The main routine of this package that aims at performing the extraction of ROIs from multisubject dataset using the localization and activation strength of extracted regions. This has been puclished in Thirion et al. High level group analysis of FMRI data based on Dirichlet process mixture models, IPMI 2007
Author : Bertrand Thirion, 2006-2009
Estimation of the population level model of activation density using dpmm and inference
Parameters : | bf list of nipy.neurospin.spatial_models.hroi.HierarchicalROI instances :
gf0, array of shape (nr) :
sub, array of shape (nr) :
gfc, array of shape (nr, coord.shape[1]) :
dmax float>0: :
thq = 0.5 (float in the [0,1] interval) :
ths=0, float in the rannge [0,nsubj] :
verbose=0, verbosity mode : |
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Returns : | crmap: array of shape (nnodes): :
LR: a instance of sbf.LandmarkRegions that describes the ROIs found :
bf: List of nipy.neurospin.spatial_models.hroi.Nroi instances :
p: array of shape (nnodes): :
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Estimation of the population level model of activation density using dpmm and inference
Parameters : | bf list of nipy.neurospin.spatial_models.hroi.HierarchicalROI instances :
gf0, array of shape (nr) :
sub, array of shape (nr) :
gfc, array of shape (nr, coord.shape[1]) :
dmax float>0: :
thq = 0.5 (float in the [0,1] interval) :
ths=0, float in the rannge [0,nsubj] :
verbose=0, verbosity mode : |
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Returns : | crmap: array of shape (nnodes): :
LR: a instance of sbf.LandmarkRegions that describes the ROIs found :
bf: List of nipy.neurospin.spatial_models.hroi.Nroi instances :
Coclust: array of shape (nr,nr): :
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Compute the Bayesian Structural Activation patterns with approach described in IPMI‘07 paper
Parameters : | domsin: StructuredDomain instance, :
lbeta: an array of shape (nbnodes, subjects): :
thq = 0.5 (float): posterior significance threshold should be in [0,1] : smin = 5 (int): minimal size of the regions to validate them : theta = 3.0 (float): first level threshold : bdensity=0 if bdensity=1, the variable p in ouput :
model: string, :
verbose=0 : verbosity mode |
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Returns : | crmap: array of shape (nnodes): :
LR: instance of sbf.LandmarkRegions, :
bf: list of nipy.neurospin.spatial_models.hroi.Nroi instances :
p: array of shape (nnodes): :
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Compute the Bayesian Structural Activation paterns - with statistical validation
Parameters : | dom: StructuredDomain instance, :
lbeta: an array of shape (nbnodes, subjects): :
dmax float>0: :
thq = 0.5 (float): :
smin = 5 (int): minimal size of the regions to validate them : theta = 3.0 (float): first level threshold : method: string, optional, :
verbose=0: verbosity mode : |
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Idem compute_BSA_simple, but this one does not estimate the full density (on small datasets, it can be much faster)
Parameters : | dom : StructuredDomain instance,
lbeta: an array of shape (nbnodes, subjects): :
dmax float>0: :
thq = 0.5 (float): :
smin = 5 (int): minimal size of the regions to validate them : theta = 3.0 (float): first level threshold : method: string, optional, :
verbose=0: verbosity mode : |
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Returns : | crmap: array of shape (nnodes): :
LR: a instance of sbf.LandmarkRegions that describes the ROIs found :
bf: List of nipy.neurospin.spatial_models.hroi.Nroi instances :
coclust: array of shape (nr,nr): :
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Compute the Bayesian Structural Activation paterns - simplified version
Parameters : | dom : StructuredDomain instance,
lbeta: an array of shape (nbnodes, subjects): :
dmax float>0: :
thq = 0.5 (float): :
smin = 5 (int): minimal size of the regions to validate them : theta = 3.0 (float): first level threshold : method: string, optional, :
verbose=0: verbosity mode : |
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Returns : | crmap: array of shape (nnodes): :
LR: a instance of sbf.LandmarkRegions that describes the ROIs found :
bf: List of nipy.neurospin.spatial_models.hroi.Nroi instances :
p: array of shape (nnodes): :
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Compute the Bayesian Structural Activation paterns - with statistical validation
Parameters : | dom : StructuredDomain instance,
lbeta: an array of shape (nbnodes, subjects) :
smin: int, optional :
theta: float, optional :
method: string, optional, :
verbose=0: verbosity mode : reshuffle=0: if nonzero, reshuffle the positions; this affects bf and gfc : |
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Returns : | bf list of nipy.neurospin.spatial_models.hroi.Nroi instances :
gf0, array of shape (nr) :
sub, array of shape (nr) :
gfc, array of shape (nr, coord.shape[1]) :
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Apply the dpmm analysis to the data: python version
Convert a set of z-values to posterior probabilities of being active
Parameters : | test: array pf shape(n_samples, 1), :
learn: array pf shape(n_samples, 1), optional :
method: string, optional, to be chosen within :
alpha: float in the [0,1], optional, :
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