Cortical thickness distinguishes between major depression and schizophrenia in adolescents

Abstract

Background Early 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. Methods We 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. Results We 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. Conclusions Our 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.

The altered network complexity of resting-state functional brain activity in schizophrenia and bipolar disorder patients

“…Zhou et al used cortical thickness as a classification features with 69.88% accuracy (NC vs . SZ) [44]. FE features achieved better accurate classification when compared with other studies.…”

“…Moreover, some research conducted binary classification. William et al utilized a set of weighted indices to categorize SZ and BD and achieved an area under the curve of 76% [43]. Zhou et al used cortical thickness as a classification features with 69.88% accuracy (NC vs .…”

Shared and distinct abnormalities of brain magnetization transfer ratio in schizophrenia and major depressive disorder: a comparative voxel-based meta-analysis

“…[ 38 ] In addition, a prior MTR study has shown potential value in distinguishing treatment-resistant depression from non-treatment-resistant major depression. [ 41 ] Apart from MTR, prior neuroimaging studies have found brain structural and functional alterations in SCZ and MDD, e.g., gray matter volume, [ 86 ] cortical thickness, [ 87 ] white matter microstructure, [ 14 ] and functional activity, [ 88 ] mainly in prefrontal, temporal, and limbic regions. Additionally, there is a study using SVM to discriminate SCZ and MDD with an accuracy of 82.6% in a latent low-dimensional space of the triple brain network.…”

Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder

“…At the macroscopic level, INTU variants are amongst the polygenic signals associating with cortical thickness (5 × 10 −9 ) in children of the Adolescent Brain Cognitive Development (ABCD) Study [ 95 ]. Cortical thickness, in turn, is dysregulated in MDD compared with controls [ 96 , 97 , 98 ], and a preliminary study suggests differences in cortical thickness between pediatric and adult onset MDD [ 99 ].…”