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Title: Multiple dipole modeling and localization from spatio-temporal MEG data

Abstract

An array of biomagnetometers may be used to measure the spatio-temporal neuromagnetic field or magnetoencephalogram (MEG) produced by neural activity in the brain. A popular model for the neural activity produced in response to a given sensory stimulus is a set of current dipoles, where each dipole represents the primary current associated with the combined activation of a large number of neutrons located in a small volume of the brain. An important problem in the interpretation of MEG data from evoked response experiments is the localization of these neural current dipoles. The authors present here a linear algebraic framework for three common spatio-temporal dipole models: (i) unconstrained dipoles, (ii) dipoles with a fixed location, and (iii) dipoles with a fixed orientation and location. In all cases, they assume that the location, orientation, and magnitude of the dipoles are unknown. With a common model, they show how the parameter estimation problem may be decomposed into the estimation of the time invariant parameter using nonlinear least-squares minimization, followed by linear estimation of the associated time varying parameters. A subspace formulation is presented and used to derive a suboptimal least-squares subspace scanning method. The resulting algorithm is a special case of the well-knownmore » MUltiple SIgnal Classification (MUSIC) method, in which the solution (multiple dipole locations) is found by scanning potential locations using a simple one dipole model.« less

Authors:
 [1];  [2];  [3]
  1. (TRW Systems Engineering and Development Division, Redondo Beach, CA (United States))
  2. (Los Alamos National Laboratory, NM (United States))
  3. (University of Southern California, Los Angeles, CA (United States))
Publication Date:
OSTI Identifier:
6968785
DOE Contract Number:
W-7405-ENG-36
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Bio-Medical Engineering (Institute of Electrical and Electronics Engineers); (United States); Journal Volume: 39:6
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; BRAIN; ELECTROPHYSIOLOGY; NERVE CELLS; BIOELECTRICITY; BIOLOGICAL LOCALIZATION; ELECTROMAGNETIC RADIATION; MAGNETOMETERS; MATHEMATICAL MODELS; ANIMAL CELLS; BODY; CENTRAL NERVOUS SYSTEM; ELECTRICITY; MEASURING INSTRUMENTS; NERVOUS SYSTEM; ORGANS; PHYSIOLOGY; RADIATIONS; SOMATIC CELLS; 551000* - Physiological Systems

Citation Formats

Mosher, J.C., Lewis, P.S., and Leahy, R. Multiple dipole modeling and localization from spatio-temporal MEG data. United States: N. p., 1992. Web. doi:10.1109/10.141192.
Mosher, J.C., Lewis, P.S., & Leahy, R. Multiple dipole modeling and localization from spatio-temporal MEG data. United States. doi:10.1109/10.141192.
Mosher, J.C., Lewis, P.S., and Leahy, R. Mon . "Multiple dipole modeling and localization from spatio-temporal MEG data". United States. doi:10.1109/10.141192.
@article{osti_6968785,
title = {Multiple dipole modeling and localization from spatio-temporal MEG data},
author = {Mosher, J.C. and Lewis, P.S. and Leahy, R.},
abstractNote = {An array of biomagnetometers may be used to measure the spatio-temporal neuromagnetic field or magnetoencephalogram (MEG) produced by neural activity in the brain. A popular model for the neural activity produced in response to a given sensory stimulus is a set of current dipoles, where each dipole represents the primary current associated with the combined activation of a large number of neutrons located in a small volume of the brain. An important problem in the interpretation of MEG data from evoked response experiments is the localization of these neural current dipoles. The authors present here a linear algebraic framework for three common spatio-temporal dipole models: (i) unconstrained dipoles, (ii) dipoles with a fixed location, and (iii) dipoles with a fixed orientation and location. In all cases, they assume that the location, orientation, and magnitude of the dipoles are unknown. With a common model, they show how the parameter estimation problem may be decomposed into the estimation of the time invariant parameter using nonlinear least-squares minimization, followed by linear estimation of the associated time varying parameters. A subspace formulation is presented and used to derive a suboptimal least-squares subspace scanning method. The resulting algorithm is a special case of the well-known MUltiple SIgnal Classification (MUSIC) method, in which the solution (multiple dipole locations) is found by scanning potential locations using a simple one dipole model.},
doi = {10.1109/10.141192},
journal = {IEEE Transactions on Bio-Medical Engineering (Institute of Electrical and Electronics Engineers); (United States)},
number = ,
volume = 39:6,
place = {United States},
year = {Mon Jun 01 00:00:00 EDT 1992},
month = {Mon Jun 01 00:00:00 EDT 1992}
}
  • An array of SQUID biomagentometers may be used to measure the spatio-temporal neuromagnetic field produced by the brain in response to a given sensory stimulus. A popular model for the neural activity that produces these fields is a set of current dipoles. We present here a common linear algebraic framework for three common spatio-temporal dipole models: moving and rotating dipoles, rotating dipoles with fixed location, and dipoles with fixed orientation and location. Our intent here is not to argue the merits of one model over another, but rather show how each model may be solved efficiently, and within the samemore » framework as the others. In all cases, we assume that the location, orientation, and magnitude of the dipoles are unknown. We present the parameter estimation problem for these three models in a common framework, and show how, in each case, the problem may be decomposed into the estimation of the dipole locations using nonlinear minimization followed by linear estimation of the associated moment time series. Numerically efficient means of calculating the cost function are presented, and problems of model order selection and missing moments are also investigated. The methods described are demonstrated in a simulated application to a three dipole problem. 21 refs., 2 figs., 1 tab.« less
  • This work was originally motivated the need to identify and track blob filaments, a feature often associated with instability in magnetically confined fusion plasma. Understanding and mitigating such instability would improve fusion reactors and make fusion a truly inexhaustible source of clean energy. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlappingmore » in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blobs in fusion plasma. On a set of 4.3TB data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.« less
  • No abstract prepared.
  • Experimental results are presented that reveal a complex route to chaos in plasma, in which a Feigenbaum scenario (cascade of temporal period-doubling bifurcation) develops simultaneously with a cascade of spatial period-doubling bifurcations, in connection with the appearance of a non-concentric multiple double layers structure. The Feigenbaum scenario is identified in the time series of the oscillations of the current through the plasma conductor.
  • A simple model for temporal bursting is introduced. This model invokes either dynamic or random forcing of a bifurcation parameter of some simple dynamical system in a way that makes the bifurcation parameter spend suitable amounts of time below and above the bifurcation threshold. This model is extended to coupled map lattices to produce spontaneous spatio-temporal burstings. It models physical systems which are embedded in a random background that is statistically homogeneous in space and time. An application of this model to optical turbulence is discussed. {copyright} {ital 1996 American Institute of Physics.}