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.signal.high_variance_confounds(series, n_confounds=5, percentile=2.0, detrend=True)¶
Return confounds time series extracted from series with highest variance.
- 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
Highest-variance series percentile to keep before computing the singular value decomposition, 0. <= percentile <= 100. series.shape * percentile / 100 must be greater than n_confounds
- detrend: bool, optional
If True, detrend timeseries before processing.
- v: numpy.ndarray
highest variance confounds. Shape: (samples, n_confounds)
This method is related to what has been published in the literature as ‘CompCor’ 1.
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.
Yashar Behzadi, Khaled Restom, Joy Liau, and Thomas T. Liu. A component based noise correction method (compcor) for bold and perfusion based fmri. NeuroImage, 37(1):90–101, 2007. URL: https://www.sciencedirect.com/science/article/pii/S1053811907003837, doi:https://doi.org/10.1016/j.neuroimage.2007.04.042.