arXiv:2606. 29098v1 Announce Type: cross Abstract: A growing number of techniques leverage the spatial structures that underlie many real-world datasets.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2609.13609v1 Announce Type: cross Abstract: Network analysis for multivariate time series is popular in many fields, from neuroscience to seismology. The inverse spectral density is a common ch...
arXiv:2603. 20452v2 Announce Type: replace Abstract: Longitudinal neuroimaging is essential for modeling disease progression in Alzheimer's disease (AD), yet irregular sampling and missing visits pose substantial challenges for learning reliable temporal representations.
arXiv:2511. 04539v2 Announce Type: replace-cross Abstract: In network neuroscience, functional brain systems are often characterized using separate yet related graph-theoretic or spectral descriptors, overlooking how these properties covary and partially overlap across individuals and conditions.
arXiv:2602. 11801v2 Announce Type: replace Abstract: Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics.
The study evaluates how different graph topologies influence the localization of the epileptogenic zone (EZ) from resting‑state stereo‑EEG recordings in 40 patients. Using a consistent learning model and leave‑one‑patient‑out validation, the authors compare dense graphs, anatomy‑ and geometry‑informed priors, budgeted sparsification, learned sparsification, and a new Region‑Bridge‑c topology. At about 30% edge retention, Region‑Bridge‑c achieves the highest mean PR‑AUC and ROC‑AUC while using roughly 69% fewer edges than a dense graph, indicating that graph construction significantly impacts EZ localization performance.
arXiv:2205.06398v2 Announce Type: cross Abstract: It has become routine in neuroscience studies to measure brain networks for different individuals using neuroimaging. These networks are typically ex...