This page is a reference documentation. It only explains the
function signature, and not how to use it. Please refer to the
user guide for the big picture.
nilearn.image.high_variance_confounds(imgs, n_confounds=5, percentile=2.0, detrend=True, mask_img=None)
Return confounds signals extracted from input signals with highest
imgs: Niimg-like object
mask_img: Niimg-like object
Number of confounds to return
Highest-variance signals percentile to keep before computing the
singular value decomposition, 0. <= percentile <= 100.
mask_img.sum() * percentile / 100. must be greater than n_confounds.
If True, detrend signals before processing.
highest variance confounds. Shape: (number of scans, n_confounds)
This method is related to what has been published in the literature
as ‘CompCor’ (Behzadi NeuroImage 2007).
The implemented algorithm does the following:
- compute sum of squares for each signals (no mean removal)
- keep a given percentile of signals with highest variance (percentile)
- compute an svd of the extracted signals
- return a given number (n_confounds) of signals from the svd with
highest singular values.
126.96.36.199. Examples using nilearn.image.high_variance_confounds