Unraveling the complex interplay between glymphatic function, age, and brain structure in school-aged children with autism spectrum disorder - Zhongfeng Xie; Di Zhou; Shuchao Wang; Ting Hua; Hong Chen; Rong Yang; Yujiao Gong; Guangyu Tang; Lin Zhang; Xiaowen Xu (Brain Imaging and Behavior, 2026)

Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression - Ying Wu; Ping Ren; Rong Chen; Hong Xu; Jianxing Xu; Lin Zeng; Donghui Wu; Wentao Jiang; NianSheng Tang; Xia Liu (Brain Imaging and Behavior, 2022)
Neuroimaging technique is a powerful tool to characterize the abnormality of brain networks in schizophrenia. However, the neurophysiological substrate of schizophrenia is still unclear. Here we investigated the patterns of brain functional and structural changes in female patients with schizophrenia using elastic net logistic regression analysis of resting-state functional magnetic resonance imaging data. Data from 52 participants (25 female schizophrenia patients and 27 healthy controls) were obtained. Using an elastic net penalty, the brain regions most relevant to schizophrenia pathology were defined in the models using the amplitude of low-frequency fluctuations (ALFF) and gray matter, respectively. The receiver operating characteristic analysis showed reliable classification accuracy with 85.7% in ALFF analysis, and 77.1% in gray matter analysis. Notably, our results showed eight common regions between the ALFF and gray matter analyses, including the Frontal-Inf-Orb-R, Rolandic-Oper-R, Olfactory-R, Angular-L, Precuneus-L, Precuenus-R, Heschl-L, and Temporal-Pole-Mid-R. In addition, the severity of symptoms was found positively associated with the ALFF within the Rolandic-Oper-R and Frontal-Inf-Orb-R. Our findings indicated that elastic net logistic regression could be a useful tool to identify the characteristics of schizophrenia -related brain deterioration, which provides novel insights into schizophrenia diagnosis and prediction.