ArticleSchizophrenia (Heidelberg, Germany)2023
Gray matter volume drives the brain age gap in schizophrenia: a SHAP study.
Article in Schizophrenia (Heidelberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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Who cites it
25 citing papers in PubMed, 43 citations in OpenAlex.
- Cortical Structures Underlying Schizophrenia and Sex Differences in Preadolescents: An Adolescent Brain Cognitive Development Study.Child psychiatry and human development · 2026Article
- Increased Brain-Age Gap in Young Adults With Psychotic Experiences.Biological psychiatry global open science · 2026Article
- Brain Age Disparities in Psychosis Across DSM Diagnoses and B-SNIP Biotypes.Schizophrenia bulletin · 2026Article
- The Retinal Age Gap as a Marker of Accelerated Aging in the Early Course of Schizophrenia.Schizophrenia bulletin · 2026Article
- Brain age gap as a predictive biomarker that links aging, lifestyle, and neuropsychiatric health.Communications medicine · 2025Article
- Volumetric Integrated Classification Index: An Integrated Voxel-Based Morphometry and Machine Learning Interpretable Biomarker for Post-Traumatic Stress Disorder.Journal of imaging informatics in medicine · 2025Article
- Regional Brain Aging Disparity Index: Region-Specific Brain Aging State Index for Neurodegenerative Diseases and Chronic Disease Specificity.Bioengineering (Basel, Switzerland) · 2025Article
- Do Transformers and CNNs Learn Different Concepts of Brain Age?Human brain mapping · 2025Article
- Developing brain asymmetry shapes cognitive and psychiatric outcomes in adolescence.Nature communications · 2025Article
- The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study.Human brain mapping · 2025Review
- 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
- ds-FCRN: three-dimensional dual-stream fully convolutional residual networks and transformer-based global-local feature learning for brain age prediction.Brain structure & function · 2025Article
- Association between cardiovascular disease risk, regional brain age gap, and cognition in healthy adults.Frontiers in aging neuroscience · 2025Article
- Structural Magnetic Resonance Imaging Brain Age Investigation in Athletes with Persistent Postconcussion Syndrome.Neurotrauma reports · 2025Article
- Metadata-conditioned generative models to synthesize anatomically-plausible 3D brain MRIs.Medical image analysis · 2024Article
- Cytokines and Madness: A Unifying Hypothesis of Schizophrenia Involving Interleukin-22.International journal of molecular sciences · 2024Review
- Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis.Schizophrenia bulletin · 2024Article
- Decoding MRI-informed brain age using mutual information.Insights into imaging · 2024Article
- Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.Tomography (Ann Arbor, Mich.) · 2024Review
Corrections and comments
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Authors and funding
18 authors at 10 institutions in 1 country.
Funding
Abstract
Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term β = 1.71 [0.53; 3.23]; p
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.