Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ebiom.2022.104027
Title: Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches
Authors: Li, Zhifei
McIntyre, Roger S
Husain, Syeda F 
Ho, Roger 
Tran, Bach X
Nguyen, Hien Thu
Soo, Shuenn-Chiang
Ho, Cyrus S 
Chen, Nanguang 
Keywords: Biomarkers discovery
Depression
Depressive disorder
Feature selection
Functional near-infrared spectroscopy
Supervised learning
Biomarkers
Depressive Disorder, Major
Humans
Machine Learning
Neuroimaging
Neurovascular Coupling
Support Vector Machine
Issue Date: 1-May-2022
Publisher: Elsevier BV
Citation: Li, Zhifei, McIntyre, Roger S, Husain, Syeda F, Ho, Roger, Tran, Bach X, Nguyen, Hien Thu, Soo, Shuenn-Chiang, Ho, Cyrus S, Chen, Nanguang (2022-05-01). Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches. eBioMedicine 79 : 104027-. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ebiom.2022.104027
Abstract: Background: Early diagnosis of major depressive disorder (MDD) could enable timely interventions and effective management which subsequently improve clinical outcomes. However, quantitative and objective assessment tools for the suspected cases who present with depressive symptoms have not been fully established. Methods: Based on a large-scale dataset (n = 363 subjects) collected with functional near-infrared spectroscopy (fNIRS) measurements during the verbal fluency task (VFT), this study proposed a data representation method for extracting spatiotemporal characteristics of NIRS signals, which emerged as candidate predictors in a two-phase machine learning framework to detect distinctive biomarkers for MDD. Supervised classifiers (e.g., support vector machine (SVM), k-nearest neighbors (KNN)) cooperated with cross-validation were implemented to evaluate the predictive capability of selected features in a training set. Another test set that was not involved in developing the algorithms enabled the independent assessment of the model's generalization. Findings: For the classification with the optimal fusion features, the SVM classifier achieved the highest accuracy of 75.6% ± 4.7% in the nested cross-validation, and the correct prediction rate of 78.0% with a sensitivity of 75.0% and a specificity of 81.4% in the test set. Moreover, the multiway ANOVA test on clinical and demographic factors confirmed that twenty out of 39 optimal features were significantly correlated with the MDD-distinctive consequence. Interpretation: The abnormal prefrontal activity of MDD may be quantified as diminished relative intensity and inappropriate activation timing of hemodynamic response, resulting in an objectively measurable biomarker for assessing cognitive deficits and screening MDD at the early stage. Funding: This study was funded by NUS iHeathtech Other Operating Expenses (R-722-000-004-731).
Source Title: eBioMedicine
URI: https://scholarbank.nus.edu.sg/handle/10635/226832
ISSN: 2352-3964
DOI: 10.1016/j.ebiom.2022.104027
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