Brain-age in ultra-low-field MRI: how well does it work?
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Introduction
Brain-age is an estimate of the brain’s biological age derived from neuroimaging data, and has been proposed as a biomarker of brain health and disease risk. While brain-age estimation commonly uses high-field (HF) magnetic resonance imaging (MRI) ( > 1.5 T) this is costly and inaccessible, limiting its applicability. Emerging ultra-low-field (ULF) MRI ( < 0.1 T) technology is a cheaper and more accessible alternative, but its lower resolution raises questions about whether biomarkers like brain-age can be estimated reliably.
Methods
We assessed different brain-age pipelines in 23 adults scanned on one HF system (GE Signa Premier at 3 T) and two identical ULF systems (Hyperfine Swoop at 64 mT). 14 distinct acquisitions were used, defined by T1-or T2-weighting, resolution, and preprocessing: raw anisotropic orientations (axial, coronal, sagittal), isotropic scans, and super-resolution derivatives from multi-resolution registration (MRR) and SynthSR. These inputs (a total of n = 573 scans) were analysed with five brain-age software packages (BrainageR, SynthBA, MIDI, DeepBrainNet, Py-BrainAge). Performance evaluation entailed validity (brain-age vs. actual age), correspondence (ULF brain-age vs. HF brain-age), and test-retest reliability (ULF1 brain-age vs. ULF2 brain-age).
Results
Overall, performance was mixed across pipelines, though several ULF pipelines achieved performance comparable to HF. The four best-performing combinations were SynthBA on T2 scans without SynthSR, MIDI on T2 scans without SynthSR, PyBrainAge on T1 scans with SynthSR and using FreeSurfer recon-all-clinical , and BrainageR on T1 scans with SynthSR. These showed moderate-to- strong validity ( r = 0.76–0.92, R 2 = 0.54–0.64, MAE = 6.49–8.21 years), moderate- to-strong correspondence to HF ( r = 0.84–0.93, ICC = 0.72–0.92), and excellent test-retest reliability ( r = 0.97–0.99, ICC = 0.97–0.99). Moreover, some anisotropic acquisitions achieved comparable validity and reliability to MRR images when tested with the best-performing model, SynthBA ( R 2 = 0.57–0.62, ICC [CI] = 0.99 [0.97– 1.00], for coronal T2).
Conclusion
This first systematic evaluation of brain-age at ULF demonstrates that accurate and reliable estimates can be achieved across multiple pipelines, with- out necessarily requiring image enhancement. Performance depended on the combination of model, scan type, and preprocessing. ULF brain-age estimation could be a practical and scalable tool for clinical decision-making, population research, and long-term patient monitoring, thereby helping to make advanced neuroimaging biomarkers more accessible worldwide.
