Abstract:
The EEG signal is a highly time-resolved but spatially coarse-grained read-out of ongoing neuronal function. Despite the complexity of the underlying connectomic brain architecture, there are several recognisable EEG-markers of healthy or pathological brain states. Increasingly we are now able to capture synaptic-level processes that may be disrupted in certain brain pathologies, but linking synaptic-level information and whole-brain neurophysiological recordings remains challenging.
In this work we illustrate how fitting generative models to neurophysiological brain data - through dynamic causal modelling - can help in identifying candidate synaptic drivers of observable health and pathological EEG patterns. We harness (1) small case series focussing on identifying EEG markers of known synaptic alterations, and (2) normative whole-brain intracranial EEG and synaptic data to demonstrate with worked examples how synaptic information can contribute to effective modelling of neurophysiological dynamics.
