Abstract
BackgroundEarly diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents.MethodsWe obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls.ResultsWe found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight.ConclusionsOur findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.
Highlights
Diagnosis of adolescent psychiatric disorder is crucial for early intervention
Sample characteristics No evidence of a group difference was found in the variables of gender (MDD: 42% male; SCZ: 49% male; and healthy controls (HC): 44% male) and age (MDD: mean age = 16.22 ± 2.02 years; SCZ: 16.02 ± 1.80 years; HC: 16.32 ± 2.99 years). there was no significant among-group difference in the intracranial volume
support vector machine (SVM) classification In case-classification, distinguishing patients with major depressive disorder (MDD) and SCZ from HC using cortical gray matter thickness resulted in an accuracy of 79.21% (p = .002, 95% Confidence interval (CI) of permutation test(per_CIs): 39.60–71.29%, sensitivity: 83.58%, specificity: 70.59%, balanced accuracy (BA): 76.20% (95% probability interval (PI): 66.72–83.88%)) and 69.88% (p = .008, 95% Confidence intervals of permutation test (per_CIs): 38.55–66.27%, sensitivity: 73.47%, specificity: 64.71%, BA: 68.45% (95% PI: 58.02–77.52%)), respectively
Summary
Diagnosis of adolescent psychiatric disorder is crucial for early intervention. There is an overlap in the clinical characteristics of adolescents with MDD and SCZ [19] At this stage, there are not wide effects on behaviors influenced by psychiatric disorders, they can have significant negative effects later in life and are potential health threat for future generations [20, 21]. A meta-analysis by Van et al found a thinner cortex in adult patients with SCZ (especially in the frontal and temporal lobe regions), Thormodsen et al reported no significant difference in the cortical thickness between adolescent patients with SCZ and healthy adolescents [26, 27] These findings indicate changes in cerebral cortex of the adolescent patients with SCZ may take time to develop
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