通过拓扑高阶功能连通性揭示精神分裂症的整合-分离失衡。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Qiang Li, Wei Huang, Chen Qiao, Huafu Chen
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引用次数: 0

摘要

背景:脑疾病的发生与脑连接组学专业化中可检测到的功能障碍相关。虽然广泛的研究已经探索了这种关系,但鉴于低阶网络的局限性,缺乏专门研究使用高阶网络检查精神病脑网络整合和分离之间的统计相关性的研究。此外,这些功能障碍被认为与大脑功能中的信息失衡有关。然而,我们对这些失衡如何引起特定精神病症状的理解仍然有限。方法:本研究旨在通过在健康个体和被诊断为精神分裂症的个体中调查系统的拓扑高阶水平的专业化变化来解决这一差距。通过图论脑网络分析,我们系统地研究了信息集成和分离,以描述脑网络连接模式的系统级差异。结果:拓扑高阶功能连接组强调了精神分裂症与健康对照组之间连接组的差异,表明精神分裂症患者扣谷-眼任务控制和显著性相互作用增加,而皮层下网络和默认模式网络、背侧注意和感觉/躯体运动口之间的相互作用减少。此外,我们观察到,与精神分裂症患者相比,健康对照组的大脑系统分离减少,这意味着精神分裂症患者大脑网络分离和整合之间的平衡被破坏,这表明恢复这种平衡可能有助于治疗这种疾病。此外,与健康对照相比,精神分裂症患者脑系统分离增加和整合减少可能作为早期精神分裂症诊断的新指标。结论:我们发现,与低阶功能连接相比,拓扑高阶功能连接突出了脑网络的相互作用。此外,我们观察到与精神分裂症相关的特定大脑区域的变化,以及精神分裂症患者大脑网络信息整合和分离的变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unraveling Integration-Segregation Imbalances in Schizophrenia Through Topological High-Order Functional Connectivity.

Background: The occurrence of brain disorders correlates with detectable dysfunctions in the specialization of brain connectomics. While extensive research has explored this relationship, there is a lack of studies specifically examining the statistical correlation between the integration and segregation of psychotic brain networks using high-order networks, given the limitations of low-order networks. Moreover, these dysfunctions are believed to be linked to information imbalances in brain functions. However, our understanding of how these imbalances give rise to specific psychotic symptoms remains limited.

Methods: This study aims to address this gap by investigating variations at the topological high-order level of the system with regard to specialization in both healthy individuals and those diagnosed with schizophrenia. By employing graph-theoretic brain network analysis, we systematically examine information integration and segregation to delineate system-level differences in the connectivity patterns of brain networks.

Results: The findings indicate that topological high-order functional connectomics highlight differences in the connectome between healthy controls and schizophrenia, demonstrating increased cingulo-opercular task control and salience interactions, while the interaction between subcortical and default mode networks, dorsal attention and sensory/somatomotor mouth decreases in schizophrenia. Furthermore, we observed a reduction in the segregation of brain systems in healthy controls compared to individuals with schizophrenia, which means the balance between segregation and integration of brain networks is disrupted in schizophrenia, suggesting that restoring this balance may aid in the treatment of the disorder. Additionally, the increased segregation and decreased integration of brain systems in schizophrenia patients compared to healthy controls may serve as a novel indicator for early schizophrenia diagnosis.

Conclusion: We discovered that topological high-order functional connectivity highlights brain network interactions compared to low-order functional connectivity. Furthermore, we observed alterations in specific brain regions associated with schizophrenia, as well as changes in brain network information integration and segregation in individuals with schizophrenia.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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