A systematic review on the potential use of machine learning to classify major depressive disorder from healthy controls using resting state fMRI measures. (January 2023)

• Record Type: Journal Article
• Authors: Bondi, Elena; Maggioni, Eleonora; Brambilla, Paolo; Delvecchio, Giuseppe
• Is Part Of: Neuroscience and biobehavioral reviews. Volume 144(2023)
• Publication Date: 2023-01
• Subjects: Major depressive disorder -- Resting state functional magnetic resonance imaging -- Rs-fMRI -- Machine learning -- Classification; Psychophysiology -- Periodicals; Human behavior -- Periodicals; Animal behavior -- Periodicals; Neurology -- Periodicals; Behavior -- Periodicals; Ethology -- Periodicals; Neurology -- Periodicals; Psychophysiologie -- Périodiques; Comportement humain -- Périodiques; Animaux -- Mœurs et comportement -- Périodiques; Neurologie -- Périodiques; Animal behavior; Human behavior; Neurology; Psychophysiology; Periodicals; Electronic journals; 573.8
• DOI: 10.1016/j.neubiorev.2022.104972
• Languages: English
• ISSNs: 0149-7634

Abstract

Abstract: Background: Major Depressive Disorder (MDD) is a psychiatric disorder characterized by functional brain deficits, as documented by resting-state functional magnetic resonance imaging (rs-fMRI) studies. Aims: In recent years, some studies used machine learning (ML) approaches, based on rs-fMRI features, for classifying MDD from healthy controls (HC). In this context, this review aims to provide a comprehensive overview of the results of these studies. Design: The studies research was performed on 3 online databases, examining English-written articles published before August 5, 2022, that performed a two-class ML classification using rs-fMRI features. The search resulted in 20 eligible studies. Results: The reviewed studies showed good performance metrics, with better performance achieved when the dataset was restricted to a more homogeneous group in terms of disease severity. Regions within the default mode network, salience network, and central executive network were reported as the most important features in the classification algorithms. Limitations: The small sample size together with the methodological and clinical heterogeneity limited the generalizability of the findings. Conclusions: In conclusion, ML applied to rs-fMRI features can be a valid approach to classify MDD and HC subjects and to discover features that can be used for additional investigation of the pathophysiology of the disease. Highlights: The reviewed studies showed that it is possible to classify with good accuracies MDD and HC subjects using rs-fMRI measures. The SVM classifier resulted to be the method with the highest performance in separating patients from controls. PCu, ACC, and DLPFC resulted to be the most discriminative features during the classification process. Future larger and homogeneous studies are needed to obtain classifiers with more reliable and accurate performances.