ArticleBrain imaging and behavior2022
Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression.
Article in Brain imaging and behavior, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Brain Morphology in Subacute Spinal Cord Injury: Insights Into Functional Recovery Across Brain Networks.Brain and behavior · 2026Article
- Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers.Frontiers in neuroscience · 2026Article
- An explainable multimodal artificial intelligence model for classifying suicide attempters with borderline personality disorder: a pilot study.Scientific reports · 2025Article
- An Improved Deep Semi-supervised JNMF Method for Biomarker Extraction of Alzheimer's Disease.Journal of molecular neuroscience : MN · 2025Article
- Multimodal prediction of psychotic-like experiences using elastic net modeling: external validation in a clinical sample.Psychological medicine · 2025Article
- Effects on Multimodal Connectivity Patterns in Female Schizophrenia During 8 Weeks of Antipsychotic Treatment.Schizophrenia bulletin · 2025Article
- Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease.Human brain mapping · 2025Article
- Diffusion MRI in 2-Month-Old Mouse Brain Predicts Alzheimer's Pathology Genotype.NMR in biomedicine · 2025Article
- Investigating heart rate variability measures during pregnancy as predictors of postpartum depression and anxiety: an exploratory study.Translational psychiatry · 2024Article
- Construction of an individualized brain metabolic network in patients with advanced non-small cell lung cancer by the Kullback-Leibler divergence-based similarity method: A study based on 18F-fluorodeoxyglucose positron emission tomography.Frontiers in oncology · 2023Article
- A greedy regression algorithm with coarse weights offers novel advantages.Scientific reports · 2022Article
- Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
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.
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