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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
This is an example where
1. An sequence of fMRI volumes are loaded
2. An ROI mask is loaded
3. A design matrix describing all the effects related to the data is computed
4. A GLM is applied to all voxels in the ROI
5. A summary of the results is provided for certain contrasts
6. A plot of the hrf is provided for the mean reponse in the hrf
7. Fitted/adjusted response plots are provided
Author : Bertrand Thirion, 2010
"""
print __doc__
import numpy as np
import os.path as op
import matplotlib.pylab as mp
from nipy.io.imageformats import load, save, Nifti1Image
import nipy.neurospin.utils.design_matrix as dm
from nipy.neurospin.utils.simul_multisubject_fmri_dataset import surrogate_4d_dataset
import get_data_light
import nipy.neurospin.glm as GLM
from nipy.neurospin.spatial_models.roi import MultipleROI
#######################################
# Simulation parameters
#######################################
# volume mask
get_data_light.getIt()
mask_path = op.expanduser(op.join('~', '.nipy', 'tests', 'data',
'mask.nii.gz'))
mask = load(mask_path)
# timing
n_scans =128
tr = 2.4
# paradigm
frametimes = np.linspace(0, (n_scans-1)*tr, n_scans)
conditions = np.arange(20)%2
onsets = np.linspace(5, (n_scans-1)*tr-10, 20) # in seconds
hrf_model = 'Canonical'
motion = np.cumsum(np.random.randn(n_scans, 6),0)
add_reg_names = ['tx','ty','tz','rx','ry','rz']
# write directory
swd = '/tmp'
########################################
# Design matrix
########################################
paradigm = np.vstack(([conditions, onsets])).T
paradigm = dm.EventRelatedParadigm(conditions, onsets)
X, names = dm.dmtx_light(frametimes, paradigm, drift_model='Cosine', hfcut=128,
hrf_model=hrf_model, add_regs=motion,
add_reg_names=add_reg_names)
#######################################
# Get the FMRI data
#######################################
fmri_data = surrogate_4d_dataset(mask=mask, dmtx=X, seed=1)
# 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()[mask.get_data()>0, :]
model = "ar1"
method = "kalman"
glm = GLM.glm()
glm.fit(Y.T, X, method=method, model=model)
# specifiy 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()
zmap = mask.get_data().astype(np.float)
zmap[zmap>0] = zmap[zmap>0]*zvals
contrast_image = Nifti1Image(zmap, mask.get_affine())
# if you want to save the contrast as an image
# contrast_path = op.join(swd, 'zmap.nii')
# save(contrast_image, contrast_path)
########################################
# Create ROIs
########################################
positions = np.array([[60, -30, 5],[50, 27, 5]])
# in mm (here in the MNI space)
radii = np.array([8,6])
mroi = MultipleROI( affine=mask.get_affine(), shape=mask.get_shape())
mroi.as_multiple_balls(positions, radii)
# to save an image of the ROIs
mroi.make_image((op.join(swd, "roi.nii")))
# exact the time courses with ROIs
mroi.set_discrete_feature_from_image('signal', image=fmri_data)
# ROI average time courses
mroi.discrete_to_roi_features('signal')
# roi-level contrast average
mroi.set_discrete_feature_from_image('contrast', image=contrast_image)
mroi.discrete_to_roi_features('contrast')
########################################
# GLM analysis on the ROI average time courses
########################################
nreg = len(names)
ROI_tc = mroi.get_roi_feature('signal')
glm.fit(ROI_tc.T, X, method=method, model=model)
mp.figure()
mp.subplot(1, 2, 1)
b1 = mp.bar(np.arange(nreg-1), glm.beta[:-1,0], width=.4, color='blue',
label='r1')
b2 = mp.bar(np.arange(nreg-1)+0.3, glm.beta[:-1,1], width=.4, color='red',
label='r2')
mp.xticks(np.arange(nreg-1), names[:-1])
mp.legend()
mp.title('parameters estimates for the roi time courses')
bx = mp.subplot(1, 2 ,2)
mroi.plot_discrete_feature('contrast', bx)
mp.show()
########################################
# fitted and adjusted response
########################################
res = ROI_tc -np.dot(glm.beta.T, X.T)
proj = np.eye(nreg)
proj[2:] = 0
fit = np.dot(np.dot(glm.beta.T,proj),X.T)
# plot it
mp.figure()
for k in range(mroi.k):
mp.subplot(mroi.k, 1, k+1)
mp.plot(fit[k])
mp.plot(fit[k] + res[k],'r')
mp.xlabel('time (scans)')
mp.legend(('effects','adjusted'))
###########################################
# hrf for condition 1
############################################
fir_order = 6
X_fir,name_dir = dm.dmtx_light(
frametimes, paradigm, hrf_model='FIR', drift_model='Cosine', drift_order=3,
fir_delays = tr*np.arange(fir_order), fir_duration=tr, add_regs=motion,
add_reg_names=add_reg_names)
glm.fit(ROI_tc.T, X_fir, method=method, model=model)
mp.figure()
for k in range(mroi.k):
mp.subplot(mroi.k, 1, k+1)
var = np.diag(glm.nvbeta[:,:,k])*glm.s2[k]
mp.errorbar(np.arange(fir_order), glm.beta[:fir_order,k],
yerr=np.sqrt(var[:fir_order]))
mp.errorbar(np.arange(fir_order), glm.beta[fir_order:2*fir_order,k],
yerr=np.sqrt(var[fir_order:2*fir_order]))
mp.legend(('condition c0','condition c1'))
mp.title('estimated hrf shape')
mp.xlabel('time(scans)')
mp.show()
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