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Neuroelectromagnetic Source Imaging of Brain Dynamics

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Computational Neuroscience

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 38))

Abstract

Neuroelectromagnetic source imaging (NSI) is the scientific field devoted to modeling and estimating the spatiotemporal dynamics of the neuronal currents that generate the electric potentials and magnetic fields measured with electromagnetic (EM) recording technologies. Unlike functional magnetic resonance imaging (fMRI), which is indirectly related to neuroelectrical activity through neurovascular coupling [e.g., the blood oxygen level-dependent (BOLD) signal], EM measurements directly relate to the electrical activity of neuronal populations. In the past few decades, researchers have developed a great variety of source estimation techniques that are well informed by anatomy, neurophysiology, and the physics of volume conduction. State-of-the-art approaches can resolve many simultaneously active brain regions and their single trial dynamics and can even reveal the spatial extent of local cortical current flows.

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Authors and Affiliations

  1. MEG Program, Department of Neurology, Medical College of Wisconsin and Froedtert Hospital, Milwaukee, WI, USA

    Rey R. Ramírez & Sylvain Baillet

  2. Biomagnetic Imaging Laboratory, University of California San Francisco, San Francisco, CA, USA

    David Wipf

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Correspondence to Rey R. Ramírez , David Wipf or Sylvain Baillet .

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  1. Department of Industrial and Systems, Engineering, Rutgers State University of New Jersey, Frelinghuysen Rd. 96, Piscataway, 08854, New Jersey, USA

    Wanpracha Chaovalitwongse

  2. Dept. Industrial & Systems, Engineering, University of Florida, Weil Hall 303, Gainesville, 32611-6595, Florida, USA

    Panos M. Pardalos

  3. , Dept. of Industrial & Systems Eng., University of Florida, Weil Hall 303, Gainesville, 32611, Florida, USA

    Petros Xanthopoulos

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Ramírez, R.R., Wipf, D., Baillet, S. (2010). Neuroelectromagnetic Source Imaging of Brain Dynamics. In: Chaovalitwongse, W., Pardalos, P., Xanthopoulos, P. (eds) Computational Neuroscience. Springer Optimization and Its Applications(), vol 38. Springer, New York, NY. https://doi.org/10.1007/978-0-387-88630-5_8

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