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

Rosch, ER; Cooray, G; Friston, KJ; (2017) Dynamic Causal Modelling of Dynamic Dysfunction in NMDA-Receptor Antibody Encephalitis. In: Erdi, P and Bhattacharya, BS and Cochran, A, (eds.) Computational Neurology and Psychiatry. (pp. 121-148). Springer: Cham, Switzerland. Green open access

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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.

Type: Book chapter
Title: Dynamic Causal Modelling of Dynamic Dysfunction in NMDA-Receptor Antibody Encephalitis
ISBN-13: 978-3-319-49959-8
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-49959-8_6
Publisher version: https://doi.org/10.1007/978-3-319-49959-8_6
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Model Inversion, Model Evidence, Neural Mass Model, Dynamic Causal Modelling, NMDAR Antibody
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: http://discovery.ucl.ac.uk/id/eprint/1569667
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