def _run_interface(self, runtime):
beta_nii = nb.load(self.inputs.beta)
if isdefined(self.inputs.mask):
mask = nb.load(self.inputs.mask).get_data() > 0
else:
mask = np.ones(beta_nii.shape[:3]) == 1
glm = GLM.glm()
nii = nb.load(self.inputs.beta)
glm.beta = beta_nii.get_data().copy()[mask,:].T
glm.nvbeta = self.inputs.nvbeta
glm.s2 = nb.load(self.inputs.s2).get_data().copy()[mask]
glm.dof = self.inputs.dof
glm._axis = self.inputs.axis
glm._constants = self.inputs.constants
reg_names = self.inputs.reg_names
self._stat_maps = []
self._p_maps = []
self._z_maps = []
for contrast_def in self.inputs.contrasts:
name = contrast_def[0]
_ = contrast_def[1]
contrast = np.zeros(len(reg_names))
for i, reg_name in enumerate(reg_names):
if reg_name in contrast_def[2]:
idx = contrast_def[2].index(reg_name)
contrast[i] = contrast_def[3][idx]
est_contrast = glm.contrast(contrast)
stat_map = np.zeros(mask.shape)
stat_map[mask] = est_contrast.stat().T
stat_map_file = os.path.abspath(name + "_stat_map.nii")
nb.save(nb.Nifti1Image(stat_map, nii.get_affine()), stat_map_file)
self._stat_maps.append(stat_map_file)
p_map = np.zeros(mask.shape)
p_map[mask] = est_contrast.pvalue().T
p_map_file = os.path.abspath(name + "_p_map.nii")
nb.save(nb.Nifti1Image(p_map, nii.get_affine()), p_map_file)
self._p_maps.append(p_map_file)
z_map = np.zeros(mask.shape)
z_map[mask] = est_contrast.zscore().T
z_map_file = os.path.abspath(name + "_z_map.nii")
nb.save(nb.Nifti1Image(z_map, nii.get_affine()), z_map_file)
self._z_maps.append(z_map_file)
return runtime
def _run_interface(self, runtime):
beta_nii = nb.load(self.inputs.beta)
if isdefined(self.inputs.mask):
mask = nb.load(self.inputs.mask).get_data() > 0
else:
mask = np.ones(beta_nii.shape[:3]) == 1
glm = GLM.glm()
nii = nb.load(self.inputs.beta)
glm.beta = beta_nii.get_data().copy()[mask, :].T
glm.nvbeta = self.inputs.nvbeta
glm.s2 = nb.load(self.inputs.s2).get_data().copy()[mask]
glm.dof = self.inputs.dof
glm._axis = self.inputs.axis
glm._constants = self.inputs.constants
reg_names = self.inputs.reg_names
self._stat_maps = []
self._p_maps = []
self._z_maps = []
for contrast_def in self.inputs.contrasts:
name = contrast_def[0]
_ = contrast_def[1]
contrast = np.zeros(len(reg_names))
for i, reg_name in enumerate(reg_names):
if reg_name in contrast_def[2]:
idx = contrast_def[2].index(reg_name)
contrast[i] = contrast_def[3][idx]
est_contrast = glm.contrast(contrast)
stat_map = np.zeros(mask.shape)
stat_map[mask] = est_contrast.stat().T
stat_map_file = os.path.abspath(name + "_stat_map.nii")
nb.save(nb.Nifti1Image(stat_map, nii.get_affine()), stat_map_file)
self._stat_maps.append(stat_map_file)
p_map = np.zeros(mask.shape)
p_map[mask] = est_contrast.pvalue().T
p_map_file = os.path.abspath(name + "_p_map.nii")
nb.save(nb.Nifti1Image(p_map, nii.get_affine()), p_map_file)
self._p_maps.append(p_map_file)
z_map = np.zeros(mask.shape)
z_map[mask] = est_contrast.zscore().T
z_map_file = os.path.abspath(name + "_z_map.nii")
nb.save(nb.Nifti1Image(z_map, nii.get_affine()), z_map_file)
self._z_maps.append(z_map_file)
return runtime
def _run_interface(self, runtime):
session_info = self.inputs.session_info
functional_runs = self.inputs.session_info[0]['scans']
if isinstance(functional_runs, str):
functional_runs = [functional_runs]
nii = nb.load(functional_runs[0])
data = nii.get_data()
if isdefined(self.inputs.mask):
mask = nb.load(self.inputs.mask).get_data() > 0
else:
mask = np.ones(nii.shape[:3]) == 1
timeseries = data.copy()[mask,:]
del data
for functional_run in functional_runs[1:]:
nii = nb.load(functional_run)
data = nii.get_data()
npdata = data.copy()
