def get_template(space='mni152_1mm', mask=None):
if space == 'mni152_1mm':
if mask is None:
img = nib.load(datasets.fetch_icbm152_2009()['t1'])
elif mask == 'brain':
img = nib.load(datasets.fetch_icbm152_2009()['mask'])
elif mask == 'gm':
img = datasets.fetch_icbm152_brain_gm_mask(threshold=0.2)
else:
raise ValueError('Mask {0} not supported'.format(mask))
elif space == 'mni152_2mm':
if mask is None:
img = datasets.load_mni152_template()
elif mask == 'brain':
img = datasets.load_mni152_brain_mask()
elif mask == 'gm':
# this approach seems to approximate the 0.2 thresholded
# GM mask pretty well
temp_img = datasets.load_mni152_template()
data = temp_img.get_data()
data = data * -1
data[data != 0] += np.abs(np.min(data))
data = (data > 1200).astype(int)
img = nib.Nifti1Image(data, temp_img.affine)
else:
raise ValueError('Mask {0} not supported'.format(mask))
else:
raise ValueError('Space {0} not supported'.format(space))
return img
def get_template(space='mni152_1mm', mask=None):
"""
Load template file.
Parameters
----------
space : {'mni152_1mm', 'mni152_2mm', 'ale_2mm'}, optional
Template to load. Default is 'mni152_1mm'.
mask : {None, 'brain', 'gm'}, optional
Whether to return the raw template (None), a brain mask ('brain'), or
a gray-matter mask ('gm'). Default is None.
Returns
-------
img : :obj:`nibabel.nifti1.Nifti1Image`
Template image object.
"""
if space == 'mni152_1mm':
if mask is None:
img = nib.load(datasets.fetch_icbm152_2009()['t1'])
elif mask == 'brain':
img = nib.load(datasets.fetch_icbm152_2009()['mask'])
elif mask == 'gm':
img = datasets.fetch_icbm152_brain_gm_mask(threshold=0.2)
else:
raise ValueError('Mask {0} not supported'.format(mask))
elif space == 'mni152_2mm':
if mask is None:
img = datasets.load_mni152_template()
elif mask == 'brain':
img = datasets.load_mni152_brain_mask()
elif mask == 'gm':
# this approach seems to approximate the 0.2 thresholded
# GM mask pretty well
temp_img = datasets.load_mni152_template()
data = temp_img.get_data()
data = data * -1
data[data != 0] += np.abs(np.min(data))
data = (data > 1200).astype(int)
img = nib.Nifti1Image(data, temp_img.affine)
else:
raise ValueError('Mask {0} not supported'.format(mask))
elif space == 'ale_2mm':
if mask is None:
img = datasets.load_mni152_template()
else:
# Not the same as the nilearn brain mask, but should correspond to
# the default "more conservative" MNI152 mask in GingerALE.
img = nib.load(
op.join(get_resource_path(),
'templates/MNI152_2x2x2_brainmask.nii.gz'))
else:
raise ValueError('Space {0} not supported'.format(space))
return img
age = oasis_dataset.ext_vars['age'].astype(float)
###############################################################################
# Sex is encoded as 'M' or 'F'. make it a binary variable
sex = oasis_dataset.ext_vars['mf'] == b'F'
###############################################################################
# Print basic information on the dataset
print('First gray-matter anatomy image (3D) is located at: %s' %
oasis_dataset.gray_matter_maps[0]) # 3D data
print('First white-matter anatomy image (3D) is located at: %s' %
oasis_dataset.white_matter_maps[0]) # 3D data
###############################################################################
# Get a mask image: A mask of the cortex of the ICBM template
gm_mask = datasets.fetch_icbm152_brain_gm_mask()
###############################################################################
# Resample the images, since this mask has a different resolution
from nilearn.image import resample_to_img
mask_img = resample_to_img(
gm_mask, gray_matter_map_filenames[0], interpolation='nearest')
#############################################################################
# Analyse data
# ------------
#
# First create an adequate design matrix with three columns: 'age',
# 'sex', 'intercept'.
import pandas as pd
import numpy as np
age = oasis_dataset.ext_vars['age'].astype(float)
###############################################################################
# Sex is encoded as 'M' or 'F'. Hence, we make it a binary variable.
sex = oasis_dataset.ext_vars['mf'] == 'F'
###############################################################################
# Print basic information on the dataset.
print('First gray-matter anatomy image (3D) is located at: %s' %
oasis_dataset.gray_matter_maps[0]) # 3D data
print('First white-matter anatomy image (3D) is located at: %s' %
oasis_dataset.white_matter_maps[0]) # 3D data
###############################################################################
# Get a mask image: A mask of the cortex of the ICBM template.
gm_mask = datasets.fetch_icbm152_brain_gm_mask()
