 'Y', 'Z',
       'RotX', 'RotY', 'RotZ'],
      dtype='object')



In [12]:

    
nuisance_vars = all_confounds[['aCompCor00', 'aCompCor01', 'aCompCor02', 'aCompCor03', 'aCompCor04', 'aCompCor05',
                              'X', 'Y', 'Z', 'RotX', 'RotY', 'RotZ']].copy()

Calculate Friston-24 (Yan et al. variant) Motion Parameters



In [14]:

    
# Create squared versions of each motion parameter at Tt
for mopar in ['X', 'Y', 'Z', 'RotX', 'RotY', 'RotZ']:
    sq_col_name = mopar+'sq'
    sq_col_data = np.square(nuisance_vars[mopar])
    nuisance_vars.loc[:,sq_col_name] = sq_col_data



In [15]:

    
zpad = pd.Series([0])
for mopar in nuisance_vars.columns[-12:]:
    pv_col_name = mopar+'_t-1'
    pv_col_data = zpad.append(nuisance_vars[mopar], ignore_index=True)[:-1]
    nuisance_vars.loc[:,pv_col_name] = pv_col_data



In [16]:

    
nuisance_vars.columns









    Out[16]:





Index(['aCompCor00', 'aCompCor01', 'aCompCor02', 'aCompCor03', 'aCompCor04',
       'aCompCor05', 'X', 'Y', 'Z', 'RotX', 'RotY', 'RotZ', 'Xsq', 'Ysq',
       'Zsq', 'RotXsq', 'RotYsq', 'RotZsq', 'X_t-1', 'Y_t-1', 'Z_t-1',
       'RotX_t-1', 'RotY_t-1', 'RotZ_t-1', 'Xsq_t-1', 'Ysq_t-1', 'Zsq_t-1',
       'RotXsq_t-1', 'RotYsq_t-1', 'RotZsq_t-1'],
      dtype='object')

Load EPI image and brain mask.



In [21]:

    
epi_img = image.load_img(bold_T1w_preproc)



In [22]:

    
mask_img = image.load_img(bold_T1w_brainmask)

Clean EPI image (do not standardize values)



In [41]:

    
clean_img = image.clean_img(epi_img, detrend=True, standardize=False, confounds=nuisance_vars.as_matrix(), low_pass=0.1, high_pass=0.01, t_r=1.671)



In [42]:

    
clean_img.to_filename('/Users/dlurie/Dropbox/Projects/avalanche/avalanche/data/ds000133/sub-01/pre/sub-01_ses-pre_task-rest_run-01_bold_space-MNI152NLin2009cAsym_clean.nii.gz')

Clean EPI image (standardize values)



In [39]:

    
clean_img_std = image.clean_img(epi_img, detrend=True, standardize=True, confounds=nuisance_vars.as_matrix(), low_pass=0.1, high_pass=0.01, t_r=1.671)



In [40]:

    
clean_img_std.to_filename('/Users/dlurie/Dropbox/Projects/avalanche/avalanche/data/ds000133/sub-01/pre/sub-01_ses-pre_task-rest_run-01_bold_space-MNI152NLin2009cAsym_clean_std.nii.gz')

Smooth standardized image



In [47]:

    
clean_img_std_fwhm6 = image.smooth_img(clean_img_std, 6)



In [48]:

    
clean_img_std_fwhm6.to_filename('/Users/dlurie/Dropbox/Projects/avalanche/avalanche/data/ds000133/sub-01/pre/sub-01_ses-pre_task-rest_run-01_bold_space-MNI152NLin2009cAsym_clean_std_fwhm6.nii.gz')

Post-Modafinil