Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression

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.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

“…Nearly half of the models (48.3% [268 of 555]) were found in studies authored by those with academic training in computers and data science (eTable 2 in Supplement 1). Schizophrenia (25.4% [141 of 555 models])…”

“…In addition, 378 of 555 models (68.1%; 95% CI, 64.2%-72.0%) lacked reports for quality control (eg, signaling question: was how to handle neuroimaging artifacts reported? ), incurring a low reporting quality rating …”

“…Approximately half of the models (258 of 555 [46.48%; 95% CI, 42.3%- 50.6%]) were assessed for poor reporting quality in the technique domain across 3 signaling questions . The unique source for the degraded reporting quality in this domain was the absence of algorithmic details, which limited reproducibility (eg, signaling question: were details for algorithm development reported?).…”

Recent Advances on Penalized Regression Models for Biological Data

“…The simulation results show that the developed classifier addresses the problem of identifying immune cell types from noisy scRNA-seq signatures. The elastic net logistic regression was also used to detect brain function and structural changes in female, schizophrenic patients [117] and to determine predictors of dengue patients, as well as to predict the length of hospital stay [118]. In the year 2021, in order to diagnose early Alzheimer's disease (AD), Cui et al proposed an adaptive LASSO penalized logistic regression model based on particle swarm optimization (PSO-ALLR) [119].…”

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