aggregate-fmri-timeseries/code/vol2shenroi_ts

#!/usr/bin/python

import sys
import os

args = dict(
    sub=sys.argv[1],
    run=sys.argv[2])

atlas_filename = "sub-{sub}/atlases/bold3Tp2/shen_fconn_atlas_150.nii.gz".format(**args)
fmri_filename = "src/aligned/sub-{sub}/in_bold3Tp2/sub-{sub}_task-avmovie_run-{run}_bold.nii.gz".format(**args)
confounds = "src/aligned/sub-{sub}/in_bold3Tp2/sub-{sub}_task-avmovie_run-{run}_bold_mcparams.txt".format(**args)
csv_filename = os.path.join("sub-{sub}".format(**args), 'shen_fconn', os.path.basename(fmri_filename)[:-6] + "csv")

##############################################################################
# Extract signals on a parcellation defined by labels using the
# NiftiLabelsMasker
from nilearn.input_data import NiftiLabelsMasker
masker = NiftiLabelsMasker(
    labels_img=atlas_filename,
    background_label=0,
    smoothing_fwhm=4.0,
    t_r=2.0,
    standardize=True,
    detrend=True,
    memory='nilearn_cache',
    verbose=5)

# Here we go from nifti files to the signal time series in a numpy
# array. Note how we give confounds to be regressed out during signal
# extraction
time_series = masker.fit_transform(
    fmri_filename,
    confounds=confounds)
labels = [int(l) for l in masker.labels_]

##############################################################################
# Store as CSV
import numpy as np
np.savetxt(
    csv_filename,
    time_series,
    delimiter=',',
    header=','.join([str(l) for l in labels]),
    comments='',
    fmt="%.10f")