Spatially Resolved Pediatric Skull Conductivities for Inhomogeneous Electrical Forward Modeling

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Tuesday, June 27, 2017: 12:45 PM  - 2:45 PM 

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Abstract Submission 

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Monday, June 26 & Tuesday, June 27 


Sergei Turovets1, Jidong Hou1, Sergei Turovets1, Kai Li1, Phan Luu1, Don Tucker1, Linda Larson-Prior2


1Electrical Geodesics, Inc, Eugene, OR, 2University of Arkansas for Medical Sciences, Little Rock, AR

First Author:

Sergei Turovets    -  Lecture Information | Contact Me
Electrical Geodesics, Inc
Eugene, OR



Misspecification of skull conductivity is a significant confounding factor in EEG /MEG source localization and in TES targeting / dosage calculations[1]. It is particularly important for children because the size, shape and electrical properties of the head tissues undergo rapid developmental changes from infancy through adolescence [2].

While some studies suggest that skull conductivity in adults might be parameterized by its thickness and structure such as sutures and bone marrow [3], there is a scarcity of published data on pediatric skull during development. Smith et al 2012 [4] performed semi-automated extraction of skull thickness and density measures of pediatric crania based on in-vivo clinical CT scans at the standard 10-20 EEG electrode placements (0 to 18 y. o.). A more spatially detailed mapping of a post-mortem human calvarium CTs (53 to 97 y.o.) was reported in [5], where both thickness and density were measured at over 2000 sites per skull.

In this work, we performed automated in vivo measurements of skull thickness and density for the same pediatric CT data pool as in [4] with higher spatial resolution to generate skull conductivity maps.


A subset of clinical CT data [4] collected at Children's Hospital, WUSTL (N=54, 24F; 0.4 to 18 y.o.) was analyzed.
Each individual CT image after resampling to the 0.5 mm x 0.5 mm x 0.5 mm resolution and converting the CT intensity to Hounsfield Units (HU) was registered in BrainK [ 6] with one of three age group atlas CTs [7]. Nineteen landmarks at the 10-20 locations were transformed from the atlas CT to the individual CT as illustrated in Figure 1 (a). Each CT volume was segmented into 5 tissues: flesh, bone, brain, air in the head, and background.
A sphere was fitted to the 10-20 landmarks. Rays were casted to intersect with the skull inner surface, Fig. 1 (b). These intersections were used to find the nearest points at the outer surface to calculate the thickness. Average densities were calculated along the line that connects these points. Similarly to petrophysics conductivity modeling, the CT density in HU was converted to porosity and then to conductivity with Archie's law [8]. The calvarium maps were limited by a plane determined by Fp1, Fp2 and O1 in order to maintain a consistent region of interest for different CT volumes.


Fig. 1 (c) shows that thickness, density and conductivity distributions are not uniform over the skull. Pediatric skulls are thinner and have lower densities and higher conductivites in sutures regions.
The relationship of skull thickness and average density with age was evaluated by linear regression. Figure 2 (a) shows both thickness and the average density increasing linearly with age. This increase seems to be divided into two stages around age 4 y.o. Development is much faster before than after this age and spatially not uniform. As shown in Figure 2 (b), skull thickness does not increase with age at T3 and T4 in terms of low R2, but grows significantly at P3 and P4. On the other hand, density does not increase with age at Fp1 and Fp2, but grows significantly at Fz, Cz and Pz.

For each individual CT, the relationship between skull thickness and density is dependent on the skull morphology. Fig. 2 (c) shows that a density and thickness correlate positively below 5 mm and negatively for the thicker plates which is likely due to the presence of a spongiform marrow filled layer.


Spatial distribution and changes of pediatric skull thickness, density and therefore conductivity are highly non-uniform and individual. Simple angularly homogeneous multi-shell spherical or MRI-based rescaled adult models are not good enough for individual pediatric forward modeling in EEG / TES or transcranial ultrasound stimulation. It requires spatially resolved skull parameter upgrades suggested in this work.
Supporting Image: JH_Fig1.jpg
   ·Figure 1. Illustration of automated skull thickness, density and conductivity mapping.
Supporting Image: JH_Fig2.jpg
   ·Figure 2. Statistical analysis of pediatric skull thickness and density.

Brain Stimulation Methods:

Non-invasive Electrical/tDCS/tACS/tRNS
Sonic/Ultrasound 2

Imaging Methods:


Modeling and Analysis Methods:

EEG/MEG Modeling and Analysis 1

Poster Session:

Poster Session - Tuesday


Computational Neuroscience
Computed Tomography (CT)
Electroencephaolography (EEG)

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Structural MRI

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1. Salman, A., Malony, A., Turovets, S. , Volkov, V., Ozog , D. , Tucker, D. (2015). Concurrency in electrical neuroinformatics: parallel computation for studying the volume conduction of brain electrical fields in human head tissues. Concurrency and Computation Practice and Experience 07/2015; DOI:10.1002/cpe.3510.

2. Song, J., Morgan, K., Turovets, S., Li, K., Davey, C., Govyadinov, P., Luu, P., Smith, K., Prior, F., Larson-Prior, L. and Tucker, D.M., 2013. Anatomically accurate head models and their derivatives for dense array EEG source localization. Functional Neurology, Rehabilitation, and Ergonomics, 3(2/3), p.275.

3. Law, S.K., 1993. Thickness and resistivity variations over the upper surface of the human skull. Brain topography, 6(2), pp.99-109.

4. Smith, K., Politte, D., Reiker, G., Nolan, T.S., Hildebolt, C., Mattson, C., Tucker, D., Prior, F., Turovets, S. and Larson-Prior, L.J., 2012, August. Automated measurement of pediatric cranial bone thickness and density from clinical computed tomography. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 4462-4465). IEEE.

5. Voie, A., Dirnbacher, M., Fisher, D. and Hölscher, T., 2014. Parametric mapping and quantitative analysis of the human calvarium. Computerized Medical Imaging and Graphics, 38(8), pp.675-682.

6. Li, K., Papademetris, X. and Tucker, D.M., 2016. BrainK for structural image processing: creating electrical models of the human head. Computational Intelligence and Neuroscience, 2016.

7. Pediatric Head Modeling Home, the project supported by NIH grant R44 MH106421 (

8. Archie, G.E., 1942. The electric resistivity log as an aid in determining some reservoir characteristics. Trans. Am. Inst. Min. Metall. Pet. Eng. 146, 54–62.