Anatomical atlas of the upper part of the human head for electroencephalography and bioimpedance applications

Abstract

Objective. The objective of this work is to develop a 4D (3D+T) statistical anatomical atlas of the electrical properties of the upper part of the human head for cerebral electrophysiology and bioimpedance applications. Approach. The atlas was constructed based on 3D magnetic resonance images (MRI) of 107 human individuals and comprises the electrical properties of the main internal structures and can be adjusted for specific electrical frequencies. T1w+T2w MRI images were used to segment the main structures of the head while angiography MRI was used to segment the main arteries. The proposed atlas also comprises a time-varying model of arterial brain circulation, based on the solution of the Navier–Stokes equation in the main arteries and their vascular territories. Main results. High-resolution, multi-frequency and time-varying anatomical atlases of resistivity, conductivity and relative permittivity were created and evaluated using a forward problem solver for EIT. The atlas was successfully used to simulate electrical impedance tomography measurements indicating the necessity of signal-to-noise between 100 and 125 dB to identify vascular changes due to the cardiac cycle, corroborating previous studies. The source code of the atlas and solver are freely available to download. Significance. Volume conductor problems in cerebral electrophysiology and bioimpedance do not have analytical solutions for nontrivial geometries and require a 3D model of the head and its electrical properties for solving the associated PDEs numerically. Ideally, the model should be made with patient-specific information. In clinical practice, this is not always the case and an average head model is often used. Also, the electrical properties of the tissues might not be completely known due to natural variability. Anatomical atlases are important tools for in silico studies on cerebral circulation and electrophysiology that require statistically consistent data, e.g. machine learning, sensitivity analyses, and as a benchmark to test inverse problem solvers.

Pressure-Poisson Equation in Numerical Simulation of Cerebral Arterial Circulation and Its Effect on the Electrical Conductivity of the Brain

“…We suggest that NSEs and their approximations can be applied directly to individual datasets to potentially improve the quality of head modeling. Thereby complementing the recently developed statistical approaches following from NSEs [36] and generally advancing the techniques applicable in EEG and MEG source localization [7] or in closely related fields such as tES [4], where any dynamical components affecting the conductivity atlases are typically absent. In particular, we have shown how to incorporate blood pressure effects into a highresolution and high-resolution MRI segmentation that includes blood vessels [5].…”

In silico study of the effects of cerebral circulation on source localization using a dynamical anatomical atlas of the human head

“…In Moura et al [14], the authors develop a statistical atlas of the electrical properties of the human head based on 3D-MRI of 107 healthy human subjects. The atlas takes into consideration the natural variability of the internal structures and the electrical properties of the main tissues.…”

“…A 0 (z) is the reference cross-sectional area, and h 0 the wall thickness at rest. Geometrical and mechanical properties of the arteries under consideration (figure 3) are presented in [14]. The boundary conditions at the terminal vessels are three-element windkessel models, and the input is a half-sinusoidal wave model of the cardiac output flow in one cycle…”

“…The relationship between blood flow velocity and electrical properties has previously been identified, both in vitro and in vivo [11][12][13]. To approximate the dynamical blood flow, we use an interpolated atlas [14] which follows from a cylindrical Navier-Stokes equations (NSE) model of a blood vessel combined with a statistical approach to take into account the effects of the circulatory system and inter-subject variations. Incorporating such an atlas into the forward model necessitates using a volumetric forward solver.…”