hemolearn.deconvolution.multi_runs_blind_deconvolution_multiple_subjects

hemolearn.deconvolution.multi_runs_blind_deconvolution_multiple_subjects(X, t_r, hrf_rois, hrf_model='scaled_hrf', shared_spatial_maps=False, deactivate_v_learning=False, deactivate_z_learning=False, deactivate_u_learning=False, n_atoms=10, n_times_atom=60, prox_z='tv', lbda_strategy='ratio', lbda=0.1, rho=2.0, delta_init=1.0, u_init_type='ica', eta=10.0, z_init=None, prox_u='l1-positive-simplex', max_iter=100, get_obj=0, get_time=0, random_seed=None, early_stopping=True, eps=1e-05, raise_on_increase=True, n_jobs=1, nb_fit_try=1, verbose=0)

Multiple initialization parallel running version of blind_deconvolution_single_subject.

Parameters

Xarray, shape (n_voxels, n_times), fMRI data

t_rfloat, Time of Repetition, fMRI acquisition parameter, the temporal

resolution

hrf_roisdict (key: ROIs labels, value: indices of voxels of the ROI)

atlas HRF

hrf_modelstr, (default=’3_basis_hrf’), type of HRF model, possible

choice are [‘3_basis_hrf’, ‘2_basis_hrf’, ‘scaled_hrf’]

shared_spatial_mapsbool, whether or not to learn a single set of

spatials maps accross subjects.

deactivate_v_learningbool, (default=False), option to force the

estimated HRF to to the initial value.

deactivate_z_learningbool, (default=False), option to force the

estimated z to its initial value.

deactivate_u_learningbool, (default=False), option to force the

estimated u to its initial value.

n_atomsint, number of components on which to decompose the neural

activity (number of temporal components and its associated spatial maps).

n_times_atomint, (default=30), number of points on which represent the

Haemodynamic Response Function (HRF), this leads to the duration of the response function, duration = n_times_atom * t_r

prox_zstr, (default=’tv’), temporal proximal operator should be in

[‘tv’, ‘l1’, ‘l2’, ‘elastic-net’]

lbda_strategy str, (default=’ratio’), strategy to fix the temporal

regularization parameter, possible choice are [‘ratio’, ‘fixed’]

lbdafloat, (default=0.1), whether the temporal regularization parameter

if lbda_strategy == ‘fixed’ or the ratio w.r.t lambda max if lbda_strategy == ‘ratio’

rhofloat, (default=2.0), the elastic-net temporal regularization

parameter

delta_initfloat, (default=1.0), the initialization value for the HRF

dilation parameter

u_init_typestr, (default=’ica’), strategy to init u, possible value are

[‘gaussian_noise’, ‘ica’, ‘patch’]

etafloat, (default=10.0), the spatial sparsity regularization parameter

z_initNone or array, (default=None), initialization of z, if None, z is

initialized to zero

prox_ustr, (default=’l2-positive-ball’), constraint to impose on the

spatial maps possible choice are [‘l2-positive-ball’, ‘l1-positive-simplex’, ‘positive’]

max_iterint, (default=100), maximum number of iterations to perform the

analysis

get_objint, the level of cost-function saving, 0 to not compute it, 1 to

compute it at each iteration, 2 to compute it at each estimation step (z, u, v)

get_timeint, the level of computation-duration saving 0 to not compute

it, 1 to compute it at each iteration, 2 to compute it at each estimation step

random_seedNone, int, random-instance, (default=None), random-instance

or random-seed used to initialize the analysis

early_stoppingbool, (default=True), whether to early stop the analysis

epsfloat, (default=1.0e-4), stoppping parameter w.r.t evolution of the

cost-function

raise_on_increasebool, (default=True), whether to stop the analysis if

the cost-function increases during an iteration. This can be due to the fact that the temporal regularization parameter is set to high

nb_fit_tryint, (default=1), number of analysis to do with different

initialization

n_jobsint, (default=1), the number of CPUs to use if multiple analysis

with different initialization is done

verboseint, (default=0), verbose level, 0 no verbose, 1 low verbose,

2 maximum verbose