Modeling of blood flow in cerebral arterial circulation and its dynamic impact on electrical conductivity in a realistic multi-compartment head model.

Background And Objective: This study aims to assess the dynamic impact of non-Newtonian cerebral arterial circulation on electrical conductivity within a realistic multi-compartment head model. Evaluating this research question is crucial and challenging due to its relevance to electrophysiological modalities like transcranial electrical stimulation (tES), electro-/magnetoencephalography (EEG/MEG), and electrical impedance tomography (EIT). In these modalities, accurate forward modeling depends on the electrical conductivity, which is affected by complex tortuous vessel networks, limited data acquisition in Magnetic Resonance Imaging (MRI), and non-linear blood flow phenomena, including shear rate and viscosity in non-Newtonian fluid.

Pressure-Poisson equation in numerical simulation of cerebral arterial circulation and its effect on the electrical conductivity of the brain.

Background And Objective: This study considers dynamic modeling of the cerebral arterial circulation and reconstructing an atlas for the electrical conductivity of the brain. Electrical conductivity is a governing parameter in several electrophysiological modalities applied in neuroscience, such as electroencephalography (EEG), transcranial electrical stimulation (tES), and electrical impedance tomography (EIT). While high-resolution 7-Tesla (T) Magnetic Resonance Imaging (MRI) data allow for reconstructing the cerebral arteries with a cross-sectional diameter larger than the voxel size, electrical conductivity cannot be directly inferred from MRI data.