ArticleHuman brain mapping2020
Improved prediction of brain age using multimodal neuroimaging data.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accelerated Aging after Traumatic Brain Injury: An ENIGMA Multi-Cohort Mega-Analysis.Annals of neurology · 2024Pooled it
- Associations of post-traumatic stress disorder and metabolic syndrome with accelerated brain aging in South African adults: A case-control study.Neurobiology of stress · 2026Article
- Divergent Multimodal Age-Association Profiles Across the Human Brain.Nature communications · 2026Article
- Aging and metabolism contribute separately to brain-body health.PLoS biology · 2026Article
- A Systematic Evaluation of the Performance of Multiple Brain Age Algorithms in Two Cohorts of Youth.Human brain mapping · 2026Article
- Association between low-frequency oscillations in blood pressure variability and brain age derived from neuroimaging.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Do Transformers and CNNs Learn Different Concepts of Brain Age?Human brain mapping · 2025Article
- Article
- Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach.Human brain mapping · 2025Article
- A review of artificial intelligence-based brain age estimation and its applications for related diseases.Briefings in functional genomics · 2025Review
- Joint Aging Patterns in Brain Function and Structure Revealed Using 27,793 Samples.Research (Washington, D.C.) · 2025Article
- A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms.Frontiers in psychiatry · 2025Article
- Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation.Brain sciences · 2024Article
- [European journal of nuclear medicine and molecular imaging · 2024Article
- 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
- Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort.GeroScience · 2024Article
- Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders.Schizophrenia bulletin · 2024Article
- Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.Human brain mapping · 2024Article
- Age Prediction Using Resting-State Functional MRI.Neuroinformatics · 2024Article
8 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Brain age prediction based on imaging data and machine learning (ML) methods has great potential to provide insights into the development of cognition and mental disorders. Though different ML models have been proposed, a systematic comparison of ML models in combination with imaging features derived from different modalities is still needed. In this study, we evaluate the prediction performance of 36 combinations of imaging features and ML models including deep learning. We utilize single and multimodal brain imaging data including MRI, DTI, and rs-fMRI from a large data set with 839 subjects. Our study is a follow-up to the initial work (Liang et al., 2019. Human Brain Mapping) to investigate different analytic strategies to combine data from MRI, DTI, and rs-fMRI with the goal to improve brain age prediction accuracy. Additionally, the traditional approach to predicting the brain age gap has been shown to have a systematic bias. The potential nonlinear relationship between the brain age gap and chronological age has not been thoroughly tested. Here we propose a new method to correct the systematic bias of brain age gap by taking gender, chronological age, and their interactions into consideration. As the true brain age is unknown and may deviate from chronological age, we further examine whether various levels of behavioral performance across subjects predict their brain age estimated from neuroimaging data. This is an important step to quantify the practical implication of brain age prediction. Our findings are helpful to advance the practice of optimizing different analytic methodologies in brain age prediction.
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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.