def get_nilearn_adhd_data(n_subjects, nilearn_download_dir):
# Load the functional datasets
datasets.get_data_dirs(data_dir=nilearn_download_dir)
adhd_data = datasets.fetch_adhd(n_subjects=n_subjects,
data_dir=nilearn_download_dir)
msdl_data = datasets.fetch_atlas_msdl(data_dir=nilearn_download_dir)
masker = input_data.NiftiMapsMasker(msdl_data.maps,
resampling_target="data",
t_r=2.5,
detrend=True,
low_pass=.1,
high_pass=.01,
memory='nilearn_cache',
memory_level=1)
pooled_subjects = []
adhd_labels = [] # 1 if ADHD, 0 if control
age = []
for func_file, confound_file, phenotypic in zip(adhd_data.func,
adhd_data.confounds,
adhd_data.phenotypic):
time_series = masker.fit_transform(func_file, confounds=confound_file)
pooled_subjects.append(time_series)
adhd_labels.append(phenotypic['adhd'])
age.append(phenotypic['age'])
correlation_measure = ConnectivityMeasure(kind='correlation')
corr_mat = correlation_measure.fit_transform(pooled_subjects)
print('Correlations are stacked in an array of shape {0}'.format(
corr_mat.shape))
beh = np.zeros((n_subjects, 2))
beh[:, 0] = adhd_labels
beh[:, 1] = age
return corr_mat, beh
Also, see :func:`nilearn.datasets.fetch_neurovault_motor_task` for details
about the plotting data and associated meta-data.
"""
###############################################################################
# Retrieve the data
# ------------------
#
# Nilearn comes with set of functions that download public data from Internet
#
# Let us first see where the data will be downloaded and stored on our disk:
#
from nilearn import datasets
print('Datasets shipped with nilearn are stored in: %r' %
datasets.get_data_dirs())
###############################################################################
# Let us now retrieve a motor task contrast map
# corresponding to a group one-sample t-test
motor_images = datasets.fetch_neurovault_motor_task()
stat_img = motor_images.images[0]
# stat_img is just the name of the file that we downloaded
stat_img
###############################################################################
# Demo glass brain plotting
# --------------------------
#
# By default, :func:`~nilearn.plotting.plot_glass_brain` uses a display mode
"""
3D and 4D niimgs: handling and visualizing
==========================================
Here we discover how to work with 3D and 4D niimgs.
"""
###############################################################################
# Downloading tutorial datasets from Internet
# --------------------------------------------
#
# Nilearn comes with functions that download public data from Internet
#
# Let's first check where the data is downloaded on our disk:
from nilearn import datasets
print('Datasets are stored in: %r' % datasets.get_data_dirs())
###############################################################################
# Let's now retrieve a motor contrast from a Neurovault repository
motor_images = datasets.fetch_neurovault_motor_task()
print(motor_images.images)
###############################################################################
# motor_images is a list of filenames. We need to take the first one
tmap_filename = motor_images.images[0]
###############################################################################
# Visualizing a 3D file
# ----------------------
#
# The file contains a 3D volume, we can easily visualize it as a
"""
3D and 4D niimgs: handling and visualizing
==========================================
Here we discover how to work with 3D and 4D niimgs.
"""
###############################################################################
# Downloading tutorial datasets from Internet
# --------------------------------------------
#
# Nilearn comes with functions that download public data from Internet
#
# Let's first check where the data is downloaded on our disk:
from nilearn import datasets
print('Datasets are stored in: %r' % datasets.get_data_dirs())
###############################################################################
# Let's now retrieve a motor contrast from a localizer experiment
tmap_filenames = datasets.fetch_localizer_button_task()['tmaps']
print(tmap_filenames)
###############################################################################
# tmap_filenames is a list of filenames. We need to take the first one
tmap_filename = tmap_filenames[0]
###############################################################################
# Visualizing a 3D file
# ----------------------
#
plt.savefig('/Users/pinheirochagas/Desktop/chan_dist.png')
plt.savefig('/Users/pinheirochagas/Desktop/connectivity_plot_memoria_ies.png')
from nilearn import datasets
print('Datasets shipped with nilearn are stored in: %r' % datasets.get_data_dirs())
motor_images = datasets.fetch_neurovault_motor_task()
stat_img = motor_images.images[0]
from nilearn import plotting
Also, see :func:`nilearn.datasets.fetch_neurovault_motor_task` for details
about the plotting data and associated meta-data.
"""
###############################################################################
# Retrieve the data
# ------------------
#
# Nilearn comes with set of functions that download public data from Internet
#
# Let us first see where the data will be downloded and stored on our disk:
#
from nilearn import datasets
print('Datasets shipped with nilearn are stored in: %r' % datasets.get_data_dirs())
###############################################################################
# Let us now retrieve a motor task contrast map
# corresponding to a group one-sample t-test
motor_images = datasets.fetch_neurovault_motor_task()
stat_img = motor_images.images[0]
# stat_img is just the name of the file that we downloded
print(stat_img)
###############################################################################
# Demo glass brain plotting
# --------------------------
from nilearn import plotting
