Rubén Sánchez-Romero

EFMouse models electric fields in the mouse brain during stimulation, enabling design and analysis of tES experiments with in-vivo validation.

This paper introduces regularized partial correlation methods to improve functional connectivity estimates in fMRI by reducing noise and confounding, showing higher reliability and accuracy compared to standard methods.

The paper demonstrates that distributed network flows can generate localized visual category selectivity in human visual cortex using fMRI data and computational models.

The paper investigates how cortical regions influence arousal systems to predict cognitive abilities using fMRI and cognitive assessments.

The paper introduces causally informed activity flow models to better understand network-generated cognitive activations by using causal functional connectivity measures.

The paper quantifies the functional importance of task-state functional connectivity in shaping cognitive task activations and finds it improves prediction accuracy beyond resting-state connectivity.

The paper introduces a combined functional connectivity method that improves causal inferences by integrating bivariate and partial correlation approaches.

The paper evaluates and introduces new statistical methods for inferring causal feedback networks from fMRI data, finding several methods achieve over 80% orientation precision and the new methods show strong recall.

The paper identifies voxel subsets driving functional connectivity between hippocampal regions using the FGES algorithm on rs-fMRI data.

The paper develops a statistical method to identify key voxels driving connectivity in brain regions, applying it to fMRI data from hippocampal and corticostriatal networks.