[Mne_analysis] extracting sources from labels using mne_compute_raw_inverse
Matt Panichello
panichem at nmr.mgh.harvard.edu
Fri Aug 19 13:27:03 EDT 2011
Matti and Hari, thanks for explaining the summary waveform option. I removed
the labeldir flag and can extract ~100 sources for each V1 ROI.
Dan, thanks also for your insight. Do you think that a highly decimated
surface will undermine an analysis in any way? What is a more typical number
of vertices for the entire brain?
Thanks again!
Matt
On Fri, Aug 19, 2011 at 12:21 PM, Matti Hamalainen
<msh at nmr.mgh.harvard.edu>wrote:
> Hi,
>
> With the labeldir option there will be only one summary waveform / label in
> the output file.
>
> - Matti
>
>
> On Aug 19, 2011, at 10:13 AM, Matt Panichello <
> panichem at nmr.mgh.harvard.edu> wrote:
>
> Hi all,
>
> I am trying to extract the sources from labels using
> mne_compute_raw_inverse. The command finishes without error, but extracts
> extremely few sources for each label: 2 for a V1 label, and 3 for a rather
> large fusiform label. Both labels are based on the freesurfer parcellation
> of the surface and look fine in tksurfer. The exact command I am dropping
> looks like this:
>
> mne_compute_raw_invers --in (raw fif file) --inv (inverse solution)
> --labeldir (label directory) --picknormalcomp --align_z --snr 3
> --out (outname)
>
> and my output is below. Any help would be appreciated!
>
> Thank you,
>
> Matt
>
>
> mne_compute_raw_inverse version 1.15 compiled at Aug 19 2011 04:09:29
>
> input file : S011/PM01_clean_raw.fif
> inverse operator file : S011/ROI_freq/PM01_raw-5-meg-inv.fif
> SNR : 3.000000
> Additional channels : 'STI 014'
> All label files in S011/ROI_freq/ will be processed.
> Source locations will be listed in head coordinates.
>
> Reading the inverse operator...
> Read 2 source spaces from S011/ROI_freq/PM01_raw-5-meg-inv.fif with
> a total of 11402 source locations
> Read the sensor covariance matrix (full)
> Read the source covariance matrix (diagonal)
> Measurement file id not found (omit matching).
> Solution is based on MEG.
> Inverse operator information was stored in head coordinates.
> Source orientation prior information read.
> Depth-weighting prior information read.
> Source orientations read.
> Singular values read.
> Eigenvectors read.
> Number of channels = 305
> Number of sources = 11402
> Free source orientations
> Location information in head coordinates
> Projection in effect:
> # 1 : axial-PCA-01 : 1 vecs : 102 chs MEG active
> # 2 : axial-PCA-02 : 1 vecs : 102 chs MEG active
> # 3 : axial-PCA-03 : 1 vecs : 102 chs MEG active
> # 4 : axial-PCA-04 : 1 vecs : 102 chs MEG active
>
> Adding derived data to the inverse operator (nave = 1)...
> Projection applied to the covariance matrix.
> Decomposing the sensor noise covariance matrix...
> Eigenvalue decomposition had been precomputed.
> Eigenleads multiplied with Cholesky decomposition of the source
> covariance matrix.
> nave change: 1 -> 1
>
> Raw data file S011/PM01_clean_raw.fif:
>
> Processing label directory S011/ROI_freq/...
> 2 left-hemisphere labels in S011/ROI_freq/
> 1 right-hemisphere labels in S011/ROI_freq/
> Processing left-hemisphere labels...[done]
> Processing right-hemisphere labels...[done]
>
> Saving raw estimates...
> Data will be split into <= 1907.3 MByte pieces
> Processing raw data [2000 samples in each buffer] .........[1000
> samples in each buffer] [done]
> 393000 samples (including skips) saved to
> S011/ROI_freq/rh.V1.PM01.normal-mne_raw.fif ... closing...[done]
>
>
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