arXiv:2604. 27277v3 Announce Type: replace-cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data.
arXiv:2607. 11962v1 Announce Type: cross Abstract: Self-supervised learning offers a compelling approach for medical imaging, where labeled data are scarce and acquisition costs are high.
arXiv:2606. 05870v1 Announce Type: cross Abstract: Neurodegenerative disorders such as Alzheimer's disease exhibit highly organized patterns of regional brain vulnerability, yet the biological mechanisms underlying this spatial selectivity remain incompletely understood.
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.
FiberGeoText (FGT) is a vision‑language model that clusters short‑range superficial white matter streamlines from ultra‑high‑resolution diffusion MRI into population‑level groups. It jointly encodes each streamline’s 3‑D trajectory, cortical anatomical context (via text from multiple parcellation schemes), and shape, using a pretrained large language model to unify heterogeneous anatomical descriptions. Evaluations on 0.76 mm diffusion data show that FGT outperforms state‑of‑the‑art methods in cortical parcel coherence, shape consistency, cluster‑size consistency, and cross‑subject correspondence, and it generalizes well to unseen subjects, recovering 96.7 % of learned clusters.
The paper investigates how ESMFold’s folding trunk transforms amino‑acid sequences into 3D protein structures by using sparse autoencoders (SAEs) to uncover interpretable features. It first analyzes representation distributions across modules and recycles to identify distinct spatial computations, then shows that single representations carry inherited semantics while pair representations compute pairwise geometry. Finally, the authors use a reasoning language model and gradient‑based attribution to trace concept composition across blocks, verifying alignment with known biochemistry and demonstrating that the trained sparse dictionaries can predict the model’s fold quality, thereby turning the black‑box trunk into an interpretable, scientifically verifiable circuit.
The paper introduces a Poisson‑equation–based framework that models cortical folding as a geometry‑driven flow derived from mean curvature on the cortical manifold. By treating folding patterns as a stationary source–sink structure, it produces a smooth, globally balanced potential field whose surface gradient defines a physically interpretable flux. This approach allows spatially coherent analysis of sulcal–gyral folding organization and provides a principled representation of geometry‑driven cortical structure in juvenile myoclonic epilepsy.
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.
Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question.
arXiv:2606.13315v2 Announce Type: replace Abstract: MRI self-supervised learning (SSL) has largely emphasized segmentation and dense prediction, while controlled studies of how alternative pretrainin...
The paper introduces a Geometric-to-Semantic Spherical Transfer Learning framework for labeling cortical sulci on brain surfaces. It first pre‑trains a spherical encoder on ~30,000 unlabeled UK Biobank subjects using only curvature and depth, then injects sulcal fundi lines as a soft‑initialized Topological Prior Injector to bridge the geometric‑semantic gap. Experiments show the method surpasses fully supervised baselines, achieving a mean Dice score of 0.77 and delivering the largest gains on variable and tertiary sulci.
arXiv:2606. 00073v1 Announce Type: cross Abstract: We investigate how internal representations emerge across hierarchical processing systems by introducing a neuroscience-inspired framework for analyzing deep spiking neural networks (SNN) through the lens of functional connectivity.