Viktor Jirsa

This paper demonstrates orientation-tunable temporally-interfering electric fields using surface electrodes to evoke seizure-like events in the mouse hippocampus without implanted electrodes.

The paper introduces virtual connectomic datasets by simulating missing connectivity features from existing neuroimaging data, enabling machine learning classification comparable to empirical data.

This study investigates whether theta-alpha activity patterns in seizure recordings are caused by spreading seizures, finding support for this hypothesis through simulations and comparisons with real patient data.

The paper uses a neural mass model combined with patient-specific brain networks to simulate seizure propagation and test clinical hypotheses about epileptic seizure dynamics.

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.

The paper quantifies changes in dynamic functional connectivity (dFC) speed after sleep deprivation and finds that modular dFC speed correlates with task-specific cognitive performance declines.

This paper introduces a taxonomy of seizure dynamotypes based on bifurcation theory and applies it to EEG data, identifying 16 dynamotypes and their variations across patients.

The paper analyzes dynamic functional connectivity (dFC) in fMRI data from healthy elderly, finding that dFC slows down and becomes less complex with aging, correlating with cognitive performance.

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 demonstrates that each individual's brain has a converging optimal working point in The Virtual Brain model that predicts fMRI and EEG features, with inter-individual differences driven by structural connectivity.

The paper develops a mathematical model of ephaptic interactions in neuronal fiber pathways, showing they can lead to complex spatio-temporal dynamics beyond simple signal transmission.

The paper builds a mouse brain model to explore functional networks and sensory processing through in-silico stimulation, revealing spatial motifs and replicating in vivo dynamics.

The paper demonstrates that individual structural connectomes predict functional brain organization in mice, with asymmetry and link weights being key predictors.

The paper uses computational modeling to link molecular pathways with neural dynamics in Alzheimer’s disease, showing how Abeta burden affects brain activity and potential reversibility with memantine.

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

This paper introduces a framework called structured flows on manifolds (SFM) to model neural system dynamics and applies it to understand brain disorders like epilepsy and dementia.

This paper develops a brain network model that integrates structural and functional data with neural dynamics to infer multi-scale neurophysiological mechanisms from EEG and fMRI data.

This study uses computational brain network modeling to show that the human brain during resting state operates at maximum metastability, revealing network mechanisms underlying non-invasive neuroimaging observations.

The paper introduces The Virtual Mouse Brain, a platform for simulating whole mouse brain dynamics using connectome data to study health and disease.