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#!/usr/bin/env python3
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
__doc__ = """
Full step-by-step example of fitting a GLM to experimental data and visualizing
the results.
More specifically:
1. A sequence of fMRI volumes are loaded
2. A design matrix describing all the effects related to the data is computed
3. a mask of the useful brain volume is computed
4. A GLM is applied to the dataset (effect/covariance,
then contrast estimation)
Note that this corresponds to a single run.
Needs matplotlib
Author : Bertrand Thirion, 2010--2012
"""
print(__doc__)
from os import getcwd, mkdir, path
import numpy as np
try:
import matplotlib.pyplot as plt
except ImportError:
raise RuntimeError("This script needs the matplotlib library")
# Local import
from get_data_light import DATA_DIR, get_first_level_dataset
from nibabel import save
from nipy.labs.viz import cm, plot_map
from nipy.modalities.fmri.design_matrix import make_dmtx
from nipy.modalities.fmri.experimental_paradigm import load_paradigm_from_csv_file
from nipy.modalities.fmri.glm import FMRILinearModel
#######################################
# Data and analysis parameters
#######################################
# volume mask
# This dataset is large
get_first_level_dataset()
data_path = path.join(DATA_DIR, 's12069_swaloc1_corr.nii.gz')
paradigm_file = path.join(DATA_DIR, 'localizer_paradigm.csv')
# timing
n_scans = 128
tr = 2.4
# paradigm
frametimes = np.linspace(0.5 * tr, (n_scans - .5) * tr, n_scans)
# confounds
hrf_model = 'canonical with derivative'
drift_model = "cosine"
hfcut = 128
# write directory
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
print(f'Computation will be performed in directory: {write_dir}')
########################################
# Design matrix
########################################
print('Loading design matrix...')
paradigm = load_paradigm_from_csv_file(paradigm_file)['0']
design_matrix = make_dmtx(frametimes, paradigm, hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut)
ax = design_matrix.show()
ax.set_position([.05, .25, .9, .65])
ax.set_title('Design matrix')
plt.savefig(path.join(write_dir, 'design_matrix.png'))
#########################################
# Specify the contrasts
#########################################
# simplest ones
contrasts = {}
n_columns = len(design_matrix.names)
for i in range(paradigm.n_conditions):
contrasts[f'{design_matrix.names[2 * i]}'] = np.eye(n_columns)[2 * i]
# and more complex/ interesting ones
contrasts["audio"] = contrasts["clicDaudio"] + contrasts["clicGaudio"] +\
contrasts["calculaudio"] + contrasts["phraseaudio"]
contrasts["video"] = contrasts["clicDvideo"] + contrasts["clicGvideo"] + \
contrasts["calculvideo"] + contrasts["phrasevideo"]
contrasts["left"] = contrasts["clicGaudio"] + contrasts["clicGvideo"]
contrasts["right"] = contrasts["clicDaudio"] + contrasts["clicDvideo"]
contrasts["computation"] = contrasts["calculaudio"] + contrasts["calculvideo"]
contrasts["sentences"] = contrasts["phraseaudio"] + contrasts["phrasevideo"]
contrasts["H-V"] = contrasts["damier_H"] - contrasts["damier_V"]
contrasts["V-H"] = contrasts["damier_V"] - contrasts["damier_H"]
contrasts["left-right"] = contrasts["left"] - contrasts["right"]
contrasts["right-left"] = contrasts["right"] - contrasts["left"]
contrasts["audio-video"] = contrasts["audio"] - contrasts["video"]
contrasts["video-audio"] = contrasts["video"] - contrasts["audio"]
contrasts["computation-sentences"] = contrasts["computation"] - \
contrasts["sentences"]
contrasts["reading-visual"] = contrasts["sentences"] * 2 - \
contrasts["damier_H"] - contrasts["damier_V"]
contrasts['effects_of_interest'] = np.eye(n_columns)[:20:2]
########################################
# Perform a GLM analysis
########################################
print('Fitting a GLM (this takes time)...')
fmri_glm = FMRILinearModel(data_path, design_matrix.matrix,
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
#########################################
# Estimate the contrasts
#########################################
print('Computing contrasts...')
for index, (contrast_id, contrast_val) in enumerate(contrasts.items()):
print(' Contrast % 2i out of %i: %s' %
(index + 1, len(contrasts), contrast_id))
# save the z_image
image_path = path.join(write_dir, f'{contrast_id}_z_map.nii')
z_map, = fmri_glm.contrast(contrast_val, con_id=contrast_id, output_z=True)
save(z_map, image_path)
# Create snapshots of the contrasts
vmax = max(- z_map.get_fdata().min(), z_map.get_fdata().max())
if index > 0:
plt.clf()
plot_map(z_map.get_fdata(), z_map.affine,
cmap=cm.cold_hot,
vmin=- vmax,
vmax=vmax,
anat=None,
cut_coords=None,
slicer='z',
black_bg=True, # looks much better thus
figure=10,
threshold=2.5)
plt.savefig(path.join(write_dir, f'{contrast_id}_z_map.png'))
print(f"All the results were witten in {write_dir}")
plt.show()
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