Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data
Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data
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DOI：
10.5334/cpsy.93
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发表时间：
2023-01
影响因子：
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通讯作者：
Yuta Takahashi;Shingo Murata;Masao Ueki;Hiroaki Tomita;Yuichi Yamashita
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文献类型：
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作者：
Yuta Takahashi;Shingo Murata;Masao Ueki;Hiroaki Tomita;Yuichi Yamashita
功能连接性（FC）和神经兴奋性可能相互作用，影响自闭症谱系障碍（ASD）的症状。我们用神经网络模拟测试了这一假设，并将其应用于功能性磁共振成像（fMRI）。一个体现预测处理理论的分层递归神经网络进行面部情绪识别任务。神经网络模拟研究了FC和神经兴奋性对发展学习神经表征变化的影响，并最终对ASD样表现的影响。接下来，通过基于fMRI参数将每个神经网络条件映射到受试者亚组，检查了模拟中的ASD样表现与相应受试者亚组中的ASD诊断之间的关联。在神经网络模拟中，较低级别网络的神经兴奋性越均匀，性能就越像ASD（降低的泛化和情感识别能力）。此外，在同构网络中，FC越高，ASD样表现越多，而在异构网络中，FC越高，ASD样表现越少，表明FC和神经兴奋性相互作用。神经兴奋性决定了自上而下预测的泛化能力，而模糊控制决定了模型的信息处理是依赖于自上而下的预测还是依赖于自下而上的感觉输入。在fMRI数据集中，ASD实际上在对应于显示ASD样表现的网络条件的受试者亚组中更普遍。目前的研究表明FC和神经兴奋性之间的相互作用，并提出了一个新的框架，计算建模和生物学应用的发展学习过程中的认知改变ASD。
Functional connectivity (FC) and neural excitability may interact to affect symptoms of autism spectrum disorder (ASD). We tested this hypothesis with neural network simulations, and applied it with functional magnetic resonance imaging (fMRI). A hierarchical recurrent neural network embodying predictive processing theory was subjected to a facial emotion recognition task. Neural network simulations examined the effects of FC and neural excitability on changes in neural representations by developmental learning, and eventually on ASD-like performance. Next, by mapping each neural network condition to subject subgroups on the basis of fMRI parameters, the association between ASD-like performance in the simulation and ASD diagnosis in the corresponding subject subgroup was examined. In the neural network simulation, the more homogeneous the neural excitability of the lower-level network, the more ASD-like the performance (reduced generalization and emotion recognition capability). In addition, in homogeneous networks, the higher the FC, the more ASD-like performance, while in heterogeneous networks, the higher the FC, the less ASD-like performance, demonstrating that FC and neural excitability interact. As an underlying mechanism, neural excitability determines the generalization capability of top-down prediction, and FC determines whether the model’s information processing will be top-down prediction-dependent or bottom-up sensory-input dependent. In fMRI datasets, ASD was actually more prevalent in subject subgroups corresponding to the network condition showing ASD-like performance. The current study suggests an interaction between FC and neural excitability, and presents a novel framework for computational modeling and biological application of a developmental learning process underlying cognitive alterations in ASD.