Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.

8.12.15.1. nilearn.glm.first_level.FirstLevelModel

class nilearn.glm.first_level.FirstLevelModel(t_r=None, slice_time_ref=0.0, hrf_model='glover', drift_model='cosine', high_pass=0.01, drift_order=1, fir_delays=[0], min_onset=-24, mask_img=None, target_affine=None, target_shape=None, smoothing_fwhm=None, memory=Memory(location=None), memory_level=1, standardize=False, signal_scaling=0, noise_model='ar1', verbose=0, n_jobs=1, minimize_memory=True, subject_label=None)

Implementation of the General Linear Model for single session fMRI data.

Parameters:

t_r : float

This parameter indicates repetition times of the experimental runs. In seconds. It is necessary to correctly consider times in the design matrix. This parameter is also passed to nilearn.signal.clean. Please see the related documentation for details.

slice_time_ref : float, optional (default 0.)

This parameter indicates the time of the reference slice used in the slice timing preprocessing step of the experimental runs. It is expressed as a percentage of the t_r (time repetition), so it can have values between 0. and 1.

hrf_model : {‘spm’, ‘spm + derivative’, ‘spm + derivative + dispersion’,

‘glover’, ‘glover + derivative’, ‘glover + derivative + dispersion’, ‘fir’, None} String that specifies the hemodynamic response function. Defaults to ‘glover’.

drift_model : string, optional

This parameter specifies the desired drift model for the design matrices. It can be ‘polynomial’, ‘cosine’ or None.

high_pass : float, optional

This parameter specifies the cut frequency of the high-pass filter in Hz for the design matrices. Used only if drift_model is ‘cosine’.

drift_order : int, optional

This parameter specifices the order of the drift model (in case it is polynomial) for the design matrices.

fir_delays : array of shape(n_onsets) or list, optional

In case of FIR design, yields the array of delays used in the FIR model, in scans.

min_onset : float, optional

This parameter specifies the minimal onset relative to the design (in seconds). Events that start before (slice_time_ref * t_r + min_onset) are not considered.

mask_img : Niimg-like, NiftiMasker object or False, optional

Mask to be used on data. If an instance of masker is passed, then its mask will be used. If no mask is given, it will be computed automatically by a NiftiMasker with default parameters. If False is given then the data will not be masked.

target_affine : 3x3 or 4x4 matrix, optional

This parameter is passed to nilearn.image.resample_img. Please see the related documentation for details.

target_shape : 3-tuple of integers, optional

This parameter is passed to nilearn.image.resample_img. Please see the related documentation for details.

smoothing_fwhm : float, optional

If smoothing_fwhm is not None, it gives the size in millimeters of the spatial smoothing to apply to the signal.

memory : string, optional

Path to the directory used to cache the masking process and the glm fit. By default, no caching is done. Creates instance of joblib.Memory.

memory_level : integer, optional

Rough estimator of the amount of memory used by caching. Higher value means more memory for caching.

standardize : boolean, optional

If standardize is True, the time-series are centered and normed: their variance is put to 1 in the time dimension.

signal_scaling : False, int or (int, int), optional,

If not False, fMRI signals are scaled to the mean value of scaling_axis given, which can be 0, 1 or (0, 1). 0 refers to mean scaling each voxel with respect to time, 1 refers to mean scaling each time point with respect to all voxels & (0, 1) refers to scaling with respect to voxels and time, which is known as grand mean scaling. Incompatible with standardize (standardize=False is enforced when signal_scaling is not False).

noise_model : {‘ar1’, ‘ols’}, optional

The temporal variance model. Defaults to ‘ar1’

verbose : integer, optional

Indicate the level of verbosity. By default, nothing is printed. If 0 prints nothing. If 1 prints progress by computation of each run. If 2 prints timing details of masker and GLM. If 3 prints masker computation details.

n_jobs : integer, optional

The number of CPUs to use to do the computation. -1 means ‘all CPUs’, -2 ‘all CPUs but one’, and so on.

minimize_memory : boolean, optional

Gets rid of some variables on the model fit results that are not necessary for contrast computation and would only be useful for further inspection of model details. This has an important impact on memory consumption. True by default.

subject_label : string, optional

This id will be used to identify a FirstLevelModel when passed to a SecondLevelModel object.

