Learn More
The influence of gray and white matter tissue anisotropy on the human electroencephalogram (EEG) and magnetoencephalogram (MEG) was examined with a high resolution finite element model of the head of an adult male subject. The conductivity tensor data for gray and white matter were estimated from magnetic resonance diffusion tensor imaging. Simulations were(More)
Knowledge of the electrical conductivity properties of excitable tissues is essential for relating the electromagnetic fields generated by the tissue to the underlying electrophysiological currents. Efforts to characterize these endogenous currents from measurements of the associated electromagnetic fields would significantly benefit from the ability to(More)
Recently, we described a Bayesian inference approach to the MEG/EEG inverse problem that used numerical techniques to estimate the full posterior probability distributions of likely solutions upon which all inferences were based [Schmidt, D.M., George, J.S., Wood, C.C., 1999. Bayesian inference applied to the electromagnetic inverse problem. Human Brain(More)
We present an analysis of atmospheric neutrino data from a 33.0 kiloton-year (535-day) exposure of the Super{Kamiokande detector. The data exhibit a zenith angle dependent decit of muon neutrinos which is inconsistent with expectations based on calculations of the atmospheric neutrino ux. Experimental biases and uncertainties in the prediction of neutrino(More)
Integrated analyses of human anatomical and functional measurements offer a powerful paradigm for human brain mapping. Magnetoencephalography (MEG) and EEG provide excellent temporal resolution of neural population dynamics as well as capabilities for source localization. Anatomical magnetic resonance imaging (MRI) provides excellent spatial resolution of(More)
We present a new approach to the electromagnetic inverse problem that explicitly addresses the ambiguity associated with its ill-posed character. Rather than calculating a single "best" solution according to some criterion, our approach produces a large number of likely solutions that both fit the data and any prior information that is used. Whereas the(More)
Collecting continuous video together with multichannel electrophysiological data and other experimental modalities requires high bandwidth and storage capacities, as well as accurate synchronization to detect correlations between different recorded events. Often, experiments are highly complex, with many variables requiring immediate analysis and feedback(More)
High-frequency oscillatory potentials (HFOPs) have been recorded from ganglion cells in cat, rabbit, frog, and mudpuppy retina and in electroretinograms (ERGs) from humans and other primates. However, the origin of HFOPs is unknown. Based on patterns of tracer coupling, we hypothesized that HFOPs could be generated, in part, by negative feedback from(More)
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(More)
We investigate the effect of the magnetic field generated by neural activity on the magnitude and phase of the MRI signal in terms of a phenomenological parameter with the dimensions of length; it involves the product of the strength and duration of these currents. We obtain an analytic approximation to the MRI signal when the neuromagnetically induced(More)