精神分裂症是一种严重的神经类疾病，其临床表现涉及到思维、认知和行为等多个方面。目前对精神分裂症的诊断和治疗主要依靠临床医生对患者的体征和症状进行主观判断，缺乏客观诊断标准。本文基于特征向量分解方法，研究精神分裂症患者静息态、任务态以及认知转迁状态下脑功能网络异常机理。研究结果有助于进一步寻找精神分裂症客观生物学标志物及揭示其潜在的发病机制、对于临床诊断和治疗具有重大意义。

研究精神分裂症静息状态大脑功能网络异常机理，分析大脑网络分离与整合动态切换特性及其与临床诊断结果的统计学关系。发现精神分裂症患者的大脑网络表现出更稳定的整合过程和更多变的分离过程，其中边缘系统的相对变化最为显著。幻觉症状与注意力系统的高度整合呈正相关，意志力缺失症状与默认模式网络和控制系统中分离过程变异程度呈负相关。基于机器学习预测模型，发现基于特征向量分解的大脑特征可以有效预测阳性症状和阴性症状，并且预测效果优于传统图论方法。多元线性回归分析表明，阳性和阴性症状对大脑网络的动态分离与整合具有相反影响。基因富集分析显示，阴性症状的影响与自闭症、攻击性和暴力行为有关；阳性症状的影响与高氨血症和酸中毒有关；阳性和阴性症状的交互作用与运动功能异常有关。

研究精神分裂症患者在任务切换实验（Task-Switching Task）状态下大脑功能网络的异常机理，研究任务态大脑功能连接与认知执行能力和临床症状之间的关系。发现精神分裂症患者存在显著的认知异常，主要体现在任务反应时间更长和认知切换成本消耗更多。精神分裂症患者具有更高的网络分离程度，以维持在执行认知任务时所需要的分离过程的可变性。默认模式网络的分离变异程度越低，阴性的快感缺乏症状越严重。随着皮层下核系统、视觉系统和控制系统的整合强度增加，阳性的思维障碍症状越严重。背侧注意系统的分离变异程度加深，幻觉症状表现增强。

研究精神分裂症患者从静息状态转迁到任务状态下大脑功能网络的异常机理，分析精神分裂症患者大脑转迁效率与认知行为和临床症状的关系。发现精神分裂症患者具有较低的大脑认知状态转迁效率，需要更强的功能分离来支持认知切换。认知行为中的长切换成本与皮层下核系统、视觉系统、边缘系统的分离变异程度转迁效率存在负相关性。揭示了精神分裂症核心的幻觉症状在大脑不同状态下功能网络中的特定反应。

Schizophrenia is a severe neurological disorder that affects multiple aspects of cognition, thinking and behavior. Currently, the diagnosis and treatment of schizophrenia primarily rely on subjective judgments made by clinicians based on patients' signs and symptoms, lacking objective diagnostic criteria. This thesis explores the underlying mechanisms of abnormal brain function networks in resting state, tasking state, and cognitive state transition of schizophrenia patients. The research results contribute to the further identification of objective biological markers and potential pathogenesis of schizophrenia, which have significant implications for clinical diagnosis and treatment.

We investigated the abnormal mechanisms of resting-state brain functional networks in patients with schizophrenia, and analyze the dynamic changes of brain network integration and segregation and their statistical relationships with clinical diagnose. The results reveal that the brain network of schizophrenia patients exhibits a more stable integrating process and a more variable segregating process, such that maintains higher segregation, especially in the limbic system. Hallucinations were associated with higher integration in attention systems, and avolition was related to a more variable segregating process in default-mode network and control systems. In a machine-learning model, eigen decomposition-based features outperformed graph measures at predicting positive and negative symptoms. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function.

We studied the abnormal mechanism of brain functional networks in schizophrenia patients during the Task-Switching Task, and analyzed the relationship between brain functional connectivity, cognitive executive abilities, and clinical symptoms. We found that schizophrenia patients exhibit significant cognitive abnormalities, primarily reflected in longer task reaction times and greater cognitive switching costs. Schizophrenia patients also demonstrated a higher functional segregation in the brain network to maintain the necessary variability of separation during cognitive execution. The lower segregation variability of default mode network was, the more severe of anhedonia was. As the integration intensity of subcortical networks, visual networks, and control systems increased, the severity of thought disorder symptom increased. Furthermore, as the segregation variability of the dorsal attention network increased, the expression of hallucinations symptom also increased.

We investigated the abnormal mechanism of brain functional network in patients with schizophrenia when the brain transferred from resting state to tasking state, and explored the relationship between brain transfer efficiency and cognitive behavior and clinical symptoms in patients with schizophrenia. It was found that patients with schizophrenia have lower brain cognitive state transition efficiency and require stronger functional separation to support cognitive switching. There was a negative correlation between the long switching cost in cognitive behavior and the transfer efficiency of the segregation variability of subcortical network, visual network and limbic system. These results revealed the specific responses of hallucinations symptom central to schizophrenia in functional networks in different states of the brain.

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