Check out the latest article from P.F. Antonietti, S. Gomez, I. Perugia, and me, "A structure-preserving LDG discretization of the Fisher–Kolmogorov equation for modeling neurodegenerative diseases" recently published in Mathematics and Computers in Simulation. 

This work presents a structure-preserving, high-order, unconditionally stable numerical method for approximating the solution to the Fisher–Kolmogorov equation on polytopal meshes, with a particular focus on its application in simulating misfolded protein spreading in neurodegenerative diseases. 

We are excited to share that our last preprint, "Predicting Alzheimer’s Disease Progression from Sparse Multimodal Data by NeuralODE Models", from A. Zanin, S. Pagani, V. Crepaldi,  G. Di Fede, P.F. Antonietti, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and me, is now out on BiorXiv. 

In this work, we introduce a new modeling framework capable of predicting individual Alzheimer's disease trajectories from sparse, irregularly sampled, multi-modal clinical data. We employ NeuralODEs to determine the current hidden state of a patient based on sparse past exam data and to forecast future disease progression, thereby illustrating how biomarkers evolve. This work provides a versatile tool for accurate diagnosis and monitoring of neurodegenerative diseases.