Machine learning using multimodal clinical, electroencephalographic, and magnetic resonance imaging data can predict incident depression in adults with epilepsy: A pilot study

Simple item page
dc.contributor.authorDelgado-García, Guillermo
dc.contributor.authorEngbers, Jordan D. T.
dc.contributor.authorWiebe, Samuel
dc.contributor.authorMouches, Pauline
dc.contributor.authorAmador, Kimberly
dc.contributor.authorForkert, Nils D.
dc.contributor.authorWhite, James
dc.contributor.authorSajobi, Tolulope
dc.contributor.authorKlein, Karl Martin
dc.contributor.authorJosephson, Colin B.
dc.contributor.authorCalgary Comprehensive Epilepsy Program Collaborators
dc.date.accessioned2024-08-09T22:11:58Z
dc.date.available2024-08-09T22:11:58Z
dc.date.issued2023-07-08
dc.description.abstractObjective: To develop a multi-modal machine learning (ML) approach for predicting incident depression in adults with epilepsy. Methods: We randomly selected 200 patients from the Calgary Comprehensive Epilepsy Program registry and linked their registry-based clinical data to their first-available clinical electroencephalogram (EEG) and magnetic resonance imaging (MRI) study. We excluded patients with a clinical or Neurological Disorders Depression Inventory for Epilepsy (NDDI-E)-based diagnosis of major depression at baseline. The NDDI-E was used to detect incident depression over a median of 2.4 years follow-up (IQR 1.5-3.3 years). A ReliefF algorithm was applied to clinical as well as quantitative EEG and MRI parameters for feature selection. Six ML algorithms were trained and tested using stratified three-fold cross-validation. Multiple metrics were used to assess model performances. Results: Of 200 patients, 150 had EEG and MRI data of sufficient quality for ML, of which 59 were excluded due to prevalent depression. Therefore, 91 patients (41 women) were included with a median age of 29 (IQR 22-44) years. A total of 42 features were selected by ReliefF, none of which were quantitative MRI or EEG variables. All models had a sensitivity >80% and 5 of 6 had an F1 score ≥0.72. Multilayer perceptron model had the highest F1 score (median 0.74; interquartile range [IQR] 0.71-0.78) and sensitivity (84.3%). Median area under the receiver operating characteristic curve and normalized Matthews correlation coefficient were 0.70 (IQR 0.64-0.78) and 0.57 (IQR 0.50-0.65), respectively. Significance: Multimodal machine learning using baseline features can predict incident depression in this population. Our pilot models demonstrated high accuracy for depression prediction. However, overall performance and calibration can be improved. This model has promise for identifying those at risk for incident depression during follow-up, though efforts to refine it in larger populations along with external validation are required.
dc.description.grantingagencyOther
dc.description.sponsorshipThis work was supported by Epilepsy Canada, the Hotchkiss Brain Institute (University of Calgary) and the Canadian Institutes of Health Research (CIHR), under the frame of ERA PerMed.
dc.identifier.citationDelgado-García, G., Engbers, J. D. T., Wiebe, S., Mouches, P., Amador, K., Forkert, N. D., White, J., Sajobi, T., Klein, K. M., Josephson, C. B., & Calgary Comprehensive Epilepsy Program Collaborators. (2023). Machine learning using multimodal clinical, electroencephalographic, and magnetic resonance imaging data can predict incident depression in adults with epilepsy: A pilot study. Epilepsia, 64(10), 2781–2791. https://doi.org/10.1111/epi.17710
dc.identifier.doihttps://doi.org/10.1111/epi.17710
dc.identifier.urihttps://hdl.handle.net/1880/119379
dc.language.isoen
dc.publisherWiley
dc.publisher.hasversionacceptedVersion
dc.publisher.institutionUniversity of Calgary
dc.publisher.policyhttps://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleMachine learning using multimodal clinical, electroencephalographic, and magnetic resonance imaging data can predict incident depression in adults with epilepsy: A pilot study
dc.typeArticle
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Predicting depression in epilepsy_accepted version.pdf
Size:
353.55 KB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
Predicting depression in epilepsy_accepted version (PDF/A)
Size:
346.9 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.25 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Cumming School of Medicine Research & Publications