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:2609.05438v1 Announce Type: cross Abstract: The human brain folds in utero, primarily during late gestation. Shortly after birth, cortical folding patterns are established and remain stable the...
The paper introduces a unified computational framework for tensor-based morphometry that integrates surface modeling, smoothing, and statistical analysis to detect brain surface shape differences between clinical groups using MRI data. It focuses on the cerebral cortex’s 2‑D convoluted topology, measuring local surface area and curvature variations that may differ across groups. Diffusion smoothing via explicit Laplace‑Beltrami operator estimation enhances signal‑to‑noise, and the method is illustrated by localizing gray‑matter growth and loss in longitudinal pediatric brain scans.
arXiv:2607. 19600v1 Announce Type: cross Abstract: The shape of a planar curve is the geometric information that remains once translation, rotation, scale and reparametrisation are removed and is of interest in many health applications, e.
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.
We introduce SMART, a framework for learning a flexible, interpretable, and scalable spatio-temporal brain atlas from longitudinal high-resolution 3D medical images. Existing approaches to spatio-temporal atlas construction rely on black-box generative models that lack flexibility, limit interpretability, and struggle to scale to high-dimensional data.
arXiv:2606. 09770v1 Announce Type: cross Abstract: Nearby neurons in cortex share similar response profiles, producing systematic spatial organization across sensory and cognitive systems.
arXiv:2605.16742v3 Announce Type: replace Abstract: Cortical surface registration is often driven by local geometric descriptors (e.g., sulcal depth and curvature). While this approach achieves geome...
arXiv:2602. 06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins?
The study introduces a Riemannian shape‑space framework for analyzing age‑related morphological changes in the midsagittal corpus callosum (CC) using 128 landmarks embedded in Kendall shape space. Applied to the OASIS‑1 cohort, multivariate geodesic regression identifies anterior sub‑regions (Genu and Anterior Body) as the primary sites of age‑related deformation, outperforming Euclidean benchmarks. In Alzheimer’s patients, age explains less than 0.5% of shape variance, and a tangent‑space classifier achieves an age‑group AUC of 0.791 from 2D contour data alone.
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.
arXiv:2608. 15306v1 Announce Type: cross Abstract: High-throughput single-cell and spatial transcriptomic technologies provide high-resolution snapshots of heterogeneous cellular states, but their destructive nature prevents repeated measurements of the same cells over time.
arXiv:2606. 09287v1 Announce Type: new Abstract: Understanding how transformer representations evolve across layers, not merely what they encode, remains an open problem in mechanistic interpretability.