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.
7.11.2. nilearn.signal.high_variance_confounds¶

nilearn.signal.
high_variance_confounds
(series, n_confounds=5, percentile=2.0, detrend=True)¶ Return confounds time series extracted from series with highest variance.
Parameters: series: numpy.ndarray
Timeseries. A timeseries is a column in the “series” array. shape (sample number, feature number)
n_confounds: int, optional
Number of confounds to return
percentile: float, optional
Highestvariance series percentile to keep before computing the singular value decomposition, 0. <= percentile <= 100. series.shape[0] * percentile / 100 must be greater than n_confounds
detrend: bool, optional
If True, detrend timeseries before processing.
Returns: v: numpy.ndarray
highest variance confounds. Shape: (samples, 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 time series (no mean removal)
 keep a given percentile of series with highest variances (percentile)
 compute an svd of the extracted series
 return a given number (n_confounds) of series from the svd with highest singular values.