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
arXiv:2608.30418v1 Announce Type: cross
Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) matrices are widely used for individual-level prediction,...
By Ce Ju, Antoine Collas, Florent Bouchard, Bertrand Thirion
arXiv:2608.20380v1 Announce Type: cross
Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) has enabled non-invasive mapping of functional brain interactions for computer-aided di...
By Dengyi Zhao, Zhiheng Zhou, Zihan Wang, Guiying Yan, Xingqin Qi
arXiv:2608. 15266v1 Announce Type: cross Abstract: Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer's disease.
By Sijing Wu, Dongyuan Li, Miaoting Huang, Weiwei Ye, Ying Zhang, Feng Xia, Renhe Jiang
arXiv:2506.15626v3 Announce Type: replace-cross
Abstract: $\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust...
By Vincent Roca, Marc Tommasi, Paul Andrey, Aur\'elien Bellet, Markus D. Schirmer, Hilde Henon, Laurent Puy, Julien Ramon, Gr\'egory Kuchcinski, Martin Bretzner, Renaud Lopes
The paper introduces a leakage‑controlled multimodal learning framework for diagnosing and predicting the progression of Alzheimer’s disease. It combines an adapted SFCN MRI encoder, four causal clinical Transformers, shared fusion, and task‑specific ODE‑GRU dynamics, with fine‑tuning and LoRA for MRI blocks. Across 2,649 subjects and 17,317 visits, the model achieves high AUROCs for diagnosis and progression, and demonstrates strong external validation on OASIS‑3 data.
By Akeem Temitope Otapo, Ghazaleh Khodabandelou, Zuheng Ming, Alice Othmani
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
arXiv:2609.09186v1 Announce Type: new
Abstract: Classification models based on resting-state functional magnetic resonance imaging (rs-fMRI) often show lower performance at imaging sites not included...
By Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin, Alan Wee-Chung Liew
BrainPFN is a Prior Data Fitted Network that predicts behavioral phenotypes from functional MRI data without needing per-score or per-cohort refitting. It is meta-trained on a domain-specific prior derived from about 7,000 real functional connectivity matrices, generating one billion synthetic training samples. In tests across four independent cohorts, BrainPFN outperforms Kernel Ridge Regression on small-sample predictions and rivals larger foundation models, all while using a modest, publicly available training pool.
By Carlo Alberto Barbano (MIND), Demian Wassermann (MIND)
The paper introduces SYMBA, a self‑supervised pretraining framework tailored for structural T1‑weighted brain MRI. SYMBA uses anatomically constrained masking, a multi‑scale structural similarity reconstruction loss, and a progressive curriculum to capture distributed morphometric patterns. Evaluated on nine heterogeneous cohorts and two unseen datasets, SYMBA outperforms existing neuroimaging foundation models, achieving high AUROC scores for Alzheimer’s disease classification, mild cognitive impairment conversion, and amyloid status prediction, and shows strong sample efficiency in low‑label settings.
By Nuria Balb\'as-Esteban, Albert Belenguer-Llorens, Elaheh Moradi, Jussi Tohka, Vanessa G\'omez-Verdejo, Carlos Sevilla-Salcedo
arXiv:2608. 07393v1 Announce Type: new Abstract: Functional Magnetic Resonance Imaging ( fMRI ) data are often pooled into collaborative multi-site consortia, as deep learning models for analyses require large datasets to generalize well.
By Deepank Girish, Yi Hao Chan, Yubin Zheng, Sukrit Gupta, Jagath C. Rajapakse
This study assessed whether day‑1 multimodal MRI can improve prediction of three‑month functional outcome after acute ischemic stroke. Using pretrained ResNet embeddings from ADC, DWI, and FLAIR scans combined with clinical data, the authors found that clinical‑only models performed well (AUC≈0.90), while MRI‑only models were less discriminative (AUC≈0.67‑0.73). Among multimodal combinations, FLAIR‑clinical fusion yielded the best performance (AUC≈0.95), with age, NIHSS subscores, ADC lesion burden, and learned FLAIR features identified as key contributors.
By Sina Raeisadigh, Henning M\"uller, Abderrahmane Hedjoudje