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

[dgw]

Hi David,

Please see my replies in text

On Thu, Apr 10, 2014 at 11:21 AM, David I Leitman
<leitman at mail.med.upenn.edu> wrote:
> 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?

No this is not true for dSPM only for the L2 solutions (both can be
calculated by mne).. By Am, I mean Ampere meters:
https://en.wikipedia.org/wiki/Ampere-meter.

HTH,
D

>
> 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
>> 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
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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
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