nilearn.glm: Generalized Linear Models

Analysing fMRI data using GLMs.

Classes:

Contrast(effect, variance[, dim, dof, ...])	The contrast class handles the estimation of statistical contrasts on a given model: student (t) or Fisher (F).
FContrastResults(effect, covariance, F, df_num)	Results from an F contrast of coefficients in a parametric model.
TContrastResults(t, sd, effect[, df_den])	Results from a t contrast of coefficients in a parametric model.
ARModel(design, rho)	A regression model with an AR(p) covariance structure.
OLSModel(design)	A simple ordinary least squares model.
LikelihoodModelResults(theta, Y, model[, ...])	Class to contain results from likelihood models.
RegressionResults(theta, Y, model, ...[, ...])	Summarize the fit of a linear regression model.
SimpleRegressionResults(results)	Contain only information of the model fit necessary for contrast computation.

Functions:

compute_contrast(labels, regression_result, ...)	Compute the specified contrast given an estimated glm.
compute_fixed_effects(contrast_imgs, ...[, ...])	Compute the fixed effects, given images of effects and variance.
expression_to_contrast_vector(expression, ...)	Convert a string describing a contrast to a contrast vector.
fdr_threshold(z_vals, alpha)	Return the Benjamini-Hochberg FDR threshold for the input z_vals.
cluster_level_inference(stat_img[, ...])	Report the proportion of active voxels for all clusters defined by the input threshold.
threshold_stats_img([stat_img, mask_img, ...])	Compute the required threshold level and return the thresholded map.

nilearn.glm.first_level

Classes:

FirstLevelModel([t_r, slice_time_ref, ...])

Implement the General Linear Model for single run fMRI data.

Functions:

check_design_matrix(design_matrix)	Check that the provided DataFrame is indeed a valid design matrix descriptor, and returns a triplet of fields.
compute_regressor(exp_condition, hrf_model, ...)	Convolve regressors with HRF model.
first_level_from_bids(dataset_path, task_label)	Create FirstLevelModel objects and fit arguments from a BIDS dataset.
glover_dispersion_derivative(t_r[, ...])	Implement the Glover dispersion derivative HRF model.
glover_hrf(t_r[, oversampling, time_length, ...])	Implement the Glover HRF model.
glover_time_derivative(t_r[, oversampling, ...])	Implement the Glover time derivative HRF (dhrf) model.
make_first_level_design_matrix(frame_times)	Generate a design matrix from the input parameters.
mean_scaling(Y[, axis])	Scaling of the data to have percent of baseline change along the specified axis.
run_glm(Y, X[, noise_model, bins, n_jobs, ...])	GLM fit for an fMRI data matrix.
spm_dispersion_derivative(t_r[, ...])	Implement the SPM dispersion derivative HRF model.
spm_hrf(t_r[, oversampling, time_length, onset])	Implement the SPM HRF model.
spm_time_derivative(t_r[, oversampling, ...])	Implement the SPM time derivative HRF (dhrf) model.

nilearn.glm.second_level

Classes:

SecondLevelModel([mask_img, target_affine, ...])

Implement the General Linear Model for multiple subject fMRI data.

Functions:

make_second_level_design_matrix(subjects_label)	Set up a second level design.
non_parametric_inference(second_level_input)	Generate p-values corresponding to the contrasts provided based on permutation testing.