The paper introduces a Bayesian framework that models BOLD dynamics as coupled Ornstein‑Uhlenbeck processes and uses Sequential Neural Posterior Estimation to produce connectivity posteriors while accounting for measurement noise. Applied to 28 healthy controls scanned at 7T, the method quantifies uncertainty from scanner noise, subject variability, and scan length, revealing that about 46 voxels per ROI and 7 minutes of 7T data suffice for 90% of asymptotic precision. It also shows that 7T achieves within‑session precision 40% faster than 3T and requires roughly 37 times less per‑subject scan time to reach population‑level convergence, offering concrete, scanner‑specific guidance for protocol optimization.
arXiv:2509. 09513v3 Announce Type: replace-cross Abstract: Biophysical diffusion MRI models like Neurite Exchange Imaging (NEXI) are essential for probing gray matter microstructure, estimating compartment diffusivities, neurite fraction, and exchange time.
arXiv:2607. 16317v1 Announce Type: cross Abstract: Deep networks now subtype brain tumors on MRI about as well as specialist readers, yet accuracy is not what keeps them out of the clinic.
The study investigates how data volume, model size, and training duration affect the performance of fMRI foundation models. Using over 200 datasets and 10,000 GPU‑hours, the authors find that larger models benefit more from additional data, and that at a fixed compute budget, increasing data yields greater gains than enlarging the model. By selecting optimal combinations of data, size, and duration, they produce models that outperform existing fMRI foundation models on out‑of‑distribution tasks while requiring less pretraining compute.
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.
arXiv:2609.36837v1 Announce Type: cross Abstract: Generative super-resolution models can turn portable 64 mT MRI into images that look like 3T scans, and the field evaluates them with PSNR, SSIM, and...
arXiv:2605.28397v2 Announce Type: replace Abstract: Predicting which people with mild cognitive impairment will develop dementia matters for early treatment. Yet structural imaging models have relied...
arXiv:2609.26867v1 Announce Type: cross Abstract: This article is motivated by an imaging application from the Adolescent Brain Cognitive Development (ABCD) study, aiming to predict task-based brain...
arXiv:2606. 00156v1 Announce Type: cross Abstract: Understanding the human brain requires access to its microscopic tissue architecture.
arXiv:2603. 05693v2 Announce Type: replace-cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines.
arXiv:2607. 29394v1 Announce Type: cross Abstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast agents (GBCAs) restricts use in contraindicated populations, prolongs scan protocols, and presents environmental toxicity concerns.
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.
arXiv:2509. 05238v2 Announce Type: replace-cross Abstract: Deep learning (DL) has transformed neuroimaging by delivering state-of-the-art performance with reduced computation times.