Neuro-metabolic systems in humans

clinical

We investigate how neural, autonomic, and metabolic signals interact in human physiology using multimodal observational data. We focus on identifying relationships and structure within real world physiological recordings in clinical and non-clinical settings. This includes wearable sensing platforms such as Fitbit derived activity and autonomic biomarkers, combined with continuous glucose monitoring. The goal is to characterise how changes in autonomic state relate to metabolic dynamics over time, and to identify predictive or coupled patterns across these systems. Particular interest lies in conditions where these interactions may be altered, including epilepsy and mild anxiety, as well as broader variations in physiological and behavioural state. This work establishes a data driven framework for understanding neuro-metabolic interactions in humans under naturalistic conditions.

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