ArticleNature neuroscience2026
A generalizable foundation model for analysis of human brain MRI.
Article in Nature neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
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Who cites it
12 citing papers in PubMed.
- Field-Orientation Effects in Amplitude-Modulation Magnetic Particle Imaging for High-Resolution Navigation.Sensors (Basel, Switzerland) · 2026Article
- Article
- On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction.Magnetic resonance in medicine · 2026Article
- Article
- A Dual-Branch Frequency-Aware Attention Framework for Rare Neurological Disease Classification from Brain MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- Article
- The portability paradox of foundation models for clinical decision support.NPJ digital medicine · 2026Article
- NeuroFM: Toward Precision Neuroimaging with Foundation Models for Individualized Brain Health Estimation.medRxiv : the preprint server for health sciences · 2026Article
- Deep learning-based cross-attention fusion of multimodal MRI for survival prediction and risk stratification in IDH-wildtype glioblastoma: a multicenter study.Frontiers in oncology · 2026Article
- A platform for investigating prompt framing as interface parameters in foundation models for robotics.Frontiers in robotics and AI · 2026Article
- Human brain data: a valuable and open-ended resource for artificial intelligence.Frontiers in human neuroscience · 2026Article
Corrections and comments
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Authors and funding
25 authors.
Funding
Abstract
Artificial intelligence applied to brain magnetic resonance imaging (MRI) holds potential to advance diagnosis, prognosis and treatment planning for neurological diseases. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. By leveraging self-supervised learning, pretraining and targeted adaptation, foundation models present a promising paradigm to overcome these limitations. Here we present Brain Imaging Adaptive Core (BrainIAC)-a foundation model designed to learn generalized representations from unlabeled brain MRI data and serve as a core basis for diverse downstream application adaptation. Trained and validated on 48,965 brain MRIs across a broad spectrum of tasks, we demonstrate that BrainIAC outperforms localized supervised training and other pretrained models, particularly in low-data, few-shot, settings and in high-difficulty prediction tasks, allowing for application in scenarios otherwise infeasible. BrainIAC can be integrated into imaging pipelines and multimodal frameworks and may lead to improved biomarker discovery and artificial intelligence clinical translation.
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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.