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I'm using the following example code to compute connectivity.
https://martinos.org/mne/stable/auto_examples/connectivity/plot_mne_inverse_label_connectivity.html#sphx-glr-auto-examples-connectivity-plot-mne-inverse-label-connectivity-py
How can I apply statistical threshold to have only significant connections?
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Dear Vivek,
significant in what way? If you have multiple subjects, you can compute a t-statistic for each connection. If you have multiple experimental conditions, you can do a paired t-test between conditions?. Could you tell us a bit more about the data?
best,
Marijn.
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Dear Marijn,
Its a single subject MEG data.
By significant I mean, if you look at the figure in the following example:
https://martinos.org/mne/stable/auto_examples/connectivity/plot_mne_inverse_label_connectivity.html#sphx-glr-auto-examples-connectivity-plot-mne-inverse-label-connectivity-py
on lower right, it shows a bar that display the strength of those
connections with values 0.4-0.6. When I plot for my data the bar displays
values from 0.15-0.20 and shows the connections below 0.15 also. How can I
restrict it to show only connections above 0.15?
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By significant connections I also mean the strongest connections. How to
keep a threshold to remove weak connections.
Hi Vivek,
In those examples, I believe the thresholding occurs from the n_lines parameter in the plot_connectivity_circle() function. n_lines=300 means that only the 300 strongest connections are plotted. See the documentation for plot_connectivity_circle: https://martinos.org/mne/stable/generated/mne.viz.plot_connectivity_circle.html#mne.viz.plot_connectivity_circle
If you wanted finer control over the threshold, you would need to either manipulate the values of the CON output (array) directly, or calculate an n_lines threshold that would capture your desired range. For example, if you wanted only those connections with a value > 0.3, you could count the number of connections with value > 0.3 and then use that number for your n_lines value. (I think this would work properly but have not tried it myself, so there might be some nuances to troubleshoot.)
It wasn?t clear to me whether you meant significant connections or just an absolute value. The above suggestions threshold based on absolute value. You could use some statistical test appropriate for your own data to determine the absolute value, but depending on your goal and data there might be more direct methods.
Cheers,
Josh
Joshua Bear, MD, MA | Assistant Professor, Pediatric Neurology and Epilepsy
University of Colorado and Children?s Hospital Colorado
13123 East 16th Avenue, Box B-155 | Aurora, CO 80045 | Phone: 720-777-6138 | Fax: 720-777-7285
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By significant connections I also mean the strongest connections. How to keep a threshold to remove weak connections.
Dear Marijn,
Its a single subject MEG data.
By significant I mean, if you look at the figure in the following example:
https://martinos.org/mne/stable/auto_examples/connectivity/plot_mne_inverse_label_connectivity.html#sphx-glr-auto-examples-connectivity-plot-mne-inverse-label-connectivity-py
on lower right, it shows a bar that display the strength of those connections with values 0.4-0.6. When I plot for my data the bar displays values from 0.15-0.20 and shows the connections below 0.15 also. How can I restrict it to show only connections above 0.15?External Email - Use Caution
Dear Vivek,
significant in what way? If you have multiple subjects, you can compute a t-statistic for each connection. If you have multiple experimental conditions, you can do a paired t-test between conditions?. Could you tell us a bit more about the data?
best,
Marijn.>
> External Email - Use Caution
>
>
> I'm using the following example code to compute connectivity.
> https://martinos.org/mne/stable/auto_examples/connectivity/plot_mne_inverse_label_connectivity.html#sphx-glr-auto-examples-connectivity-plot-mne-inverse-label-connectivity-py
>
> How can I apply statistical threshold to have only significant connections?
>
> --
> Vivek Sharma
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Thanks Josh,
I tried changing the n_lines parameter in the plot_connectivity_circle()
function, it reduces the connections only. It still shows weak connections
or connections below desired value. I'll try other way you mentioned.