Multi-scale Modeling of Brain Dynamics

We investigated the sensor-based and data system dynamic multi-scale models to track and predict the progression of deep brain structure related disorders such as Epileptic seizure, Alzheimer’s and Parkinson’s diseases. Specifically, we attempt to show how theoretical studies performed in physiologically-plausible computational models of neuronal assemblies (“neural mass models”) can enable us to set up some relationships between excitability-related parameters in models and some characteristic electrophysiological patterns typically observed in local field potentials (LFPs) or the EEG recorded under normal or epileptic conditions and other deep brain disorders.