Integrated Modelling of Brain Dynamics, Genetics, and Behavior

Understanding how genetics and brain circuits shape dynamic brain states and behaviour is a central challenge in neuroscience and medicine. This requires building models that can link genetic variation, brain structure and activity (e.g. fMRI), and behavioural outcomes. These models are highly nonlinear, time-dependent, and high-dimensional. Classical methods struggle to efficiently learn and fit such models, particularly at population scale.

Quantum neural network models (e.g. Quantum Boltzmann Machines, Quantum Variational Autoencoders, Hidden Quantum Markov Models) can more efficiently model complex, nonlinear relationships and time-dependent brain dynamics. Quantum solvers for linear and nonlinear differential equations can accelerate the fitting of dynamical brain models. Together, these approaches enable more accurate, scalable modelling of integrated brain-behaviour-genetics systems.

These methods could provide richer, more actionable insights into the links between brain function, genetics, and behaviour, improving next-generation digital phenotyping and clinical decision-making.