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

Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterising their residual function with functional magnetic resonance imaging (fMRI) has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is challenging as such detailed characterisation requires testing patients on multitudes of cognitive tasks in the scanner, rendering experimental sessions unfeasibly lengthy. Thus, the current status quo in clinical neuroimaging research involves testing patients on a very limited number of tasks, in the hope that it will reveal a useful neuroimaging biomarker for the whole cohort. Given the great heterogeneity among stroke patients and the volume of possible tasks this approach is unsustainable. Advancing task-based fMRI biomarker discovery requires a paradigm shift in order to be able to swiftly characterise residual network activity in individual patients using a diverse range of cognitive tasks. Here, we overcome this problem by leveraging neuroadaptive Bayesian optimisation, 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. We employ this technique in a cross-sectional study with 11 left-hemispheric stroke patients with chronic aphasia (4 female, age ± SD: 59 ± 10.9 years) and 14 healthy, age-matched controls (8 female, age ± SD: 55.6 ± 6.8 years). To assess intra-subject reliability of the functional profiles obtained, we conducted two independent runs per subject, for which the algorithm was entirely re-initialized. Our results demonstrate that this technique is both feasible and robust, yielding reliable patient-specific functional profiles. Moreover, we show that group-level results are not representative of patient-specific results: Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. In summary, our study highlights the importance of moving beyond traditional "one-size-fits-all" approaches where patients are treated as one group and single tasks are used. Our approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting a diverse range of neurological and psychiatric conditions.