calculating phase locking values on MNE source estimate within labels- using mne.minimum_norm.source_induced_power

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Hi all and happy holidays:

I am trying to render a a time- frequency map containing phase locking values (0-1) from label files containing SVD?s of MNE source estimate timeseries. I began with the example:

https://martinos.org/mne/stable/auto_examples/time_frequency/plot_source_label_time_frequency.html#sphx-glr-auto-examples-time-frequency-plot-source-label-time-frequency-py

which gives ITC for evoked plus induced and induced alone using mne.minimum_norm.source_induced_power. my questions are the following parameters:
If I choose baseline =None and baseline_mode=None, will I get PLV values of 0-1 across all the epoch where the locking is relative to stimulus onset (i.e. at time zero).

2) zero_mean when should this be used? are all wavelets not zero-meaned by default?
3) the parameter prepare: When calling a saved inverse operator file at the beginning of a script like the example as in my caee executing the full inverse_operator = read_inverse_operator(inv_fname)
src = inverse_operator['src?] initially and then looping thought the labels. Can setting the Prepare?= True be done and will it save time?

thanks in advance

david

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1 Like
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hi David,

see inline comments

I am trying to render a a time- frequency map containing phase locking
values (0-1) from label files containing SVD?s of MNE source estimate
timeseries. I began with the example:

Page Redirection

which gives ITC for evoked plus induced and induced alone using
mne.minimum_norm.source_induced_power. my questions are the following
parameters:
If I choose baseline =None and baseline_mode=None, will I get PLV values
of 0-1 across all the epoch where the locking is relative to stimulus onset
(i.e. at time zero).

yes

2) zero_mean when should this be used? are all wavelets not zero-meaned by
default?

very often Morlet wavelets are used in Neuroscience without the \kappa_\sigma
in defintion from Morlet wavelet - Wikipedia

this constant makes sure the wavelets are zero mean.

it's not a big problem if you use many cycles (lets say > 5) but otherwise
your wavelet coefficient will capture a DC component.

Historically I had written the code without the \kappa_\sigma and when I added
it, I made it an option with zero_mean. I suggest to always use zero_mean=True.

3) the parameter prepare: When calling a saved inverse operator file at the
beginning of a script like the example as in my caee executing the full
inverse_operator = read_inverse_operator(inv_fname)
src = inverse_operator['src?] initially and then looping thought the
labels. Can setting the Prepare?= True be done and will it save time?

yes if you prepare the inverse operator you should save time.

HTH
Alex