Ricardo Salvador

cyto7 is a vertex-level cortical-type atlas of the human cortex that improves upon area-level maps by capturing finer type boundaries and validating its structure with multimodal data.

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.

This paper introduces a group optimization framework for tES dose planning using representative head models, showing improved consistency and predictive power compared to individualized approaches.

The paper evaluates biophysical models to characterize electric fields from intracranial electrodes in DRE patients, finding that electrode geometry and tissue properties influence field distribution.

The paper quantifies how weak electric fields affect neuron models using spherical harmonics, showing dipole dominance and other terms' roles in non-resting states.

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 paper investigates how variations in electric field distribution from tDCS affect cognitive-motor outcomes in older adults and shows that optimized multichannel montages can improve targeting and reduce variability.

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 study shows that multifocal tDCS modulates resting-state connectivity in older adults, with effects dependent on electric field distribution and baseline connectivity.

StarNEt, a deep learning model, improves MRI segmentation for transcranial electrical stimulation by providing faster and more accurate white and gray matter masks compared to FreeSurfer.

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