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.

7.5.1. nilearn.image.clean_img

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:

  • detrend

  • low- and high-pass filter

  • remove confounds

  • standardize

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.

imgs: Niimg-like object

See 4D image. The signals in the last dimension are filtered.

sessionsnumpy array, optional

Add a session level to the cleaning process. Each session will be cleaned independently. Must be a 1D array of n_samples elements.

detrend: bool

If detrending should be applied on timeseries (before confound removal)

standardize: bool

If True, returned signals are set to unit variance.

confounds: numpy.ndarray, str or list of

Confounds timeseries. 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[0] == confounds.shape[0]). 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_pass, high_pass: float

Respectively low and high cutoff frequencies, in Hertz.

t_r: float, optional

Repetition time, in second (sampling period). Set to None if not specified. Mandatory if used together with low_pass or high_pass.

ensure_finite: bool, optional

If True, the non-finite values (NaNs and infs) found in the images will be replaced by zeros.

mask_img: Niimg-like object, optional

See 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.

cleaned_img: Niimg-like object

Input images, cleaned. Same shape as imgs.


Confounds removal is based on a projection on the orthogonal of the signal space. See Friston, K. J., A. P. Holmes, K. J. Worsley, J.-P. Poline, C. D. Frith, et R. S. J. Frackowiak. “Statistical Parametric Maps in Functional Imaging: A General Linear Approach”. Human Brain Mapping 2, no 4 (1994): 189-210.

Orthogonalization between temporal filters and confound removal is based on suggestions in Lindquist, M., Geuter, S., Wager, T., & Caffo, B. (2018). Modular preprocessing pipelines can reintroduce artifacts into fMRI data. bioRxiv, 407676.