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 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()
meg_path = data_path / 'MEG' / 'sample'
stc_fname = meg_path / '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

    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) × time × 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.')
F_obs, clusters, cluster_p_values, H0 = clu =\
    spatio_temporal_cluster_test(
        X, adjacency=adjacency, n_jobs=None, 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]

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

  0%|          | Permuting : 0/49 [00:00<?,       ?it/s]
  2%|2         | Permuting : 1/49 [00:00<00:03,   14.76it/s]
  4%|4         | Permuting : 2/49 [00:00<00:02,   19.85it/s]
  8%|8         | Permuting : 4/49 [00:00<00:01,   30.31it/s]
 10%|#         | Permuting : 5/49 [00:00<00:01,   30.15it/s]
 14%|#4        | Permuting : 7/49 [00:00<00:01,   35.57it/s]
 16%|#6        | Permuting : 8/49 [00:00<00:01,   34.58it/s]
 18%|#8        | Permuting : 9/49 [00:00<00:01,   33.84it/s]
 22%|##2       | Permuting : 11/49 [00:00<00:01,   37.25it/s]
 24%|##4       | Permuting : 12/49 [00:00<00:01,   36.29it/s]
 29%|##8       | Permuting : 14/49 [00:00<00:00,   38.93it/s]
 31%|###       | Permuting : 15/49 [00:00<00:00,   37.91it/s]
 33%|###2      | Permuting : 16/49 [00:00<00:00,   37.05it/s]
 37%|###6      | Permuting : 18/49 [00:00<00:00,   39.20it/s]
 39%|###8      | Permuting : 19/49 [00:00<00:00,   38.31it/s]
 43%|####2     | Permuting : 21/49 [00:00<00:00,   40.16it/s]
 45%|####4     | Permuting : 22/49 [00:00<00:00,   39.25it/s]
 49%|####8     | Permuting : 24/49 [00:00<00:00,   40.90it/s]
 51%|#####1    | Permuting : 25/49 [00:00<00:00,   39.99it/s]
 53%|#####3    | Permuting : 26/49 [00:00<00:00,   39.18it/s]
 57%|#####7    | Permuting : 28/49 [00:00<00:00,   40.69it/s]
 59%|#####9    | Permuting : 29/49 [00:00<00:00,   39.86it/s]
 63%|######3   | Permuting : 31/49 [00:00<00:00,   41.25it/s]
 65%|######5   | Permuting : 32/49 [00:00<00:00,   40.42it/s]
 67%|######7   | Permuting : 33/49 [00:00<00:00,   39.65it/s]
 71%|#######1  | Permuting : 35/49 [00:00<00:00,   40.97it/s]
 73%|#######3  | Permuting : 36/49 [00:00<00:00,   40.20it/s]
 78%|#######7  | Permuting : 38/49 [00:00<00:00,   41.44it/s]
 80%|#######9  | Permuting : 39/49 [00:00<00:00,   40.68it/s]
 84%|########3 | Permuting : 41/49 [00:01<00:00,   41.85it/s]
 86%|########5 | Permuting : 42/49 [00:01<00:00,   41.08it/s]
 88%|########7 | Permuting : 43/49 [00:01<00:00,   40.36it/s]
 92%|#########1| Permuting : 45/49 [00:01<00:00,   41.52it/s]
 94%|#########3| Permuting : 46/49 [00:01<00:00,   40.79it/s]
 98%|#########7| Permuting : 48/49 [00:01<00:00,   41.88it/s]
100%|##########| Permuting : 49/49 [00:01<00:00,   42.00it/s]
100%|##########| Permuting : 49/49 [00:01<00:00,   40.58it/s]

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

# 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]))

Visualizing clusters.

Total running time of the script: ( 0 minutes 13.828 seconds)

Estimated memory usage: 275 MB

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