Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: A multicenter study based on machine learning

Background: Objective biomarkers are crucial for overcoming the clinical dilemma in major depressive disorder (MDD), and the individualized diagnosis is essential to facilitate the precise medicine for MDD. Methods: Sleep disturbance-related magnetic resonance imaging (MRI) features was identified in the internal dataset (92 MDD patients) using the relevance vector regression algorithm, which was further verified in 460 MDD patients of an independent, multicenter dataset. Subsequently, using these MRI features, the eXtreme Gradient Boosting classification model was constructed in the current multicenter dataset (460 MDD patients and 470 normal controls). Meanwhile, the association between classification outputs and the severity of depressive symptoms was also investigated. Results: In MDD patients, the combination of gray matter density and fractional amplitude of low-frequency fluctuation can accurately predict individual sleep disturbance score that was calculated by the sum of item 4 score, item 5 score, and item 6 score of the 17-Item Hamilton Rating Scale for Depression (HAMD-17) (R-2 = 0.158 in the internal dataset; R-2 = 0.110 in multicenter dataset). Furthermore, the classification model based on these MRI features distinguished MDD patients from normal controls with 86.3% accuracy (area under the curve = 0.937). Importantly, the classification outputs significantly correlated with HAMD-17 scores in MDD patients. Limitation: Lacking some specialized tools to assess the personal sleep quality, e.g. Pittsburgh Sleep Quality Index. Conclusion: Neuroimaging features can reflect accurately individual sleep disturbance manifestation and serve as potential diagnostic biomarkers of MDD.

Automated summary

Neuroimaging features accurately reflect individual sleep disturbance levels and can serve as potential diagnostic biomarkers for major depressive disorder. A classification model based on these features effectively distinguishes MDD patients from normal controls.