Viktor Jirsa

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.

The paper proposes a computational framework where cortico-subcortical circuits implement multi-head self-attention, aligning with human brain recordings during speech perception.

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

The paper introduces siibra, a software tool suite that integrates diverse brain data into a Multilevel Human Brain Atlas, enabling interoperable analysis and visualization.

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

This paper optimizes temporal interference stimulation (TIS) targeting in mice to reduce off-target effects while maintaining efficacy in the prefrontal cortex.

This paper investigates age-related changes in EEG phase synchronization dynamics across childhood to old age, identifying two developmental trajectories and a computational model explaining the observed patterns.

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 {xi}-Net, a generative model of cortical activity that estimates EEG components and connectivity across lifespan using rsEEG data.

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 paper investigates how temporal interference stimulation affects memory encoding in humans, finding that different frequencies and brain targets can either enhance or impair recall.

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.

This study uses PHATE to reduce the dimensionality of resting-state MEG data, revealing dynamic brain states and their transitions in a low-dimensional space.

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.

This paper introduces multipolar temporal interference (mTI), a method that improves focal control of non-invasive deep brain stimulation in animals.

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 compares non-invasive and invasive methods to estimate brain connectivity and signal propagation delays, finding they yield similar results.

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.

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

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

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 develops Bayesian inference methods for Dynamic Causal Modelling using probabilistic programming languages to improve neural dynamics prediction.