Examples masker reports#

Nifti masker#

Adapted from Simple example of NiftiMasker use

HTML report

NiftiMasker Applying a mask to extract time-series from Niimg-like objects. NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask :term:`voxels<voxel>` is necessary. Use case: working with time series of :term:`resting-state` or task maps.

image

This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image.

Parameters
Parameter Value
detrend False
dtype None
high_pass None
high_variance_confounds False
low_pass None
mask_args None
mask_img None
mask_strategy epi
memory Memory(location=nilearn_cache/joblib)
memory_level 2
reports True
runs None
smoothing_fwhm 8
standardize zscore_sample
standardize_confounds True
t_r None
target_affine None
target_shape None
verbose 0

Adapted from Encoding models for visual stimuli from Miyawaki et al. 2008

HTML report

MultiNiftiMasker Applying a mask to extract time-series from multiple Niimg-like objects. MultiNiftiMasker is useful when dealing with image sets from multiple subjects. Use case: integrates well with decomposition by MultiPCA and CanICA (multi-subject models)

image

No image provided to fit in NiftiMasker. Setting image to mask for reporting.

This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image.

Parameters
Parameter Value
detrend True
dtype None
high_pass None
high_variance_confounds False
low_pass None
mask_args None
mask_img /home/himanshu/nilearn_data/miyawaki2008/mask/mask.nii.gz
mask_strategy background
memory Memory(location=None)
memory_level 0
n_jobs 2
smoothing_fwhm None
standardize zscore_sample
standardize_confounds True
t_r None
target_affine None
target_shape None
verbose 0
HTML report

MultiNiftiMasker Applying a mask to extract time-series from multiple Niimg-like objects. MultiNiftiMasker is useful when dealing with image sets from multiple subjects. Use case: integrates well with decomposition by MultiPCA and CanICA (multi-subject models)

image

A list of 4D subject images were provided to fit. Only first subject is shown in the report.

This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image.

Parameters
Parameter Value
detrend True
dtype None
high_pass None
high_variance_confounds False
low_pass None
mask_args None
mask_img /home/himanshu/nilearn_data/miyawaki2008/mask/mask.nii.gz
mask_strategy background
memory Memory(location=None)
memory_level 0
n_jobs 2
smoothing_fwhm None
standardize zscore_sample
standardize_confounds True
t_r None
target_affine None
target_shape None
verbose 0

Nifti labels masker#

Adapted from Extracting signals from brain regions using the NiftiLabelsMasker

HTML report

NiftiLabelsMasker Class for extracting data from Niimg-like objects using labels of non-overlapping brain regions. NiftiLabelsMasker is useful when data from non-overlapping volumes should be extracted (contrarily to :class:`nilearn.maskers.NiftiMapsMasker`). Use case: summarize brain signals from clusters that were obtained by prior K-means or Ward clustering. For more details on the definitions of labels in Nilearn, see the :ref:`region` section.