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较弱的自上而下认知控制和较强的自下而上信号传递作为精神分裂症的一种发病机制。

Weaker top-down cognitive control and stronger bottom-up signaling transmission as a pathogenesis of schizophrenia.

作者信息

Lyu Xiaodan, Liu Tiantian, Ma Yunxiao, Wang Li, Wu Jinglong, Yan Tianyi, Liu Miaomiao, Yang Jiajia

机构信息

Cognitive Neuroscience Lab, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan.

School of Medical Technology, Beijing Institute of Technology, Beijing, China.

出版信息

Schizophrenia (Heidelb). 2025 Mar 5;11(1):36. doi: 10.1038/s41537-025-00587-0.

Abstract

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.

摘要

精神分裂症的临床症状高度异质,最显著的症状是认知缺陷和感知障碍。认知缺陷通常与自上而下机制的异常有关,而感知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驱动,仍不清楚。我们假设自上而下和自下而上的异常相互作用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,我们基于静息态功能磁共振成像数据开发了一种改进的堆叠自动编码器 - 支持向量机(ISAE - SVM)模型来诊断精神分裂症。使用置换检验从模型的输出特征中识别出213个最具区分性的功能连接。提取连接更高认知功能区域和更低感知任务区域的功能连接,以进一步检查它们与临床症状的相关性。最后,使用频谱动态因果模型(sDCM)分析与这些功能连接相对应的脑区之间的动态因果相互作用。我们的结果表明,ISAE - SVM模型的平均分类准确率达到了82%。值得注意的是,跨越认知和感觉脑区的五个静息态功能连接与阳性和阴性症状量表得分显著相关。此外,sDCM分析显示精神分裂症患者自上而下的调节减弱,自下而上的信号增强。这些发现支持了我们的假设,即自上而下调节受损和自下而上信号增强促成了精神分裂症的神经机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae3a/11883009/370287bbdf29/41537_2025_587_Fig1_HTML.jpg

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