ArticleHuman brain mapping2021
Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
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
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- BrainAGE, brain health, and mental disorders: A systematic review.Neuroscience and biobehavioral reviews · 2024Pooled it
- Impact of meditation on brain age derived from multimodal neuroimaging in experts and older adults from a randomized trial.Scientific reports · 2025Trial
- Resting-state brain age is associated with cognitive and sensorimotor abnormalities in schizophrenia spectrum disorders: a validation & longitudinal study.Translational psychiatry · 2026Article
- A comparative evaluation of brain MRI modalities, modality-specific measures, brain regions, and cognitive tests for brain age prediction.GeroScience · 2026Article
- Brain aging patterns among nine neurological disorders: A case-control study.PLoS medicine · 2026Article
- Metabolomic signatures of brain aging: A multimodal and genetic study.Molecular psychiatry · 2026Article
- Aging and metabolism contribute separately to brain-body health.PLoS biology · 2026Article
- Aging beyond diagnosis: the MRI brain age gap across disorders.GeroScience · 2026Review
- superLPNet: a super lightweight parameter deep learning model for brain age estimation from structural MRI.Magma (New York, N.Y.) · 2026Article
- MRI-derived estimation of biological aging in patients with affective disorders in a 9-year follow-up - a prospective marker of future recurrence.Molecular psychiatry · 2026Article
- Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration.GeroScience · 2026Article
- Brain Age Estimation on T2-FLAIR Scans for Application to Multiple Sclerosis.Human brain mapping · 2026Article
- Brain Age Disparities in Psychosis Across DSM Diagnoses and B-SNIP Biotypes.Schizophrenia bulletin · 2026Article
- Synchrony between brain age and internalising and externalising symptoms across adolescence.medRxiv : the preprint server for health sciences · 2026Article
- A linked independent component analysis framework for characterizing site-effect patterns in multi-site structural and functional MRI.Frontiers in bioinformatics · 2026Article
- Brain Age Acceleration on MRI Due to Poor Sleep: Associations, Mechanisms, and Clinical Implications.Brain sciences · 2025Review
- Article
- Longitudinal Trajectory of Cognition, Brain Morphometry, and Brain Predicted Age in Unaffected First-Degree Relatives of Patients With Bipolar Disorder.Bipolar disorders · 2025Article
- Predicting Mental and Neurological Illnesses Based on Cerebellar Normative Features.Biological psychiatry global open science · 2025Article
- Development and Validation of a Brain Aging Biomarker in Middle-Aged and Older Adults: Deep Learning Approach.JMIR aging · 2025Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub-cortical volumes, cortical and subcortical T1/T2-weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age-matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two-group case-control classifications revealed highest accuracy for AD using global T1-weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF-based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain-based mapping of overlapping and distinct pathophysiology in common disorders.
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.