Dynamical neural network reconstruction based on stimulation data

Abstract

Single-pulse electrical stimulation (SPES) during clinical intracranial electrocorticography (iEEG) monitoring allows active probing of epileptogenic tissue. Two electrodes are stimulated, and the responses at other electrodes are recorded.These responses show physiological early responses (ERs) and more epileptogenic delayed responses (DRs). Earlier, we have demonstrated that neural mass models also produce and explain these responses. Here we propose to combine neural masses to create individualized neural mass networks reproducing the measured responses.We first show a dynamical inventory of small interacting neural masses using simulations and bifurcation analysis. Here we vary parameters related to intrinsic excitability, connectivity strength and background input. Second, we fit networks of small artificial topology and four real patient networks changing those parameters. We find that the ERs are nearly all reproduced. For the DRs, we can fit them in the small networks but to a lesser extent for the patient data. The resulting models may be helpful to explore the effect of stimulation protocols and surgery