| name | from-read-out-geometry-to-in-silico-stimulation |
| description | Distributed functional-connectivity signature of Alzheimer's disease methodology using subject-specific reservoir-computing models to reconstruct individual lagged functional connectivity and develop personalized neuromodulation strategies. Shows that optimal stimulation targets are distributed patterns rather than focal sites, requiring model-informed targeting based on therapeutic responsiveness rather than read-out deviation magnitude. |
| metadata | {"arxiv_id":"2607.24356","published":"2026-07-27","authors":"Cristiano Capone, Enza Cece, Andrea Ciardiello, Guido Gigante, Evaristo Cisbani, Maurizio Mattia","tags":["alzheimer-disease","functional-connectivity","reservoir-computing","neuromodulation","personalized-medicine","brain-networks","computational-neuroscience"]} |
| license | Complete terms in LICENSE.txt |
From Read-Out Geometry to In-Silico Stimulation
Overview
This methodology addresses a critical question in Alzheimer's disease (AD) treatment: whether the functional connectivity (FC) signature reduces to focal sites or requires distributed network intervention. Using subject-specific reservoir-computing models, the approach reconstructs individual lagged FC and develops personalized neuromodulation strategies that account for network-level therapeutic responsiveness.
Key Contributions
1. Subject-Specific Reservoir Computing Models
- Cross-subject-identifiable models: Each individual's model can be mapped to a common template
