plot_adhd_covariance.py

"""
Computation of covariance matrix between brain regions
======================================================

This example shows how to extract signals from regions defined by an atlas,
and to estimate a covariance matrix based on these signals.
"""

import pylab as pl
import matplotlib

from sklearn import covariance

import nilearn.datasets
import nilearn.image
import nilearn.signal
import nilearn.io

# Copied from matplotlib 1.2.0 for matplotlib 0.99 compatibility.
_bwr_data = ((0.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 0.0, 0.0))
pl.cm.register_cmap(cmap=matplotlib.colors.LinearSegmentedColormap.from_list(
    "bwr", _bwr_data))


def plot_matrices(cov, prec, title, subject_n=0):
    """Plot covariance and precision matrices, for a given processing. """

    # Put zeros on the diagonal, for graph clarity.
    size = prec.shape[0]
    prec[range(size), range(size)] = 0

    span = max(abs(prec.min()), abs(prec.max()))
    title = "{0:d} {1}".format(subject_n, title)

    # Display covariance matrix
    pl.figure()
    pl.imshow(cov, interpolation="nearest",
              vmin=-1, vmax=1, cmap=pl.cm.get_cmap("bwr"))
    pl.colorbar()
    pl.title(title + " / covariance")

    # Display precision matrix
    pl.figure()
    pl.imshow(prec, interpolation="nearest",
              vmin=-span, vmax=span,
              cmap=pl.cm.get_cmap("bwr"))
    pl.colorbar()
    pl.title(title + " / precision")

subject_n = 1

dataset = nilearn.datasets.fetch_adhd()
filename = dataset["func"][subject_n]
confound_file = dataset["confounds"][subject_n]

print("-- Loading raw data ({0:d}) and masking ...".format(subject_n))
msdl_atlas = nilearn.datasets.fetch_msdl_atlas()

print("-- Computing confounds ...")
hv_confounds = nilearn.image.high_variance_confounds(filename)

print("-- Computing region signals ...")
masker = nilearn.io.NiftiMapsMasker(msdl_atlas["maps"], resampling_target="maps",
                                 low_pass=None, high_pass=0.01, t_r=2.5,
                                 verbose=1)
region_ts = masker.fit_transform(filename,
                                 confounds=[hv_confounds, confound_file])

print("-- Computing covariance matrices ...")
estimator = covariance.GraphLassoCV()
estimator.fit(region_ts)

plot_matrices(estimator.covariance_, -estimator.precision_,
              title="Graph Lasso CV ({0:.3f})".format(estimator.alpha_),
              subject_n=subject_n)
pl.show()