2 samples permutation test on source data with spatio-temporal clustering

Tests if the source space data are significantly different between 2 groups of subjects (simulated here using one subject’s data). The multiple comparisons problem is addressed with a cluster-level permutation test across space and time.

# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#          Eric Larson <larson.eric.d@gmail.com>
# License: BSD-3-Clause
import os.path as op

import numpy as np
from scipy import stats as stats

import mne
from mne import spatial_src_adjacency
from mne.stats import spatio_temporal_cluster_test, summarize_clusters_stc
from mne.datasets import sample

print(__doc__)

Set parameters

data_path = sample.data_path()
stc_fname = data_path + '/MEG/sample/sample_audvis-meg-lh.stc'
subjects_dir = data_path + '/subjects'
src_fname = subjects_dir + '/fsaverage/bem/fsaverage-ico-5-src.fif'

# Load stc to in common cortical space (fsaverage)
stc = mne.read_source_estimate(stc_fname)
stc.resample(50, npad='auto')

# Read the source space we are morphing to
src = mne.read_source_spaces(src_fname)
fsave_vertices = [s['vertno'] for s in src]
morph = mne.compute_source_morph(stc, 'sample', 'fsaverage',
                                 spacing=fsave_vertices, smooth=20,
                                 subjects_dir=subjects_dir)
stc = morph.apply(stc)
n_vertices_fsave, n_times = stc.data.shape
tstep = stc.tstep * 1000  # convert to milliseconds

n_subjects1, n_subjects2 = 6, 7
print('Simulating data for %d and %d subjects.' % (n_subjects1, n_subjects2))

#    Let's make sure our results replicate, so set the seed.
np.random.seed(0)
X1 = np.random.randn(n_vertices_fsave, n_times, n_subjects1) * 10
X2 = np.random.randn(n_vertices_fsave, n_times, n_subjects2) * 10
X1[:, :, :] += stc.data[:, :, np.newaxis]
# make the activity bigger for the second set of subjects
X2[:, :, :] += 3 * stc.data[:, :, np.newaxis]

#    We want to compare the overall activity levels for each subject
X1 = np.abs(X1)  # only magnitude
X2 = np.abs(X2)  # only magnitude

Out:

    Reading a source space...
    [done]
    Reading a source space...
    [done]
    2 source spaces read
Simulating data for 6 and 7 subjects.

Compute statistic

To use an algorithm optimized for spatio-temporal clustering, we just pass the spatial adjacency matrix (instead of spatio-temporal)

print('Computing adjacency.')
adjacency = spatial_src_adjacency(src)

#    Note that X needs to be a list of multi-dimensional array of shape
#    samples (subjects_k) x time x space, so we permute dimensions
X1 = np.transpose(X1, [2, 1, 0])
X2 = np.transpose(X2, [2, 1, 0])
X = [X1, X2]

# Now let's actually do the clustering. This can take a long time...
# Here we set the threshold quite high to reduce computation,
# and use a very low number of permutations for the same reason.
n_permutations = 50
p_threshold = 0.001
f_threshold = stats.distributions.f.ppf(1. - p_threshold / 2.,
                                        n_subjects1 - 1, n_subjects2 - 1)
print('Clustering.')
T_obs, clusters, cluster_p_values, H0 = clu =\
    spatio_temporal_cluster_test(
        X, adjacency=adjacency, n_jobs=1, n_permutations=n_permutations,
        threshold=f_threshold, buffer_size=None)
#    Now select the clusters that are sig. at p < 0.05 (note that this value
#    is multiple-comparisons corrected).
good_cluster_inds = np.where(cluster_p_values < 0.05)[0]

Out:

Computing adjacency.
-- number of adjacent vertices : 20484
Clustering.
stat_fun(H1): min=0.000000 max=303.632172
Running initial clustering
Found 361 clusters
Permuting 49 times...

