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.

A systematic review on the potential use of machine learning to classify major depressive disorder from healthy controls using resting state fMRI measures / E. Bondi, E. Maggioni, P. Brambilla, G. Delvecchio. - In: NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS. - ISSN 0149-7634. - 144:(2023 Jan), pp. 104972.1-104972.16. [Epub ahead of print] [10.1016/j.neubiorev.2022.104972]

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

P. Brambilla
Penultimo
;
2023

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.
Classification; Machine learning; Major depressive disorder; Resting state functional magnetic resonance imaging; Rs-fMRI
Settore MED/25 - Psichiatria
gen-2023
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1000468
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