Parvin Zarei Eskikand

This study uses a neural mass model to predict seizure duration from neural connectivity patterns in a rat epilepsy model, finding that preictal connectivity parameters can forecast seizure length with high accuracy.

This study identifies dynamic multiday seizure cycles in a Tetanus Toxin rat model, showing evolving rhythms that may inform seizure prediction and treatment.

This study identifies cyclical patterns in seizure duration in the Tetanus Toxin rat model, showing predictable rhythms linked to EEG power, which could improve epilepsy management.

This study uses a neural mass model to analyze iEEG data from a Tetanus Toxin rat epilepsy model, finding correlations between connectivity weights and seizure frequency, and showing longer recovery times before seizures.

This study uses a neural mass model to show reduced synaptic heterogeneity in a Tetanus Toxin rat epilepsy model, linked to increased network synchrony.

This paper introduces a predictive coding model to study motion detection, showing how prior knowledge and sensory data balance in schizophrenia, with longer convergence times in higher noise.

The paper develops a neural mass model of a cortical column to study inhibitory stabilization across layers, finding that layers 4 and 5 exhibit ISN behavior with paradoxical responses, while layer 2/3 does not.