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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.
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Electroencephalography (EEG) is a brain imaging technology that is noninvasive, cost effective, and provides millisecond temporal resolution. Improved spatial resolution of EEG measures can benefit multiple clinical and research applications, including the assessment of Traumatic Brain Injury (TBI), stroke, and neurodevelopmental disorders. Recent advances in electrode arrays have made it feasible to achieve dense array sampling (128, 256 and 512 channels) of brain potentials on the head surface, and then localize the sources of the measured fields to the surface of the cortex to provide spatially resolved information. Accurate dense array source localization requires i) moving beyond simplistic models of the human head (such as homogeneous multi-shell spheres) and ii) accurate knowledge of regional conductivities of head tissues. These requirements are particularly important for children because the size, shape and electrical properties of the head tissues undergo rapid developmental changes from infancy through adolescence. In this paper we apply high performance computing with finite difference methods (FDM) to solve the forward EEG problem with skull and head conductivity models that are appropriate for children as well as adults. We show that the improved structural (MRI and CT based) head models may improve high-resolution EEG source localization by correcting systematic biases in EEG source localization due to conductivity misspecifications and structural uncertainties. We also demonstrate how these same advances in electromagnetic head models may be used to model effects of non-invasive brain stimulation such as Transcranial Magnetic Stimulation (TMS) and Transcranial Electrical Stimulation (TES)
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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.