def compute_BSA_ipmi(domain, lbeta, dmax, thq=0.5, smin=5, ths=0, theta=3.0,
bdensity=0, model="gam_gauss", verbose=0):
"""
Compute the Bayesian Structural Activation patterns
with approach described in IPMI'07 paper
Parameters
----------
domsin: StructuredDomain instance,
Description of the spatial context of the data
lbeta: an array of shape (nbnodes, subjects):
the multi-subject statistical maps
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
contains the likelihood of the data under H1
on the set of input nodes
model: string,
model used to infer the prior p-values
can be 'gamma_gauss' or 'gauss_mixture'
verbose=0 : verbosity mode
Returns
-------
crmap: array of shape (nnodes):
the resulting group-level labelling of the space
LR: instance of sbf.LandmarkRegions,
that describes the ROIs found in inter-subject inference
bf: list of nipy.neurospin.spatial_models.hroi.Nroi instances
representing individual ROIs
p: array of shape (nnodes):
likelihood of the data under H1 over some sampling grid
Note
----
This is historically the first version,
but probably not the most optimal
It should not be changed for historical reason
"""
nbsubj = lbeta.shape[1]
nvox = domain.size
bf, gf0, sub, gfc = compute_individual_regions(
domain, lbeta, smin, theta, 'gam_gauss', verbose)
crmap = -np.ones(nvox, np.int)
u = []
AF = sbf.LandmarkRegions(bf[0].domain, 0, [], [])
p = np.zeros(nvox)
if len(sub)<1:
return crmap, AF, bf, u, p
# inter-subject analysis
# use the DPMM (core part)
dim = domain.em_dim
sub = np.concatenate(sub).astype(np.int)
gfc = np.concatenate(gfc)
gf0 = np.concatenate(gf0)
p = np.zeros(np.size(nvox))
g0 = 1./(np.sum(domain.local_volume))
g1 = g0
dof = 1000
prior_precision = 1./(dmax*dmax)*np.ones((1,dim))
if bdensity:
spatial_coords = domain.coord
else:
spatial_coords = gfc
p,q = dpmm(gfc, 0.5, g0, g1, dof, prior_precision,
1-gf0, sub, 100, spatial_coords, nis=300)
# inference
valid = q>thq
# remove non-significant regions
for s in range(nbsubj):
bfs = bf[s]
if bfs.k>0: # is not None
valids = -np.ones(bfs.k).astype('bool')
valids[bfs.isleaf()] = valid[sub==s]
valids = bfs.make_forest().propagate_upward_and(valids)
bfs.select(valids)
if bfs.k>0: # is not None
bfs.merge_descending()
bfs.make_feature('position', domain.coord)
pos = bfs.representative_feature('position', 'cumulated_mean')
bfs.set_roi_feature('position', pos)
# compute probabilitsic correspondences across subjects
gc = _hierarchical_asso(bf, np.sqrt(2)*dmax)
if gc == []:
return crmap,AF,bf,p
# make hard clusters through clustering
u, cost = average_link_graph_segment(gc, 0.2, gc.V*1.0/nbsubj)
q = 0
for s in range(nbsubj):
if bf[s].k>0: # is not None
bf[s].set_roi_feature('label', u[q:q+bf[s].k])
q += bf[s].k
LR, mlabel = sbf.build_LR(bf, ths=ths)
if LR is not None:
crmap = LR.map_label(domain.coord, pval=0.95, dmax=dmax)
return crmap, LR, bf, p
def Compute_Amers (Fbeta, Beta, xyz, affine, shape, coord, dmax=10.,
thr=3.0, ths=0, pval=0.2,verbose=0):
"""
This is the main function for building the BFLs
Parameters
----------
Fbeta : nipy.neurospin.graph.field.Field instance
an describing the spatial relationships
in the dataset. nbnodes = Fbeta.V
Beta: an array of shape (nbnodes, subjects):
the multi-subject statistical maps
xyz array of shape (nnodes,3):
the grid coordinates of the field
affine=np.eye(4), array of shape(4,4)
coordinate-defining affine transformation
shape=None, tuple of length 3 defining the size of the grid
implicit to the discrete ROI definition
coord array of shape (nnodes,3):
spatial coordinates of the nodes
dmax=10.: spatial relaxation allowed in the preocedure
thr = 3.0: thrshold at the first-level
ths = 0, number of subjects to validate a BFL
pval = 0.2 : significance p-value for the spatial inference
Results
-------
crmap: array of shape (nnodes):
the resulting group-level labelling of the space
AF : a instance of Landmark_regions that describes the ROIs found
in inter-subject inference
If no such thing can be defined LR is set to None
BFLs: List of nipy.neurospin.spatial_models.hroi.Nroi instances
representing individual ROIs
Newlabel: labelling of the individual ROIs
"""
BFLs = []
LW = []
nbsubj = Beta.shape[1]
