Computational modelling of cardiac control following myocardial infarction using an in silico patient cohort.

Loss of cardiac physiological function following myocardial infarction (MI) is accompanied by neural adaptations in the baroreflex that are compensatory in the short term, but then become associated with long-term disease progression. One marker of these adaptations is decreased baroreflex sensitivity, a strong predictor of post-MI mortality. The relative contributions of cardiac remodelling and neural adaptation in the sensory, central brainstem and peripheral ganglionic loci to baroreflex sensitivity changes remain underexplored.

Closed-loop modeling of central and intrinsic cardiac nervous system circuits underlying cardiovascular control.

The baroreflex is a multi-input, multi-output control physiological system that regulates blood pressure by modulating nerve activity between the brainstem and the heart. Existing computational models of the baroreflex do not explictly incorporate the intrinsic cardiac nervous system (ICN), which mediates central control of the heart function. We developed a computational model of closed-loop cardiovascular control by integrating a network representation of the ICN within central control reflex circuits.

Unpacking the multimodal, multi-scale data of the fast and slow lanes of the cardiac vagus through computational modelling.

The vagus nerve is a key mediator of brain-heart signaling, and its activity is necessary for cardiovascular health. Vagal outflow stems from the nucleus ambiguus, responsible primarily for fast, beat-to-beat regulation of heart rate and rhythm, and the dorsal motor nucleus of the vagus, responsible primarily for slow regulation of ventricular contractility. Due to the high-dimensional and multimodal nature of the anatomical, molecular and physiological data on neural regulation of cardiac function, data-derived mechanistic insights have proven elusive.