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.clean_img(imgs, sessions=None, detrend=True, standardize=True, confounds=None, low_pass=None, high_pass=None, t_r=None, ensure_finite=False, mask_img=None)¶
Improve SNR on masked fMRI signals.
This function can do several things on the input signals, in the following order:
low- and high-pass filter
Low-pass filtering improves specificity.
High-pass filtering should be kept small, to keep some sensitivity.
Filtering is only meaningful on evenly-sampled signals.
According to Lindquist et al. (2018), removal of confounds will be done orthogonally to temporal filters (low- and/or high-pass filters), if both are specified.
New in version 0.2.5.
- imgsNiimg-like object
4D image. The signals in the last dimension are filtered (see http://nilearn.github.io/manipulating_images/input_output.html for a detailed description of the valid input types).
Add a session level to the cleaning process. Each session will be cleaned independently. Must be a 1D array of n_samples elements.
If detrending should be applied on timeseries (before confound removal). Default=True.
If True, returned signals are set to unit variance. Default=True.
Confounds timeseries. optional Shape must be (instant number, confound number), or just (instant number,) The number of time instants in signals and confounds must be identical (i.e. signals.shape == confounds.shape). If a string is provided, it is assumed to be the name of a csv file containing signals as columns, with an optional one-line header. If a list is provided, all confounds are removed from the input signal, as if all were in the same array.
Low cutoff frequencies, in Hertz.
High cutoff frequencies, in Hertz.
Repetition time, in second (sampling period). Set to None if not specified. Mandatory if used together with low_pass or high_pass.
If True, the non-finite values (NaNs and infs) found in the images will be replaced by zeros. Default=False.
- mask_imgNiimg-like object, optional
If provided, signal is only cleaned from voxels inside the mask. If mask is provided, it should have same shape and affine as imgs. If not provided, all voxels are used. See http://nilearn.github.io/manipulating_images/input_output.html.
- Niimg-like object
Input images, cleaned. Same shape as imgs.
Confounds removal is based on a projection on the orthogonal of the signal space .
Orthogonalization between temporal filters and confound removal is based on suggestions in .
K. J. Friston, A. P. Holmes, K. J. Worsley, J.-P. Poline, C. D. Frith, and R. S. J. Frackowiak. Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping, 2(4):189–210, 1994. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.460020402, arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.460020402, doi:https://doi.org/10.1002/hbm.460020402.
Martin A. Lindquist, Stephan Geuter, Tor D. Wager, and Brian S. Caffo. Modular preprocessing pipelines can reintroduce artifacts into fmri data. bioRxiv, 2018. URL: https://www.biorxiv.org/content/early/2018/09/04/407676, arXiv:https://www.biorxiv.org/content/early/2018/09/04/407676.full.pdf, doi:10.1101/407676.