ArticleFrontiers in neuroscience2023
The accuracy of different mismatch negativity amplitude representations in predicting the levels of consciousness in patients with disorders of consciousness.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational predictive processing models of consciousness: a systematic review of non-invasive brain signal analysis in disorders of consciousness.Frontiers in computational neuroscience · 2026Pooled it
- Development of an exploratory mismatch negativity-based prognostic nomogram for consciousness recovery in patients with prolonged disorders of consciousness: a derivation study.BMC neurology · 2025Article
- Clinical Characteristics of Auditory Mismatch Negativity in Patients with Chronic Disorders of Consciousness Following Intracerebral Haemorrhage: A Prognostic Prediction Study.Journal of multidisciplinary healthcare · 2025Article
- Evaluating Brain Activity in Patients With Chronic Disorders of Consciousness After Traumatic Brain Injury Using EEG Microstate Analysis During Hyperbaric Oxygen Therapy.CNS neuroscience & therapeutics · 2025Article
- Differences in the effect of repetitive transcranial magnetic stimulation and median nerve electrical stimulation in patients with prolonged disorders of consciousness after intracerebral hemorrhage: a randomized controlled trial protocol.Frontiers in neurology · 2024Article
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Authors and funding
10 authors.
Funding
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Abstract
Introduction: The mismatch negativity (MMN) index has been used to evaluate consciousness levels in patients with disorders of consciousness (DoC). Indeed, MMN has been validated for the diagnosis of vegetative state/unresponsive wakefulness syndrome ( Methods: Task-state electroencephalography (EEG) data were obtained from 67 patients with DoC (35 Results: The results showed that among the four microstates clustered, MS1 showed statistical significance in terms of time proportion during the 100-250 ms period. Our results confirmed the activation patterns of MMN through functional connectivity analysis. Among the four MMN amplitude representations, the microstate-based representation showed the highest accuracy in distinguishing different levels of consciousness in patients with DoC (AUC = 0.89). Conclusion: We discovered a prediction model based on microstate calculation of MMN amplitude can accurately distinguish between MCS and
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