Abstract
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.
Highlights
Schizophrenia (SZ) is a severe mental disorder characterized by hallucinations, delusions and cognitive impairments
Biswal et al found that the spontaneous low-frequency oscillations (LFO) of blood-oxygenlevel-dependent (BOLD) signals measured in rs-fMRI are physiologically meaningful (Biswal et al, 1995), and the LFO has been successfully applied in studying neural substrates of brain dysfunction and psychiatric disorders (Woodward & Cascio, 2015)
In the elastic net logistic regression model, the two parameters α and λ were defined according to the receiver operating characteristic (ROC) curve for amplitude of low-frequency fluctuations (ALFF) and gray matter (GM) measures, respectively
Summary
Schizophrenia (SZ) is a severe mental disorder characterized by hallucinations, delusions and cognitive impairments. Compared with healthy controls (HC), accumulated evidence from magnetic resonance imaging (MRI) studies have shown widespread brain dysfunction in SZ patients, including the frontal cortex, temporal lobe and subcortical regions (Mwansisya et al, 2017). It is still unclear about the neurophysiological substrate of SZ, and how to accurately diagnose and predict SZ using regional features derived from imaging data. Biswal et al found that the spontaneous low-frequency oscillations (LFO) of blood-oxygenlevel-dependent (BOLD) signals measured in rs-fMRI are physiologically meaningful (Biswal et al, 1995), and the LFO has been successfully applied in studying neural substrates of brain dysfunction and psychiatric disorders (Woodward & Cascio, 2015). The neuroimaging findings indicate the functional and structural changes could be potential biomarkers for SZ diagnosis, the neural substrate of SZ is still under-investigated
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