ArticleHuman brain mapping2022
Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 55 citations in OpenAlex.
- Hybrid deep learning model for brain age prediction using time-distributed convolutional and bidirectional LSTM networks.Scientific reports · 2026Article
- Regional brain aging patterns reveal disease-specific pathways of neurodegeneration.medRxiv : the preprint server for health sciences · 2025Article
- Machine Learning-Based Age Prediction with Feature Subset Selection from Magnetic Resonance Angiography Data.Healthcare informatics research · 2025Article
- Network Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction.Human brain mapping · 2025Article
- Brain Aging in Patients With Cardiovascular Disease From the UK Biobank.Human brain mapping · 2025Article
- Explainable Artificial Intelligence in Radiological Cardiovascular Imaging-A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- A lightweight generative model for interpretable subject-level prediction.Medical image analysis · 2025Article
- Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study.Journal of medical imaging (Bellingham, Wash.) · 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
- Second-order instantaneous causal analysis of spontaneous MEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Unravelling pathological ageing with brain age gap estimation in Alzheimer's disease, diabetes and schizophrenia.Brain communications · 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
- Exploring the relationship among Alzheimer's disease, aging and cognitive scores through neuroimaging-based approach.Scientific reports · 2024Article
- Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits.Brain sciences · 2024Article
- Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.NPJ Parkinson's disease · 2024Article
- Article
- Geroscience-Centric Perspective for Geriatric Psychiatry: Integrating Aging Biology With Geriatric Mental Health Research.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2024Review
- Analysis and visualization of the effect of multiple sclerosis on biological brain age.Frontiers in neurology · 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
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1-weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.
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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.