"""

Group analysis of resting-state fMRI with ICA: CanICA

=====================================================

An example applying CanICA to resting-state data. This example applies it

to 40 subjects of the ADHD200 datasets.

CanICA is an ICA method for group-level analysis of fMRI data. Compared

to other strategies, it brings a well-controlled group model, as well as a

thresholding algorithm controlling for specificity and sensitivity with

an explicit model of the signal. The reference papers are:

* G. Varoquaux et al. "A group model for stable multi-subject ICA on

fMRI datasets", NeuroImage Vol 51 (2010), p. 288-299

* G. Varoquaux et al. "ICA-based sparse features recovery from fMRI

datasets", IEEE ISBI 2010, p. 1177

Pre-prints for both papers are available on hal

(http://hal.archives-ouvertes.fr)

"""

import numpy as np

### Load ADHD rest dataset ####################################################

from nilearn import datasets

# Here we use a limited number of subjects to get faster-running code. For

# better results, simply increase the number.

dataset = datasets.fetch_adhd()

func_files = dataset.func[:5]

### Preprocess ################################################################

from nilearn import io

# This is a multi-subject method, thus we need to use the

# MultiNiftiMasker, rather than the NiftiMasker

# We specify the target_affine to downsample to 3mm isotropic

# resolution

masker = io.MultiNiftiMasker(smoothing_fwhm=6,

target_affine=np.diag((3, 3, 3)),

memory="nilearn_cache", memory_level=1,

verbose=True)

data_masked = masker.fit_transform(func_files)

mean_epi = masker.inverse_transform(data_masked[0].mean(axis=0)).get_data()

### Apply CanICA ##############################################################

from nilearn.decomposition.old_canica import CanICA

n_components = 20

ica = CanICA(n_components=n_components, random_state=42, memory="nilearn_cache",

maps_only=True)

components_masked = ica.fit(data_masked).components_

# We normalize the estimated components, for thresholding to make sense

# XXX: this should probably be integrated in the CanICA object

components_masked -= components_masked.mean(axis=1)[:, np.newaxis]

components_masked /= components_masked.std(axis=1)[:, np.newaxis]

# Threshold

#threshold = (stats.norm.isf(0.5*threshold_p_value)

# /np.sqrt(components_masked.shape[0]))

threshold = .9

components_masked[np.abs(components_masked) < threshold] = 0

# Now invert the masking operation, to go back to a full 3D

# representation

components_img = masker.inverse_transform(components_masked)

components = components_img.get_data()

# Using a masked array is important to have transparency in the figures

components = np.ma.masked_equal(components, 0, copy=False)

### Visualize the results #####################################################

# Show some interesting components

import pylab as pl

from scipy import ndimage

for i in range(n_components):

pl.figure()

pl.axis('off')

cut_coord = ndimage.maximum_position(np.abs(components[..., i]))[2]

vmax = np.max(np.abs(components[:, :, cut_coord, i]))

pl.imshow(np.rot90(mean_epi[:, :, cut_coord]), interpolation='nearest',

cmap=pl.cm.gray)

pl.imshow(np.rot90(components[:, :, cut_coord, i]),

interpolation='nearest', cmap=pl.cm.jet, vmax=vmax, vmin=-vmax)

pl.show()