[Mne_analysis] source estimate stc files and current source density estimates

Thu Apr 10 11:21:42 EDT 2014

Hi D,

To be clear: the " dSPM value "units reported on  the y axis of figures such as plot_compute_mne_inverse.py are real and by "Am" do you mean micro Amperes?

if so I am then really confused by the MNE manual(6.2): in the section on Minimum norm estimates : It really suggests that the dSPM output is a stat because of the noise normalization procedure conducted: see below:

Noise normalization
The noise-normalized linear estimates introduced by Dale et al. require division of the expected current amplitude by its variance. Noise normal- ization serves three purposes:

It converts the expected current value into a dimensionless statistical test variable. Thus the resulting time and location dependent values are often referred to as dynamic statistical parameter maps (dSPM).

It reduces the location bias of the estimates. In particular, the tendency of the MNE to prefer superficial currents is eliminated.

The width of the point-spread function becomes less dependent on the source location on the cortical mantle. The point-spread is defined as the MNE resulting from the signals coming from a point current source (a current dipole) located at a certain point on the cortex.

In practice, noise normalization requires the computation of the diagonal elements of the matrix

M C M T = M ̃ M ̃ T .
With help of the singular-value decomposition approach we see directly

that

M ̃ M ̃ T = V Γ 2 V T .

Under the conditions expressed at the end of Section 6.2.5, it follows that the t-statistic values associated with fixed-orientation sources) are thus proportional to L while the F-statistic employed with free-orientation sources is proportional to L , correspondingly.

Note: A section discussing statistical considerations related to the noise normalization procedure will be added to this manual in one of the subse- quent releases.

Note: The MNE software usually computes the square roots of the F-sta- tistic to be displayed on the inflated cortical surfaces. These are also pro- portional to L . 

On Apr 9, 2014, at 10:23 PM, dgw wrote:

> Hi David
> The l2 results are in "real units" Am.
> 
> For the second question, you could ( though it would only make some sense to do with the l2 ( for the above reason). But it is not likely to provide you with different information if running it on averaged data(epochs may be interesting though). As for the other details the units would depend on what analysis you ran. There are examples in mne-python of several available functions. As for the normality question some support for permutation testing is implemented,but obviously choosing the right statistic will depend on your particular question and data.
> Hth
> D
> 
> David I Leitman <leitman at mail.med.upenn.edu> wrote:
> HI all,
> 
> I have been trying to figure out how to get a measure of current source density from the l2 min-norm source model. It seems that both methods dSPM and MNE that I use, yield  arbitrary or r  statistical values (MNe manual 6.2.6). From what i can tell this is do to the noise normalization step as described in the MNE manual is there any way to get a CSD measure in microamps or something real?
> 
> My second question is if I pass my epoched or evoked data through the mne L2 min-norm source model using  mne or dSPM as the method, Can the resulting time-series data comprising these MNE or dSPM values be submitted to temporo-spectral analysis? If so how is power at a particular frequency calculated? and if you can only get db change, how do wavelet or FFT procedure used to measure oscillations conform to statistical distributions like the f distribution in-terms of their non-normality and tendency to be skewed? I sho uld say I am not a statistician ... so this latter point may be a non-starter:)
> 
> Finally, is the above a problem for the beamforming routines detailed in MNE python? I do not know much about this approach.
> 
> thanks so much 
> 
> david
> 
> 
> David I. Leitman PhD
> 
> Research Assistant Professor
> Department of Psychiatry-
> Neuropsychiatry program
> Perelman School of Medicine
> University of Pennsylvania
> Gates Pavilion 10th floor room 1042
> 3400 Spruce St
> Philadelphia, PA 19104-4283
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> F: (215) 662-7903
> E: leitman at mail.med.upenn.edu
> Faculty page: http://www.med.upenn.edu/apps/faculty/index.php/g275/p8174343
> Lab website:
>  http://davidileitman.com
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David I. Leitman PhD

Research Assistant Professor
Department of Psychiatry-
Neuropsychiatry program
Perelman School of Medicine
University of Pennsylvania
Gates Pavilion 10th floor room 1042
3400 Spruce St
Philadelphia, PA 19104-4283
P: (215) 662-7389
F: (215) 662-7903
E: leitman at mail.med.upenn.edu
Faculty page:	http://www.med.upenn.edu/apps/faculty/index.php/g275/p8174343
Lab website:
 http://davidileitman.com

The information contained in this e-mail message is intended only for the personal and confidential use of the recipient(s) named above.  If the reader of this message is not the intended recipient or an agent responsible for delivering it to the intended recipient, you are hereby notified that you have received this document in error and that any review, dissemination, distribution, or copying of this message is strictly prohibited.  If you have received this communication in error, please notify the the Neuropsychiatry Section immediately by e-mail, and delete the original message.

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