functional network from raw resting state MEG

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1

External Email - Use Caution

Hello everyone,

I am almost new to MNE, Please be patient with me.
I am trying to calculate the functional network from raw resting state MEG
data from here
<http://How_to_Build_a_Functional_Connectomic_Biomarker_for_Mild_Cognitive_Impairment_from_Source_Reconstructed_MEG_Resting-State_Activity_-_Raw_data_I_Controls_/6210158>
.
I am following this example:
Compute source space connectivity and visualize it using a circular graph
<https://mne.tools/stable/auto_examples/connectivity/plot_mne_inverse_label_connectivity.html>

The problem is there is no *meg-inv.fif* file in the dataset.
an I am stuck in these lines:

inverse_operator
<https://mne.tools/stable/generated/mne.minimum_norm.InverseOperator.html#mne.minimum_norm.InverseOperator>
= read_inverse_operator
<https://mne.tools/stable/generated/mne.minimum_norm.read_inverse_operator.html#mne.minimum_norm.read_inverse_operator>(fname_inv
<https://docs.python.org/3/library/stdtypes.html#str>)

stcs = apply_inverse_epochs
<https://mne.tools/stable/generated/mne.minimum_norm.apply_inverse_epochs.html#mne.minimum_norm.apply_inverse_epochs>(epochs
<https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs>,
inverse_operator
<https://mne.tools/stable/generated/mne.minimum_norm.InverseOperator.html#mne.minimum_norm.InverseOperator>,
lambda2 <https://docs.python.org/3/library/functions.html#float>,
method <https://docs.python.org/3/library/stdtypes.html#str>,
                            pick_ori="normal", return_generator=True)

Gramfort, 2013. MEG and EEG data analysis with MNE-Python. *Frontiers in
neuroscience*, *7*, p.267.
This article clearly explain why we need a SourceEstimate object

Is it still possible to calculate the functional network from this dataset
or I should look for something else?

here is the output of *raw.info <http://raw.info>*:
*MADCN0001_tsss_mc.fif*
<Info | 23 non-empty values
acq_pars: ACQch001 110113 ACQch002 110112 ACQch003 110111 ACQch004 110122
...
bads: []
ch_names: MEG0113, MEG0112, MEG0111, MEG0122, MEG0123, MEG0121, MEG0132,
...
chs: 204 GRAD, 102 MAG, 1 EEG, 3 STIM, 9 CHPI
custom_ref_applied: False
description: Vectorview system
dev_head_t: MEG device -> head transform
dig: 322 items (3 Cardinal, 4 HPI, 315 Extra)
events: 1 item (list)
experimenter: Common account for MEG work (meg)
file_id: 4 items (dict)
highpass: 0.1 Hz
hpi_meas: 1 item (list)
hpi_results: 1 item (list)
hpi_subsystem: 2 items (dict)
line_freq: 50.0
lowpass: 330.0 Hz
meas_date: 2013-11-05 12:09:28 UTC
meas_id: 4 items (dict)
nchan: 319
proc_history: 1 item (list)
proj_id: 1 item (ndarray)
proj_name: qsm
projs: []
sfreq: 1000.0 Hz
subject_info: 7 items (dict)

2

External Email - Use Caution

there are many ways to do what you suggest.

can you refer to a paper from which you would like to replicate the analysis?

to get started with mne I would start with these tutorials:

https://mne.tools/stable/auto_tutorials/index.html

Alex

3

External Email - Use Caution

Thank you Alex,
I used the connectivity in sensor space for now to get an initial result.
I think I need to have some background study about source localization.
I am using this paper paper.

Marcos, A., Maestu, F. and Pereda, E., How to Build a Functional
Connectomic Biomarker for Mild Cognitive Impairment from Source
Reconstructed MEG Resting-State Activity: The Combination of ROI
Representation and Connectivity Estimator Matters.

I am eager to hear if you have any suggestions.

best,
Abolfazl
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