The study presents a multimodal BrainAGE framework that fuses structural T1-weighted MRI with DeepCBV maps—vascular information synthesized from non‑contrast MRI—to estimate brain age more accurately. Using two separate 3D convolutional neural networks, the combined model achieved a mean absolute error of 3.95 years on cognitively normal controls, outperforming single‑modality models. Saliency analyses showed MRI highlighted white matter and cortical atrophy, while DeepCBV emphasized vascular‑rich regions, and the integrated approach revealed stronger differentiation between stable and progressive MCI, indicating sensitivity to early vascular changes.
By Jordan Jomsky, Zongyu Li, Kay C. Igwe, Yiren Zhang, Max Lashley, Tal Nuriel, Andrew Laine, Scott A. Small, Jia Guo, for the Frontotemporal Lobar Degeneration Neuroimaging Initiative, for the Alzheimer's Disease Neuroimaging Initiative
arXiv:2606. 01237v1 Announce Type: new Abstract: Mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are closely associated with the early Alzheimer's disease continuum, where accurate and explainable diagnosis is important for early risk assessment and intervention.
By Xiongri Shen, Jiaqi Wang, Zhenxi Song, Yi Zhong, Leilei Zhao, Xin He, Baiying Lei, Zhiguo Zhang
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
BrainFedFM is a structural brain MRI foundation model that was federatively pretrained on 164,707 3‑D scans from 42 sites using a dual‑priority approach that emphasizes informative anatomical regions locally and prioritizes site contributions globally. The model outperformed seven baseline models—including four centralized foundation models—across 20 downstream tasks (classification, regression, segmentation), achieving a mean rank of 1.68 and a 50% performance gain, especially in classification and regression and among underrepresented populations. These results demonstrate the model’s generalizability and show that federated pretraining can effectively develop neuroimaging foundation models without pooling raw images.
By Zhen Yu, Yang Liu, Xiahai Zhuang, Qingchao Chen
The paper introduces TAFNet, a temporal attention fusion network that uses both a baseline and a follow‑up T1‑weighted brain scan to predict which individuals with mild cognitive impairment (MCI) will progress to dementia. The model employs a pretrained Siamese encoder for each scan and fuses the two scans through anatomical difference, cross‑temporal attention, joint context, and a learned per‑patient gate. Evaluated on paired scans from the Alzheimer’s Disease Neuroimaging Initiative, TAFNet outperforms single‑scan networks and a simple scan‑difference model, achieving significant gains in cross‑validation and maintaining high sensitivity at clinically reasonable specificity.
By Sara Fin, Alireza Moayedikia, David J. White, Uffe Kock Wiil, Alicia Troncoso
arXiv:2607. 16325v1 Announce Type: cross Abstract: Foundation models provide powerful representations for brain MRI analysis, but their predictions remain difficult to interpret in anatomically meaningful terms.
By Wei Zhang
The paper investigates combining a domain‑specific self‑supervised task—voxel‑level brain age prediction—with a general task—image inpainting—to pretrain models for brain MRI segmentation. A multitask pretraining framework jointly optimizes both objectives, yielding representations that outperform single‑task pretraining and training from scratch on three segmentation benchmarks (multiple sclerosis lesions, ischemic stroke lesions, and cortical structures). The study demonstrates that integrating domain‑specific and general self‑supervised tasks benefits the development of generalizable neuroimaging foundation models.
By Tasneem Nasser, Susanne Schmid, Roberto Souza, Naser El-Sheimy
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
arXiv:2609.15888v1 Announce Type: cross
Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...
By Paul-Gabriel Nicolae, Irina Georgiana Mocanu
arXiv:2607. 17782v1 Announce Type: cross Abstract: Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data.
By Moona Mazher, Abdul Qayyum, Steven A. Niederer, Daniel C. Alexander
arXiv:2609.06807v1 Announce Type: cross
Abstract: In this work, we comprehensively evaluate three popular feature-extraction paradigms in AI-based neuroimaging modeling: (1) computation of anatomical...
By Boyang Yu, Miquel Lopez Escoriza, Long Chen, Arjun V. Masurkar, Narges Razavian, Carlos Fernandez-Granda
arXiv:2604. 22700v2 Announce Type: replace Abstract: Modeling and predicting neurodegenerative disease progression from medical images remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning.
By Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh, Qingyu Zhao, Miaomiao Zhang