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<body><p><span style="padding: 3px 10px; border-radius: 5px; color: #ffffff; font-weight: bold; display: inline-block; background-color: #ff0000;"> External Email - Use Caution </span></p><p></p>
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<div style="direction: ltr;">2. Re: 1 Samp Cluster (Eric Larson)</div>
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<div dir="ltr" style="text-align: left;">Even setting a different threshold returns no clusters. </div>
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<div style="direction: ltr;">Jason Geller, Ph.D. </div>
<div style="direction: ltr;">Postdoctoral Scholar</div>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif"><b>From:</b> mne_analysis-bounces@nmr.mgh.harvard.edu <mne_analysis-bounces@nmr.mgh.harvard.edu> on behalf of mne_analysis-request@nmr.mgh.harvard.edu <mne_analysis-request@nmr.mgh.harvard.edu><br>
<b>Sent:</b> Wednesday, October 30, 2019 7:01 PM<br>
<b>To:</b> mne_analysis@nmr.mgh.harvard.edu<br>
<b>Subject:</b> [External] Mne_analysis Digest, Vol 141, Issue 37
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Today's Topics:<br>
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1. Re: Computing Connectivity 1Samp Cluster (Eric Larson)<br>
2. Re: 1 Samp Cluster (Eric Larson)<br>
3. DICS beamformer (Ramakrishnan, Nithya)<br>
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Message: 1<br>
Date: Wed, 30 Oct 2019 12:21:39 -0400<br>
From: Eric Larson <larson.eric.d@gmail.com><br>
Subject: Re: [Mne_analysis] Computing Connectivity 1Samp Cluster<br>
To: Discussion and support forum for the users of MNE Software<br>
<mne_analysis@nmr.mgh.harvard.edu><br>
Message-ID:<br>
<CAGu2niW6Hn_o1q_uaRgk9wwd_w4XoePBK05_dA9Byu-BJx40Sw@mail.gmail.com><br>
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External Email - Use Caution <br>
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><br>
> inverse_operator = read_inverse_operator(inverse + subject +<br>
> '-inv.fif') # each subject inv file<br>
> stcs_morph = mne.compute_source_morph(src,subject_to,<br>
><br>
> subjects_dir=subjects_dir).apply(stcs)<br>
><br>
<br>
Here the "spacing" parameter controls the destination source space. The<br>
default is "5", which means "ico-5 source space for fsaverage", which has<br>
20484 total vertices. If you look at `stcs_morph.vertices`, it should be a<br>
list of two arrays, each array containing np.arange(10242) (fsaverage is<br>
special in that its ico-5 source space consists of the first 10242 vertices<br>
in each hemisphere).<br>
<br>
If you want to use your ico-6 fsaverage source space (which has 8196<br>
vertices) as the destination, you'd want to do something like:<br>
<br>
src_fs = mne.read_source_spaces(path6 +'fsaverage-oct-6-src.fif')<br>
morpher = mne.compute_source_morph(src, subject_to,<br>
spacing=[src_fs[0]['vertno'], src_fs[1]['vertno']])<br>
stcs_morph = morpher.apply(stcs)<br>
<br>
Eric<br>
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Message: 2<br>
Date: Wed, 30 Oct 2019 12:23:52 -0400<br>
From: Eric Larson <larson.eric.d@gmail.com><br>
Subject: Re: [Mne_analysis] 1 Samp Cluster<br>
To: Discussion and support forum for the users of MNE Software<br>
<mne_analysis@nmr.mgh.harvard.edu><br>
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<CAGu2niV-XetnOSOmvhtYjR7mVra9U_Jua14EAiYtZAddFX+sfw@mail.gmail.com><br>
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> When running 1 Samp Cluster on the difference between two conditions, the<br>
> H1 Stat always comes back as min = 0 and max = 0 with no clusters found.<br>
> Now, it is possible that there is no difference between the conditions, but<br>
> given the fact that min and max are both 0 has me concerned I did something<br>
> wrong. Any insight?<br>
><br>
<br>
It suggests that no source points survive the initial thresholding due to<br>
`threshold`. if you look at the "t_obs" output, you should see that no<br>
points exceed your threshold. You can decrease it so it actually has some<br>
points to cluster.<br>
<br>
Eric<br>
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Message: 3<br>
Date: Thu, 31 Oct 2019 00:00:42 +0000<br>
From: "Ramakrishnan, Nithya" <nramakri@bcm.edu><br>
Subject: [Mne_analysis] DICS beamformer<br>
To: "mne_analysis@nmr.mgh.harvard.edu"<br>
<mne_analysis@nmr.mgh.harvard.edu><br>
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Hi,<br>
<br>
I am working on getting some source estimates from my resting state MEG data using the DICS beamformer. In order to do that, I am computing the CSD matrix and the DICS spatial filter. I read in a few papers that computed the DICS beamformer using only the gradiometers
and I found some information that if we were to mix sensors, we would need to use a covariance matrix. Is this accurate? And if I were to use only the gradiometers, would it be better/faster to select the gradiometer data when computing the CSD matrix?<br>
<br>
Thanks<br>
<br>
Nithya Ramakrishnan<br>
Senior Scientific Programmer<br>
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End of Mne_analysis Digest, Vol 141, Issue 37<br>
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