Spase Petkoski

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 develops ADAM, a model linking dopamine clearance and structural connectivity to cognitive aging, finding elevated clearance timescales and a structure-age-dependent association with cognitive performance.

This paper develops a normative modeling framework to analyze static and dynamic functional connectivity across lifespan, revealing distinct age-related trajectories for each.

The paper develops a digital twin brain model that simulates and manipulates functional brain networks to predict mental illness symptoms and responses to interventions.

This paper identifies a deformable attractor manifold that organizes human resting-state brain dynamics, showing it relates to cognitive variability and pathology.

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

The paper introduces DeepTaskGen, a deep learning method that generates synthetic task-based contrast maps from resting-state fMRI data, outperforming benchmarks and enabling biomarker development for cognitive tasks.

The paper quantifies consciousness using spatiotemporal brain complexity metrics from resting-state and stimulation data, showing agreement with perturbational complexity in a more accessible paradigm.

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

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 uses Brain Network Modeling to analyze how anatomical constraints shape resting state dynamics and identify degeneracy patterns in the human brain.

This paper introduces VBI, a toolkit for Bayesian inference on virtual brain models, demonstrating its accuracy and reliability through in-silico testing.

This paper uses computational modeling and dynamical systems analysis to show that symmetry breaking in brain connectivity generates the resting state manifold, producing key features observed in empirical data.

This paper introduces SBI-VBMs, a method using deep neural networks to estimate generative parameters in brain disorders through probabilistic inference on virtual brain models.

The paper analyzes age-related changes in brain structure and function using MRI data, finding that compensation mechanisms and reduced propagation velocity explain functional alterations.

This paper develops a causal inference framework to link structural connectivity and brain function in aging, showing increased structural modulation in poor cognitive performers.

The paper normalizes brain connectomes to account for synchronization-based communication, revealing frequency-specific network cores in human brain data.

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.

This paper introduces a data-driven method to infer neural mass models and parameters from brain data, showing it can recover original parameters and produce realistic dynamics in synthetic and resting-state fMRI data.

The paper introduces a method to estimate functional transmission delays in the brain using in vivo measurements and structural connectome data, showing that larger bundles have faster velocities and delays are greater in multiple sclerosis patients.

This study integrates experimental and computational models of motor control and recovery after stroke to improve experimental design through continuous data integration and simulation.

The paper quantifies temporal attention sampling capacities in auditory and visual perception, revealing optimal rates and motor-related modulation.

This paper develops a seizure control strategy using patient-specific brain network models to predict and modulate seizure propagation patterns in epilepsy patients.