[Mne_analysis] [Mne analysis] two sample t-test with spatio_temporal_cluster_test

Talitha Ford tcford at swin.edu.au
Mon Jun 12 17:42:43 EDT 2017
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Thanks Dennis, I had included a ttest as the stat_fun in the previous command, though.

From what I understand, for a ttest, my code should look more like this:

#~ X1= np.abs(X1)
#~ X2 np.abs(X2)

print('Computing connectivity.')
connectivity = spatial_tris_connectivity(grade_to_tris(5))

#    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])
all_data = [X2, X1]

p_threshold = 0.0001

#~ f_threshold = stats.distributions.f.ppf(1. - p_threshold / 2.,
    #~ len(X2) - 1, len(X1) - 1)

t_threshold = stats.distributions.t.ppf(p_threshold / 2.,
    len(X2) - 1, len(X1) - 1)

print('Clustering.')
T_obs, clusters, cluster_p_values, H0 = clu =\
spatio_temporal_cluster_test(all_data, connectivity=connectivity, n_jobs=2, threshod= t_threshold, stat_fun= scipy.stats.ttest_ind)

Commenting out the conversation of the data to absolute values, calculating a t_threshold, and including ttest_ind as the stat_fun? Sorry if I have misunderstood something.

Thanks,
Talitha



On 12 Jun 2017, at 17:27, Denis-Alexander Engemann <denis.engemann at gmail.com<mailto:denis.engemann at gmail.com>> wrote:

Ahh. For two conditions F should be the abs(T**2). You can just use a t-test for indpendent samples here instead as statfun.
On Mon, 12 Jun 2017 at 10:13, Talitha Ford <tcford at swin.edu.au<mailto:tcford at swin.edu.au>> wrote:
Hi Dennis,
Thank you, this is the script I’ve been working from. The problem I am having though, is that as f-stats are >0, they do not indicate which group is larger than the other, which is what I would like to know. I have tried to use scipy.stats.ttest_ind but I get this error:
ValueError: could not broadcast input array from shape (1000) into shape (614520)

The command is:
 T_obs, clusters, cluster_p_values, H0 = clu =\
spatio_temporal_cluster_test(all_data, connectivity=connectivity, n_jobs=2, stat_fun= scipy.stats.ttest_ind)

all_data is 2 lists (2 groups) of 16 and 19 participants, with 30 time points of 20484 vertices for each participant.

I hope that makes sense (and there is a possible work around!). Thanks again for you help,

Talitha


On 12 Jun 2017, at 05:33, Denis-Alexander Engemann <denis.engemann at gmail.com<mailto:denis.engemann at gmail.com>> wrote:

Hi Talitha,

you can run the permutation clustering with a wide array of contrasts. This might be what you are looking for:

http://martinos.org/mne/dev/auto_tutorials/plot_stats_cluster_spatio_temporal_2samp.html

I hope this helps,
Denis

On Sun, Jun 11, 2017 at 12:00 PM Talitha Ford <tcford at swin.edu.au<mailto:tcford at swin.edu.au>> wrote:
Dear all,

Similar to conducing a pair samples t-test on source data using spatio-temporal clustering, allowing the visualisation of clusters where condition A > condition B and vice versa, is it possible to run an independent samples t-test to visualise differences between two groups? The 2 samples permutation tests currently available are limited in that they plot F statistics that don’t give a direction of the difference between the groups. Basically, is it possible to identify clusters that differ significantly between the groups, as well as identify/visualise the direction in which they differ?

I am currently attempting to extract the cluster values for each participant to get an overall mean for each vertex within the cluster to compare between groups, but this seems very inefficient.

Cheers,
Talitha

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