[Mne_analysis] Visualizing power spectrum density of sources on the brain

[Gladia Hotan]

Hi Denis,

Thanks for your reply!

I noted that in the compute_source_psd code, the output is already scaled
to dB (psd = 10*np.log10(psd) is taken in line 486). Is it still necessary
to take 10*log10 of the output?

I tried detrending the data prior to computing the inverse solution and
noise covariance matrix, but I get a similar result in which most of the
power is localized in the white matter. An example of the average I get
over many epochs is at this link:
https://www.dropbox.com/s/vs7jjd8u2szjrbo/Screen%20Shot%202016-07-14%20at%202.19.20%20PM.png?dl=0

I tried plotting spectra (power vs frequency) of the sources and the
spectra look reasonable; it's just the spatial distribution that's strange.

This is roughly how I'm extracting power at a certain frequency:

# Compute psd of sources:
stc_psd = mne.minimum_norm.comptue_source_psd(...)

# Extract the frequency band of interest:
data = stc_psd.data[:,1:5]  # Shape: numsources*4

# Sum the power in the frequency band of interest:
av_data = np.transpose(np.array([np.sum(data,1)]))  # Shape: numsources*1

# Make a copy of the stc object and overwrite its data:
stc_psd1 = stc_psd.copy()
stc_psd1.times = stc_psd_slow.times[0:1]   # Doesn't matter what this
number is; just get the dimensions right
stc_psd1._data = av_data

# Visualize:
mne.viz.plot_source_estimates(stc_psd1,...)

Is there any other troubleshooting method I can try?

Thanks and Best,
Gladia


On Thu, Jul 7, 2016 at 12:22 PM, Denis-Alexander Engemann <
denis.engemann at gmail.com> wrote:

> Sorry Gladia,
>
> I was a bit unclear I fear. I was referring to the fact, that if you want
> to see things in dB and want to look at the power spectrum in one ROI or
> vertex the same way you want to look at it in the sensor space, you need to
> use the MNE option. You can then intuitively take the decadic logarithm and
> scale to dB.
> Then for the surface plotting it is a bit tricky with regard to color
> maps. Both dSPM and MNE can be intuitively used but the entire MNE viz
> system sort of expects time domain data as you may have noticed, where data
> are dont' have any tend.
> With frequency domain data this is not the case, you have local peaks but
> a global 1/f trend which will mess up your display. You then either need to
> detrend the spectrum or choose fmin, fmid, fmax, carefully, essentially
> setting them at each frequency where you make sure that your stc.times
> vector is not longer than 1; by default histograms for determining viz
> values are computed over all time points so the auto option won't work.
> Lot's of manual tuning required unfortunately.
> As to your plots I'm not fully sure I get your message. Intuitively make
> sure you get the relationship between the time domain and the specteal data
> right, maybe the time domain localization is driven by a certain band. And
> as said, colormaps can be difficult and misleading. Plotting historgrams
> along the way and looking at slices can help understand what you do.
>
> I hope this helps a bit,
> Denis
>
>
> On Thu, Jul 7, 2016 at 4:04 PM Gladia Hotan <gladiach at gmail.com> wrote:
>
>> Thanks Alex!
>>
>> I am wondering why when I plot the psd on the brain I get a lot of
>> activity in the corpus callosum and white matter rather than in the grey
>> matter, which is where the sources are localized to. Here is a link to
>> example screenshots of the source distribution vs the psd distribution on
>> the brain, which don't seem to match:
>> https://www.dropbox.com/sh/blstn3xitewbg5t/AACia--q9mF6d0ZjluhXq_TGa?dl=0
>>
>>  How can I check that the source psd is being plotted correctly?
>>
>> Thanks and Best,
>> Gladia
>>
>>
>> On Tue, Jul 5, 2016 at 4:54 PM, Alexandre Gramfort <
>> alexandre.gramfort at telecom-paristech.fr> wrote:
>>
>>> > Could anyone explain why MNE should be used instead of dSPM to get the
>>> power
>>> > plots? The tutorial on the MNE website says to use dSPM as the solver.
>>>
>>> dSPM is just a normalized/scaled version of MNE (scaling is obtained
>>> form the noise standard deviation derived from the noise cov). So
>>> basically it's just a scaling factor. If you apply a baseline /
>>> contrast with a log ratio of power then there is no diff between the
>>> 2.
>>>
>>> HTH
>>> Alex
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