def _run_interface(self, runtime):
from nltools.data import Brain_Data
import os
in_file = self.inputs.in_file
mask = self.inputs.mask
low_pass = self.inputs.low_pass_cutoff
high_pass = self.inputs.high_pass_cutoff
TR = self.inputs.sampling_rate
if low_pass == 0:
low_pass = None
if high_pass == 0:
high_pass = None
dat = Brain_Data(in_file, mask=mask)
# Handle no filtering
if low_pass or high_pass:
dat = dat.filter(sampling_rate=TR,
low_pass=low_pass,
high_pass=high_pass)
# Generate output file name
out_file = os.path.split(in_file)[-1].split(
'.nii.gz')[0] + '_filtered.nii.gz'
dat.write(out_file)
self._out_file = out_file
runtime.returncode = 0
return runtime
import seaborn as sns
from nltools.data import Brain_Data
from nltools.plotting import component_viewer
base_dir = '../data/localizer/derivatives/preproc/fmriprep'
base_dir = '/Users/lukechang/Dropbox/Dartbrains/Data/preproc/fmriprep'
sub = 'S01'
data = Brain_Data(os.path.join(base_dir, f'sub-{sub}','func', f'sub-{sub}_task-localizer_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'))
## More Preprocessing
Even though, we have technically already run most of the preprocessing there are a couple of more steps that will help make the ICA cleaner.
First, we will run a high pass filter to remove any low frequency scanner drift. We will pick a fairly arbitrary filter size of 0.0078hz (1/128s). We will also run spatial smoothing with a 6mm FWHM gaussian kernel to increase a signal to noise ratio at each voxel. These steps are very easy to run using nltools after the data has been loaded.
data = data.filter(sampling_freq=1/2.4, high_pass=1/128)
data = data.smooth(6)
## Independent Component Analysis (ICA)
Ok, we are finally ready to run an ICA analysis on our data.
ICA attempts to perform blind source separation by decomposing a multivariate signal into additive subcomponents that are maximally independent.
We will be using the `decompose()` method on our `Brain_Data` instance. This runs the [FastICA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.fastica.html) algorithm implemented by scikit-learn. You can choose whether you want to run spatial ICA by setting `axis='voxels` or temporal ICA by setting `axis='images'`. We also recommend running the whitening flat `whiten=True`. By default `decompose` will estimate the maximum components that are possible given the data. We recommend using a completely arbitrary heuristic of 20-30 components.
tr = 2.4
output = data.decompose(algorithm='ica', n_components=30, axis='images', whiten=True)
## Viewing Components