nvox = Beta.shape[0]
for s in range(nbsubj):
beta = np.reshape(Beta[:,s],(nvox,1))
Fbeta.set_field(beta)
bfls = hroi.NROI_from_watershed(Fbeta, affine, shape, xyz,
refdim=0, th=thr)
if bfls!=None:
bfls.set_discrete_feature_from_index('position',coord)
bfls.discrete_to_roi_features('position','average')
#bfls.make_feature(coord,'position','mean')
BFLs.append(bfls)
# _clean_density(BFLs,dmax,coord,pval,verbose=1,dev=0,nrec=5)
_clean_density_redraw(BFLs,dmax,coord,pval,verbose=0,dev=0,
nrec=1,nsamples=10)
Gc = _hierarchical_asso(BFLs,dmax)
Gc.weights = np.log(Gc.weights)-np.log(Gc.weights.min())
if verbose:
print Gc.V,Gc.E,Gc.weights.min(),Gc.weights.max()
# building cliques
#u = segment_graph_rd(Gc,1)
u,cost = average_link_graph_segment(Gc,0.1,Gc.V*1.0/nbsubj)
# relabel the BFLs
q = 0
for s in range(nbsubj):
BFLs[s].set_roi_feature('label',u[q:q+BFLs[s].k])
q += BFLs[s].k
LR,mlabel = build_LR(BFLs,ths)
if LR!=None:
crmap = LR.map_label(coord,pval = 0.95,dmax=dmax)
return crmap, LR, BFLs
def compute_BSA_dev (Fbeta, lbeta, coord, dmax, xyz, affine=np.eye(4),
shape=None, thq=0.9,smin=5, ths=0, theta=3.0, g0=1.0,
bdensity=0, verbose=0):
"""
Compute the Bayesian Structural Activation paterns
Parameters
----------
Fbeta : nipy.neurospin.graph.field.Field instance
an describing the spatial relationships
in the dataset. nbnodes = Fbeta.V
lbeta: an array of shape (nbnodes, subjects):
the multi-subject statistical maps
coord array of shape (nnodes,3):
spatial coordinates of the nodes
xyz array of shape (nnodes,3):
the grid coordinates of the field
affine=np.eye(4), array of shape(4,4)
coordinate-defining affine transformation
shape=None, tuple of length 3 defining the size of the grid
implicit to the discrete ROI definition
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
g0 = 1.0 (float): constant values of the uniform density
over the (compact) volume of interest
bdensity=0 if bdensity=1, the variable p in ouput
contains the likelihood of the data under H1
on the set of input nodes
verbose=0 : verbosity mode
Results
-------
crmap: array of shape (nnodes):
the resulting group-level labelling of the space
LR: a instance of sbf.Landmrak_regions that describes the ROIs found
in inter-subject inference
If no such thing can be defined LR is set to None
bf: List of nipy.neurospin.spatial_models.hroi.Nroi instances
representing individual ROIs
p: array of shape (nnodes):
likelihood of the data under H1 over some sampling grid
Note
----
This version is probably the best one to date
the intra subject Gamma-Gaussian MM has been replaces by a Gaussian MM
which is probably mroe robust
"""
bf = []
gfc = []
gf0 = []
sub = []
gc = []
nsubj = lbeta.shape[1]
nvox = lbeta.shape[0]
# intra-subject analysis: get the blobs,
# with their position and their significance
for s in range(nsubj):
# description in terms of blobs
beta = np.reshape(lbeta[:,s],(nvox,1))
Fbeta.set_field(beta)
nroi = hroi.NROI_from_field(Fbeta, affine, shape, xyz, refdim=0,
th=theta,smin=smin)
bf.append(nroi)
if nroi!=None:
sub.append(s*np.ones(nroi.k))
nroi.set_discrete_feature_from_index('activation',beta)
bfm = nroi.discrete_to_roi_features('activation','average')
# compute the region position
nroi.set_discrete_feature_from_index('position',coord)
bfc = nroi.discrete_to_roi_features('position',
'cumulated_average')
gfc.append(bfc)
# compute the prior proba of being null
beta = np.squeeze(beta)
beta = beta[beta!=0]
alpha = 0.01
prior_strength = 100
fixed_scale = True
bfp = en.three_classes_GMM_fit(beta, bfm, alpha,
prior_strength,verbose,fixed_scale)
bf0 = bfp[:,1]
gf0.append(bf0)
crmap = -np.ones(nvox, np.int)
u = []
AF = []
p = np.zeros(nvox)
if len(sub)<1:
return crmap,AF,bf,u,p
# inter-subject analysis
# use the DPMM (core part)
sub = np.concatenate(sub).astype(np.int)
gfc = np.concatenate(gfc)
gf0 = np.concatenate(gf0)
p = np.zeros(np.size(nvox))
g1 = g0
dof = 0
prior_precision = 1./(dmax*dmax)*np.ones((1,3), np.int)
if bdensity:
spatial_coords = coord
else:
spatial_coords = gfc
p,q = fc.fdp(gfc, 0.5, g0, g1, dof,prior_precision, 1-gf0,
sub, 100, spatial_coords,10,1000)
valid = q>thq
if verbose:
import matplotlib.pylab as mp
mp.figure()
mp.plot(1-gf0,q,'.')
