Damien Depannemaecker

The paper shows that different biophysical parameters can produce equivalent bifurcation sequences in neuronal models, revealing degeneracy in neuronal dynamics.

The paper examines how slow ionic dynamics affect bistability in a biophysical mean-field model of brain activity, showing that conditional bistability from a fast subsystem is not inherited by the physiological resting branch.

The paper examines how structural brain connectivity influences the initiation and termination of neuronal avalanches using MEG and dMRI data from healthy individuals.

The paper analyzes neuronal avalanche dynamics in resting-state MEG data to show temporal persistence and structural organization in brain activity.

The paper analyzes seizure dynamics in epileptic mice, finding structured, animal-specific trajectories and circadian modulation in seizure microstructure.

This study shows that changes in whole-brain microstate dynamics after levodopa administration correlate with clinical improvement in Parkinson's disease.

BRIDGE is a Python pipeline that reconstructs and validates mean-field models from single neuron dynamics, enabling reproducible and reusable large-scale brain simulations.

This study develops a microcircuit model to investigate how astrocytic potassium buffering and gap-junction coupling influence neuronal synchronization and prevent pathological states in the hippocampus.

This study develops a computational model showing how circadian rhythms modulate excitation-inhibition balance to drive seizure-like dynamics in neuronal networks.

This study shows that hormone levels across the menstrual cycle affect brain activity patterns and predict psychological well-being.

This paper introduces a modular neural mass model incorporating neuromodulation, specifically dopamine dynamics, and analyzes its effects on oscillatory states.

This paper develops a computational model to simulate how amyloid-beta accumulation affects brain network dynamics in Alzheimer's disease, revealing a two-phase deterioration and identifying critical thresholds for network collapse.

The paper identifies criticality signatures in cortical networks during synchronization onset, showing both intra- and inter-hemispheric coordination patterns.

This paper develops a multiscale computational framework to study how brain criticality at local and global scales interacts through structural connectivity.

This study compares a dopamine-modulated mean-field model with its corresponding spiking network to evaluate the model's accuracy in capturing network dynamics.

The paper develops a minimal mathematical model to study degeneracy in astrocytic potassium buffering, revealing robustness through local and long-range mechanisms.

The paper systematically investigates the dynamics and bifurcation structure of a mean-field model of Adaptive Exponential integrate-and-fire networks to aid in interpreting results.

This paper introduces Virtual Brain Twins (VBTs) as personalized digital brain models that integrate structural and functional data to improve understanding of brain function and support personalized treatment.

The paper introduces an extended Adaptive Exponential integrate-and-fire model to simulate ionic regulation impairment and demonstrates its ability to generate normal and epileptic neural network activity.

The paper adapts a mean-field model to quadratic integrate-and-fire neuron models with adaptation and conductance-based synapses, validating it against a spiking network model.

This paper develops a mean-field neural model of brain states that reproduces empirical dynamics across scales and brain states, using the human connectome and the Virtual Brain simulator.

This paper develops computational models to study seizure propagation in neural networks, showing that inhibitory connectivity and network structure determine seizure spread and identifying a time window for intervention.

This paper develops a mean-field model of neuronal populations incorporating ion-exchange mechanisms to simulate brain activity and validate its effects through in vitro experiments.

The paper develops a brain-wide model using biologically realistic population dynamics to simulate slow-wave synchrony and asynchronous states, showing their distinct responses to stimulation.

This paper introduces the Conductance-based Adaptive Exponential integrate-and-fire (CAdEx) model to address limitations of the AdEx model in simulating neuronal behavior.

The paper develops a mean-field formalism to predict the collective dynamics of large networks of complex spiking neurons, linking population models to electrophysiology.