Brain-age in ultra-low-field MRI: how well does it work?

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.

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, PyBrainAge). 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).

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).

This first systematic evaluation of brain-age at ULF demonstrates that accurate and reliable estimates can be achieved across multiple pipelines, without 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.