Maurizio Corbetta

This study links receptor profiles and cortical excitability to acute and long-term stroke outcomes using patient-specific whole-brain models.

This study shows that structural disconnection after stroke leads to electrophysiological changes in both perilesional and structurally disconnected cortical regions, correlating with neurological severity.

The paper identifies orchestrator and integrator communities in resting-state brain activity, linking them to intelligence scores using a fractional modeling framework.

This paper introduces a non-invasive framework linking consciousness measures to non-equilibrium thermodynamics by analyzing FDT violations in humans and rodents across different states of consciousness.

This study examines how domain-general cognition (g) affects lesion-deficit mapping by analyzing 473 patients with focal brain damage using structural and functional lesion network mapping.

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 study shows that structural brain networks influence individual brain atrophy patterns after stroke, with atrophy spreading through the network from the initial lesion.

The paper identifies age-dependent neuromodulation targets for visual memory by analyzing stimulation and lesion connectivity across multiple datasets.

The paper uses computational whole-brain models to show that perilesional excitability predicts long-term motor recovery after stroke, but not acute impairment.

This paper models post-lesional slow waves in brain networks, identifying mechanisms for their generation and propagation.

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.

This study links long-range temporal correlations in brain activity to glucose metabolism and biosynthetic processes, showing higher correlations correlate with greater metabolic costs.

The paper shows spontaneous motor cortex activity in humans reflects somatotopic organization by replaying task-specific movement patterns more in corresponding motor subregions.

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

The paper predicts [18F]FDG PET parameters using rs-fMRI and 15O PET data, showing rs-fMRI and CMRO2 explain 45% of Ki variance.

This study benchmarks predictive models for long-term stroke outcomes using a global consortium, revealing mechanistic diversity and key predictors across motor, cognitive, and emotional domains.

This study examines how motor task-induced changes in brain connectivity relate to manual dexterity, finding that dexterous individuals show increased network segregation while low performers exhibit greater integration.

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.

This paper uses deep learning to generate disconnectomes from stroke lesion masks, achieving faster computation and better prediction of post-stroke cognitive and behavioral outcomes.

The paper develops a method to map white and grey matter contributions to resting-state networks and finds that 89% of white matter is shared across multiple networks.

This study identifies cognitive components and their relationship with brain network connectivity in Alzheimer's disease and frontotemporal dementia, revealing convergent and divergent patterns.

This study examines how stroke affects neural criticality in individual brain models and shows recovery over time linked to white matter remodeling.

The paper examines whether spontaneous brain activity in the somatomotor cortex is influenced by visual hand stimuli and their inferred use, finding stronger correlations with natural hand images than other stimuli.

The paper introduces a MATLAB toolkit for quantifying grey matter damage and white matter disconnections in focal brain lesions, showing consistency with prior literature.

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.

The paper shows that spontaneous activity in human motor cortex resembles common hand movements more than rare ones, suggesting it encodes behaviorally relevant patterns.

The paper investigates whether spontaneous brain activity in human visual cortex reflects stimulus-evoked patterns, finding that resting-state patterns are not biased toward specific stimuli but show category-specific fluctuations.

This paper introduces MOU-EC, a whole-brain effective connectivity model for fMRI data to study brain cognition in health and disease, showing its application in extracting task-specific biomarkers and interpreting connectivity changes.