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#Setup
import sys, subprocess
sys.path.append('..')


    

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# %load examples/plot_functional_brain_networks.py
"""
Estimate Functional Connectivity using an estimator for Sparse Inverse Covariances
==================================================================================
This example constructs a functional connectome using the sparse penalized MLE 
estimator implemented using QUIC.

This function extracts time-series from the ABIDE dataset, with nodes defined using 
regions of interest from the 
    Power-264 atlas (Power, 2011).
    Power, Jonathan D., et al. "Functional network organization of the human brain." 
    Neuron 72.4 (2011): 665-678.

Then we estimate separate inverse covariance matrices for one subject.
"""

import numpy as np
import matplotlib.pyplot as plt
from nilearn import datasets, connectome, plotting, input_data
from inverse_covariance import QuicGraphLasso

# Fetch the coordinates of power atlas, ROI labels not available.
power = datasets.fetch_coords_power_2011()
coords = np.vstack((
    power.rois['x'],
    power.rois['y'],
    power.rois['z'],
)).T

# Loading the functional datasets
abide = datasets.fetch_abide_pcp(n_subjects=1)
abide.func = abide.func_preproc

# print basic information on the dataset
print('First subject functional nifti images (4D) are at: %s' %abide.func[0])  # 4D data

      
# Masking: taking the signal in a sphere of radius 5mm around Power coords
masker = input_data.NiftiSpheresMasker(seeds=coords,
                                       smoothing_fwhm=4,
                                       radius=5.,
                                       standardize=True,
                                       detrend=True,
                                       low_pass=0.1,
                                       high_pass=0.01,
                                       t_r=2.5)

timeseries = masker.fit_transform(abide.func[0])


    

    




First subject functional nifti images (4D) are at: /Users/jaska/nilearn_data/ABIDE_pcp/cpac/nofilt_noglobal/Pitt_0050003_func_preproc.nii.gz

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# Compute the sparse inverse covariance
estimator = QuicGraphLasso(init_method='cov', lam=0.5, mode='default', verbose=1)
estimator.fit(timeseries)


    

    
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QuicGraphLasso(Sigma0=None, Theta0=None, auto_scale=True, init_method='cov',
        lam=0.5, max_iter=1000, method='quic', mode='default', path=None,
        score_metric='log_likelihood', tol=1e-06, verbose=1)

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%matplotlib inline

# Display the sparse inverse covariance
plt.figure(figsize=(7.5, 7.5))
plt.imshow(np.triu(-estimator.precision_, 1), interpolation="nearest", cmap=plt.cm.PRGn)
plt.title('Precision (Sparse Inverse Covariance) matrix')
plt.colorbar()

# And now display the corresponding graph
plotting.plot_connectome(estimator.precision_, coords,
                         title='Functional Connectivity using Precision Matrix',
                         edge_threshold="99.2%",
                         node_size=20)
plotting.show()


    

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