A Bayesian optimisation approach for rapidly mapping residual network function in stroke

Post-stroke cognitive and linguistic impairments are debilitating conditions, with current therapies only showing small improvements. Domain-general brain networks seem to play a critical role in stroke recovery and characterising their residual function with functional neuroimaging (fMRI) has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is currently challenging in patients as such detailed characterisation requires too many different cognitive tasks. Here, we use neuroadaptive Bayesian optimisation to overcome this problem, an approach combining real-time fMRI with machine-learning. By intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. This approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting diverse neurological and psychiatric conditions.