ArticleImaging neuroscience (Cambridge, Mass.)2025
BrainAgeNeXt: Advancing brain age modeling for individuals with multiple sclerosis.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Accelerated Brain Aging Identifies Functional Vulnerability Beyond Chronological Age in Multiple Sclerosis.Sensors (Basel, Switzerland) · 2026Trial
- Improving racial fairness in brain age models using style-transfer synthesis.bioRxiv : the preprint server for biology · 2026Article
- Accelerated Brain Aging in Multiple Sclerosis: Microstructural and Metabolic Correlates of the Brain Age Gap.Neurology international · 2026Article
- Brain Age Estimation on T2-FLAIR Scans for Application to Multiple Sclerosis.Human brain mapping · 2026Article
- Real-world federated learning for brain imaging scientists.Frontiers in digital health · 2026Article
- MRI signature of brain age underlying post-traumatic stress disorder in World Trade Center responders.Translational psychiatry · 2025Article
- The effect of lesion filling on brain age estimation in multiple sclerosis.BMC medical imaging · 2025Article
- Current challenges and future directions for brain age prediction in children and adolescents.Nature communications · 2025Review
- Bias and generalizability of brain age prediction models: A multi-cohort evaluation with anatomical and interpretability insights.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
- Update of
Authors and funding
17 authors.
Funding
Abstract
Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 ± 3.64 years, lower than the compared methods (MAE range 3.55-4.16 years). We also tested all methods across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 ± 6.51 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs 0.75,
Indexed as
Identifiers
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.