[Mne_analysis] extracting sources from labels using mne_compute_raw_inverse
Matti Hamalainen
msh at nmr.mgh.harvard.edu
Fri Aug 19 14:00:35 EDT 2011
Hi again,
On Aug 19, 2011, at 1:27 PM, Matt Panichello wrote:
> 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?
The about 11000 is the total number of sources which is ok. Nothing to worry about there.
- Matti
>
> 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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