Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning

Abstract

Major depressive disorder (MDD), a psychiatric disorder characterized by functional brain deficits, poses considerable diagnostic and treatment challenges, especially in adolescents owing to varying clinical presentations. Biomarkers hold substantial clinical potential in the field of mental health, enabling objective assessments of physiological and pathological states, facilitating early diagnosis, and enhancing clinical decision-making and patient outcomes. Recent breakthroughs combine neuroimaging with machine learning (ML) to distinguish brain activity patterns between MDD patients and healthy controls, paving the way for diagnostic support and personalized treatment. However, the accuracy of the results depends on the selection of neuroimaging features and algorithms. Ensuring privacy protection, ML model accuracy, and fostering trust are essential steps prior to clinical implementation. Future research should prioritize the establishment of comprehensive legal frameworks and regulatory mechanisms for using ML in MDD diagnosis while safeguarding patient privacy and rights. By doing so, we can advance accuracy and personalized care for MDD.

Keywords: Biomarkers; Functional connectivity; Machine learning; Major depressive disorder; Major depressive disorder diagnosis; Model accuracy; Neuroimaging; Personalized treatment; Resting-state functional magnetic resonance imaging.

Figure 1

Core enhancing factors, risks, challenges, and future directions in medical machine learning. ALEF: Amplitude of low-frequency fluctuations; DFNC: Dynamic functional network connectivity; DT: Decision tree; FC: Functional connectivity; LASSO: Least absolute shrinkage and selection operator; MDD: Major depressive disorder; ReHo: Regional homogeneity; RF: Random forest; Rs-fMRI: Resting-state functional magnetic resonance imaging; SVM: Support vector machine; XGBoost: Extreme Gradient Boosting.