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pyhrf.rfir module

class pyhrf.rfir.RFIREstim(hrf_nb_coeffs=42, hrf_dt=0.6, drift_type='cosine', stop_crit1=0.0001, stop_crit2=1e-05, nb_its_max=5, nb_iterations=500, nb_its_min=1, average_bold=False, taum=0.01, lambda_reg=100.0, fixed_taum=False, discarded_scan_indexes=None, output_fit=False)

Bases: pyhrf.xmlio.Initable

Class handling the estimation of HRFs from fMRI data. Analysis is voxel-wise and can be multissession (heteroscedastic noise and session dependent drift). Simultaneous analysis of several conditions is treated. One HRF is considered at each voxel.

Compute_INV_R_and_R_and_DET_R()

Both computes self.InvR and self.DetR

Requires:

  • K-1
  • InvR initialized
Compute_onset_matrix3()

Computes the onset matrix. Each stimulus onset is considered over a period of LengthOnsets seconds if (LengthOnsets > DetlaT) and a time step otherwise.

Requires:

  • X initialized
  • OnsetList
  • TR
  • DeltaT
  • K
  • LengthOnsets

where self.X[i][m,n,k] is such that:

  • session i (in 0:I-1)
  • condition m (in 0:M-1)
  • data nb n (in 0:Ni[i]-1)
  • hrf coef nb k (in 0:K-2)
CptFctQ(CptType)

Computes the function Q(\Theta',\tilde{\Theta};y) at a given iteration

Notes

It requires:

  • All parameters and hyperparameters
  • Sigma
  • InvR
  • CptType = ‘K_Km1’ or ‘K_K’
CptSigma()

Computes the Sigma at a given iteration.

self.Sigma[m*SBS:(m+1)*SBS,n*SBS:(n+1)*SBS]] -> (m,n)^th block of Sigma in session i.

EM_solver(POI)

requires: * everything in the class is supposed initialized

InitMatrixAndVectors(POI)

Initialize to zeros: X, y, P, l, h, InvR, Sigma. Initialize to ones: TauM, rb (<-scalar).

InitStorageMat()

Initialization of the matrices that will store all voxel results.

Notes

Input signals must have been read (in ReadRealSignal)

ReadPointOfInterestData(POI)

Initialize the parameters for a voxel analysis. The voxel ID is ‘POI’ in ‘ConsideredCoord’ initialized in ‘ReadRealSignal’

Notes

Input signals must have been read (in ReadRealSignal)

StoreRes(POI)

Store results computed in the voxel defined in POI.

Notes

The estimation at this voxel must have been performed

buildCosMat(fctNb, tr, ny)

Build a cosine low frequency basis in P (adapted from samplerbase.py)

Parameters:
  • fctNb – columns number in the current session
  • tr – the time resolution of the BOLD data (in second)
  • ny – number of data for the current session
buildLowFreqMat()

Build the low frequency basis matrix P.

buildPolyMat(fctNb, tr, ny)

Build a polynomial low frequency basis in P (adapted from samplerbase.py)

Parameters:
  • fctNb – columns number in the current session
  • tr – the time resolution of the BOLD data (in second)
  • ny – number of data for the current session

Notes

  • there may have no constant column in the orthogonal matrix (the algorithm suppose there is one such column)
  • the columns number is not always as expected
clean_memory()

Clean all objects that are useless for outputs

compute_fit(POI)
cpt_XSigmaX(tempTerm2i, SBS, i)
default_nb_its = 500
default_stop_crit1 = 0.0001
default_stop_crit2 = 1e-05
getOutputs()
linkToData(data)
parametersComments = {'drift_type': 'Basis type in the drift model. Either "cosine" or "poly"', 'hrf_dt': 'Required HRF temporal resolution', 'hrf_nb_coeffs': 'Number of values in the discrete HRF. Discretization is homogeneous HRF time length is then: nb_hrf_coeffs * hrf_dt '}
parametersToShow = ['hrf_nb_coeffs', 'hrf_dt', 'drift_type', 'nb_iterations']
run()

function to launch the analysis

pyhrf.rfir.init_dict()
pyhrf.rfir.rfir(func_data, fir_duration=42, fir_dt=0.6, nb_its_max=100, nb_its_min=5, fixed_taum=False, lambda_reg=100.0)

Fit a Regularized FIR on functional data func_data:

  • multisession voxel-based fwd model: y = sum Xh + Pl + b
  • heteroscedastic noise
  • session dependent drift coefficients
  • one HRF per condition
  • solved by Expectation-Minimization (EM) (iterative scheme)

Reference: “Unsupervised robust non-parametric estimation of the hemodynamic response function for any fMRI experiment.” Ciuciu, J.-B. Poline, G. Marrelec, J. Idier, Ch. Pallier, and H. Benali. IEEE Trans. Med. Imag., 22(10):1235-1251, Oct. 2003.

Parameters:
  • func_data (pyhrf.core.FmriData) –
  • fir_duration (float) – FIR duration in seconds
  • fir_dt (float) – FIR temporal resolution
  • fixed_taum (bool) – enable faster (drafter) RFIR version where the HRF variance hyper-parameter is fixed.
  • lambda_reg (float) – amount of temporal regularization for the HRF. Only used if fixed_taum is true.
  • nb_its_min – minimum number of iterations for the EM
  • nb_its_max – maximum number of iterations for the EM
Returns:
Return type:

dict of xndarray instances