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Abstract：
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.
摘要：
目的：精神分裂症是一种复杂的神经发育性疾病，与大脑皮层和神经网络的不同缺陷有关，导致脑电波不规则。针对这种不规则性提出了各种神经病理学假设，我们打算在本计算研究中进行检查。方法：我们使用基于细胞自动机的神经元群体的数学模型来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；其次，增加兴奋性神经元的百分比和减少抑制性神经元的百分比，以增加神经元群体中的兴奋与抑制率。然后，我们使用Lempel-Ziv复杂性度量将两种情况下模型产生的输出信号的复杂性与真实健康的静息状态脑电图(EEG)信号进行比较,并观察这些变化是否改变(增加或减少)神经元种群动态的复杂性.结果：通过降低神经元刺激阈值(即，第一个假设)，没有观察到网络复杂性的模式和幅度的显著变化，模型复杂度与真实脑电信号的复杂度非常相似(P>0.05)。然而,增加激发与抑制率(即，第二个假设)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EEG（P=0.002）以及未改变条件（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着增加。结论：我们的计算模型表明，神经网络中兴奋与抑制率的失衡可能是异常神经元放电模式的根源，因此是精神分裂症脑电活动复杂性增加的原因。
