[Mne_analysis] 2. 1 Samp Cluster (Eric Larson)

[Geller, Jason]

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2. Re: 1 Samp Cluster (Eric Larson)


Even setting a different threshold returns no clusters.

Jason Geller, Ph.D.
Postdoctoral Scholar
The University of Iowa


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Today's Topics:

   1. Re: Computing Connectivity 1Samp Cluster (Eric Larson)
   2. Re: 1 Samp Cluster (Eric Larson)
   3. DICS beamformer (Ramakrishnan, Nithya)


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Message: 1
Date: Wed, 30 Oct 2019 12:21:39 -0400
From: Eric Larson <larson.eric.d at gmail.com>
Subject: Re: [Mne_analysis] Computing Connectivity 1Samp Cluster
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>
Message-ID:
        <CAGu2niW6Hn_o1q_uaRgk9wwd_w4XoePBK05_dA9Byu-BJx40Sw at mail.gmail.com>
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>
>     inverse_operator = read_inverse_operator(inverse + subject +
> '-inv.fif') # each subject inv file
>     stcs_morph = mne.compute_source_morph(src,subject_to,
>
> subjects_dir=subjects_dir).apply(stcs)
>

Here the "spacing" parameter controls the destination source space. The
default is "5", which means "ico-5 source space for fsaverage", which has
20484 total vertices. If you look at `stcs_morph.vertices`, it should be a
list of two arrays, each array containing np.arange(10242) (fsaverage is
special in that its ico-5 source space consists of the first 10242 vertices
in each hemisphere).

If you want to use your ico-6 fsaverage source space (which has 8196
vertices) as the destination, you'd want to do something like:

src_fs = mne.read_source_spaces(path6 +'fsaverage-oct-6-src.fif')
morpher = mne.compute_source_morph(src, subject_to,
spacing=[src_fs[0]['vertno'], src_fs[1]['vertno']])
stcs_morph = morpher.apply(stcs)

Eric
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Message: 2
Date: Wed, 30 Oct 2019 12:23:52 -0400
From: Eric Larson <larson.eric.d at gmail.com>
Subject: Re: [Mne_analysis] 1 Samp Cluster
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>
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        <CAGu2niV-XetnOSOmvhtYjR7mVra9U_Jua14EAiYtZAddFX+sfw at mail.gmail.com>
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>
> When running 1 Samp Cluster on the difference between two conditions, the
> H1 Stat always comes back as min =  0 and max = 0 with no clusters found.
> Now, it is possible that there is no difference between the conditions, but
> given the fact that min and max are both 0 has me concerned I did something
> wrong. Any insight?
>

It suggests that no source points survive the initial thresholding due to
`threshold`. if you look at the "t_obs" output, you should see that no
points exceed your threshold. You can decrease it so it actually has some
points to cluster.

Eric
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Message: 3
Date: Thu, 31 Oct 2019 00:00:42 +0000
From: "Ramakrishnan, Nithya" <nramakri at bcm.edu>
Subject: [Mne_analysis] DICS beamformer
To: "mne_analysis at nmr.mgh.harvard.edu"
        <mne_analysis at nmr.mgh.harvard.edu>
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Hi,

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?

Thanks

Nithya Ramakrishnan
Senior Scientific Programmer


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