Yonatan Sanz Perl

The paper introduces a computational engine to systematically discover cause-effect relationships between brain structure and function using in-silico brain models.

This paper applies an off-equilibrium thermodynamical framework based on the fluctuation-dissipation theorem to differentiate brain states like wakefulness and deep sleep.

This paper introduces CHARM, a framework that captures low-dimensional manifolds of critical brain dynamics using complex harmonics, revealing differences between wakefulness and sleep.

This paper models low-dimensional brain networks to show they organize human cognition through flexible reconfiguration during tasks.

This study examines how classical psychedelics alter brain hierarchy and complexity, finding they reduce hierarchy while increasing neural complexity.

This paper investigates the long-term effects of psilocybin on fronto-striatal-thalamic circuit dynamics and connectivity using longitudinal fMRI data and computational modeling.

This study uses computational whole-brain modeling to show that competitive interactions in mammalian connectomes enhance computational capacity and fit brain dynamics better than cooperative-only models.

This study identifies dynamic structure-function correlations as robust markers of consciousness in humans across different unconscious states.

The paper uses whole-brain models to simulate the effects of serotonergic psychedelics on disorders of consciousness, showing they can shift brain dynamics toward conscious states.

This study uses off-equilibrium fluctuation-dissipation theorem to analyze brain dynamics in Alzheimer's disease, finding increased deviations in MCI and decreased deviations in AD, suggesting potential biomarkers for early detection.

This paper uses computational models to simulate brain perturbations in disorders of consciousness, showing that transitions to healthy brain states can be achieved through targeted stimulation.

The paper shows that combining rare long-range connections with local connectivity improves the capture of functional brain dynamics compared to geometry alone.

This paper models the transient brain state changes induced by DMT using a time-dependent whole-brain model and fMRI data, showing heightened reactivity in specific brain regions correlated with serotonin receptor density.

The paper measures brain irreversibility in ferrets during different behavioral states to reveal functional hierarchies in cortical dynamics.

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 a turbulent dynamics framework to analyze whole-brain fMRI data from TBI patients and healthy controls, showing reduced synchronization and reactivity during recovery.

The paper maps brain states of reduced consciousness into a low-dimensional space using whole-brain modeling and deep learning, revealing an orderly trajectory from wakefulness to brain injury.

The paper uses computational modeling to identify key brain regions for personalized neurostimulation strategies to improve cognitive interventions in Alzheimer's disease.

This paper uses deep learning models to analyze whole-brain dynamics in neurodegenerative diseases, identifying key regions for inducing transitions from pathological to healthy states.

The paper integrates structural and functional connectivity data from marmoset brains to map functional networks, develop a deep-learning parcellation generator, and investigate structural-functional relationships.

This paper uses strength-dependent perturbations to show the human brain operates at a balance between fluctuation and oscillation, enhancing flexibility.

This study identifies canonical brain dynamics by optimizing computational models to match empirical data from functional MRI, revealing stable spiral attractors and bifurcation-related dynamics.

This paper introduces a framework using turbulent dynamics to differentiate brain states by analyzing neuroimaging data with model-free and model-based measures.

The paper shows that neural activity during conscious wakefulness has a preferred temporal direction, distinguishable from inverted signals, while this asymmetry is reduced during sleep and anesthesia.

The paper introduces FastDMF, a computationally efficient implementation of the Dynamic Mean Field model, enabling simulations of whole-brain dynamics with significantly faster performance and reduced memory usage.

The paper uses in silico stimulation on a resting-state fMRI dataset to identify brain regions that can shift aging brain states to middle-aged states.

This study uses whole-brain network dynamics to differentiate four gender groups, finding distinct patterns in neural activity propagation and metastability.

The paper models human brain connectivity with and without long-range exceptions to show they improve information processing.

The paper develops a framework to distinguish brain states by their stability and reversibility, using whole-brain models to analyze consciousness levels and their response to perturbations.

This paper uses a deep learning framework to quantify non-equilibrium in brain activity, showing higher levels during tasks than rest and distinguishing between health and neuropsychiatric diseases.