Davide Momi

This study evaluates how neural complexity metrics relate to network architecture by simulating neural dynamics across diverse network structures and analyzing both observational and perturbational measures.

In awake mice, psilocybin changed burst firing rather than mean firing rate across thalamus and hippocampus. Ketanserin pretreatment abolished most burst effects, but EEG power and firing-rate changes persisted.

This paper introduces a whole-brain model linking resting-state EEG activity to TMS-evoked responses, showing it can predict TEPs without time-domain simulations.

This paper shows that pre-stimulus brain states predict and reduce variability in stimulation responses by timing interventions to optimal neural states.

This study shows that ibogaine treatment in PTSD veterans is linked to reorganization of high-beta brain networks, which correlates with symptom improvement.

This study investigates how corticothalamic circuits contribute to age-related changes in resting-state alpha oscillations using MEG data from the Cam-CAN dataset.

The paper uses computational modeling to explore how theta burst stimulation affects brain oscillations and plasticity through calcium-dependent mechanisms, revealing resonance with alpha rhythms and implications for personalized treatment.

This study identifies neurophysiological signatures predicting response to TMS in treatment-resistant depression using combined EEG and computational modeling.

This paper systematically compares four theoretical models of alpha rhythm generation in neural circuits, identifying common elements and differences, and classifying two dynamical regimes for alpha rhythms.

This paper applies a corticothalamic model to analyze sleep EEG data from multiple datasets, revealing consistent physiological patterns and drug-related effects during sleep stages.

This study investigates how functional connectivity in resting-state networks depends on structural connectivity, showing state-dependent interactions between lower and higher order networks.

This study evaluates the validity and reliability of HD-DOT TD-fNIRS in rapid succession data collection, finding consistent performance with less spatial detail than fMRI.

This paper investigates how inhibition levels affect fMRI resting-state negative correlations (NCs) using simulations and connectome-based modeling, showing that greater inhibition increases NC magnitude and spatial extent.

The paper uses electrophysiology and modeling to show that high-order brain regions have higher excitability due to recurrent feedback, revealing a gradient linked to cortical hierarchy.

This study examines the reliability of resting-state EEG modulation by cTBS and iTBS on the primary motor cortex, finding no significant group-level effects and low-to-moderate reliability across sessions.

This paper investigates how TMS-evoked responses are influenced by recurrent large-scale network dynamics and local reverberatory activity, using TMS-EEG and connectome-based modeling.

This study examines inter-individual variability in functional connectivity of TMS targets for MDD using E-field maps and identifies major functional networks involved.

The paper introduces a deep learning approach to estimate parameters in connectome-based neural mass models using fMRI data and demonstrates its effectiveness on synthetic and real resting-state fMRI data.