[Mne_analysis] how to extract the label time series using MNE-toolbox

[Alexandre Gramfort]

hi,

> Recently I am conducting an epilepy MEG analysis project.
> I used MNE toolbox to create a cortex constrained source space (5891
> sources) and calculated the lead fead matrix (free orientaion, 5891
> (sources) x 3 (directions xyz) x 306 (channels) matrix ).
> By on house code, I applied  the inverse problem (beamformer) operator to a
> segment of epileptiform MEG and then got a 5891 (sources) x 3 (directions) x
> 500 (time points) matrix (SDTM), which is source time series.

so you have 3 time series per location. Which is not make stc contain.

> My questions are as,
> 1) how to get the information on which source belong to which regions by
> label operations (for example source A  is included in temporal cortex). If
> I got such information, I can get a collective time series for a region by
> averaging (or other ways to get a representative time series) all the source
> time series in this region. anyone have a quick code  to share it to me?

if you use instances of SourceEstimates (stc objects) you can use labels.
I assume you use a surface source space. You can look at the code
of the in_label method.

http://martinos.org/mne/stable/generated/mne.SourceEstimate.html#mne.SourceEstimate.in_label

> 2) epileptiform MEG is difficult to average. and for such MEG, we have only
> one "trial".  MNE python is quite suitable to process multiple trials MEG
> data. How to use the several functions to process single "trials"
> epileptiform MEG?
> for example,
> if I know SDTM in matlab format, how to use it as the input of the function
> mne.extract_label_time_course?
> The stcs para equals to the SDTM?

you can get an Evoked instance by average a single Epoch.

> The following is the three examples for the functions extracted from
> http://martinos.org/mne/stable/auto_examples/connectivity/plot_mne_inverse_label_connectivity.html#example-connectivity-plot-mne-inverse-label-connectivity-py
>
> stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method,
>                             pick_ori="normal", return_generator=True)
>
> # Get labels for FreeSurfer 'aparc' cortical parcellation with 34
> labels/hemi
> labels, label_colors = mne.labels_from_parc('sample', parc='aparc',
>                                             subjects_dir=subjects_dir)
>
> # Average the source estimates within each label using sign-flips to reduce
> # signal cancellations, also here we return a generator
> src = inverse_operator['src']
> label_ts = mne.extract_label_time_course(stcs, labels, src,
> mode='mean_flip',
>                                          return_generator=True)

it does not make sense to you mean_flip unless you have fixed orient
source space
or used pick_ori='normal'. You can just average you have positive
values in the stc.

Hope this helps,

Alex