ReviewWorld journal of psychiatry2024
Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents.
Review in World journal of psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Raphe subregion seeds reveal whole-brain functional connectivity differences underlying adolescent depression.BMC psychiatry · 2026Article
- Decoding Post-Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework.CNS neuroscience & therapeutics · 2026Article
- Unveiling the invisible: How cutting-edge neuroimaging transforms adolescent depression diagnosis.World journal of psychiatry · 2025Article
- Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning.World journal of psychiatry · 2025Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundResearch has found that the amygdala plays a significant role in underlying pathology of major depressive disorder (MDD). However, few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity (FC).
aimTo investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.
methodsForty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study. Using resting-state functional magnetic resonance imaging, the FC was compared between the adolescents with MDD and the healthy controls, with the bilateral amygdala serving as the seed point, followed by statistical analysis of the results. The support vector machine (SVM) method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.
resultsCompared to the controls and using the bilateral amygdala as the region of interest, patients with MDD showed significantly lower FC values in the left inferior temporal gyrus, bilateral calcarine, right lingual gyrus, and left superior occipital gyrus. However, there was an increase in the FC value in Vermis-10. The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls, achieving a diagnostic accuracy of 83.91%, sensitivity of 79.55%, specificity of 88.37%, and an area under the curve of 67.65%.
conclusionThe results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.
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