http://disconnectomestudio.bcblab.com
In this channel, you can post your questions on the disconnectome studio web application and the Disconnectome Symptom Discoverer (DSD) tool functionality to predict the 1-year aspected neuropsychological scores from acute stroke disconnectome images.
Hi, I am attempting to predict motor scores from lesion masks using the DSD. Do I need to first calculate disconnectomes and then upload those to http://165.232.73.88/motor_page_calc/? If so, I believe the link to calculate the disconnectomes (“Calculate disconnectome first”) is broken. Thank you!
Hi, yes, you need to calculate the Disconnectome maps. They must be registered to the MNI152 2mm space registered, and .nii.gz format. If those requirements are fulfilled, the web-application runs smoothly. For this reason, you thought the link was broken. To overcome this issue, press the “Download patient Disconnectome example” on the home page, and you will download a Disconnectome template. Try it on the app, and you will see that the web-application is working. Thanks. Lia
Hi, thank you for the response. I am not trying to upload a disconnectome map at this point.
When I click “Calculate disconnectomes first” from the homepage (http://165.232.73.88/), I am taken to this page with this message:
Hi Emily, apologies about that. We moved the website, and some links still need to be updated.
Essentially this page is supposed to lead you to bcbtoolkit so that you can compute your disconnectome map. For maximal compatibility with the DSD you might want to use 2x2x2mm lesions and boost your disconnectome with [DISCONNECTOME Package X (180 additional subjects HCP7T 2mm) – Thiebaut de Schotten et al. Nat Comm 2020 available at Open_data cheers mich
The link has just been updated!
Hello,
I have just used the DSD to predict some motor scores for a sample of chronic stroke subjects. I am noticing that the variance of the predicted scores is quite low for some of the measures. Can you confirm that that is the expected behavior of the model?
Thank you,
Emily
Hi Emily,
Yes, we also noticed that when an impairment is expected, the variance of the predicted scores is reduced compared to measured scores. This is due to the UMAP coordinate smoothing and consequent averaging of the neuropsychological score in the UMAP space. However, in our analyses, the predicted score variations agreed with the measured scores. The obtained model R2s are reported in our preprint and upcoming publication. Moreover, in the supplementary materials, the mean absolute error % is reported to estimate the difference between the predicted and measured scores. On average, it was below 20% among the scores.
I hope this was helpful.
Cheers
Lia
Hello! Thank you for creating the disconnectome web application.
I was wondering if there is any way to upload multiple .nii.gz disconnectomes and then download the corresponding .csv files, as opposed to uploading one .nii.gz disconnectome file at a time?
Thank you
Best regards
Hi Beatrice, unfortunately, the web application does not have multiple image processing. However, we can provide standalone Python codes that you could run on your server at a population level. Here my email for more information on this (liatalozzi@gmail.com). Thanks. Lia
Hello, thank you very much for your reply. I sent you an email. Best, Beatrice
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