print np.sum(valid),np.size(valid)
# remove non-significant regions
for s in range(nsubj):
bfs = bf[s]
if bfs!=None:
valids = valid[sub==s]
valids = bfs.propagate_upward_and(valids)
bfs.clean(valids)
bfs.merge_descending()
# re-compute the region position
bfs.set_discrete_feature_from_index('position',coord)
bfc = bfs.discrete_to_roi_features('position',
'cumulated_average')
# Alan's choice
#beta = np.reshape(lbeta[:,s],(nvox,1))
#bfsc = coord[bfs.feature_argmax(beta)]
#bfs.set_roi_feature(bfsc,'position')
# compute a model of between-regions associations
gc = _hierarchical_asso(bf,np.sqrt(2)*dmax)
# Infer the group-level clusters
if gc == []:
return crmap,AF,bf,p
# either replicator dynamics or agglomerative clustering
#u = sbf.segment_graph_rd(gc,1)
u,cost = average_link_graph_segment(gc,0.1,gc.V*1.0/nsubj)
q = 0
for s in range(nsubj):
if bf[s]!=None:
bf[s].set_roi_feature('label',u[q:q+bf[s].k])
q += bf[s].k
LR,mlabel = sbf.build_LR(bf,ths)
if LR!=None:
crmap = LR.map_label(coord,pval = 0.95,dmax=dmax)
return crmap,LR,bf,p
def Compute_Amers(dom, lbeta, dmax=10., thr=3.0, ths=0, pval=0.2, verbose=0):
"""
This is the main function for building the BFLs
Parameters
----------
dom : StructuredDomain instance,
generic descriptor of the space domain
lbeta: an array of shape (nbnodes, subjects):
the multi-subject statistical maps
dmax:float, optional, spatial relaxation allowed in the preocedure
thr: float, optional, threshold at the first-level
ths: float, optional, number of subjects to validate a BFL
pval: float, optional : significance p-value for the spatial inference
Returns
-------
crmap: array of shape (nnodes):
the resulting group-level labelling of the space
AF : a instance of LandmarkRegions that describes the ROIs found
in inter-subject inference
If no such thing can be defined LR is set to None
BFLs: List of nipy.neurospin.spatial_models.hroi.Nroi instances
representing individual ROIs
Newlabel: labelling of the individual ROIs
"""
BFLs = []
nbsubj = lbeta.shape[1]
nvox = lbeta.shape[0]
from nipy.neurospin.graph.field import field_from_coo_matrix_and_data
Fbeta = field_from_coo_matrix_and_data(dom.topology, lbeta[:, 0])
for s in range(nbsubj):
beta = np.reshape(lbeta[:,s],(nvox,1))
Fbeta.set_field(beta)
bfls = hroi.HROI_from_watershed(dom, beta, threshold=thr)
if bfls.k>0:
bfls.make_feature('position', dom.coord)
pos = bfls.representative_feature('position', 'mean')
bfls.set_roi_feature('position', pos)
BFLs.append(bfls)
_clean_density_redraw(BFLs, dmax, dom.coord, pval, verbose=0,
nrec=1, nsamples=10)
Gc = _hierarchical_asso(BFLs,dmax)
Gc.weights = np.log(Gc.weights)-np.log(Gc.weights.min())
if verbose:
print Gc.V, Gc.E, Gc.weights.min(), Gc.weights.max()
# building cliques
u,cost = average_link_graph_segment(Gc, 0.1, Gc.V*1.0/nbsubj)
# relabel the BFLs
q = 0
for s in range(nbsubj):
BFLs[s].set_roi_feature('label',u[q:q+BFLs[s].k])
q += BFLs[s].k
LR, mlabel = build_LR(BFLs, ths=ths)
if LR!=None:
crmap = LR.map_label(dom.coord, pval=0.95, dmax=dmax)
return crmap, LR, BFLs
def compute_BSA_ipmi(Fbeta,lbeta, coord,dmax, xyz, affine=np.eye(4),
shape=None, thq=0.5,
smin=5, ths=0, theta=3.0, g0=1.0, bdensity=0, verbose=0):
"""
Compute the Bayesian Structural Activation patterns
with approach described in IPMI'07 paper
Parameters
----------
Fbeta : nipy.neurospin.graph.field.Field instance
an describing the spatial relationships
in the dataset. nbnodes = Fbeta.V
lbeta: an array of shape (nbnodes, subjects):
the multi-subject statistical maps
