def fetch_atlas(atlas_name, rois=False):
"""Retruns selected atlas path
"""
if atlas_name == 'msdl':
atlas = fetch_msdl_atlas()['maps']
elif atlas_name == 'harvard_oxford':
atlas = os.path.join(CACHE_DIR, 'atlas',
'HarvardOxford-cortl-maxprob-thr0-2mm.nii.gz')
elif atlas_name == 'juelich':
atlas = os.path.join(CACHE_DIR, 'atlas',
'Juelich-maxprob-thr0-2mm.nii.gz')
elif atlas_name == 'mayo':
atlas = os.path.join(CACHE_DIR, 'atlas', 'atlas_68_rois.nii.gz')
elif atlas_name == 'canica':
atlas = os.path.join(CACHE_DIR, 'atlas', 'atlas_canica_61_rois.nii.gz')
elif atlas_name == 'canica141':
atlas = os.path.join(CACHE_DIR, 'atlas',
'atlas_canica_141_rois.nii.gz')
elif atlas_name == 'tvmsdl':
atlas = os.path.join(CACHE_DIR, 'atlas', 'atlas_tv_msdl.nii.gz')
dmn = None
if (atlas_name in ['msdl', 'mayo', 'canica']) and rois:
dmn = fetch_dmn_atlas(atlas_name, atlas)
atlas_img = index_img(atlas, dmn['rois'])
atlas = os.path.join(CACHE_DIR, 'atlas', 'atlas_dmn.nii.gz')
atlas_img.to_filename(atlas)
return atlas, dmn
def fetch_atlas(atlas_name):
"""Retruns selected atlas path
"""
if atlas_name == 'msdl':
atlas = fetch_msdl_atlas()['maps']
elif atlas_name == 'harvard_oxford':
# atlas = os.path.join(CACHE_DIR, 'atlas',
# 'HarvardOxford-cortl-prob-2mm.nii.gz')
atlas = os.path.join(CACHE_DIR, 'atlas',
'HarvardOxford-cortl-maxprob-thr0-2mm.nii.gz')
elif atlas_name == 'juelich':
# atlas = os.path.join(CACHE_DIR, 'atlas',
# 'Juelich-prob-2mm.nii.gz')
atlas = os.path.join(CACHE_DIR, 'atlas',
'Juelich-maxprob-thr0-2mm.nii.gz')
elif atlas_name == 'mayo':
atlas = os.path.join(CACHE_DIR, 'atlas', 'atlas_68_rois.nii.gz')
elif atlas_name == 'canica':
atlas = os.path.join(CACHE_DIR, 'atlas', 'atlas_canica_61_rois.nii.gz')
elif atlas_name == 'canica141':
atlas = os.path.join(CACHE_DIR, 'atlas',
'atlas_canica_141_rois.nii.gz')
elif atlas_name == 'tvmsdl':
atlas = os.path.join(CACHE_DIR, 'atlas', 'atlas_tv_msdl.nii.gz')
return atlas
def load_msdl_names_and_coords():
""" Returns msdl atlas ROIs
"""
atlas = fetch_msdl_atlas()
roi_coords = np.loadtxt(atlas['labels'], dtype=np.float,
delimiter=',', skiprows=1, usecols=(0,1,2))
roi_names = np.loadtxt(atlas['labels'], dtype=np.str,
delimiter=',', skiprows=1, usecols=(3,))
for i in range(len(roi_names)):
roi_names[i] = roi_names[i].strip()
roi_names[-1] = roi_names[-1][:10]
roi_names[-2] = roi_names[-2][:10]
return roi_names, roi_coords
def load_msdl_names_and_coords():
""" Returns msdl atlas ROIs
"""
atlas = fetch_msdl_atlas()
roi_coords = np.loadtxt(atlas['labels'],
dtype=np.float,
delimiter=',',
skiprows=1,
usecols=(0, 1, 2))
roi_names = np.loadtxt(atlas['labels'],
dtype=np.str,
delimiter=',',
skiprows=1,
usecols=(3, ))
for i in range(len(roi_names)):
roi_names[i] = roi_names[i].strip()
roi_names[-1] = roi_names[-1][:10]
roi_names[-2] = roi_names[-2][:10]
return roi_names, roi_coords
plt.colorbar()
plt.title("%s / covariance" % title)
# Display precision matrix
plt.figure()
plt.imshow(prec, interpolation="nearest",
vmin=-span, vmax=span,
cmap=plotting.cm.bwr)
plt.colorbar()
plt.title("%s / precision" % title)
# Fetching datasets ###########################################################
print("-- Fetching datasets ...")