del data
timeseries = np.concatenate((timeseries,npdata[mask,:]), axis=1)
del npdata
nscans = timeseries.shape[1]
if 'hpf' in session_info[0].keys():
hpf = session_info[0]['hpf']
drift_model=self.inputs.drift_model
else:
hpf=0
drift_model = "Blank"
reg_names = []
for reg in session_info[0]['regress']:
reg_names.append(reg['name'])
reg_vals = np.zeros((nscans,len(reg_names)))
for i in range(len(reg_names)):
reg_vals[:,i] = np.array(session_info[0]['regress'][i]['val']).reshape(1,-1)
frametimes= np.linspace(0, (nscans-1)*self.inputs.TR, nscans)
conditions = []
onsets = []
duration = []
for i,cond in enumerate(session_info[0]['cond']):
onsets += cond['onset']
conditions += [cond['name']]*len(cond['onset'])
if len(cond['duration']) == 1:
duration += cond['duration']*len(cond['onset'])
else:
duration += cond['duration']
if conditions:
paradigm = BlockParadigm(con_id=conditions, onset=onsets, duration=duration)
else:
paradigm = None
design_matrix, self._reg_names = dm.dmtx_light(frametimes, paradigm, drift_model=drift_model, hfcut=hpf,
hrf_model=self.inputs.hrf_model,
add_regs=reg_vals,
add_reg_names=reg_names
)
if self.inputs.normalize_design_matrix:
for i in range(len(self._reg_names)-1):
design_matrix[:,i] = (design_matrix[:,i]-design_matrix[:,i].mean())/design_matrix[:,i].std()
if self.inputs.plot_design_matrix:
if pylab_available:
pylab.pcolor(design_matrix)
pylab.savefig("design_matrix.pdf")
pylab.close()
pylab.clf()
else:
Exception('Pylab not available for saving design matrix image')
glm = GLM.glm()
glm.fit(timeseries.T, design_matrix, method=self.inputs.method, model=self.inputs.model)
self._beta_file = os.path.abspath("beta.nii")
beta = np.zeros(mask.shape + (glm.beta.shape[0],))
beta[mask,:] = glm.beta.T
nb.save(nb.Nifti1Image(beta, nii.get_affine()), self._beta_file)
self._s2_file = os.path.abspath("s2.nii")
s2 = np.zeros(mask.shape)
s2[mask] = glm.s2
nb.save(nb.Nifti1Image(s2, nii.get_affine()), self._s2_file)
if self.inputs.save_residuals:
explained = np.dot(design_matrix,glm.beta)
residuals = np.zeros(mask.shape + (nscans,))
residuals[mask,:] = timeseries - explained.T
self._residuals_file = os.path.abspath("residuals.nii")
nb.save(nb.Nifti1Image(residuals, nii.get_affine()), self._residuals_file)
self._nvbeta = glm.nvbeta
self._dof = glm.dof
self._constants = glm._constants
self._axis = glm._axis
if self.inputs.model == "ar1":
self._a_file = os.path.abspath("a.nii")
a = np.zeros(mask.shape)
a[mask] = glm.a.squeeze()
nb.save(nb.Nifti1Image(a, nii.get_affine()), self._a_file)
self._model = glm.model
self._method = glm.method
return runtime
from nipy.labs import glm dimt = 100 dimx = 10 dimy = 11 dimz = 12 # axis defines the "time direction" y = np.random.randn(dimt, dimx * dimy * dimz) axis = 0 X = np.array([np.ones(dimt), range(dimt)]) X = X.T ## the design matrix X must have dimt lines mod = glm.glm(y, X, axis=axis, model='ar1') # Define a t contrast tcon = mod.contrast([1, 0]) # Compute the t-stat t = tcon.stat() ## t = tcon.stat(baseline=1) to test effects > 1 # Compute the p-value p = tcon.pvalue() # Compute the z-score z = tcon.zscore() # Perform a F test without keeping the F stat
fmri_data = surrogate_4d_dataset(shape=shape, n_scans=n_scans)[0] # if you want to save it as an image data_file = op.join(swd, 'fmri_data.nii') save(fmri_data, data_file) ######################################## # Perform a GLM analysis ######################################## # GLM fit Y = fmri_data.get_data() model = "ar1" method = "kalman" glm = GLM.glm() glm.fit(Y.T, X, method=method, model=model) # specify the contrast [1 -1 0 ..] contrast = np.zeros(X.shape[1]) contrast[0] = 1 contrast[1] = - 1 my_contrast = glm.contrast(contrast) # compute the constrast image related to it zvals = my_contrast.zscore() contrast_image = Nifti1Image(np.reshape(zvals, shape), affine) # if you want to save the contrast as an image contrast_path = op.join(swd, 'zmap.nii') save(contrast_image, contrast_path)
