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Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is challenging as such detailed characterization 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 functional MRI biomarker discovery requires a paradigm shift in order to be able to swiftly characterize residual network activity in individual patients using a diverse range of cognitive tasks. Here, we overcome this problem by leveraging neuroadaptive Bayesian optimization, an approach combining real-time functional MRI with machine-learning, by intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. We used this technique in a cross-sectional study with 11 left-hemispheric stroke patients with chronic aphasia (four female, age \u00b1 standard deviation: 59\u2009\u00b1\u200910.9\u2009years) and 14 healthy, age-matched control subjects (eight female, age \u00b1 standard deviation: 55.6\u2009\u00b1\u20096.8\u2009years). To assess intra-subject reliability of the functional profiles obtained, we conducted two independent runs per subject, for which the algorithm was entirely reinitialized. 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 \u2018one-size-fits-all\u2019 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.<\/jats:p>","DOI":"10.1093\/brain\/awab109","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T15:12:15Z","timestamp":1615389135000},"page":"2120-2134","source":"Crossref","is-referenced-by-count":27,"title":["A Bayesian optimization approach for rapidly mapping residual network function in stroke"],"prefix":"10.1093","volume":"144","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7742-3162","authenticated-orcid":false,"given":"Romy","family":"Lorenz","sequence":"first","affiliation":[{"name":"MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK"},{"name":"Stanford University, Stanford, CA 94305, USA"},{"name":"Max-Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04303, 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UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Geranmayeh","sequence":"additional","affiliation":[{"name":"Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London W12 0NN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,3,16]]},"reference":[{"key":"2021081715520456000_awab109-B1","author":"B\u00e9jot"},{"key":"2021081715520456000_awab109-B2","author":"Brady","year":"2016"},{"key":"2021081715520456000_awab109-B3","author":"Elsner","year":"2019"},{"issue":"2","key":"2021081715520456000_awab109-B4","doi-asserted-by":"crossref","first-page":"e024429","DOI":"10.1136\/bmjopen-2018-024429","article-title":"Addressing cognitive impairment following stroke: Systematic review and meta-analysis of non-randomized controlled studies of psychological 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