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.

nilearn.image.clean_img

nilearn.image.clean_img(imgs, runs=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.

Parameters:

imgsNiimg-like object

4D image. The signals in the last dimension are filtered (see Input and output: neuroimaging data representation for a detailed description of the valid input types).

runsnumpy.ndarray, optional

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

Warning

‘runs’ replaces ‘sessions’ after release 0.10.0. Using ‘session’ will result in an error after release 0.10.0.

Default=``None``.

detrendbool, optional

If detrending should be applied on timeseries (before confound removal). Default=True.

standardizebool, optional

If True, returned signals are set to unit variance. Default=True.

confoundsnumpy.ndarray, str or list of

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[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_passfloat, optional

Low cutoff frequencies, in Hertz.

high_passfloat, optional

High cutoff frequencies, in Hertz.

t_rfloat, optional

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

ensure_finitebool, optional

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 Input and output: neuroimaging data representation.

Returns:

Niimg-like object

Input images, cleaned. Same shape as imgs.

See also

nilearn.signal.clean

Notes

Confounds removal is based on a projection on the orthogonal of the signal space [[1]].

Orthogonalization between temporal filters and confound removal is based on suggestions in [[2]].

References

[1]

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.

[2]

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.

Examples using nilearn.image.clean_img

Predicted time series and residuals

Predicted time series and residuals