coord array of shape (nnodes,3):
spatial coordinates of the nodes
xyz array of shape (nnodes,3):
the grid coordinates of the field
affine=np.eye(4), array of shape(4,4)
coordinate-defining affine transformation
shape=None, tuple of length 3 defining the size of the grid
implicit to the discrete ROI definition
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
g0 = 1.0 (float): constant values of the uniform density
over the (compact) volume of interest
bdensity=0 if bdensity=1, the variable p in ouput
contains the likelihood of the data under H1
on the set of input nodes
verbose=0 : verbosity mode
Results
-------
crmap: array of shape (nnodes):
the resulting group-level labelling of the space
LR: a instance of sbf.Landmrak_regions that describes the ROIs found
in inter-subject inference
If no such thing can be defined LR is set to None
bf: List of nipy.neurospin.spatial_models.hroi.Nroi instances
representing individual ROIs
p: array of shape (nnodes):
likelihood of the data under H1 over some sampling grid
Note
----
This is historically the first version,
but probably not the most optimal
It should not be changed for historical reason
"""
bf = []
gfc = []
gf0 = []
sub = []
gc = []
nbsubj = lbeta.shape[1]
nvox = lbeta.shape[0]
# intra-subject part: compute the blobs
# and assess their significance
for s in range(nbsubj):
beta = np.reshape(lbeta[:,s],(nvox,1))
Fbeta.set_field(beta)
nroi = hroi.NROI_from_field(Fbeta, affine, shape, xyz, refdim=0,
th=theta, smin=smin)
bf.append(nroi)
if nroi!=None:
sub.append(s*np.ones(nroi.k))
# find some way to avoid coordinate averaging
nroi.set_discrete_feature_from_index('activation',beta)
bfm = nroi.discrete_to_roi_features('activation','average')
nroi.set_discrete_feature_from_index('position',coord)
bfc = nroi.discrete_to_roi_features('position',
'cumulated_average')
gfc.append(bfc)
# get some prior on the significance of the regions
beta = np.reshape(beta,(nvox))
beta = beta[beta!=0]
# use a Gamma-Gaussian Mixture Model
bfp = en.Gamma_Gaussian_fit(beta,bfm,verbose)
bf0 = bfp[:,1]
gf0.append(bf0)
crmap = -np.ones(nvox, np.int)
u = []
AF = []
p = np.zeros(nvox)
if len(sub)<1:
return crmap,AF,bf,u,p
# inter-subject analysis
# use the DPMM (core part)
sub = np.concatenate(sub).astype(np.int)
gfc = np.concatenate(gfc)
gf0 = np.concatenate(gf0)
p = np.zeros(np.size(nvox))
g1 = g0
dof = 0
prior_precision = 1./(dmax*dmax)*np.ones((1,3), np.float)
if bdensity:
spatial_coords = coord
else:
spatial_coords = gfc
p,q = fc.fdp(gfc, 0.5, g0, g1,dof, prior_precision,
1-gf0, sub, 100, spatial_coords,10,1000)
# inference
valid = q>thq
if verbose>1:
import matplotlib.pylab as mp
mp.figure()
mp.plot(1-gf0,q,'.')
print np.sum(valid),np.size(valid)
# remove non-significant regions
for s in range(nbsubj):
bfs = bf[s]
if bfs!=None:
valids = valid[sub==s]
valids = bfs.propagate_upward_and(valids)
bfs.clean(valids)
if bfs!=None:
bfs.merge_descending()
bfs.set_discrete_feature_from_index('position',coord)
bfs.discrete_to_roi_features('position','cumulated_average')
# compute probabilitsic correspondences across subjects
gc = _hierarchical_asso(bf,np.sqrt(2)*dmax)
if gc == []:
return crmap,AF,bf,p
# make hard clusters
# choose one solution...
#u = sbf.segment_graph_rd(gc,1)
u,cost = average_link_graph_segment(gc,0.2,gc.V*1.0/nbsubj)
q = 0
for s in range(nbsubj):
if bf[s]!=None:
bf[s].set_roi_feature('label',u[q:q+bf[s].k])
q += bf[s].k
LR,mlabel = sbf.build_LR(bf,ths)
if LR!=None:
crmap = LR.map_label(coord,pval=0.95,dmax=dmax)
return crmap,LR,bf,p