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

Romy Lorenz1,2,3, Michelle Johal4, Frederic Dick5

  • 1MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK.

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|March 16, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neuroimaging approach using machine learning to rapidly map individual brain network function after stroke. This method reveals unique patient profiles, improving precision medicine for neurological conditions.

Keywords:
chronic strokeclosed-loopcognitionfunctional neuroimagingmachine learning

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomarker Discovery

Background:

  • Post-stroke cognitive and linguistic impairments significantly impact recovery, with limited therapeutic options.
  • Current neuroimaging methods for stroke recovery are time-consuming and often use a limited task set, hindering personalized rehabilitation.
  • Heterogeneity among stroke patients necessitates a shift from group-level analysis to individual-specific characterization of brain function.

Purpose of the Study:

  • To develop and validate a novel neuroimaging technique for rapid, patient-specific mapping of domain-general brain network function.
  • To overcome the limitations of lengthy experimental sessions in task-based functional MRI for stroke patients.
  • To establish reliable biomarkers for guiding personalized rehabilitation strategies in stroke recovery.

Main Methods:

  • Leveraged neuroadaptive Bayesian optimization, combining real-time functional MRI with machine learning.
  • Intelligently searched across a diverse range of cognitive tasks to map residual network activity.
  • Conducted a cross-sectional study with 11 chronic aphasia stroke patients and 14 healthy controls, with two independent runs per subject for reliability assessment.

Main Results:

  • Demonstrated the feasibility and robustness of the neuroadaptive Bayesian optimization technique in generating reliable patient-specific functional profiles.
  • Showed that group-level results are not representative of patient-specific findings, highlighting individual variability.
  • Identified idiosyncratic network abnormality profiles in stroke patients associated with behavioral performance, contrasting with highly similar control profiles.

Conclusions:

  • The developed technique enables swift characterization of residual network activity, moving beyond traditional 'one-size-fits-all' approaches.
  • Patient-specific functional profiles are crucial for understanding stroke recovery and guiding personalized interventions.
  • This approach holds promise for precision medicine in neurological and psychiatric conditions, with potential extensions to other brain networks and therapeutic combinations.