Neuromagnetic source reconstruction
- Los Alamos National Lab., NM (United States)
- University of Southern California, Los Angeles, CA (United States)
In neuromagnetic source reconstruction, a functional map of neural activity is constructed from noninvasive magnetoencephalographic (MEG) measurements. The overall reconstruction problem is under-determined, so some form of source modeling must be applied. We review the two main classes of reconstruction techniques-parametric current dipole models and nonparametric distributed source reconstructions. Current dipole reconstructions use a physically plausible source model, but are limited to cases in which the neural currents are expected to be highly sparse and localized. Distributed source reconstructions can be applied to a wider variety of cases, but must incorporate an implicit source, model in order to arrive at a single reconstruction. We examine distributed source reconstruction in a Bayesian framework to highlight the implicit nonphysical Gaussian assumptions of minimum norm based reconstruction algorithms. We conclude with a brief discussion of alternative non-Gaussian approachs.
- Research Organization:
- Los Alamos National Lab., NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 10115240
- Report Number(s):
- LA-UR--94-4378; CONF-9505158--1; ON: DE95005242; CNN: NIMH Grant R01-MH-53213
- Country of Publication:
- United States
- Language:
- English
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