ArticleFrontiers in psychiatry2020
Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019.
Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Article
- Integration of Genetic Information to Improve Brain Age Gap Estimation Models in the UK Biobank.Human brain mapping · 2025Article
- A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms.Frontiers in psychiatry · 2025Article
- Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines.Brain informatics · 2024Article
- ANTsX neuroimaging-derived structural phenotypes of UK Biobank.Scientific reports · 2024Article
- BrainAGE: Revisited and reframed machine learning workflow.Human brain mapping · 2024Article
- A deep neural network estimation of brain age is sensitive to cognitive impairment and decline.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions.Frontiers in robotics and AI · 2024Review
- The value of arterial spin labelling perfusion MRI in brain age prediction.Human brain mapping · 2023Article
- Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults.Sensors (Basel, Switzerland) · 2023Article
- Gray matter volume drives the brain age gap in schizophrenia: a SHAP study.Schizophrenia (Heidelberg, Germany) · 2023Article
- A large-scale ENIGMA multisite replication study of brain age in depression.Neuroimage. Reports · 2022Article
- Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors.Frontiers in neurology · 2022Article
- An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors.Frontiers in aging neuroscience · 2022Article
- Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features.Frontiers in aging neuroscience · 2022Article
- Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples.Current opinion in neurology · 2021Review
- Explainable Deep Learning for Personalized Age Prediction With Brain Morphology.Frontiers in neuroscience · 2021Article
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6 authors.
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Abstract
Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with
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