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).

See also

pyhrf.ui.analyser_ui, pyhrf.ui.treatment, pyhrf.ui.jde, pyhrf.ui.vb_jde_analyser

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
  • 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.