[Mne_analysis] spatio_temporal_clustering

pooja prabhu prabhuppooja at gmail.com
Sat Apr 25 03:56:54 EDT 2020
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Hai list,
while computing the spatio-temporal clustering on power generated from 19
subject whole brain volume (after morphing), we observed two problems.
1. as the p_threshold is decreased there should be the less number of
clusters. In our case its reverse. why? refer the table

P_threshold

F_threshold

Number of clusters (p<0.05)

10-2

2.22

0

10-3

3.56

0

10-4

5.22

0

10-5

7.34

1

10-6

10.05

1

10-7

13.55

1

10-8

18.05

4

10-9

23.85

3
2. each time the clusters are localised at different regions.
Can any one us say what is cause of this problem?
please refer to the snippet below,

src_fs = mne.setup_volume_source_space( 'fsaverage',
pos=5.,mri=aseg_fname, subjects_dir=subjects_dir,verbose=True)
connectivity=spatial_src_connectivity(src_fs)
power_test=np.zeros((2,n_subjects,n_vertices,n_timepoints)) ## here we
are checking the power values are significant with respect to
baseline, so the first sample is zero and the second sample is the
contrast.
contrast=l_gamma_power_dist8-l_gamma_power_dist1 ##
l_gamma_power_dist8 and l_gamma_power_dist1 are the power generated
for whole brain volume (after morphing) for n_subjects using Hilbert
beamforming  [contrast.shape=n_subjects,n_vertices,n_timepoints]
power_test[1,:,:,:]=contrast
p_accept = 0.05
power_test=np.transpose(power_test,[0,1,3,2]) ## to rearrange the
dimension 2 x n_subjects x n_timepoints x n_vertices
p_threshold=10e-5
n_subjects=19
f_threshold=stats.distributions.f.ppf(1.-p_threshold/2.,n_subjects-1,n_subjects-1)
cluster_stats = spatio_temporal_cluster_test(power_test,
n_permutations=1000,connectivity=connectivity,
                                             threshold=f_threshold,
tail=1,seed=8,
                                             n_jobs=1)T_obs, clusters,
p_values, _ = cluster_stats
good_cluster_inds = np.where(p_values < p_accept)[0]
for i_clu, clu_idx in enumerate(good_cluster_inds):
    # unpack cluster information, get unique indices
    time_inds, space_inds = np.squeeze(clusters[clu_idx])
    time_inds = np.unique(time_inds)
    space_inds = np.unique(space_inds)




-- 
Thank You
Pooja Prabhu
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