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A Bayesian optimization approach for rapidly mapping residual network function in stroke. ...
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Predictive Neural Computations Support Spoken Word Recognition: Evidence from MEG and Competitor Priming. ...
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A Bayesian optimization approach for rapidly mapping residual network function in stroke.
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Abstract:
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 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 ± standard deviation: 59 ± 10.9 years) and 14 healthy, age-matched control subjects (eight female, age ± standard deviation: 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 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 '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.
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Keyword:
Adult; Aged; Bayes Theorem; Brain; Brain Mapping; Computer-Assisted; Female; Humans; Image Interpretation; Machine Learning; Magnetic Resonance Imaging; Male; Middle Aged; Stroke
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URL: https://www.repository.cam.ac.uk/handle/1810/316163
https://doi.org/10.17863/CAM.63271
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| 4 |
Predictive Neural Computations Support Spoken Word Recognition: Evidence from MEG and Competitor Priming.
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Rapid computations of spectrotemporal prediction error support perception of degraded speech. ...
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Chunking and redintegration in verbal short-term memory. ...
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Comparison of Frequency Transposition and Frequency Compression for People With Extensive Dead Regions in the Cochlea. ...
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Dynamic integration of conceptual information during learning.
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In: PloS one, vol 13, iss 11 (2018)
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ИНТЕЛЛЕКТУАЛЬНЫЙ ФИЛЬТР ЭЛЕКТРОННЫХ СООБЩЕНИЙ ... : INTELLIGENT FILTER FOR THE ELECTRONIC MESSAGES ...
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Rational irrationality: modeling climate change belief polarization using bayesian networks
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Language phylogenies reveal expansion pulses and pauses in Pacific settlement
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In: Science (2015)
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A Bayesian framework for knowledge attribution: evidence from semantic integration.
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Forensic Speaker Recognition at the beginning of the twenty-first century - an overview and a demonstration
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In: Australian Journal of Forensic Sciences (2015)
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Strength of forensic speaker identification evidence: multispeaker formant- and cepstrum-based segmental discrimination with a Bayesian likelihood ratio as threshold
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In: Forensic Linguistics: The International Journal of Speech, Language and the Law (2015)
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Mapping the Origins and Expansion of the Indo-European Language Family
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In: Science (2015)
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Strength of forensic speaker identification evidence: multispeaker formant- and cepstrum-based segmental discrimination with a Bayesian likelihood ratio as threshold
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In: Forensic Linguistics: The International Journal of Speech, Language and the Law (2015)
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The Riddle of Tasmanian languages
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In: Royal Society of London. Proceedings B. Biological Sciences (2015)
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Forensic Speaker Recognition at the beginning of the twenty-first century - an overview and a demonstration
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In: Australian Journal of Forensic Sciences (2015)
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