arXiv:2610.34825v1 Announce Type: new
Abstract: Diffusion MRI (dMRI) provides information about white matter microstructure that can support brain age prediction. However, many existing prediction mo...
By Chenyang Fan, Robert D. Sanders, Elie Matar, Yixuan Yuan, Xiang Li, Sharon L. Naismith, Fernando Calamante, Jinglei Lv
Brain-PACE is a deep Siamese MRI framework that directly estimates the pace of structural brain ageing from paired T1‑weighted MRI scans, extending the LILAC model with spatial attention, soft label distribution learning, and a Cramér distance objective. In a study of participants with mild cognitive impairment, 42.6 % showed accelerated ageing, and faster Brain‑PACE scores correlated with greater functional and cognitive impairment as well as higher regional tau burden in key brain regions. The method improves probabilistic performance, reduces prediction bias, and provides predictive uncertainty, offering a complementary longitudinal imaging phenotype sensitive to early neurodegeneration.
By Samuel Maddox (School of Computing Sciences, University of East Anglia), Jacob Newman (School of Computing Sciences, University of East Anglia), Saber Sami (Norwich Medical School, University of East Anglia), Michal Mackiewicz (School of Computing Sciences, University of East Anglia), for the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers, Lifestyle flagship study of ageing
arXiv:2608. 08173v1 Announce Type: cross Abstract: Longitudinal MRI enables sensitive measurement of structural brain change for studying aging and neurodegenerative disease.
By Jingru Fu, Kathleen E. Larson, Douglas N. Greve, Bruce Fischl, Malte Hoffmann
arXiv:2511.15188v3 Announce Type: replace
Abstract: Accurate brain-age estimation from structural MRI provides a valuable biomarker for studying healthy aging and neurodegenerative disease. We propos...
By Wasif Jalal, Md Nafiu Rahman, Atif Hasan Rahman, M. Sohel Rahman
The paper introduces DB‑SUiT, a surface‑based diffusion bridge that translates cortical MRI to PET images directly on the cortical manifold. It employs a conditional spherical U‑shaped vision Transformer to capture multi‑scale surface features and long‑range dependencies while incorporating demographic and subcortical information. Evaluations on two dementia datasets show that the synthesized PET surfaces outperform MRI and PET volumes in automated classification and achieve high diagnostic accuracy in a blinded reader study.
By Yitong Li, Alexandra Samoylova, Fabian Bongratz, Timo Grimmer, Dennis M. Hedderich, Igor Yakushev, Christian Wachinger
The paper introduces a two-stage multi‑modal MRI framework that processes different MRI modalities independently before integrating them through late fusion. First, the model estimates a probability distribution over six developmental stages, then it predicts age using probability‑weighted, stage‑specialized experts. Experiments across nine datasets—from fetal to elderly—show the method outperforms existing baselines, reducing mean absolute error by 13% and 78% in in‑domain and out‑of‑domain settings, and multi‑modal integration yields 12‑13% performance gains. Analysis on ADNI clinical groups indicates that the predicted brain age gap could help characterize Alzheimer’s‑related brain aging.
By Dingyi Zhang, Ruiying Liu, Yun Wang