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Session: Poster Session 1 (Even numbers)
Session starts: Thursday 26 January, 16:00
Presentation starts: 16:00

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Nina Doorn (University of Twente)

Abstract:
The brain is still the least understood organ in the human body, and there is a lack of understanding of many neurological disorders. It is vital to develop efficient models of these disorders to uncover disease mechanisms and develop effective therapeutic strategies. Studying the brain in vivo is complicated by the limited possibility to control and invasively measure neuronal functioning. Neuronal networks derived from human induced pluripotent stem cells (hiPSCs) can potentially overcome these issues, as they allow to study physiological and pathological network behaviour in vitro with a patient-specific genetic signature. These networks, derived from healthy subjects and patients, are spontaneously active and show robust and replicable electrophysiological signatures that can be observed using multi-electrode arrays (MEAs). Various genotype/phenotype correlations have been established with this method [1]. Despite these advances, the identification of cellular and synaptic mechanisms underlying abnormal phenotypes remains challenging, as this is not trivial to deduce from the networks’ electrical phenomenology. Therefore, we developed a biophysical in silico model that faithfully reproduces the activity of healthy control neuronal networks on MEAs. To illustrate the potential of the model to test hypotheses and predict underlying disease mechanisms, we studied excitatory neuronal networks derived from a Dravet Syndrome (DS) patient. DS is a highly studied, severe infantile epileptic encephalopathy, caused by mutations in SCN1A encoding part of the voltage-gated sodium channel NaV1.1. It remains a paradox how impaired sodium currents result in epilepsy, especially in excitatory neurons. Our in silico model revealed that sodium channel dysfunctions were insufficient to transition from a model of healthy networks to a model resembling the in vitro DS network behaviour, and that additional alterations were needed. In particular, the model predicted the influence of reduced after-hyperpolarizing currents and synaptic strengths in DS neuronal networks. We subsequently substantiated these in silico generated predictions in vitro, providing new hypotheses about cellular mechanisms at play in the DS neuronal networks. This illustrates the potential of our in silico model to identify important mechanisms that can then be investigated in vitro in a targeted and efficient manner, expanding our understanding of healthy and patient-derived neuronal networks.