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SPM  March 2009

SPM March 2009

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Subject:

Re: SPM8b-MEG invert warning

From:

Vladimir Litvak <[log in to unmask]>

Reply-To:

Vladimir Litvak <[log in to unmask]>

Date:

Mon, 23 Mar 2009 17:11:10 +0000

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (145 lines)

Dear Haiteng,

We could not reproduce this warning and the line numbers that appear
there do not make sense for the most recent SPM8b version. Could you
download the latest SPM8b (or update the version you have), make sure
that there is only one SPM in your path and if you still get the
warning, send us the message again?

Thanks,

Vladimir

2009/3/23 hiten <[log in to unmask]>:
> Dear SPMers,
> Now,I am using spm8 to process VSM CTF-275 data. After preprocessing ,then
> go to 3D source reconstruction,choose template ,co-register,Forward
> model,then follwing warning emerges:
>
> computing surface normals
>
> Warning: Matrix is close to singular or badly scaled.
>
>          Results may be inaccurate. RCOND = 3.627436e-017.
>
>> In forwinv\private\meg_ini>getcoeffs at 94
>
>   In forwinv\private\meg_ini at 36
>
>   In forwinv\private\prepare_vol_sens at 176
>
>   In forwinv_prepare_vol_sens at 11
>
>   In spm_eeg_inv_forward at 46
>
>   In spm_eeg_inv_forward_ui at 28
>
>   In spm_eeg_inv_imag_api>Forward_Callback at 88
>
>   In spm_eeg_inv_imag_api at 54
>
> Foward model complete - thank you
>
> Vladimir had told me this warning is normal and does not indicate a problem.
>
> But then go on invert  using standard imaging methods,the follwing warning
> produces:
>
> Computing Green function from graph Laplacian: - done
>
> Using 81 spatial modes
>
> Using 8 temporal modes
>
> accounting for 53.80 percent variance
>
> Warning: File: spm_mvb.m Line: 150 Column: 18
>
> Function with duplicate name "spm_mvb" cannot be called.
>
>> In spm_eeg_invert at 369
>
>   In spm_eeg_invert_ui at 82
>
>   In spm_eeg_inv_imag_api>Inverse_Callback at 95
>
>   In spm_eeg_inv_imag_api at 54
>
> Percent variance explained 99.72 (53.66)
>
>       type: 'GS'
>
>     trials: {'eat'  'neural'  'sad'  'happy'  'cross'}
>
>     smooth: 0.6000
>
>        xyz: [0 0 0]
>
>        rad: 128
>
>      scale: 1.0000e+012
>
>          M: [7204x275 double]
>
>          J: {[7204x8 double]  [7204x8 double]  [7204x8 double]  [7204x8
> double]  [7204x8 double]}
>
>          Y: {[275x8 double]  [275x8 double]  [275x8 double]  [275x8 double]
> [275x8 double]}
>
>          L: [275x7204 double]
>
>          R: [275x275 double]
>
>         qC: [7204x1 double]
>
>         qV: [3577x3577 double]
>
>          T: [3577x8 double]
>
>          U: [275x275 double]
>
>         Is: [7204x1 double]
>
>         It: [1x3577 double]
>
>         Ic: [275x1 double]
>
>         Nd: 7204
>
>        pst: [1x3577 double]
>
>        dct: [1x281 double]
>
>          F: 5.0153e+004
>
>         R2: 99.7219
>
>         VE: 0.5380
>
>        woi: [-1990 990]
>
> I don't know whether these warning would bring out inaccurate results?
>
> I would be happy about any comments!
> Thanks in advance,
>                    haiteng
>
>
>
>
>
>
> --
> haiteng  jiang
> Research Center for Learning Science,
> Southeast University
> Si Pai Lou 2 # , Nanjing, 210096, P.R.China
> Brain Imaging  Lab
> Mobile:+86-13813998827
> Email: [log in to unmask]
>
>
> ________________________________
> 网易邮箱,中国第一大电子邮件服务商

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