Multi-scale computational modelling of neuronal dynamics in genetic epilepsies Rosch RE1,2, Heilbron M1, Peters C3, Ruben P3, Lim M4, Pal D4,5, Goyal S6, Hughes E4, Baldeweg T2, Friston KJ1 1Wellcome Trust Centre for Neuroimaging, University College London. 2Developmental Neurosciences Programme, University College London. 3Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada. 4Department of Paediatric Neurology, Evelina London Children’s Hospital. 5Department of Basic and Clinical Neuroscience, King’s College London. 6Department of Clinical Neurophysiology, Evelina London Children’s Hospital. Richard E Rosch BMBCh r.rosch@ucl.ac.uk dynamic-brains.com roschkoenig τ3 Thalamic Reticular Thalamo- cortical Intrinsic Thalamic τ4 h3 h1 h4 h2 A τt1 τt2 τt3 RF LF LT RT Th Frontal Cortical Thalamic Temporal Cortical B Neuronal models bridge observational scales in neurological disorders with genetic causes SCN1A channel gating abnormalities Network changes in GRIN2A-epilepsy Single SCN1A mutant causes loss of function and gain of function Sleep dynamics associated with GRIN2A are explained by thalamic microcircuitry Multi-scale models of dynamic pathology reveal links between genotype and phenotype • Exonic deletion identified in GRIN2A gene in a child with acquired aphasia and EEG features suggestive of focal epilepsy • Overnight sleep EEG was performed using telemetry setup to monitor for continuous spike-wave discharges in sleep (CSWS) as observed in affected sibling Can network modelling identify possible causes for abnormal sleep dynamics in this patient? fMRI Developmental Period EEG / MEG Animal Models Cell Models Genetics SCN1A ECOG / SEEG microscale macroscale mesoscale GRIN2A Spatial Scale Modelling describes hidden states • SCN1A-mutation (A1273V, AV) identified in a child with diagnosis of Dravet-spectrum epilepsy, onset at 6 months of age (Fig 1) • Patch clamp measurements in CHOk1 cells at 37 and 40ºC were used to estimate steady state activation, fast inactivation, and fast inactivation recovery curves Can changes of channel dynamics in this patient explain the temperature-sensitive phenotype? Fig 1: SCN1A variant. The patient had heat-induced seizures and a de novo mutation in a transmembrane domain of the SCN1A gene. Fig 3: Simple corticothalamic network model for sleep dynamics. Using dynamic causal modelling, EEG dynamics are explained with a network of cortical (not shown) and thalamic (A) neural mass models connected in a simple distributed sleep network . • Empirical measurements alone often focus on macro- or microscale abnormalities • Emergent properties may be better understood in terms of mesoscale features at the level of neuronal circuits • Computational models allow bottom up or top down inference based on empirical data Fig 4: Hodgkin Huxley model of abnormal cortical action potential dynamics. Empirical patch clamp measures were implemented in HH model for wild type (left) and variant (right) SCN1A, for normal (black) and febrile (red) temperatures. Bottom panels show bifurcation analysis for varying input currents. The AV variant can sustain firing at higher input currents than the wildtype, especially at high temperatures. Fig 2: Empirical techniques. Currently used empirical techniques allow inference on restricted developmental and spatial scales only • A single variant in SCN1A can explain both loss of function (reduced firing frequency), and gain of function (increased tolerance to high inout states) • High temperatures cause distinct gating patterns that cause reduction in depolarision block during febrile temperatures, which may relate to seizure disorder Fig 5: Sleep dynamics can be reduced to limited set of model parameters. Different sleep stages have distinct spectral signatures (A). The differences in spectra between sleep stages can be summaries in a subset of parameters identified using Bayesian Model Comparison (via free energy, B). Two thalamic