Note
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.
7.5.5. nilearn.image.high_variance_confounds

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
variance.
Parameters:  imgs: Niimglike object
mask_img: Niimglike object
n_confounds: int
Number of confounds to return
percentile: float
Highestvariance signals percentile to keep before computing the
singular value decomposition, 0. <= percentile <= 100.
mask_img.sum() * percentile / 100. must be greater than n_confounds.
detrend: bool
If True, detrend signals before processing.

Returns:  v: numpy.ndarray
highest variance confounds. Shape: (number of scans, n_confounds)

Notes
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.
7.5.5.1. Examples using nilearn.image.high_variance_confounds