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Spatially Resolved Pediatric Skull Conductivities for Inhomogeneous Electrical Forward Modeling



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Spatially Resolved Pediatric Skull Conductivities for Inhomogeneous Electrical
Forward Modeling
Presented During: Poster Session
Tuesday, June 27, 2017: 12:45 PM - 02:45 PM
Presented During: Poster Session
Tuesday, June 27, 2017: 12:45 PM - 02:45 PM
Stand-By Time
Tuesday, June 27, 2017: 12:45 PM - 2:45 PM
Submission No:
Submission Type:
Abstract Submission
On Display:
Monday, June 26 & Tuesday, June 27
Jidong Hou1, Sergei Turovets1, Kai Li1, Phan Luu1, Linda Larson-Prior2, Don Tucker1
1Electrical Geodesics, Inc, Eugene, OR, 2University of Arkansas for Medical Sciences, Little Rock, AR
First Author:
Jidong Hou - 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.
·Figure 1. Illustration of automated skull thickness, density and conductivity mapping.
·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.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
BrainK is a set of automated procedures for characterizing the tissues of the human head from MRI, CT, and photogrammetry images. The tissue segmentation and cortical surface extraction support the primary goal of modeling the propagation of electrical currents through head tissues with a finite difference model (FDM) or finite element model (FEM) created from the BrainK geometries. The electrical head model is necessary for accurate source localization of dense array electroencephalographic (dEEG) measures from head surface electrodes. It is also necessary for accurate targeting of cerebral structures with transcranial current injection from those surface electrodes. BrainK must achieve five major tasks: image segmentation, registration of the MRI, CT, and sensor photogrammetry images, cortical surface reconstruction, dipole tessellation of the cortical surface, and Talairach transformation. We describe the approach to each task, and we compare the accuracies for the key tasks of tissue segmentation and cortical surface extraction in relation to existing research tools (FreeSurfer, FSL, SPM, and BrainVisa). BrainK achieves good accuracy with minimal or no user intervention, it deals well with poor quality MR images and tissue abnormalities, and it provides improved computational efficiency over existing research packages.
Full-text available
Advances in human brain neuroimaging for high-temporal and high-spatial resolution will depend on localization of Electroencephalography (EEG) signals to their cortex sources. The source localization inverse problem is inherently ill-posed and depends critically on the modeling of human head electromagnetics. We present a systematic methodology to analyze the main factors and parameters that affect the EEG source-mapping accuracy. These factors are not independent and their effect must be evaluated in a unified way. To do so requires significant computational capabilities to explore the problem landscape, quantify uncertainty effects, and evaluate alternative algorithms. Bringing high-performance computing (HPC) to this domain is necessary to open new avenues for neuroinformatics research. The head electromagnetics forward problem is the heart of the source localization inverse. We present two parallel algorithms to address tissue inhomogeneity and impedance anisotropy. Highly-accurate head modeling environments will enable new research and clinical neuroimaging applications. Cortex-localized dEEG analysis is the next-step in neuroimaging domains such as early childhood reading, understanding of resting state brain networks, and models of full brain function. Therapeutic treatments based on neurostimulation will also depend significantly on HPC integration.
Full-text available
Skull thickness and density measures of normal pediatric crania would inform multiple disciplines including neurosurgery, optical and magnetoelectrophysiological imaging, and biomechanical modeling of head trauma. We report on a new method for automated extraction of in vivo skull thickness and density measures of pediatric crania based on x-ray computed tomography scans (CT). Data were obtained from a clinical image repository for pediatric populations in whom no pathology was noted. Skull thickness and density measures were systematically obtained across the calvarium. We find a set of measures that correlated with physiological age that are likely to prove useful in multiple disciplines.
The usefulness of the electrical resistivity log in determining reservoir characteristics is governed largely by: (i) the accuracy with which the true resistivity of the formation can be determined; (2) the scope of detailed data concerning the relation of resistivity measurements to formation characteristics; (3) the available information concerning the conductivity of connate or formation waters; (4) the extent of geologic knowledge regarding probable changes in facies within given horizons, both vertically and laterally, particularly in relation to the resultant effect on the electrical properties of the reservoir. Simple examples are given in the following pages to illustrate the use of resistivity logs in the solution of some problems dealing with oil and gas reservoirs. From the available information, it is apparent that much care must be exercised in applying to more complicated cases the methods suggested. It should be remembered that the equations given are not precise and represent only approximate relationships. It is believed, however, that under favorable conditions their application falls within useful limits of accuracy.
A study of skull thickness and resistivity variations over the upper surface was made for an adult human skull. Physical measurements of thickness and qualitative analysis of photographs and CT scans of the skull were performed to determine internal and external features of the skull. Resistivity measurements were made using the four-electrode method and ranged from 1360 to 21400 Ohm-cm with an overall mean of 7560 +/- 4130 Ohm-cm. The presence of sutures was found to decrease resistivity substantially. The absence of cancellous bone was found to increase resistivity, particularly for samples from the temporal bone. An inverse relationship between skull thickness and resistivity was determined for trilayer bone (n = 12, p < 0.001). The results suggest that the skull cannot be considered a uniform layer and that local resistivity variations should be incorporated into realistic geometric and resistive head models to improve resolution in EEG. Influences of these variations on head models, methods for determining these variations, and incorporation into realistic head models, are discussed.