###############################################################################
# Resample the images, since this mask has a different resolution.
from nilearn.image import resample_to_img
mask_img = resample_to_img(
gm_mask, gray_matter_map_filenames[0], interpolation='nearest')
#############################################################################
# Analyse data
# ------------
#
# First, we create an adequate design matrix with three columns: 'age',
# 'sex', 'intercept'.
import pandas as pd
import numpy as np
motion_confounds = data_store[name].motion_param
connectome_regress_confounds = None
from utils import data_info
shape, affine, _ = data_info(func_imgs[0])
###########################################################################
# Masker
# ------
# Masking the data
from nilearn import datasets
# Fetch grey matter mask from nilearn shipped with ICBM templates
gm_mask = datasets.fetch_icbm152_brain_gm_mask(threshold=0.2)
from nilearn.input_data import MultiNiftiMasker
masker = MultiNiftiMasker(mask_img=gm_mask, target_shape=shape,
target_affine=affine, smoothing_fwhm=6.,
standardize=True, detrend=True, mask_strategy='epi',
memory=mem, memory_level=2, n_jobs=2,
verbose=10)
##############################################################################
# Cross Validator
# ---------------
from sklearn.model_selection import StratifiedShuffleSplit
# # Masking consists in selecting the region of the image on which the # model is run: it is useless to run it outside of the brain. # # The approach taken by FirstLeveModel is to estimate it from the fMRI # data itself when no mask is explicitly provided. Since the data # have been resampled into MNI space, we can use instead a mask of the # grey matter in MNI space. The benefit is that it makes voxel-level # comparisons easier across subjects and datasets, and removes # non-grey matter regions, in which no BOLD signal is expected. The # downside is that the mask may not fit very well this particular # data. data_mask = first_level_model.masker_.mask_img_ from nilearn.datasets import fetch_icbm152_brain_gm_mask icbm_mask = fetch_icbm152_brain_gm_mask() from nilearn.plotting import plot_roi plt.figure(figsize=(16, 4)) ax = plt.subplot(121) plot_roi(icbm_mask, title='ICBM mask', axes=ax) ax = plt.subplot(122) plot_roi(data_mask, title='Data-driven mask', axes=ax) plt.show() ######################################################################### # For the sake of time saving, we resample icbm_mask to our data. # For this we call the resample_to_img routine of Nilearn. # We use interpolation = 'nearest' to keep the mask as a binary image. from nilearn.image import resample_to_img resampled_icbm_mask = resample_to_img(icbm_mask,
# First we load the ADHD200 data
from nilearn import datasets
import scipy as sp
from fmri_methods_sipi import rot_sub_data, hotelling_t2
import matplotlib.pyplot as plt
from nilearn import input_data
from nilearn.plotting import plot_roi, show, plot_stat_map
from nilearn.image.image import mean_img
from nilearn.image import index_img
from sklearn.decomposition import PCA
from scipy.stats import levene
from statsmodels.sandbox.stats.multicomp import multipletests
from nilearn.image import resample_to_img
std_msk = datasets.load_mni152_brain_mask()
gm_msk = datasets.fetch_icbm152_brain_gm_mask(threshold=0.3)
gm_msk.set_data_dtype(sp.float32)
affine_tar = 1.0 * sp.eye(4)
affine_tar[3, 3] = .3
adhd_dataset = datasets.fetch_adhd()
func_filenames = adhd_dataset.func # list of 4D nifti files for each subject
mean_func_img = mean_img(func_filenames[0])
gm_msk = resample_to_img(source_img=gm_msk,
target_img=mean_func_img,
interpolation='nearest')
#affine_tar,
#interpolation='nearest', target_shape=sp.array([61,73,61]))
# # Masking consists in selecting the region of the image on which the # model is run: it is useless to run it outside of the brain. # # The approach taken by FirstLeveModel is to estimate it from the fMRI # data themselves when no mask is explicitly provided. Since the data # have been resampled into MNI space, we can use instead a mask of the # grey matter in MNI space. The benefit is that it makes voxel-level # comparisons easier across subjects and datasets, and removed # non-grey matter regions, in which no BOLD signal is expected. The # downside is that the mask may not fit very well these particular # data. data_mask = first_level_model.masker_.mask_img_ from nilearn.datasets import fetch_icbm152_brain_gm_mask icbm_mask = fetch_icbm152_brain_gm_mask() from nilearn.plotting import plot_roi plt.figure(figsize=(16, 4)) ax = plt.subplot(121) plot_roi(icbm_mask, title='ICBM mask', axes=ax) ax = plt.subplot(122) plot_roi(data_mask, title='Data-driven mask', axes=ax) plt.show() ######################################################################### # For the sake of time saving, we reample icbm_mask to our data # For this we call the resample_to_img routine of Nilearn. # We use interpolation = 'nearest' to keep the mask a binary image from nilearn.image import resample_to_img resampled_icbm_mask = resample_to_img(icbm_mask, data_mask,
