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Dynamic Causal Modelling of Dynamic Dysfunction in NMDA-Receptor Antibody Encephalitis

  • Richard E. RoschEmail author
  • Gerald Cooray
  • Karl J. Friston
Chapter
Part of the Springer Series in Bio-/Neuroinformatics book series (SSBN, volume 6)

Abstract

Using electroencephalography (EEG) dynamic brain function can be measured and its abnormalities identified and described. However, inferring pathological mechanisms from EEG recordings is an ill-posed, inverse problem. Here we illustrate the use of neural mass model based dynamic causal modelling to address this inverse problem. Using Bayesian model inversion and model comparison, DCM allows evaluation of different hypotheses regarding pathomechanisms leading to dynamic brain dysfunction in NMDA receptor encephalitis.

Keywords

Model Inversion Model Evidence Neural Mass Model Dynamic Causal Modelling NMDAR Antibody 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

RER is funded by a Wellcome Trust Clinical Research (106556/Z/14/Z). KJF is funded by a Wellcome Trust Principal Research Fellowship (088130/Z/09/Z).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Richard E. Rosch
    • 1
    • 2
    Email author
  • Gerald Cooray
    • 1
    • 3
  • Karl J. Friston
    • 1
  1. 1.Wellcome Trust Centre for NeuroimagingInstitute of Neurology, University College LondonLondonUK
  2. 2.Centre for Developmental Cognitive NeuroscienceInstitute of Child Health, University College LondonLondonUK
  3. 3.Department of Clinical NeurophysiologyKarolinska University HospitalStockholmSweden

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