精神分裂症患者脑电活动复杂性增加的可能神经病理机制:一项计算研究。

Ali Khaleghi, Mohammad Reza Mohammadi, Kian Shahi, Ali Motie Nasrabadi
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引用次数: 7

摘要

目的:精神分裂症是一种复杂的神经发育疾病,与大脑皮层和神经网络的不同缺陷有关,导致脑电波的不规则性。我们打算在这个计算研究中检查的这种不规则性已经提出了各种神经病理学假设。方法:采用基于细胞自动机的神经元群数学模型,检验关于精神分裂症神经病理学的两个假设:第一,降低神经元刺激阈值以增加神经元兴奋性;第二,增加兴奋性神经元的比例,减少抑制性神经元的比例,以增加神经元群体中兴奋与抑制的比例。然后,我们使用Lempel-Ziv复杂性度量将模型在两种情况下产生的输出信号的复杂性与真实健康静息状态脑电图(EEG)信号进行比较,看看这些变化是否会改变(增加或减少)神经元种群动态的复杂性。结果:降低神经元刺激阈值(即第一个假设)后,神经网络复杂度的模式和幅度没有明显变化,模型复杂度与真实脑电信号的复杂度非常接近(P > 0.05)。然而,增加激励抑制比(即第二个假设)会导致设计网络的复杂性模式发生显著变化(P < 0.05)。更有趣的是,在这种情况下,与真实健康脑电图(P = 0.002)和不变条件(P = 0.028)和第一个假设(P = 0.001)的模型输出相比,模型输出信号的复杂性显著增加。结论:我们的计算模型表明,神经网络中兴奋抑制比的不平衡可能是异常神经元放电模式的来源,从而导致精神分裂症患者脑电活动的复杂性增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study.

Objective: Schizophrenia is a complex neurodevelopmental illness that is associated with different deficits in the cerebral cortex and neural networks, resulting in irregularity of brain waves. Various neuropathological hypotheses have been proposed for this irregularity that we intend to examine in this computational study. Method : We used a mathematical model of a neuronal population based on cellular automata to examine two hypotheses about the neuropathology of schizophrenia: first, reducing neuronal stimulation thresholds to increase neuronal excitability; and second, increasing the percentage of excitatory neurons and decreasing the percentage of inhibitory neurons to increase the excitation to inhibition ratio in the neuronal population. Then, we compare the complexity of the output signals produced by the model in both cases with real healthy resting-state electroencephalogram (EEG) signals using the Lempel-Ziv complexity measure and see if these changes alter (increase or decrease) the complexity of the neuronal population dynamics. Results: By lowering the neuronal stimulation threshold (i.e., the first hypothesis), no significant change in the pattern and amplitude of the network complexity was observed, and the model complexity was very similar to the complexity of real EEG signals (P > 0.05). However, increasing the excitation to inhibition ratio (i.e., the second hypothesis) led to significant changes in the complexity pattern of the designed network (P < 0.05). More interestingly, in this case, the complexity of the output signals of the model increased significantly compared to real healthy EEGs (P = 0.002) and the model output of the unchanged condition (P = 0.028) and the first hypothesis (P = 0.001). Conclusion: Our computational model suggests that imbalances in the excitation to inhibition ratio in the neural network are probably the source of abnormal neuronal firing patterns and thus the cause of increased complexity of brain electrical activity in schizophrenia.

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来源期刊
Iranian Journal of Psychiatry
Iranian Journal of Psychiatry Medicine-Psychiatry and Mental Health
CiteScore
4.00
自引率
0.00%
发文量
42
审稿时长
4 weeks
期刊最新文献
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