Roberta Maria Lorenzi

The paper presents a multiscale brain modelling framework combining DCM and TVB to study excitatory-inhibitory balance in multiple sclerosis, finding altered connectivity patterns and their association with clinical and behavioural outcomes.

The paper introduces Auto-MFM, a tool that automatically derives mean field models from spiking neural networks, accurately reproducing population dynamics and enabling exploration of physiological and pathological brain circuits.

This paper develops a region-specific mean field model for the cerebellum, improving virtual brain simulations of local and global dynamics compared to standard oscillators.

The paper uses DCM on fMRI data to show cerebellar control switches from excitatory to inhibitory during action execution versus observation, revealing distinct functional roles in motor planning and sensorimotor feedback.

This paper introduces a multiscale modelling framework for hippocampal CA1 that bridges micro and macro scales using a novel mean-field model, validated against spiking network models.

This study investigates spinal cord atrophy in Alzheimer's disease and finds it contributes to distinguishing patients from controls and explains cognitive scores.