Dynamic Signatures of Molecular Disorders

Over the last few decades, new technologies have revolutionised our ability to identify faulty genes and other molecular causes of childhood conditions. Yet even where we identify genetic mutations, or abnormal auto-antibodies as the cause for a particular condition, understanding the link between these abnormalities at the smallest scale with the whole brain dysfunction they cause remains challenging. 

One possible approach to improve our understanding is computational modelling. We can try out how well different models explain the EEG abnormalities we can observe, and link the model parameters back to disruptions at the scale of individual neurons. This work integrates recent advances in how models can be 'inverted' to explain EEG data (e.g. through dynamic causal modelling) and a rich history of models of neuronal populations (e.g. neural mass models). 

We are applying this approach to patient cohorts, animal models, and some healthy study participants to understand the convergent paths towards specific EEG abnormalities. This may help us to develop biomarkers of specific disorders in the future, and in turn holds the potential to improve our ability to tailor treatments to those patients that are most likely to respond. 

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Collaborators

Torsten Baldeweg
Professor of Developmental Cognitive Neuroscience
University College London,
London (UK)

Gerald Cooray
Clinical Neurophysiologist
Karolinska Institute
Stockholm (SWE)

Karl Friston
Professor of Theoretical Neurobiology
University College London
London (UK)

Sushma Goyal
Consultant Neurophysiologist
Evelina London Children's Hospital,
London (UK)

Ming Lim
Consultant Paediatric Neurologist
Evelina London Children's Hospital, 
London (UK)

Deb Pal
Professor of Paediatric Epilepsy
King's College London
London (UK)

Sukhvir Wright
ERUK Research Fellow
Aston University
Birmingham (UK)


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Using computational models allows us to translate between different types of observations: Here we link patient EEG recordings with observations made in an experimental mouse model. 
Rosch et al (2017) bioRxiv: 10.1101/160309

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Linking EEG abnormalities and model parameters [chapter]

Here we discuss the different ways computational models can be used to explain EEG abnormalities and illustrate one approach using NMDAR-Ab encephalitis as an example. 
Rosch et al (2016) 10.1007/978-3-319-49959-8_6 [link to author manuscript]