def bandpass_filter(in_file, lowpass, highpass, TR):
import numpy as np
import nibabel as nb
from os import path
from nipype.interfaces.afni.preprocess import Bandpass
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
out_file = 'func_filtered.nii'
bp = Bandpass()
bp.inputs.highpass = highpass
bp.inputs.lowpass = lowpass
bp.inputs.in_file = in_file
bp.inputs.tr = TR
bp.inputs.out_file = out_file
bp.inputs.outputtype = 'NIFTI'
bp.run()
out_file = path.abspath(out_file)
return (out_file)
def get_alff(input, TR, path_output, hp_freq=0.01, lp_freq=0.08, cleanup=True):
"""
This function calculates ALFF and fALFF from a preprocessed (motion correction, nuisance
regression, etc.) resting-state time series. ALFF is computed by bandpass filtering the time
time series and computing the voxel-wise standard deviation of the filtered time series. fALFF
is computed by dividing ALFF by the voxel-wise standard deviation of the unfiltered time series.
Additionally, ALFF and fALFF are expressed in z-score. This function follows the script found in
https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/alff/alff.py
Inputs:
*input: input time series.
*TR: repetition time in s.
*hp_freq: highpass cutoff frequency in Hz.
*lp_freq: lowpass cutoff frequency in Hz.
*cleanup: delete intermediate files.
created by Daniel Haenelt
Date created: 27-02-2019
Last modified: 15-04-2020
"""
import os
import nibabel as nb
from scipy.stats import zscore
from nipype.interfaces.afni.preprocess import Bandpass
from nipype.interfaces.afni.utils import TStat, Calc
from lib.io.get_filename import get_filename
# make output folder
if not os.path.exists(path_output):
os.makedirs(path_output)
# get path and filename
_, file, _ = get_filename(input)
# filtering
bandpass = Bandpass()
bandpass.inputs.in_file = input
bandpass.inputs.highpass = hp_freq
bandpass.inputs.lowpass = lp_freq
bandpass.inputs.tr = TR
bandpass.inputs.outputtype = 'NIFTI'
bandpass.inputs.out_file = os.path.join(path_output,
file + "_filtered.nii")
bandpass.run()
# standard deviation over frequency
stddev_filtered = TStat()
stddev_filtered.inputs.in_file = os.path.join(path_output,
file + "_filtered.nii")
stddev_filtered.inputs.args = "-stdev"
stddev_filtered.inputs.outputtype = 'NIFTI'
stddev_filtered.inputs.out_file = os.path.join(path_output, 'alff.nii')
stddev_filtered.run()
# standard deviation of the unfiltered nuisance corrected image
stddev_unfiltered = TStat()
stddev_unfiltered.inputs.in_file = input
stddev_unfiltered.inputs.args = "-stdev"
stddev_unfiltered.inputs.outputtype = 'NIFTI'
stddev_unfiltered.inputs.out_file = os.path.join(path_output, 'temp.nii')
stddev_unfiltered.run()
# falff calculations
falff = Calc()
falff.inputs.in_file_a = os.path.join(path_output, 'alff.nii')
falff.inputs.in_file_b = os.path.join(path_output, 'temp.nii')
falff.inputs.args = '-float'
falff.inputs.expr = '(1.0*a)/(1.0*b)'
falff.inputs.outputtype = 'NIFTI'
falff.inputs.out_file = os.path.join(path_output, 'falff.nii')
falff.run()
# alff in z-score
alff_img = nb.load(os.path.join(path_output, "alff.nii"))
alff_array = alff_img.get_fdata()
alff_array = zscore(alff_array, axis=None)
output = nb.Nifti1Image(alff_array, alff_img.affine, alff_img.header)
nb.save(output, os.path.join(path_output, "alff_z.nii"))
# falff in z-score
falff_img = nb.load(os.path.join(path_output, "falff.nii"))
falff_array = falff_img.get_fdata()
falff_array = zscore(falff_array, axis=None)
output = nb.Nifti1Image(falff_array, falff_img.affine, falff_img.header)
nb.save(output, os.path.join(path_output, "falff_z.nii"))
# cleanup
if cleanup:
os.remove(os.path.join(path_output, "temp.nii"))
os.remove(os.path.join(path_output, file + "_filtered.nii"))
