Li Qiang, Huang Wei, Qiao Chen, Chen Huafu
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, 30303, Atlanta, GA, USA.
Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, 611731, Chengdu, China.
Neuroinformatics. 2025 Feb 22;23(2):21. doi: 10.1007/s12021-025-09718-5.
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.
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.
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.
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.
脑部疾病的发生与脑连接组学特化中可检测到的功能障碍相关。尽管已有广泛研究探索了这种关系,但鉴于低阶网络的局限性,缺乏专门使用高阶网络研究精神分裂症脑网络整合与分离之间统计相关性的研究。此外,这些功能障碍被认为与脑功能中的信息失衡有关。然而,我们对这些失衡如何导致特定精神症状的理解仍然有限。
本研究旨在通过调查健康个体和被诊断为精神分裂症的个体在系统拓扑高阶水平上的特化差异来填补这一空白。通过采用基于图论的脑网络分析,我们系统地检查信息整合与分离,以描绘脑网络连接模式中的系统级差异。
研究结果表明,拓扑高阶功能连接组学突出了健康对照组和精神分裂症患者在连接组上的差异,显示扣带回 - 脑岛任务控制和显著性交互增加,而在精神分裂症中,皮层下与默认模式网络、背侧注意和感觉/躯体运动口之间的交互减少。此外,我们观察到与精神分裂症患者相比,健康对照组脑系统的分离减少,这意味着精神分裂症中脑网络分离与整合之间的平衡被打破,表明恢复这种平衡可能有助于该疾病的治疗。此外,与健康对照组相比,精神分裂症患者脑系统分离增加和整合减少可能作为精神分裂症早期诊断的新指标。
我们发现,与低阶功能连接性相比,拓扑高阶功能连接性突出了脑网络交互。此外,我们观察到与精神分裂症相关的特定脑区的改变,以及精神分裂症患者脑网络信息整合与分离的变化。