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.

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, random_state=None)[source]#

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

Parameters:
t_rfloat

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_reffloat, optional

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. Default=0.

hrf_modelstr, function, list of functions, or None

This parameter defines the HRF model to be used. It can be a string if you are passing the name of a model implemented in Nilearn. Valid names are:

It can also be a custom model. In this case, a function should be provided for each regressor. Each function should behave as the other models implemented within Nilearn. That is, it should take both t_r and oversampling as inputs and return a sample numpy array of appropriate shape.

Note

It is expected that spm standard and glover models would not yield large differences in most cases.

Note

In case of glover and spm models, the derived regressors are orthogonalized wrt the main one.

Default=’glover’.

drift_modelstring, optional

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

high_passfloat, 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’. Default=0.01.

drift_orderint, optional

This parameter specifies the order of the drift model (in case it is polynomial) for the design matrices. Default=1.

fir_delaysarray of shape(n_onsets) or list, optional

In case of FIR design, yields the array of delays used in the FIR model, in scans. Default=[0].

min_onsetfloat, 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. Default=-24.

mask_imgNiimg-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_affine3x3 or 4x4 matrix, optional

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

target_shape3-tuple of integers, optional

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

smoothing_fwhmfloat, optional.

If smoothing_fwhm is not None, it gives the full-width at half maximum in millimeters of the spatial smoothing to apply to the signal.

memorystring or pathlib.Path, 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_levelinteger, optional

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

standardizeboolean, optional

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

signal_scalingFalse, 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). Default=0.

noise_model{‘ar1’, ‘ols’}, optional

The temporal variance model. Default=’ar1’.

verboseinteger, 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. Default=0.

n_jobsinteger, optional

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

minimize_memoryboolean, 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. Default=True.

subject_labelstring, optional

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

random_stateint or numpy.random.RandomState, optional

Random state seed to sklearn.cluster.KMeans for autoregressive models of order at least 2 (‘ar(N)’ with n >= 2). Default=None.

New in version 0.9.1.

Notes

This class is experimental. It may change in any future release of Nilearn.

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, random_state=None)[source]#
property scaling_axis#
fit(run_imgs, events=None, confounds=None, sample_masks=None, design_matrices=None, bins=100)[source]#

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_imgsNiimg-like object or list of Niimg-like objects,

Data on which the GLM will be fitted. If this is a list, the affine is considered the same for all.

eventspandas Dataframe or string or list of pandas DataFrames or strings, optional

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.

confoundspandas Dataframe, numpy array or string or

list of pandas DataFrames, numpy arrays or strings, optional 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.

sample_masksarray_like, or list of array_like, optional

shape of array: (number of scans - number of volumes removed, ) Indices of retained volumes. Masks the niimgs along time/fourth dimension to perform scrubbing (remove volumes with high motion) and/or remove non-steady-state volumes. Default=None.

New in version 0.9.2.

design_matricespandas DataFrame or list of pandas DataFrames, optional

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

binsint, optional

Maximum number of discrete bins for the AR coef histogram. If an autoregressive model with order greater than one is specified then adaptive quantification is performed and the coefficients will be clustered via K-means with bins number of clusters. Default=100.

compute_contrast(contrast_def, stat_type=None, output_type='z_score')[source]#

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_defstr 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. 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 +-*/. In this case, the string defining the contrasts must be a valid expression for compatibility with pandas.DataFrame.eval.

stat_type{‘t’, ‘F’}, optional

Type of the contrast.

output_typestr, optional

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

Returns:
outputNifti1Image 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_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:
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:
X_newndarray 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))[source]#

Returns a HTMLReport which shows all important aspects of a fitted GLM.

The HTMLReport 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).

Note

The FirstLevelModel or SecondLevelModel must have been fitted prior to calling generate_report.

Parameters:
contrastsdict [ str, ndarray ] or str or list [ str ] or ndarray or list [ ndarray ]

Contrasts information for a FirstLevelModel or SecondLevelModel.

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 through compute_contrast, and second_level_contrast for SecondLevelModel through compute_contrast.

titlestr, optional
  • If a str, it 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, optional

Default is the MNI152 template (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, optional

Cluster forming threshold in same scale as stat_img (either a t-scale or z-scale value). Used only if height_control is None. Default=3.09

alphafloat, optional

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. Default=0.001

cluster_thresholdint, optional

Cluster size threshold, in voxels. Default=0

height_controlstr or None, optional

False positive control meaning of cluster forming threshold: ‘fpr’, ‘fdr’, ‘bonferroni’ or None. Default=’fpr’.

min_distancefloat, optional

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

plot_type{‘slice’, ‘glass’}, optional

Specifies the type of plot to be drawn for the statistical maps. Default=’slice’.

display_mode{‘ortho’, ‘x’, ‘y’, ‘z’, ‘xz’, ‘yx’, ‘yz’, ‘l’, ‘r’, ‘lr’, ‘lzr’, ‘lyr’, ‘lzry’, ‘lyrz’}, optional

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

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

report_dimsSequence[ int, int ], optional

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. Default=(1600, 800).

Returns:
report_textHTMLReport

Contains the HTML code for the GLM report.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

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

Returns:
paramsdict

Parameter names mapped to their values.

predicted()[source]#

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

Returns:
outputlist

A list of Nifti1Image(s).

r_square()[source]#

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

Returns:
outputlist

A list of Nifti1Image(s).

residuals()[source]#

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

Returns:
outputlist

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 Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

Examples using nilearn.glm.first_level.FirstLevelModel#

Intro to GLM Analysis: a single-session, single-subject fMRI dataset

Intro to GLM Analysis: a single-session, single-subject fMRI dataset

Intro to GLM Analysis: a single-session, single-subject fMRI dataset
Decoding of a dataset after GLM fit for signal extraction

Decoding of a dataset after GLM fit for signal extraction

Decoding of a dataset after GLM fit for signal extraction
Example of explicit fixed effects fMRI model fitting

Example of explicit fixed effects fMRI model fitting

Example of explicit fixed effects fMRI model fitting
Default Mode Network extraction of ADHD dataset

Default Mode Network extraction of ADHD dataset

Default Mode Network extraction of ADHD dataset
Analysis of an fMRI dataset with a Finite Impule Response (FIR) model

Analysis of an fMRI dataset with a Finite Impule Response (FIR) model

Analysis of an fMRI dataset with a Finite Impule Response (FIR) model
Single-subject data (two sessions) in native space

Single-subject data (two sessions) in native space

Single-subject data (two sessions) in native space
Simple example of two-session fMRI model fitting

Simple example of two-session fMRI model fitting

Simple example of two-session fMRI model fitting
Predicted time series and residuals

Predicted time series and residuals

Predicted time series and residuals
First level analysis of a complete BIDS dataset from openneuro

First level analysis of a complete BIDS dataset from openneuro

First level analysis of a complete BIDS dataset from openneuro
Understanding parameters of the first-level model

Understanding parameters of the first-level model

Understanding parameters of the first-level model
BIDS dataset first and second level analysis

BIDS dataset first and second level analysis

BIDS dataset first and second level analysis
Beta-Series Modeling for Task-Based Functional Connectivity and Decoding

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding