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Family history of FXTAS is associated with age-related cognitive-linguistic decline among mothers with the FMR1 premutation.
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In: Journal of neurodevelopmental disorders, vol 14, iss 1 (2022)
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Increased connectivity among sensory and motor regions during visual and audiovisual speech perception
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In: Open Access Publications (2022)
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A Therapeutic Relational Agent for Reducing Problematic Substance Use (Woebot): Development and Usability Study.
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In: Journal of medical Internet research, vol 23, iss 3 (2021)
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A Bayesian optimization approach for rapidly mapping residual network function in stroke. ...
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Parental Perceptions and Decisions Regarding Maintaining Bilingualism in Autism. ...
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Age-related differences in the neural bases of phonological and semantic processes in the context of task-irrelevant information.
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Influence of encoding difficulty, word frequency, and phonological regularity on age differences in word naming.
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Infant and Toddler Child-Care Quality and Stability in Relation to Proximal and Distal Academic and Social Outcomes.
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Age-related differences in the neural bases of phonological and semantic processes.
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Age-related differences in resolving semantic and phonological competition during receptive language tasks.
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Predictive Neural Computations Support Spoken Word Recognition: Evidence from MEG and Competitor Priming. ...
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Sustained neural rhythms reveal endogenous oscillations supporting speech perception. ...
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Language networks in aphasia and health: A 1000 participant activation likelihood estimation meta-analysis. ...
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Next-gen sequencing identifies non-coding variation disrupting miRNA-binding sites in neurological disorders
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Validation of the Portuguese version of the Evidence-Based Practice Questionnaire
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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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| 18 |
Predictive Neural Computations Support Spoken Word Recognition: Evidence from MEG and Competitor Priming.
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Parental Perceptions and Decisions Regarding Maintaining Bilingualism in Autism.
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DYT-TUBB4A (DYT4 Dystonia): New Clinical and Genetic Observations.
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In: Neurology, vol. 96, no. 14, pp. e1887-e1897 (2021)
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