Neurodevelopmental deviations in schizophrenia: Evidences from multimodal connectome-based brain ages

Background Pathologic schizophrenia process originate early in brain development, leading to detectable brain alterations via structural and functional magnetic resonance imaging (MRI). Recent MRI studies have sought to characterize disease effects from a brain age perspective, but developmental deviations from the typical brain age trajectory in youths with schizophrenia remain unestablished. Aim This study investigated brain development deviations in early-onset schizophrenia (EOS) patients by applying machine learning algorithms to structural and functional MRI data. Methods Multimodal MRI data, including T1-weighted MRI (T1w-MRI), diffusion MRI, and resting-state functional MRI (rs-fMRI) data, were collected from 80 antipsychotic-naive first-episode EOS patients and 91 typically developing (TD) controls. The morphometric similarity connectome (MSC), structural connectome (SC), and functional connectome (FC) were separately constructed by using these three modalities. According to these connectivity features, eight brain age estimation models were first trained with the TD group, the best of which was then used to predict brain ages in patients. Individual brain age gaps (BAGs) were assessed as brain ages minus chronological ages. Results Both the SC and MSC features performed well in brain age estimation, whereas the FC features did not. Compared with the TD controls, the EOS patients presented widened structural BAGs, with opposite trends between childhood and adolescence. These increased absolute BAG scores for EOS patients were positively correlated with the severity of their clinical symptoms. Conclusion These findings from a multimodal brain age perspective suggest that advanced BAGs exist early in youths with schizophrenia.