Michele Allegra

This paper introduces a scalable method for estimating effective connectivity in fMRI data, showing it matches advanced DCM results and outperforms functional connectivity for connectome fingerprinting.

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.

The paper introduces a robust method to estimate the intrinsic dimension of neural manifolds, showing it is reliable across different data types and conditions.

This study estimates the dimension of structural connectomes to understand their influence on brain dynamics in health and disease, finding it correlates with cognitive performance and lesion effects.

The paper analyzes dynamic functional connectivity in the human brain, identifying two recurring states of cortical-subcortical interactions in healthy subjects.

The paper uses experimental and computational methods to show that spontaneous network activity patterns predict the effects of targeted stimulation in hippocampal neuronal cultures.

The paper compares Granger Causality and Effective Connectivity methods for inferring directed neural interactions in fMRI data, showing their mathematical relationship and limitations.

This paper applies network control theory to identify optimal input nodes for controlling specific brain regions using effective connectivity models.

The paper uses generative whole-brain models trained on healthy data to predict functional connectivity changes in stroke patients by incorporating lesion-induced structural disconnections.

The paper introduces a non-parametric permutation-based framework for group-level analysis of cognitive brain networks using neurophysiological data and presents an open-source Python toolbox called Frites.

This study investigates how stroke alters long-range brain communication by analyzing functional connectivity and information transfer using resting-state fMRI and Granger causality, revealing inter-hemispheric asymmetries and reduced information transfer.

This study investigates how brain networks support strategy optimization and spontaneous strategy shifts during a perceptual decision-making task using fMRI.