Attributes

labels_(array of shape (n_voxels,),) a map of values on voxels used to identify the corresponding model
results_(dict,) with keys corresponding to the different labels values. Values are SimpleRegressionResults corresponding to the voxels, if minimize_memory is True, RegressionResults if minimize_memory is False
__init__(t_r=None, slice_time_ref=0.0, hrf_model='glover', drift_model='cosine', high_pass=0.01, drift_order=1, fir_delays=[0], min_onset=-24, mask_img=None, target_affine=None, target_shape=None, smoothing_fwhm=None, memory=Memory(location=None), memory_level=1, standardize=False, signal_scaling=0, noise_model='ar1', verbose=0, n_jobs=1, minimize_memory=True, subject_label=None)

Initialize self. See help(type(self)) for accurate signature.

compute_contrast(contrast_def, stat_type=None, output_type='z_score')

Generate different outputs corresponding to the contrasts provided e.g. z_map, t_map, effects and variance. In multi-session case, outputs the fixed effects map.

Parameters:

contrast_def : str or array of shape (n_col) or list of (string or

array of shape (n_col))

where n_col is the number of columns of the design matrix, (one array per run). If only one array is provided when there are several runs, it will be assumed that the same contrast is desired for all runs. The string can be a formula compatible with pandas.DataFrame.eval. Basically one can use the name of the conditions as they appear in the design matrix of the fitted model combined with operators +- and combined with numbers with operators +-*/.

stat_type : {‘t’, ‘F’}, optional

type of the contrast

output_type : str, optional

Type of the output map. Can be ‘z_score’, ‘stat’, ‘p_value’, ‘effect_size’, ‘effect_variance’ or ‘all’

Returns:

output : Nifti1Image or dict

The desired output image(s). If output_type == 'all', then the output is a dictionary of images, keyed by the type of image.

fit(run_imgs, events=None, confounds=None, design_matrices=None)

Fit the GLM

For each run: 1. create design matrix X 2. do a masker job: fMRI_data -> Y 3. fit regression to (Y, X)

Parameters:

run_imgs: Niimg-like object or list of Niimg-like objects,

See http://nilearn.github.io/manipulating_images/input_output.html#inputing-data-file-names-or-image-objects # noqa:E501 Data on which the GLM will be fitted. If this is a list, the affine is considered the same for all.

events: pandas Dataframe or string or list of pandas DataFrames or

strings

fMRI events used to build design matrices. One events object expected per run_img. Ignored in case designs is not None. If string, then a path to a csv file is expected.

confounds: pandas Dataframe, numpy array or string or

list of pandas DataFrames, numpy arays or strings

Each column in a DataFrame corresponds to a confound variable to be included in the regression model of the respective run_img. The number of rows must match the number of volumes in the respective run_img. Ignored in case designs is not None. If string, then a path to a csv file is expected.

design_matrices: pandas DataFrame or list of pandas DataFrames,

Design matrices that will be used to fit the GLM. If given it takes precedence over events and confounds.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features)

Input samples.

y : ndarray of shape (n_samples,), default=None

Target values (None for unsupervised transformations).

**fit_params : dict

Additional fit parameters.

Returns:

X_new : ndarray array of shape (n_samples, n_features_new)

Transformed array.

generate_report(contrasts, title=None, bg_img='MNI152TEMPLATE', threshold=3.09, alpha=0.001, cluster_threshold=0, height_control='fpr', min_distance=8.0, plot_type='slice', display_mode=None, report_dims=(1600, 800))

Returns HTMLDocument 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.

The GLM must be fitted and have the computed design matrix(ces).

Parameters:

A fitted first or second level model object.

contrasts: Dict[string, ndarray] or String or List[String] or ndarray or

List[ndarray]

Contrasts information for a first or second level model.

Example:

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)

title: String, 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_img: Niimg-like object

Default is the MNI152 template See http://nilearn.github.io/manipulating_images/input_output.html The background image for mask and stat maps to be plotted on upon. To turn off background image, just pass “bg_img=None”.

threshold: float

Default is 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.

alpha: float

Default is 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_threshold: int, optional

Default is 0 Cluster size threshold, in voxels.

height_control: string or None

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

min_distance: `float`

For display purposes only. Minimum distance between subpeaks in mm. Default is 8 mm.

plot_type: String. [‘slice’ (default) or ‘glass’]

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

display_mode: string

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_dims: Sequence[int, int]

Default is (1600, 800) pixels. 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.

Returns:

report_text: HTMLDocument Object

Contains the HTML code for the GLM Report.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

predicted()

Transform voxelwise predicted values to the same shape as the input Nifti1Image(s)

Returns:

output : list

a list of Nifti1Image(s)

r_square()

Transform voxelwise r-squared values to the same shape as the input Nifti1Image(s)

Returns:

output : list

a list of Nifti1Image(s)

residuals()

Transform voxelwise residuals to the same shape as the input Nifti1Image(s)

Returns:

output : list

a list of Nifti1Image(s)

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params : dict

Estimator parameters.

Returns:

self : object

Estimator instance.