Frontiers in Neuroscience (Dec 2023)

The accuracy of different mismatch negativity amplitude representations in predicting the levels of consciousness in patients with disorders of consciousness

  • Kang Zhang,
  • Kexin Li,
  • Chunyun Zhang,
  • Xiaodong Li,
  • Shuai Han,
  • Chuanxiang Lv,
  • Jingwei Xie,
  • Xiaoyu Xia,
  • Xiaoyu Xia,
  • Li Bie,
  • Yongkun Guo,
  • Yongkun Guo

DOI
https://doi.org/10.3389/fnins.2023.1293798
Journal volume & issue
Vol. 17

Abstract

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IntroductionThe 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 (VS/UWS) and minimally conscious state (MCS). In this study, we evaluated the accuracy of different MMN amplitude representations in predicting levels of consciousness.MethodsTask-state electroencephalography (EEG) data were obtained from 67 patients with DoC (35 VS and 32 MCS). We performed a microstate analysis of the task-state EEG and used four different representations (the peak amplitude of MMN at electrode Fz (Peak), the average amplitude within a time window −25– 25 ms entered on the latency of peak MMN component (Avg for peak ± 25 ms), the average amplitude of averaged difference wave for 100–250 ms (Avg for 100–250 ms), and the average amplitude difference between the standard stimulus (“S”) and the deviant stimulus (“D”) at the time corresponding to Microstate 1 (MS1) (Avg for MS1) of the MMN amplitude to predict the levels of consciousness.ResultsThe 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).ConclusionWe discovered a prediction model based on microstate calculation of MMN amplitude can accurately distinguish between MCS and VS states. And the functional connection of the MS1 is consistent with the activation mode of MMN.

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