pyhrf.vbjde.vem_bold module¶

This module implements the VEM for BOLD data.

The function uses the C extension for expectation and maximization steps (see src/pyhrf/vbjde/utilsmodule.c file).

Notes

TODO: add some refs?

pyhrf.vbjde.vem_bold.eps¶

float – mimics the machine epsilon to avoid zero values

pyhrf.vbjde.vem_bold.logger¶

logger – logger instance identifying this module to log informations

pyhrf.vbjde.vem_bold.jde_vem_bold(graph, bold_data, onsets, durations, hrf_duration, nb_classes, tr, beta, dt, estimate_sigma_h=True, sigma_h=0.05, it_max=-1, it_min=0, estimate_beta=True, contrasts=None, compute_contrasts=False, hrf_hyperprior=0, estimate_hrf=True, constrained=False, zero_constraint=True, drifts_type='poly', seed=6537546)¶

This is the main function that computes the VEM analysis on BOLD data. This function uses optimized python functions.

Parameters:

graph (ndarray of lists) – represents the neighbours indexes of each voxels index bold_data (ndarray, shape (nb_scans, nb_voxels)) – raw data onsets (dict) – dictionnary of onsets durations (# TODO) – # TODO hrf_duration (float) – hrf total time duration (in s) nb_classes (int) – the number of classes to classify the nrls. This parameter is provided for development purposes as most of the algorithm implies two classes tr (float) – time of repetition beta (float) – the initial value of beta dt (float) – hrf temporal precision estimate_sigma_h (bool, optional) – toggle estimation of sigma H sigma_h (float, optional) – initial or fixed value of sigma H it_max (int, optional) – maximal computed iteration number it_min (int, optional) – minimal computed iteration number estimate_beta (bool, optional) – toggle the estimation of beta contrasts (OrderedDict, optional) – dict of contrasts to compute compute_contrasts (bool, optional) – if True, compute the contrasts defined in contrasts hrf_hyperprior (float) – # TODO estimate_hrf (bool, optional) – if True, estimate the HRF for each parcel, if False use the canonical HRF constrained (bool, optional) – if True, add a constrains the l2 norm of the HRF to 1 zero_constraint (bool, optional) – if True, add zeros to the beginning and the end of the estimated HRF. drifts_type (str, optional) – set the drifts basis type used. Can be “poly” for polynomial or “cos” for cosine seed (int, optional) – seed used by numpy to initialize random generator number

Returns:

loop (int) – number of iterations before convergence nrls_mean (ndarray, shape (nb_voxels, nb_conditions)) – Neural response level mean value hrf_mean (ndarray, shape (hrf_len,)) – Hemodynamic response function mean value hrf_covar (ndarray, shape (hrf_len, hrf_len)) – Covariance matrix of the HRF labels_proba (ndarray, shape (nb_conditions, nb_classes, nb_voxels)) – probability of voxels being in one class noise_var (ndarray, shape (nb_voxels,)) – estimated noise variance nrls_class_mean (ndarray, shape (nb_conditions, nb_classes)) – estimated mean value of the gaussians of the classes nrls_class_var (ndarray, shape (nb_conditions, nb_classes)) – estimated variance of the gaussians of the classes beta (ndarray, shape (nb_conditions,)) – estimated beta drift_coeffs (ndarray, shape (# TODO)) – estimated coefficient of the drifts drift (ndarray, shape (# TODO)) – estimated drifts contrasts_mean (ndarray, shape (nb_voxels, len(contrasts))) – Contrasts computed from NRLs contrasts_var (ndarray, shape (nb_voxels, len(contrasts))) – Variance of the contrasts compute_time (list) – computation time of each iteration compute_time_mean (float) – computation mean time over iterations nrls_covar (ndarray, shape (nb_conditions, nb_conditions, nb_voxels)) – # TODO stimulus_induced_signal (ndarray, shape (nb_scans, nb_voxels)) – # TODO mahalanobis_zero (float) – Mahalanobis distance between estimated hrf_mean and the null vector mahalanobis_cano (float) – Mahalanobis distance between estimated hrf_mean and the canonical HRF mahalanobis_diff (float) – difference between mahalanobis_cano and mahalanobis_diff mahalanobis_prod (float) – product of mahalanobis_cano and mahalanobis_diff ppm_a_nrl (ndarray, shape (nb_voxels,)) – The posterior probability map using an alpha ppm_g_nrl (ndarray, shape (nb_voxels,)) – # TODO ppm_a_contrasts (ndarray, shape (nb_voxels,)) – # TODO ppm_g_contrasts (ndarray, shape (nb_voxels,)) – # TODO variation_coeff (float) – coefficient of variation of the HRF free_energy (list) – # TODO

Notes

See A novel definition of the multivariate coefficient of variation article for more information about the coefficient of variation.