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.reporting.make_glm_report(model, contrasts, title=None, bg_img='MNI152TEMPLATE', threshold=3.09, alpha=0.001, cluster_threshold=0, height_control='fpr', two_sided=False, min_distance=8.0, plot_type='slice', cut_coords=None, display_mode=None, report_dims=(1600, 800))[source]

Return HTMLReport object for a report which shows all important aspects of a fitted GLM.

The object can be opened in a browser, displayed in a notebook, or saved to disk as a standalone HTML file.

modelFirstLevelModel or SecondLevelModel object

A fitted first or second level model object. Must have the computed design matrix(ces).

contrastsDict[string, ndarray] or String or List[String] or ndarray or


Contrasts information for a first or second level model.


Dict of contrast names and coefficients, or list of contrast names or list of contrast coefficients or contrast name or contrast coefficient

Each contrast name must be a string. Each contrast coefficient must be a list or numpy array of ints.

Contrasts are passed to contrast_def for FirstLevelModel (nilearn.glm.first_level.FirstLevelModel.compute_contrast) & second_level_contrast for SecondLevelModel (nilearn.glm.second_level.SecondLevelModel.compute_contrast)

titleString, optional

If string, represents the web page’s title and primary heading, model type is sub-heading. If None, page titles and headings are autogenerated using contrast names.

bg_imgNiimg-like object, default=’MNI152TEMPLATE’

See Input and output: neuroimaging data representation. The background image for mask and stat maps to be plotted on upon. To turn off background image, just pass “bg_img=None”.

thresholdfloat, default=3.09

Cluster forming threshold in same scale as stat_img (either a t-scale or z-scale value). Used only if height_control is None.

alphafloat, default=0.001

Number controlling the thresholding (either a p-value or q-value). Its actual meaning depends on the height_control parameter. This function translates alpha to a z-scale threshold.

cluster_thresholdint, default=0

Cluster size threshold, in voxels.

height_controlstring, default=’fpr’

false positive control meaning of cluster forming threshold: ‘fpr’ (default) or ‘fdr’ or ‘bonferroni’ or None.

two_sidedbool, default=False

Whether to employ two-sided thresholding or to evaluate positive values only.

min_distancefloat, default=8

For display purposes only. Minimum distance between subpeaks in mm.

plot_typeString, {‘slice’, ‘glass’}, default=’slice’

Specifies the type of plot to be drawn for the statistical maps.

cut_coordsNone, a tuple of float, or int, optional

The MNI coordinates of the point where the cut is performed.

  • If display_mode is ‘ortho’ or ‘tiled’, this should be a 3-tuple: (x, y, z)

  • For display_mode == “x”, “y”, or “z”, then these are the coordinates of each cut in the corresponding direction.

  • If None is given, the cuts are calculated automatically.

  • If display_mode is ‘mosaic’, and the number of cuts is the same for all directions, cut_coords can be specified as an integer. It can also be a length 3 tuple specifying the number of cuts for every direction if these are different.


If display_mode is “x”, “y” or “z”, cut_coords can be an integer, in which case it specifies the number of cuts to perform.

display_modestring, optional

Default is ‘z’ if plot_type is ‘slice’; ‘ ortho’ if plot_type is ‘glass’.

Choose the direction of the cuts: ‘x’ - sagittal, ‘y’ - coronal, ‘z’ - axial, ‘l’ - sagittal left hemisphere only, ‘r’ - sagittal right hemisphere only, ‘ortho’ - three cuts are performed in orthogonal directions.

Possible values are: ‘ortho’, ‘x’, ‘y’, ‘z’, ‘xz’, ‘yx’, ‘yz’, ‘l’, ‘r’, ‘lr’, ‘lzr’, ‘lyr’, ‘lzry’, ‘lyrz’.

report_dimsSequence[int, int], default=(1600, 800)

Specifies width, height (in pixels) of report window within a notebook. Only applicable when inserting the report into a Jupyter notebook. Can be set after report creation using report.width, report.height.

report_textHTMLReport Object

Contains the HTML code for the GLM Report.


report = make_glm_report(model, contrasts) report.open_in_browser() report.save_as_html(destination_path)

Examples using nilearn.reporting.make_glm_report

Decoding of a dataset after GLM fit for signal extraction

Decoding of a dataset after GLM fit for signal extraction

Default Mode Network extraction of ADHD dataset

Default Mode Network extraction of ADHD dataset

First level analysis of a complete BIDS dataset from openneuro

First level analysis of a complete BIDS dataset from openneuro

Voxel-Based Morphometry on OASIS dataset

Voxel-Based Morphometry on OASIS dataset