IF 3 Q2 PSYCHIATRY
Xiaodan Lyu, Tiantian Liu, Yunxiao Ma, Li Wang, Jinglong Wu, Tianyi Yan, Miaomiao Liu, Jiajia Yang
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引用次数: 0

摘要

精神分裂症的临床症状千差万别,其中最显著的症状是认知障碍和知觉障碍。认知障碍通常与自上而下机制的异常有关,而知觉障碍则源于自下而上处理过程的功能障碍。然而,精神分裂症主要是由自上而下的控制机制、自下而上的感知过程还是它们之间的相互作用所导致的,目前仍不清楚。我们假设,自上而下和自下而上的异常互动构成了精神分裂症的神经机制。考虑到自动编码器可以识别隐藏的数据特征,支持向量机能够自动定位分类超平面,我们开发了一种改进的堆叠自动编码器-支持向量机(ISAE-SVM)模型,用于基于静息态功能磁共振成像数据诊断精神分裂症。采用置换检验从模型输出特性中识别出213个最具鉴别力的功能连接。提取了连接高级认知功能区和低级感知任务区的功能连接,以进一步研究它们与临床症状的相关性。最后,利用频谱动态因果建模(sDCM)分析了这些功能连接对应的脑区之间的动态因果互动。结果显示,ISAE-SVM 模型的平均分类准确率达到了 82%。值得注意的是,横跨认知和感觉两个脑区的五个静息态功能连接与正性和负性综合征量表评分显著相关。此外,sDCM 分析表明,精神分裂症患者自上而下的调节作用减弱,自下而上的信号转导增强。这些发现支持了我们的假设,即自上而下的调节功能受损和自下而上的信号转导功能增强是精神分裂症的神经机制之一。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weaker top-down cognitive control and stronger bottom-up signaling transmission as a pathogenesis of schizophrenia.

The clinical symptoms of schizophrenia are highly heterogeneous, with the most striking symptoms being cognitive deficits and perceptual disturbances. Cognitive deficits are typically linked to abnormalities in top-down mechanisms, whereas perceptual disturbances stem from dysfunctions in bottom-up processing. However, it remains unclear whether schizophrenia is primarily driven by top-down control mechanisms, bottom-up perceptual processes, or their interaction. We hypothesized that abnormal top-down and bottom-up interactions constitute the neural mechanisms of schizophrenia. Considering that autoencoders can identify hidden data features and support vector machines are capable of automatically locating the classification hyperplane, we developed an improved stacked autoencoder-support vector machine (ISAE-SVM) model for diagnosing schizophrenia based on resting-state functional magnetic resonance imaging data. A permutation test was used to identify the 213 most discriminative functional connections from the model's output features. Functional connections linking regions of higher cognitive functions and lower perceptual tasks were extracted to further examine their relevance to clinical symptoms. Finally, spectral dynamic causal modeling (sDCM) was used to analyze the dynamic causal interaction between brain regions corresponding to these functional connections. Our results showed that the ISAE-SVM model achieved an average classification accuracy of 82%. Notably, five resting-state functional connections spanning both cognitive and sensory brain areas were significantly correlated with Positive and Negative Syndrome Scale scores. Furthermore, sDCM analysis revealed weakened top-down regulation and enhanced bottom-up signaling in schizophrenia. These findings support our hypothesis that impaired top-down regulation and enhanced bottom-up signaling contribute to the neural mechanisms of schizophrenia.

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