ArticleNeuroImage2019
Brain age prediction: Cortical and subcortical shape covariation in the developing human brain.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations.Frontiers in neuroinformatics · 2024Pooled it
- A comparative evaluation of brain MRI modalities, modality-specific measures, brain regions, and cognitive tests for brain age prediction.GeroScience · 2026Article
- Neuroanatomical features reveal accelerated brain age in alcohol use disorder.bioRxiv : the preprint server for biology · 2025Article
- Network Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction.Human brain mapping · 2025Article
- Screen time, sleep, brain structural neurobiology, and sequential associations with child and adolescent psychopathology: Insights from the ABCD study.Journal of behavioral addictions · 2024Article
- Article
- Relative brain age is associated with socioeconomic status and anxiety/depression problems in youth.Developmental psychology · 2024Article
- Predicting 'Brainage' in late childhood to adolescence (6-17yrs) using structural MRI, morphometric similarity, and machine learning.Scientific reports · 2023Article
- Brain structural co-development is associated with internalizing symptoms two years later in the ABCD cohort.Journal of behavioral addictions · 2023Article
- Predicting 'Brainage' in the Developmental Period using Structural MRI, Morphometric Similarity, and Machine Learning.Research square · 2023Article
- sJIVE: Supervised Joint and Individual Variation Explained.Computational statistics & data analysis · 2022Article
- Brain structural covariation linked to screen media activity and externalizing behaviors in children.Journal of behavioral addictions · 2022Article
- DeepParcellation: A novel deep learning method for robust brain magnetic resonance imaging parcellation in older East Asians.Frontiers in aging neuroscience · 2022Article
- Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms.NeuroImage. Clinical · 2022Article
- Structural and Functional Trajectories of Middle Temporal Gyrus Sub-Regions During Life Span: A Potential Biomarker of Brain Development and Aging.Frontiers in aging neuroscience · 2022Article
- Interpretive JIVE: Connections with CCA and an application to brain connectivity.Frontiers in neuroscience · 2022Article
- The contributions of brain structural and functional variance in predicting age, sex and treatment.Neuroimage. Reports · 2021Article
- Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?Psychiatry research. Neuroimaging · 2021Article
- Explainable Deep Learning for Personalized Age Prediction With Brain Morphology.Frontiers in neuroscience · 2021Article
- A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Cortical development is characterized by distinct spatial and temporal patterns of maturational changes across various cortical shape measures. There is a growing interest in summarizing complex developmental patterns into a single index, which can be used to characterize an individual's brain age. We conducted this study with two primary aims. First, we sought to quantify covariation patterns for a variety of cortical shape measures, including cortical thickness, gray matter volume, surface area, mean curvature, and travel depth, as well as white matter volume, and subcortical gray matter volume. We examined these measures in a sample of 869 participants aged 5-18 from the Healthy Brain Network (HBN) neurodevelopmental cohort using the Joint and Individual Variation Explained (Lock et al., 2013) method. We validated our results in an independent dataset from the Nathan Kline Institute - Rockland Sample (NKI-RS; N = 210) and found remarkable consistency for some covariation patterns. Second, we assessed whether covariation patterns in the brain can be used to accurately predict a person's chronological age. Using ridge regression, we showed that covariation patterns can predict chronological age with high accuracy, reflected by our ability to cross-validate our model in an independent sample with a correlation coefficient of 0.84 between chronologic and predicted age. These covariation patterns also predicted sex with high accuracy (AUC = 0.85), and explained a substantial portion of variation in full scale intelligence quotient (R
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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.