ArticleSchizophrenia bulletin2024
Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders.
Article in Schizophrenia bulletin, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Harnessing Brain Age-Specific Effects on the Associations Between Sleep Quality, Glymphatic Function, and Cognition in Normal Ageing Adults: Insights for Gerotherapeutics.Neurology and therapy · 2026Article
- Depression and aging: insights from brain age prediction models.Psychological medicine · 2026Article
- Characterizing multivariate regional hubs for schizophrenia classification, sex differences, and brain age estimation using explainable AI.medRxiv : the preprint server for health sciences · 2025Article
- Disparities in accelerated brain aging in recent-onset and chronic schizophrenia.Psychological medicine · 2025Article
- A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms.Frontiers in psychiatry · 2025Article
- Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.Human brain mapping · 2024Article
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Authors and funding
15 authors.
Funding
Abstract
background and hypothesisThe brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI). STUDY
designWe employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n = 541). The brain-PAD was analyzed in independent HCs (n = 209) and patients (n = 233). Correlations between the brain-PAD and clinical measures were investigated. STUDY
resultsThe SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs.
conclusionsOur findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age.
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