The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.
School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Med Biol Eng Comput. 2024 Nov;62(11):3327-3341. doi: 10.1007/s11517-024-03133-9. Epub 2024 Jun 5.
Cognitive disturbance in identifying, processing, and responding to salient or novel stimuli are typical attributes of schizophrenia (SCH), and P300 has been proven to serve as a reliable psychosis endophenotype. The instability of neural processing across trials, i.e., trial-to-trial variability (TTV), is getting increasing attention in uncovering how the SCH "noisy" brain organizes during cognition processes. Nevertheless, the TTV in the brain network remains unrevealed, notably how it varies in different task stages. In this study, resorting to the time-varying directed electroencephalogram (EEG) network, we investigated the time-resolved TTV of the functional organizations subserving the evoking of P300. Results revealed anomalous TTV in time-varying networks across the delta, theta, alpha, beta1, and beta2 bands of SCH. The TTV of cross-band time-varying network properties can efficiently recognize SCH (accuracy: 83.39%, sensitivity: 89.22%, and specificity: 74.55%) and evaluate the psychiatric symptoms (i.e., Hamilton's depression scale-24, r = 0.430, p = 0.022, RMSE = 4.891; Hamilton's anxiety scale-14, r = 0.377, p = 0.048, RMSE = 4.575). Our study brings new insights into probing the time-resolved functional organization of the brain, and TTV in time-varying networks may provide a powerful tool for mining the substrates accounting for SCH and diagnostic evaluation of SCH.
识别、处理和响应显著或新颖刺激时的认知障碍是精神分裂症（SCH）的典型特征，并且已经证明 P300 可作为可靠的精神病潜在表型。在认知过程中，神经处理在试验之间的不稳定性，即试验间变异性（TTV），越来越受到关注，它揭示了 SCH“嘈杂”大脑是如何组织的。然而，大脑网络中的 TTV 仍然未知，特别是它在不同任务阶段如何变化。在这项研究中，我们借助时变定向脑电图（EEG）网络，研究了引发 P300 的功能组织的时变 TTV。结果表明，SCH 横跨 delta、theta、alpha、beta1 和 beta2 频段的时变网络中存在异常 TTV。跨频带时变网络特性的 TTV 可以有效地识别 SCH（准确率：83.39%，灵敏度：89.22%，特异性：74.55%）并评估精神症状（即，汉密尔顿抑郁量表-24，r=0.430，p=0.022，RMSE=4.891；汉密尔顿焦虑量表-14，r=0.377，p=0.048，RMSE=4.575）。我们的研究为探测大脑的时变功能组织提供了新的见解，并且时变网络中的 TTV 可能为挖掘导致 SCH 的基质以及 SCH 的诊断评估提供了有力工具。
