Jakub Vohryzek

The paper investigates brain dynamics in anhedonia using resting-state fMRI, finding a shift in cortico-subcortical hierarchy from bottom-up to balanced.

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

This study introduces vorticity-based metrics to analyze brain dynamics during DMT experiences, finding increased rotational organization and spatial heterogeneity associated with subjective intensity changes.

This study examines brain dynamics during unusual bodily experiences and out-of-body experiences, finding reduced neural hierarchy and increased EEG complexity.

This paper proposes that cross-frequency coupling (CFC) mechanisms in a laminar neural mass model support prediction error evaluation in predictive coding, with implications for disorders like Alzheimer's disease and the effects of serotonergic psychedelics.

This study shows that cognitive co-activation dominates fMRI synchrony in the awake brain but is replaced by anatomical and molecular constraints during consciousness perturbations.

This study uses functional connectome harmonics to analyze neonatal brain gradients and finds that power and energy metrics differ between term and preterm neonates, predicting postmenstrual age.

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 uses a whole-brain model to simulate how serotonergic psychedelics affect brain dynamics in Alzheimer's disease, showing restored oscillatory patterns and correlation with serotonin receptor distribution.

This study uses computational modeling to simulate functional connectivity changes in Huntington's disease and assess recovery potential by perturbing striatal activity.

This study uses fMRI to assess functional and effective connectivity changes across HD-ISS stages, finding attenuated connectivity in early stages and sensitivity to motion.

The Turbulent Brain toolbox applies turbulence physics to analyze brain activity dynamics, showing reduced information transmission during movie watching compared to resting state.

This paper identifies disrupted hierarchical organization in disorders of consciousness using fluctuation-dissipation theorem deviations from fMRI data.

The paper uses whole-brain modeling to analyze brain dynamics during advanced meditation states, finding increased functional integration and near-critical dynamics in later states associated with minimal phenomenal experience.

This study examines changes in brain network dynamics after 60 minutes of aerobic cycling, finding reduced striato-frontoparietal connectivity.

Inception simulates long-term recovery in Disorders of Consciousness using whole-brain models and fMRI data, showing improved patient classification and potential for capturing pharmacological effects.

This paper applies whole-brain models to study state transitions in depression following psilocybin and escitalopram treatments, showing differential effects on susceptibility and drivability to healthier states.

The paper examines how dimensionality reduction techniques preserve synergistic interactions in brain dynamics using resting-state fMRI data from 100 healthy participants.

The paper introduces a whole-brain model called NEMO that uses neurotransmitter maps to simulate brain dynamics and shows it correlates with behavioral performance and intelligence.

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

The paper explores how symmetry and structure in data influence the dynamics and organization of algorithmic agents, linking concepts from group theory, information theory, and neural networks.

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.

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 connectome harmonic decomposition to show that ketamine-induced unresponsiveness increases localised harmonics, similar to psychedelic states, unlike traditional sedation which increases global harmonics.

This study shows that DMT alters connectome harmonics, increasing their repertoire entropy and correlating with subjective experience intensity over time.

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

The paper explores how psychedelics affect neural dynamics by flattening the neural landscape, increasing complexity, and promoting adaptability through changes in plasticity mechanisms across different timescales.