arXiv:2606. 07135v1 Announce Type: new Abstract: Huntington's disease (HD) is a progressive neurodegenerative disorder that affects motor, cognitive, and behavioral functions, where accurate characterization of disease progression remains essential to improve patient outcome and quality of life.
By Lubna Mahmoud Abu Zohair, Hind Zantout
arXiv:2608. 06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction.
By Anirudh Rayas, Yuan Wang, Pavan Turaga
DiaSeg extracts diagonal segments from Dynamic Time Warping (DTW) paths, characterizing each with five geometric features to preserve local alignment information. In a study of 91 subjects across six clinical conditions, these segments revealed consistent unsupervised patterns aligned with biomechanical phases and achieved near-perfect separation of healthy and pathological gait. While cycle‑based methods reached higher overall accuracy, DiaSeg offers phase‑specific interpretability, pinpointing where coordination breaks down within the gait cycle.
By Tresor Y. Koffi, Amel Hidouri, Corentin Legrand, Aur\'elie Bertaux
The paper introduces Explainable Graph-theoretical Machine Learning (XGML) to build individual metabolic brain graphs from FDG-PET data and identify subgraphs predictive of multivariate Alzheimer’s disease outcomes. Using ADNI data, the best model—kernel density estimation with Hellinger distance and random forest—achieved a Pearson correlation of 0.595 across eight cognitive scores, with the highest performance on ADAS13, ADAS11, and ADASQ4. Key edges were found to be jointly but differentially predictive, indicating potential network biomarkers for cognitive decline, though external validation on OASIS3 showed weaker performance likely due to cohort differences.
By Narmina Baghirova, Duy-Thanh V\~u, Duy-Cat Can, Christelle Schneuwly Diaz, Julien Bodlet, Guillaume Blanc, Georgi Hrusanov, Bernard Ries, Oliver Y. Ch\'en
arXiv:2607. 25609v1 Announce Type: cross Abstract: Understanding disease trajectories from longitudinal clinical data remains challenging due to complex temporal dynamics and heterogeneous patient cohorts.
By Bastian Pfeifer
arXiv:2606. 03322v1 Announce Type: cross Abstract: The graphical representation of the brain offers critical insights into diagnosing and prognosing neurodegenerative disease via relationships between regions of interest (ROIs).
By Jaeyoon Sim, Minjae Lee, Guorong Wu, Won Hwa Kim