Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents

Abstract

Background: Research 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).

Aim: To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.

Methods: Forty-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.

Results: Compared 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%.

Conclusion: The 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.

Keywords: Adolescent; Biomarker; Machine learning; Major depressive disorder; Neuroimaging; Resting-state functional magnetic resonance imaging; Support vector machine.

Figure 1

Flow chart of the selection process for the major depressive disorder patients and the healthy controls. MDD: Major depressive disorder; HCs: Healthy controls; MINI-kid: The mini international neuropsychiatric interview for children and adolescents; HAMD-17: The 17-item Hamilton depression rating scale; DSM-IV: The diagnostic and statistical manual of mental disorders, 4^th edition; rs-fMRI: Resting-state functional magnetic resonance imaging.

Figure 2

Differences in functional connectivity values between patients with major depressive disorder patients and the healthy controls. The color bar represents the t-values in the group analysis.

Figure 3

Visualization of the support vector machine classification based on reduced functional connectivity values in the right lingual gyrus for the differentiation of patients with major depressive disorder from healthy controls. A: Three-dimensional visualization of support vector machine with the most optimal parameters; B: Classification map of functional connectivity values for the right lingual gyrus.

Figure 4

Assessment of the accuracy of the use of abnormal functional connectivity values in different regions of the brain for distinguishing between patients with major depressive disorder patients and the healthy controls. FC: Functional connection.

Figure 5

The functional connectivity value of the right lingual gyrus is effective for distinguishing between patients with major depressive disorder patients and the healthy controls, based on the receiver operating characteristic curve. AUC: Area under the curve.

Figure 6

Radar map showing the accuracy, sensitivity, and specificity of the use of the functional connectivity value in the right lingual gyrus for distinguishing between patients with major depressive disorder patients and the healthy controls, together with the corresponding area under the curve values.