The role of blood vessels in high-resolution volume conductor head modeling of EEG

Abstract

Reconstruction of the electrical sources of human EEG activity at high spatiotemporal accuracy is an important aim in neuroscience and neurological diagnostics. Over the last decades, numerous studies have demonstrated that realistic modeling of head anatomy improves the accuracy of source reconstruction of EEG signals. For example, including a cerebrospinal fluid compartment and the anisotropy of white matter electrical conductivity were both shown to significantly reduce modeling errors. Here, we for the first time quantify the role of detailed reconstructions of the cerebral blood vessels in volume conductor head modeling for EEG. To study the role of the highly arborized cerebral blood vessels, we created a submillimeter head model based on ultra-high-field-strength (7 T) structural MRI datasets. Blood vessels (arteries and emissary/intraosseous veins) were segmented using Frangi multi-scale vesselness filtering. The final head model consisted of a geometry-adapted cubic mesh with over 17 × 106 nodes. We solved the forward model using a finite-element-method (FEM) transfer matrix approach, which allowed reducing computation times substantially and quantified the importance of the blood vessel compartment by computing forward and inverse errors resulting from ignoring the blood vessels. Our results show that ignoring emissary veins piercing the skull leads to focal localization errors of approx. 5 to 15 mm. Large errors (>2 cm) were observed due to the carotid arteries and the dense arterial vasculature in areas such as in the insula or in the medial temporal lobe. Thus, in such predisposed areas, errors caused by neglecting blood vessels can reach similar magnitudes as those previously reported for neglecting white matter anisotropy, the CSF or the dura — structures which are generally considered important components of realistic EEG head models. Our findings thus imply that including a realistic blood vessel compartment in EEG head models will be helpful to improve the accuracy of EEG source analyses particularly when high accuracies in brain areas with dense vasculature are required.

Optimization of focality and direction in dense electrode array transcranial direct current stimulation (tDCS)

“…Because the current flow in the head is very complex due to factors such as anatomical structures, tissue characteristics, electrode positions and shape [4, 50], there may be need, in particular settings, for additional current power constraints to prevent excessive current delivery to the critical regions in the brain. Although here we only constrained the current power in the brain outside the ROI, the optimization problem is readily capable of incorporating multiple critical regions, each assigned with its own safety bound, to allow the flexibility of defining subject-specific critical regions.…”

Skull Modeling Effects in Conductivity Estimates Using Parametric Electrical Impedance Tomography

“…Furthermore, electrode modeling can also affect scalp and skull conductivity estimates, but this has been shown to be relatively minor [60]. The effects of other modeling enhancements such as the inclusion of the dura layer [105] or the major blood vessels [20] should be analyzed in future studies.…”

“…Initially, simplified three layered spherical models [16] were used, but technological advances have enabled progressively more realistic models that employ anatomically realistic three or four nested layers [17], models that include marrow bone (MB) [18], small foramina in the skull [10], [19], and even blood vessels [20]. The quality of these models depends on both accuracy of characterizing numerous head tissues and their electrical properties, including anisotropy.…”

Cerebral artery segmentation based on magnetization-prepared two rapid acquisition gradient echo multi-contrast images in 7 Tesla magnetic resonance imaging

“…Uncertain segmentation of brain tissues and blood vessels can lead to misidentifying the localization of the electroencephalographic signal source and lead to anatomical confounding in certain cortical regions with large vessels in studies of brain function and of cortical characteristics, such as cortical thickness (Fiederer et al, 2016;Viviani, 2016). Therefore, accurate segmentation of blood vessels is essential for neuroimaging research and medical image analysis.…”