Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a Transformer

Epilepsy is one of the most common neurological disorders, and many patients require surgical intervention when medication fails to control seizures. For effective surgical outcomes, precise localisation of the epileptogenic focus - often approximated through the Seizure Onset Zone (SOZ) - is critical yet remains a challenge. Active probing through electrical stimulation is already standard clinical practice for identifying epileptogenic areas. This paper advances the application of deep learning for SOZ localisation using Single Pulse Electrical Stimulation (SPES) responses. We achieve this by introducing Transformer models that incorporate cross-channel attention. We evaluate these models on held-out patient test sets to assess their generalisability to unseen patients and electrode placements. Our study makes three key contributions: Firstly, we implement an existing deep learning model to compare two SPES analysis paradigms - namely, divergent and convergent. These paradigms evaluate outward and inward effective connections, respectively. Our findings reveal a notable improvement in moving from a divergent (AUROC: 0.574) to a convergent approach (AUROC: 0.666), marking the first application of the latter in this context. Secondly, we demonstrate the efficacy of the Transformer models in handling heterogeneous electrode placements, increasing the AUROC to 0.730. Lastly, by incorporating inter-trial variability, we further refine the Transformer models, with an AUROC of 0.745, yielding more consistent predictions across patients. These advancements provide a deeper insight into SOZ localisation and represent a significant step in modelling patient-specific intracranial EEG electrode placements in SPES. Future work will explore integrating these models into clinical decision-making processes to bridge the gap between deep learning research and practical healthcare applications.

Bayesian Belief Updating of Spatiotemporal Seizure Dynamics

Epileptic seizure activity shows complicated dynamics in both space and time. To understand the evolution and propagation of seizures spatially extended sets of data need to be analysed. We have previously described an efficient filtering scheme using variational Laplace that can be used in the Dynamic Causal Modelling (DCM) framework [Friston, 2003] to estimate the temporal dynamics of seizures recorded using either invasive or non-invasive electrical recordings (EEG/ECoG). Spatiotemporal dynamics are modelled using a partial differential equation -- in contrast to the ordinary differential equation used in our previous work on temporal estimation of seizure dynamics [Cooray, 2016]. We provide the requisite theoretical background for the method and test the ensuing scheme on simulated seizure activity data and empirical invasive ECoG data. The method provides a framework to assimilate the spatial and temporal dynamics of seizure activity, an aspect of great physiological and clinical importance.