Publications by authors named "Matlachov A"

A variety of techniques have been developed to noninvasively image human brain function that are central to research and clinical applications endeavoring to understand how the brain works and to detect pathology (e.g. epilepsy, schizophrenia, etc.

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Interest in nuclear magnetic resonance measurements at ultra-low magnetic fields (ULF, approximately microT fields) has been motivated by various benefits and novel applications including narrow NMR peak-width, negligible susceptibility artifacts, imaging of samples inside metal containers, and possibility of directly imaging neuronal currents. ULF NMR/MRI is also compatible with simultaneous measurements of biomagnetic signals. However the most widely used technique in ULF NMR-prepolarization at high field and measurement at lower field-results in large transient signals which distort the free induction decay signal.

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We have obtained 1H NMR spectra simultaneously with high temporal resolution biomagnetic signals such as the magnetocardiogram (MCG) and magnetomyogram (MMG). The NMR spectra are acquired at measurement fields of 2-50 microT, with corresponding proton Larmor frequencies of 80-2000 Hz. Our measurements demonstrate a method suitable for MR imaging with concurrent measurement of biomagnetic signals that can provide sub-millisecond temporal resolution.

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A system that simultaneously measures magnetoencephalography (MEG) and nuclear magnetic resonance (NMR) signals from the human brain was designed and fabricated. A superconducting quantum interference device (SQUID) sensor coupled to a gradiometer pickup coil was used to measure the NMR and MEG signals. 1H NMR spectra with typical Larmor frequencies from 100-1000 Hz acquired simultaneously with the evoked MEG response from a stimulus to the median nerve are reported.

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We have built an NMR system that employs a superconducting quantum interference device (SQUID) detector and operates in measurement fields of 2-25 microT. The system uses a pre-polarizing field from 4 to 30 mT generated by simple room-temperature wire-wound coils that are turned off during measurements. The instrument has an open geometry with samples located outside the cryostat at room-temperature.

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Perhaps the greatest impediment to acquiring high-quality magnetoencephalography (MEG) recordings is the ubiquitous ambient magnetic field noise. We have designed and built a whole-head MEG system using a helmet-like superconducting imaging surface (SIS) surrounding the array of superconducting quantum interference device (SQUID) magnetometers used to measure the MEG signal. We previously demonstrated that the SIS passively shields the SQUID array from ambient magnetic field noise, independent of frequency, by 25-60 dB depending on sensor location.

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A mathematical model (sigma(omega) approximately equal to A omega alpha, where, sigma is identical with conductivity, omega = 2 pi f is identical with applied frequency (Hz), A (amplitude) and alpha (unit less) is identical with search parameters) was used to fit the frequency dependence of electrical conductivities of compact, spongiosum, and bulk layers of the live and, subsequently, dead human skull samples. The results indicate that the fit of this model to the experimental data is excellent. The ranges of values of A and alpha were, spongiform (12.

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Electrical conductivities of compact, spongiosum, and bulk layers of the live human skull were determined at varying frequencies and electric fields at room temperature using the four-electrode method. Current, at higher densities that occur in human cranium, was applied and withdrawn over the top and bottom surfaces of each sample and potential drop across different layers was measured. We used a model that considers variations in skull thicknesses to determine the conductivity of the tri-layer skull and its individual anatomical structures.

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In this study, electrical conductivities of compact, spongiosum, and bulk layers of cadaver skull were determined at varying electric fields at room temperature. Current was applied and withdrawn over the top and bottom surfaces of each sample and potential drop across different layers was measured using the four-electrode method. We developed a model, which considers of variations in skull thicknesses, to determine the conductivity of the tri-layer skull and its individual anatomical structures.

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