Tracking abnormal network dynamics
Epilepsy in the neonatal period and in childhood can often profoundly disrupt normal brain function. We can use different types of brain recordings to characterise these disruptions. In patients, this is done most commonly through scalp EEG; but visual EEG analysis – particularly in young children – is time-consuming and challenging. Furthermore, certain features of the EEG that describe the relationship between multiple challenge are difficult to appreciate by visual analysis alone.
In this work stream, we are exploring the use of computational analysis of clinical and experimental recordings of brain function to track dynamic network changes in epilepsy and other disorders of brain function. This work aims to identify both biomarkers that can support the diagnostic process in the clinical setting, and dynamic features that may point to a new understanding of underlying disease mechanism.
Multimodal in vivo recording using transparent graphene microelectrodes [paper]
Here we present a technique to map the onset and spatiotemporal spread of acute epileptic seizures in vivo by simultaneously recording high bandwidth microelectrocorticography and calcium fluorescence using transparent graphene microelectrode arrays.
Driscoll et al. (2021) Comms Biol: 10.1038/s42003-021-01670-9
Tracking seizure dynamics with multimodal recordings [preprint]
Through the use of novel transparent electrodes, we can record seizure dynamics with both electrophysiology and calcium imaging - here we present the method and an analysis pipeline.
Driscoll et al (2020) bioRxiv: 10.1101/2020.06.04.134189
Oscillatory brain markers of network function in adults with Down Syndrome [paper]
In this paper we use EEG and Dynamic Causal Modelling to link brain oscillations, cognitive ability, and synaptic function in people living with Down Syndrome.
In this paper we illustrate how summary measures of network dynamics can separate subgroups of epilepsies in early infancy.
Rosch et al (2017) Net Neurosci: 10.1162/netn_a_00026
This code applies dynamic network analysis on clinical EEG recordings, using a dynamic delay-delay matrix approach.
Github: Dynamics Matrices