Generative models of brain dynamics in epilepsy

ICNC2024
Symposia: Big Data, Artificial Intelligence and Machine Learning In The Diagnosis and Management of Epilepsy

Generative models of brain dynamics in epilepsy
Jamie Norris

Epilepsy is characterized by abnormal brain dynamics, which can be measured with increasing sophistication in patients and preclinical models. Concurrently, the growing availability of detailed genomic and neuroimaging diagnostics aids our understanding of the synaptic and network constraints under which these pathological brain dynamics emerge. Generative models of brain dynamics facilitate the investigation of how changes in synaptic efficacy or network topology may influence abnormal brain activity. While these models often identify qualitative features of the dynamic landscape, they can be challenging to relate to specific empirical data, particularly at the single-patient level.

The development of efficient artificial intelligence (AI) methods for fitting generative models to empirical data now enables the connection between quantitative observations from preclinical models and patient data to models of epilepsy pathology. Here, we will explore how AI enables the inference of constraints underlying abnormal dynamics in brain recordings from animal models and patients. We will illustrate this using specific examples, ranging from whole-brain single-cell calcium imaging in zebrafish to large-scale normative datasets of intracranial EEG data. We will focus on certain Bayesian model fitting techniques, such as dynamic causal modelling, and demonstrate how these AI methods are poised to impact clinical practice in the near future.

Other Lectures in this symposium:
Machine Learning to improve diagnosis of epilepsy in infancy and potential solution for remote detection.
Multicentre Epilepsy Lesion Detection project: bringing AI into epilepsy presurgical planning
Artificial Intelligence in the diagnosis of epilepsy in resource poor areas.