  0%|          |  : 0/49 [00:00<?,       ?it/s]
  2%|2         |  : 1/49 [00:00<00:04,    9.88it/s]
  4%|4         |  : 2/49 [00:00<00:03,   15.00it/s]
  6%|6         |  : 3/49 [00:00<00:02,   18.14it/s]
  8%|8         |  : 4/49 [00:00<00:02,   20.24it/s]
 12%|#2        |  : 6/49 [00:00<00:01,   26.57it/s]
 14%|#4        |  : 7/49 [00:00<00:01,   27.01it/s]
 18%|#8        |  : 9/49 [00:00<00:01,   31.30it/s]
 20%|##        |  : 10/49 [00:00<00:01,   31.09it/s]
 22%|##2       |  : 11/49 [00:00<00:01,   30.91it/s]
 27%|##6       |  : 13/49 [00:00<00:01,   33.95it/s]
 29%|##8       |  : 14/49 [00:00<00:01,   33.51it/s]
 33%|###2      |  : 16/49 [00:00<00:00,   35.98it/s]
 35%|###4      |  : 17/49 [00:00<00:00,   35.39it/s]
 39%|###8      |  : 19/49 [00:00<00:00,   37.49it/s]
 41%|####      |  : 20/49 [00:00<00:00,   36.81it/s]
 43%|####2     |  : 21/49 [00:00<00:00,   36.22it/s]
 47%|####6     |  : 23/49 [00:00<00:00,   38.05it/s]
 49%|####8     |  : 24/49 [00:00<00:00,   37.39it/s]
 53%|#####3    |  : 26/49 [00:00<00:00,   39.02it/s]
 55%|#####5    |  : 27/49 [00:00<00:00,   38.33it/s]
 59%|#####9    |  : 29/49 [00:00<00:00,   39.83it/s]
 61%|######1   |  : 30/49 [00:00<00:00,   39.11it/s]
 63%|######3   |  : 31/49 [00:00<00:00,   38.45it/s]
 67%|######7   |  : 33/49 [00:00<00:00,   39.85it/s]
 69%|######9   |  : 34/49 [00:00<00:00,   39.16it/s]
 73%|#######3  |  : 36/49 [00:00<00:00,   40.47it/s]
 76%|#######5  |  : 37/49 [00:00<00:00,   39.77it/s]
 80%|#######9  |  : 39/49 [00:01<00:00,   41.00it/s]
 82%|########1 |  : 40/49 [00:01<00:00,   40.29it/s]
 86%|########5 |  : 42/49 [00:01<00:00,   41.46it/s]
 88%|########7 |  : 43/49 [00:01<00:00,   40.73it/s]
 90%|########9 |  : 44/49 [00:01<00:00,   40.06it/s]
 94%|#########3|  : 46/49 [00:01<00:00,   41.19it/s]
 96%|#########5|  : 47/49 [00:01<00:00,   40.50it/s]
100%|##########|  : 49/49 [00:01<00:00,   41.84it/s]
100%|##########|  : 49/49 [00:01<00:00,   39.25it/s]
Computing cluster p-values
Done.

Visualize the clusters

print('Visualizing clusters.')

#    Now let's build a convenient representation of each cluster, where each
#    cluster becomes a "time point" in the SourceEstimate
fsave_vertices = [np.arange(10242), np.arange(10242)]
stc_all_cluster_vis = summarize_clusters_stc(clu, tstep=tstep,
                                             vertices=fsave_vertices,
                                             subject='fsaverage')

#    Let's actually plot the first "time point" in the SourceEstimate, which
#    shows all the clusters, weighted by duration
subjects_dir = op.join(data_path, 'subjects')
# blue blobs are for condition A != condition B
brain = stc_all_cluster_vis.plot('fsaverage', hemi='both',
                                 views='lateral', subjects_dir=subjects_dir,
                                 time_label='temporal extent (ms)',
                                 clim=dict(kind='value', lims=[0, 1, 40]))
30 cluster ftest spatiotemporal

Out:

Visualizing clusters.

Total running time of the script: ( 0 minutes 19.147 seconds)

Estimated memory usage: 202 MB

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