def __init__(self, vols, age, tiv=None, sex=None):
self.X = make_design(age, tiv, sex)
M = glm(vols, self.X)
self.beta = M.beta
self.s2 = M.s2
self.vols = vols
def _run_interface(self, runtime):
session_info = self.inputs.session_info
functional_runs = self.inputs.session_info[0]['scans']
if isinstance(functional_runs, str):
functional_runs = [functional_runs]
nii = nb.load(functional_runs[0])
data = nii.get_data()
if isdefined(self.inputs.mask):
mask = nb.load(self.inputs.mask).get_data() > 0
else:
mask = np.ones(nii.shape[:3]) == 1
timeseries = data.copy()[mask, :]
del data
for functional_run in functional_runs[1:]:
nii = nb.load(functional_run)
data = nii.get_data()
npdata = data.copy()
del data
timeseries = np.concatenate((timeseries, npdata[mask, :]), axis=1)
del npdata
nscans = timeseries.shape[1]
if 'hpf' in session_info[0].keys():
hpf = session_info[0]['hpf']
drift_model = self.inputs.drift_model
else:
hpf = 0
drift_model = "Blank"
reg_names = []
for reg in session_info[0]['regress']:
reg_names.append(reg['name'])
reg_vals = np.zeros((nscans, len(reg_names)))
for i in range(len(reg_names)):
reg_vals[:, i] = np.array(
session_info[0]['regress'][i]['val']).reshape(1, -1)
frametimes = np.linspace(0, (nscans - 1) * self.inputs.TR, nscans)
conditions = []
onsets = []
duration = []
for i, cond in enumerate(session_info[0]['cond']):
onsets += cond['onset']
conditions += [cond['name']] * len(cond['onset'])
if len(cond['duration']) == 1:
duration += cond['duration'] * len(cond['onset'])
else:
duration += cond['duration']
paradigm = BlockParadigm(con_id=conditions,
onset=onsets,
duration=duration)
design_matrix, self._reg_names = dm.dmtx_light(
frametimes,
paradigm,
drift_model=drift_model,
hfcut=hpf,
hrf_model=self.inputs.hrf_model,
add_regs=reg_vals,
add_reg_names=reg_names)
if self.inputs.normalize_design_matrix:
for i in range(len(self._reg_names) - 1):
design_matrix[:, i] = (
design_matrix[:, i] -
design_matrix[:, i].mean()) / design_matrix[:, i].std()
if self.inputs.plot_design_matrix:
if pylab_available:
pylab.pcolor(design_matrix)
pylab.savefig("design_matrix.pdf")
pylab.close()
pylab.clf()
else:
Exception('Pylab not available for saving design matrix image')
glm = GLM.glm()
glm.fit(timeseries.T,
design_matrix,
method=self.inputs.method,
model=self.inputs.model)
self._beta_file = os.path.abspath("beta.nii")
beta = np.zeros(mask.shape + (glm.beta.shape[0], ))
beta[mask, :] = glm.beta.T
nb.save(nb.Nifti1Image(beta, nii.get_affine()), self._beta_file)
self._s2_file = os.path.abspath("s2.nii")
s2 = np.zeros(mask.shape)
s2[mask] = glm.s2
nb.save(nb.Nifti1Image(s2, nii.get_affine()), self._s2_file)
if self.inputs.save_residuals:
explained = np.dot(design_matrix, glm.beta)
residuals = np.zeros(mask.shape + (nscans, ))
residuals[mask, :] = timeseries - explained.T
self._residuals_file = os.path.abspath("residuals.nii")
nb.save(nb.Nifti1Image(residuals, nii.get_affine()),
self._residuals_file)
self._nvbeta = glm.nvbeta
self._dof = glm.dof
self._constants = glm._constants
self._axis = glm._axis
if self.inputs.model == "ar1":
self._a_file = os.path.abspath("a.nii")
a = np.zeros(mask.shape)
a[mask] = glm.a.squeeze()
nb.save(nb.Nifti1Image(a, nii.get_affine()), self._a_file)
self._model = glm.model
self._method = glm.method
return runtime
