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Adaptive Array-Gain Spatial Filtering in Magnetoencephalography

Maloney, Thomas C.
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1273001694

Abstract Details

2010, MS, University of Cincinnati, Arts and Sciences : Physics.
This paper analyzes adaptive array-gain spatial filtering as a way of interpreting magnetoencephalography (MEG) data to locate centers of neuronal activity within the brain. When neurons in the brain communicate they generate an electric current which, in turn, generates a magnetic field. MEG can record this magnetic signal when it is generated simultaneously in multiple neurons. The features of MEG include good spatial resolution, very good temporal resolution, and no radiation. Currently, inverse modeling is used in the clinical setting to interpret the MEG signal. Since there is no unique solution to the inverse method, the results rely on the skilled interpretation of a neurologist to evaluate their concordance with the patient’s clinical symptoms. Spatial filtering is a way of locating sources of power within the brain that avoids having to use inverse methods and, therefore, avoids many of the localization errors and artifacts. Spatial filtering accomplishes this by using the relationship between the signals from the individual detectors, in the form of a covariance matrix, along with the known detector sensitivity to magnetic dipoles, known as the lead field. In this study, the head is modeled as a homogenous conducting sphere and the neuronal activity is modeled as an electrical current dipole. Both patient data and simulated data are analyzed in this study. For the simulated data, dipoles at various positions and orientations are analyzed with and without various levels of Gaussian white noise added in. The results of this study show that adaptive array-gain spatial filtering has the potential to become a standard method in the localization of neuronal activity centers in the brain. With its propensity for reduced error and speed in interpreting the data, spatial filtering may one day help to make MEG a standard procedure in pre-surgical brain mapping, thereby reducing patient morbidity.
Frank Pinski, PhD (Committee Chair)
F Paul Esposito, PhD (Committee Member)
Douglas Rose, MD (Committee Member)
70 p.
Physics
Magnetoencephalography; spatial filtering; epilepsy; neuronal activity

Recommended Citations

Citations

  • Maloney, T. C. (2010). Adaptive Array-Gain Spatial Filtering in Magnetoencephalography [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1273001694

    APA Style (7th edition)

  • Maloney, Thomas. Adaptive Array-Gain Spatial Filtering in Magnetoencephalography. 2010. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1273001694.

    MLA Style (8th edition)

  • Maloney, Thomas. "Adaptive Array-Gain Spatial Filtering in Magnetoencephalography." Master's thesis, University of Cincinnati, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1273001694

    Chicago Manual of Style (17th edition)

ucin1273001694
532
© 2010, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.