摘要
精神分裂症是一种持久的精神障碍,表现为感知、情感和行为的显著异常,但其神经机制仍不完全清楚。为了探讨精神分裂症患者与健康对照组在静息状态下全脑因果连接的差异,基于特征模态方法,提出了一种分层度指标,克服了传统图论中节点度在单层次上测量的不足。研究发现精神分裂症患者的全脑因果网络的节点度降低,并且运动系统的入度变化最为显著,而默认系统的出度变化最为显著。进一步提取高阶节点度,并基于机器学习方法,发现高阶节点度在区分精神分裂症患者和健康对照组上优于传统图论度,并且能更准确地预测精神分裂症的阳性和阴性症状,表明高阶网络特征可以作为精神分裂症的生物学指标。研究成果揭示了精神分裂症的异常高阶网络特征,有助于精神分裂症客观化诊断技术的发展。
Schizophrenia is a persistent mental disorder manifested by significant abnormalities in perception,emotion,and behavior.Nevertheless,the neural mechanisms underlying this disorder are still not fully understood.In order to explore the differences in whole-brain causal connectivity between patients with schizophrenia and healthy controls in the resting state,a hierarchical degree(HD)index was proposed based on eigenmode method to overcome the inadequacy of node degree measured at a single level in traditional graph theory.It was found that the node degree of the whole-brain causal network of schizophrenia patients reduced.In addition,the most significant changes in in-degree were found in the motor system,whereas the most significant changes in out-degree were found in the default mode system.Higher-order node degree was further extracted and found to be superior to traditional graph theory degree in distinguishing schizophrenia patients from healthy controls based on a machine learning approach,and more accurately predicted positive and negative symptoms of schizophrenia,suggesting that higher-order network features can be used as biological indicators of schizophrenia.The findings of this paper reveal abnormal higher-order network features of schizophrenia and contribute to the advancement of objective diagnostic technologies for schizophrenia.
作者
孟湘媛
王荣
MENG Xiang-yuan;WANG Rong(College of Science,Xi'an University of Science and Technology,Xi'an 710054,China)
出处
《科学技术与工程》
北大核心
2025年第19期7986-7994,共9页
Science Technology and Engineering
基金
国家自然科学基金(12272292)。
关键词
精神分裂症
因果连接
分层度
特征模态方法
schizophrenia
causal connectivity
hierarchical degree
eigenmode method
