Roser Sanchez-Todo

This paper proposes that cross-frequency coupling (CFC) mechanisms in a laminar neural mass model support prediction error evaluation in predictive coding, with implications for disorders like Alzheimer's disease and the effects of serotonergic psychedelics.

This paper analyzes a laminar neural mass model to show it can generate periodic, quasiperiodic, and chaotic oscillations, including cross-frequency couplings, and examines how Alzheimer's disease affects these dynamics.

This paper develops a laminar neural mass model to simulate Alzheimer's disease progression, showing that PV interneuron dysfunction drives early hyperexcitability and later hypoactivity through electrophysiological biomarkers.

This paper uses a whole-brain model to simulate how serotonergic psychedelics affect brain dynamics in Alzheimer's disease, showing restored oscillatory patterns and correlation with serotonin receptor distribution.

This paper develops personalized whole-brain models of seizure propagation using multimodal clinical data to simulate and evaluate individualized therapeutic strategies.

The paper explores how psychedelics affect neural dynamics by flattening the neural landscape, increasing complexity, and promoting adaptability through changes in plasticity mechanisms across different timescales.

This paper introduces a physical neural mass model framework to infer oscillatory generator locations from laminar electrophysiological recordings in macaque monkeys.

The paper develops a framework linking neural mass models with physical models to simulate SEEG recordings more accurately by incorporating current source density and volume conductor effects.

This paper introduces a laminar neural mass model that realistically reproduces SEEG recordings during epileptic seizures and demonstrates its ability to capture key electrophysiological features.

This study models ephaptic fields in the human brain across aging, finding they decrease with age and follow gyrification patterns, with higher involvement in sensorimotor and medial regions.

This paper develops a hybrid brain model personalized with neuroimaging and electrophysiology data to simulate individual brain activity and functional connectivity.