Cortical abnormalities and identification for first-episode schizophrenia via high-resolution magnetic resonance imaging

Abstract

Background: Evidence from neuroimaging has implicated abnormal cerebral cortical patterns in schizophrenia. Application of machine learning techniques is required for identifying structural signature reflecting neurobiological substrates of schizophrenia at the individual level. We aimed to develop a method to identify patients with schizophrenia from healthy individuals via the features of cerebral cortex using high-resolution magnetic resonance imaging (MRI). Method: In this study, cortical features were measured, including volumetric (cortical thickness, surface area, and gray matter volume) and geometric (mean curvature, metric distortion, and sulcal depth) features. Patients with first-episode schizophrenia (n = 52, ranging 17–45 years old) and healthy controls (n = 66, ranging 18–46 years old) were included from the Department of Psychiatry at Xijing Hospital. Multivariate computation was used to examine the abnormalities of cortical features in schizophrenia. Features were selected by least absolute shrinkage and selection operator (LASSO) method. The diagnostic capacity of multi-dimensional neuroanatomical patterns-based classification was evaluated based on receiver operating characteristic (ROC) analysis. Results: Mean curvature (left insula and left inferior frontal gyrus), cortical thickness (left fusiform gyrus), and metric distortion (left cuneus and right superior temporal gyrus) revealed both group differences and diagnostic capacity. Area under ROC curve was 0.88, and the sensitivity, specificity, and accuracy were 94 %, 82 %, and 88 %, respectively. Confirming these findings, similar results were observed in the independent validation (sensitivity of 91 %, specificity of 78 %, and accuracy of 85 %). There was a positive association between index score derived from the multi-dimensional patterns and the severity of symptoms (r = 0.33, P < .05) for patients. Discussion: Our findings demonstrate a view of cortical differences with capacity to discriminate between patients with schizophrenia and healthy population. Structural neuroimaging-based measurements hold great promise of paving the road for their clinical utility in schizophrenia.

Keywords

• Multi-dimensional patterns
• Classification
• Schizophrenia
• High-resolution MRI
• Machine learning