# Vertexes to time course

**URL:** <https://mne.discourse.group/t/vertexes-to-time-course/6187>\
**Category:** Support & Discussions\
**Created:** [January 12, 2023, 2:07pm UTC](https://mne.discourse.group/t/vertexes-to-time-course/6187 "2023-01-12T14:07:32Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![Rauf](https://avatars.discourse-cdn.com/v4/letter/r/d26b3c/32.png) [@Rauf](https://mne.discourse.group/u/Rauf)\
**Post date:** [January 12, 2023, 2:07pm UTC](https://mne.discourse.group/t/vertexes-to-time-course/6187/1 "2023-01-12T14:07:32Z")

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Hi  
I believe its a simple and naive question. I am using the code below (taken from the tutorial as it is). The aud\_lh variable has details of 1097 vertices. Moreover stc\_lh has only 33 time courses, How those many number of vertices result in 33 time courses ? Furthermore, is there a way to calculate the euclidean distance between vertices that contributed to these 33 time courses ?  
Thanks

```python
import mne
from mne.datasets import sample
import matplotlib.pyplot as plt

print( __doc__ )

data_path = sample.data_path()
subjects_dir = '/network/lustre/home/mne_data/MNE-sample-data/subjects'
meg_path = '/network/lustre/home/mne_data/MNE-sample-data/MEG/sample'

# load the stc
stc = mne.read_source_estimate('/network/lustre/home/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg')

# load the labels
aud_lh = mne.read_label('/network/lustre/home/mne_data/MNE-sample-data/MEG/sample/labels/Aud-lh.label')
aud_rh = mne.read_label('/network/lustre/home/mne_data/MNE-sample-data/MEG/sample/labels/Aud-rh.label')

# extract the time course for different labels from the stc
stc_lh = stc.in_label(aud_lh)
stc_rh = stc.in_label(aud_rh)

```

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<div class="post-metadata">

**Author:** ![drammock](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/drammock/32/4_2.png) [@drammock](https://mne.discourse.group/u/drammock)\
**Post date:** [January 12, 2023, 9:47pm UTC](https://mne.discourse.group/t/vertexes-to-time-course/6187/2 "2023-01-12T21:47:02Z")

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This has to do with the different sampling densities of the source space. The label `aud_lh` generally has all vertices from the highest resolution source space, while this STC is based on a lower density source space. When you call `stc.in_label()` it’s going to take only the vertices that are in the label _and_ in the STC.

```python
assert set(stc.lh_vertno).intersection(set(aud_lh.vertices)) == 33

```

see [The typical M/EEG workflow — MNE 1.3.0 documentation](https://mne.tools/stable/overview/cookbook.html#setting-up-source-space) for more info on source space density.