from nilearn import datasets
msdl_atlas_dataset = datasets.fetch_msdl_atlas()
adhd_dataset = datasets.fetch_adhd(n_subjects=1)
# Extracting region signals ###################################################
import nilearn.image
import nilearn.input_data
from sklearn.externals.joblib import Memory
mem = Memory('nilearn_cache')
masker = nilearn.input_data.NiftiMapsMasker(
msdl_atlas_dataset.maps, resampling_target="maps", detrend=True,
low_pass=None, high_pass=0.01, t_r=2.5, standardize=True,
memory=mem, memory_level=1, verbose=2)
masker.fit()
# Display precision matrix
plt.figure()
plt.imshow(prec, interpolation="nearest",
vmin=-span, vmax=span,
cmap=plt.cm.get_cmap("bwr"))
plt.colorbar()
plt.title("%s / precision" % title)
###############################################################################
###############################################################################
CACHE_DIR = os.path.join('/', 'disk4t', 'mehdi',
'data', 'tmp')
dataset = fetch_adni_rs_fmri()
atlas = fetch_msdl_atlas()
func_files = dataset['func']
dx_group = np.array(dataset['dx_group'])
idx = {}
for g in ['AD', 'LMCI', 'EMCI', 'Normal']:
idx[g] = np.where(dx_group == g)
atlas4d = nib.load(atlas['maps'])
atlas4d_data = atlas4d.get_data()
atlas3d_data = np.sum(atlas4d_data, axis=3)
atlas3d = nib.Nifti1Image(atlas3d_data, atlas4d.get_affine())
n_subjects = len(func_files)
subjects = []
cov_feat = []
of functional regions in rest, and the
:class:`nilearn.input_data.NiftiMapsMasker` to extract time series.
Note that the inverse covariance (or precision) contains values that can
be linked to *negated* partial correlations, so we negated it for
display.
As the MSDL atlas comes with (x, y, z) MNI coordinates for the different
regions, we can visualize the matrix as a graph of interaction in a
brain. To avoid having too dense a graph, we represent only the 20% edges
with the highest values.
"""
from nilearn import datasets
atlas = datasets.fetch_msdl_atlas()
atlas_filename = atlas['maps']
# Load the labels
import numpy as np
csv_filename = atlas['labels']
# The recfromcsv function can load a csv file
labels = np.recfromcsv(csv_filename)
names = labels['name']
from nilearn.input_data import NiftiMapsMasker
masker = NiftiMapsMasker(maps_img=atlas_filename,
standardize=True,
memory='nilearn_cache',
verbose=5)
<https://team.inria.fr/parietal/research/spatial_patterns/spatial-patterns-in-resting-state/>`_
of functional regions in rest.
The key to extract signals is to use the
:class:`nilearn.input_data.NiftiMapsMasker` that can transform nifti
objects to time series using a probabilistic atlas.
As the MSDL atlas comes with (x, y, z) MNI coordinates for the different
regions, we can visualize the matrix as a graph of interaction in a
brain. To avoid having too dense a graph, we represent only the 20% edges
with the highest values.
"""
from nilearn import datasets
atlas = datasets.fetch_msdl_atlas()
atlas_filename = atlas['maps']
# Load the labels
import numpy as np
csv_filename = atlas['labels']
# The recfromcsv function can load a csv file
labels = np.recfromcsv(csv_filename)
names = labels['name']
from nilearn.input_data import NiftiMapsMasker
masker = NiftiMapsMasker(maps_img=atlas_filename, standardize=True,
memory='nilearn_cache', verbose=5)
data = datasets.fetch_adhd(n_subjects=1)
# Display precision matrix
plt.figure()
plt.imshow(prec,
interpolation="nearest",
vmin=-span,
vmax=span,
cmap=plotting.cm.bwr)
plt.colorbar()
plt.title("%s / precision" % title)
# Fetching datasets ###########################################################
print("-- Fetching datasets ...")
from nilearn import datasets
msdl_atlas_dataset = datasets.fetch_msdl_atlas()
adhd_dataset = datasets.fetch_adhd(n_subjects=n_subjects)
# print basic information on the dataset
print('First subject functional nifti image (4D) is at: %s' %
adhd_dataset.func[0]) # 4D data
# Extracting region signals ###################################################
from nilearn import image
from nilearn import input_data
from sklearn.externals.joblib import Memory
mem = Memory('nilearn_cache')
masker = input_data.NiftiMapsMasker(msdl_atlas_dataset.maps,
resampling_target="maps",
def test_fetch_msdl_atlas():
dataset = datasets.fetch_msdl_atlas(data_dir=tmpdir, verbose=0)
assert_true(isinstance(dataset.labels, _basestring))
assert_true(isinstance(dataset.maps, _basestring))
assert_equal(len(url_request.urls), 1)
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 1 10:11:24 2015
@author: mr243268
"""
import os, time
import numpy as np
from loader import load_dynacomp
from nilearn.datasets import fetch_msdl_atlas
from nilearn.input_data import NiftiMapsMasker
dataset = load_dynacomp(preprocessing_folder='pipeline_2',
prefix='resampled_wr')
atlas = fetch_msdl_atlas()
# add mask, smoothing, filtering and detrending
masker = NiftiMapsMasker(maps_img=atlas['maps'],
mask_img=dataset.mask,
low_pass=.1,
high_pass=.01,
t_r=1.05,
smoothing_fwhm=6.,
detrend=True,
standardize=False,
resampling_target='data',
memory_level=0,
verbose=5)
for i in range(len(dataset.subjects)):
def test_fetch_msdl_atlas():
dataset = datasets.fetch_msdl_atlas(data_dir=tmpdir, verbose=0)
assert_true(isinstance(dataset.labels, _basestring))
assert_true(isinstance(dataset.maps, _basestring))
assert_equal(len(url_request.urls), 1)