The study presents an explainable multimodal deep‑learning framework that combines a 3D CNN for T1‑weighted MRI with a feedforward network for harmonized clinical and demographic data to diagnose Alzheimer’s disease. Using 6,479 ADNI records and 1,703 OASIS‑3 records, the authors compare various model configurations on three‑way and pairwise diagnostic tasks, finding that performance and explanations vary by task, modality, fusion strategy, and cohort. SHAP and Integrated Gradients consistently highlight the MMSE score as the most influential tabular feature, while CAM‑based explanations differ across model setups and cohorts, indicating that explainability is not a stable property under cohort shift.
By Yusuf Brima, Marcellin Atemkeng, Lakshmana Rao Namamula, Antoine Vacavant
arXiv:2512. 10966v3 Announce Type: replace-cross Abstract: Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data.
By Farica Zhuang, Shu Yang, Dinara Aliyeva, Zixuan Wen, Duy Duong-Tran, Christos Davatzikos, Tianlong Chen, Song Wang, Li Shen
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
The study investigates whether a compact, supervised 3D CNN pretrained for brain‑age prediction can act as a reusable foundation model for various Alzheimer's‑related neuroimaging tasks. By freezing the 7.18 million weights and adding only ~1 % of trainable parameters via Low‑Rank Adaptation, the model achieved high performance across six experiments, including dementia classification, MCI progression prediction, amyloid positivity detection, and volume estimation of hippocampal and white matter hypointensities. The results demonstrate that the pretrained brain‑age model generalizes well to new datasets without retraining, offering a data‑efficient alternative to larger networks.
By Reza Rajabli, D. Louis Collins
M$^2$PFN is an end‑to‑end multimodal framework that extends the TabPFN in‑context learning engine to Alzheimer’s disease diagnosis by aligning 3D‑MRI and tabular features in a shared subspace. It performs differentiable inference through TabPFN’s transformer, back‑propagates gradients into the encoders, and incorporates a frozen tabular‑only prediction via a gated shortcut. On the ADNI cohort it achieves 65.55 % macro‑F1 and 82.21 % macro‑AUC, surpassing unimodal and multimodal baselines, and it generalizes to external cohorts without retraining.
By Lujia Zhong, Shuo Huang, Jianwei Zhang, Xinyu Nie, Yonggang Shi
arXiv:2606. 19371v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a fatal disorder that destroys memory and cognitive skills in the elderly population.
By Long Doan, Branden Chen, Ethan Litton, Huan Huang, Jiajing Huang, Yixin Xie, Weihua Zhou, Nandakumar Narayanan, Chen Zhao
The paper presents a multimodal 3D convolutional neural network that classifies Alzheimer’s disease using raw OASIS 1 MRI volumes. It fuses structural T1 images with gray matter, white matter, and cerebrospinal fluid probability maps to capture complementary neuroanatomical information. Evaluated with 5‑fold subject‑level cross‑validation, the model achieves a mean accuracy of 72.34 % and an ROC AUC of 0.7781, with GradCAM visualizations highlighting anatomically relevant regions such as the medial temporal lobe and ventricles.
By Md Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid, Abu Saleh Musa Miah, Najmul Hassan, Md Abdur Rahim, Jungpil Shin
arXiv:2607. 26746v1 Announce Type: cross Abstract: Accurate identification of Alzheimers disease (AD) using resting-state functional magnetic resonance imaging (rs-fMRI) remains challenging due to the high dimensionality, noise, and complex inter-regional dependencies inherent in functional brain connectivity, which limit the effectiveness of traditional approaches based on handcrafted connectivity features or conventional machine learning models.
By Harshiddhi Pathak, Gowtham Reddy N, Mrinal Acharya, Manjunatha Mahadevappa
MINT (Multimodal Imaging-to-Speech Knowledge Transfer) is a three-stage framework that transfers MRI-derived biomarkers to speech representations for early Alzheimer’s screening. An MRI teacher creates a compact embedding space for CN‑versus‑MCI classification, and a residual projection head aligns speech features to this space using a geometric loss, allowing imaging‑free inference. Experiments on ADNI‑4 show that aligned speech matches speech baselines, while multimodal fusion outperforms MRI alone, and ablations highlight dropout regularization and self‑supervised pretraining as key design choices.
By Vrushank Ahire, Yogesh Kumar, Anouck Girard, M. A. Ganaie
The paper introduces Neuro‑JEPA, a sparse multimodal foundation model that learns unified representations of brain MRI across T1w, T2w, and FLAIR sequences using a latent predictive objective and a Mixture‑of‑Experts architecture. It was pretrained on over 1.5 million scans from 428,647 studies and systematically evaluates architectural, masking, objective, and sparsity choices for robust multimodal representation learning. Across 47 tasks from three health systems and 12 public datasets, Neuro‑JEPA consistently outperforms a simple CNN baseline, demonstrating its effectiveness for both clinical and research applications.
By Haoxu Huang, Long Chen, Jingyun Chen, Jinu Hyun, James Ryan Loftus, Kara Melmed, Daniel Orringer, Jennifer Frontera, Seena Dehkharghani, Arjun Masurkar, Narges Razavian
The paper introduces MCNet, a neural network that assigns a Modality Contribution Score (MCS) to each patient, indicating how much structural MRI versus amyloid PET drives the diagnostic decision for Alzheimer’s disease. Across 327 ADNI-3 participants, MCNet achieved strong three‑class staging (AUC = 0.881) and the MCS showed a clear, statistically significant increase in PET dominance from cognitively normal to AD. The method was validated on an independent OASIS‑3 cohort and compared favorably to SHAP, suggesting it can guide personalized imaging and clinical trial decisions.
By Dawa Chyophel Lepcha, Aaliya Ali, Sophie A. Martin, Deepika Koundal, Pierrick Coupe, Shabbir Syed-Abdul
arXiv:2607. 07091v1 Announce Type: cross Abstract: In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits.
By Xinyue Du, Yibo Liu, Zhenglei Zhou, Xuancheng Yao, Weimin Zhong, Qiuhui Chen