Traumatic brain injury (TBI) is one of the most common diseases in clinical neurosurgery characterized with high incidence rate, mortality and many complications. Objectives: The purpose of this study is to explore th...Traumatic brain injury (TBI) is one of the most common diseases in clinical neurosurgery characterized with high incidence rate, mortality and many complications. Objectives: The purpose of this study is to explore the roles of Xuesaitong in the therapeutic effect of brain trauma and alteration of expression in Bax, a kind of promoting apoptosis factor. Methods: The rat traumatic brain injury models were established by using modified free falling body impact method. Thereafter, Xuesaitong was employed to be administered to TBI rats, and NSS Score Rating Scale was used to evaluate the effect of Xuesaitong. Moreover, real-time PCR was used to detect the Bax expression changes before and after the Xuesaitong administration. Results: Xuesaitong could accelerate the neurofunctional recovery of TBI rats, accompanied by NSS Scores significant decrease. Simultaneously, it also could inhibit the expression of Bax factor. Conclusions: Xusaitong could markedly ameliorate TBI restoration, in which it promotes the neurofunctional recovery, and at the same time it inhibits the expression of Bax.展开更多
Importance:Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders.Self-supervised learning(SSL)models offer ...Importance:Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders.Self-supervised learning(SSL)models offer a transformative approach for mapping dependencies in functional neuroimaging data.Leveraging the intrinsic organization of brain signals for comprehensive feature extraction,these models enable the analysis of critical neurofunctional features within a clinically relevant framework,overcoming challenges related to data heterogeneity and the scarcity of labeled data.Highlight:This paper provides a comprehensive overview of SSL techniques applied to functional neuroimaging data,such as functional magnetic resonance imaging and electroencephalography,with a specific focus on their applications in various neuropsychiatric disorders.We discuss 3 main categories of SSL methods:contrastive learning,generative learning,and generative-contrastive methods,outlining their basic principles and representative methods.Critically,we highlight the potential of SSL in addressing data scarcity,multimodal integration,and dynamic network modeling for disease detection and prediction.We showcase successful applications of these techniques in understanding and classifying conditions such as Alzheimer’s disease,Parkinson’s disease,and epilepsy,demonstrating their potential in downstream neuropsychological applications.Conclusion:SSL models provide a scalable and effective methodology for individual detection and prediction in brain disorders.Despite current limitations in interpretability and data heterogeneity,the potential of SSL for future clinical applications,particularly in the areas of transdiagnostic psychosis subtyping and decoding task-based brain functional recordings,is substantial.展开更多
文摘Traumatic brain injury (TBI) is one of the most common diseases in clinical neurosurgery characterized with high incidence rate, mortality and many complications. Objectives: The purpose of this study is to explore the roles of Xuesaitong in the therapeutic effect of brain trauma and alteration of expression in Bax, a kind of promoting apoptosis factor. Methods: The rat traumatic brain injury models were established by using modified free falling body impact method. Thereafter, Xuesaitong was employed to be administered to TBI rats, and NSS Score Rating Scale was used to evaluate the effect of Xuesaitong. Moreover, real-time PCR was used to detect the Bax expression changes before and after the Xuesaitong administration. Results: Xuesaitong could accelerate the neurofunctional recovery of TBI rats, accompanied by NSS Scores significant decrease. Simultaneously, it also could inhibit the expression of Bax factor. Conclusions: Xusaitong could markedly ameliorate TBI restoration, in which it promotes the neurofunctional recovery, and at the same time it inhibits the expression of Bax.
基金supported by grants from the National Natural Science Foundation of P.R.China(62276081 and 62106113)Guangdong Basic and Applied Basic Research Foundation(2023A1515010792 and 2023B1515120065)Shenzhen Science and Technology Program(GXWD20231129121139001 and JCYJ20240813110522029).
文摘Importance:Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders.Self-supervised learning(SSL)models offer a transformative approach for mapping dependencies in functional neuroimaging data.Leveraging the intrinsic organization of brain signals for comprehensive feature extraction,these models enable the analysis of critical neurofunctional features within a clinically relevant framework,overcoming challenges related to data heterogeneity and the scarcity of labeled data.Highlight:This paper provides a comprehensive overview of SSL techniques applied to functional neuroimaging data,such as functional magnetic resonance imaging and electroencephalography,with a specific focus on their applications in various neuropsychiatric disorders.We discuss 3 main categories of SSL methods:contrastive learning,generative learning,and generative-contrastive methods,outlining their basic principles and representative methods.Critically,we highlight the potential of SSL in addressing data scarcity,multimodal integration,and dynamic network modeling for disease detection and prediction.We showcase successful applications of these techniques in understanding and classifying conditions such as Alzheimer’s disease,Parkinson’s disease,and epilepsy,demonstrating their potential in downstream neuropsychological applications.Conclusion:SSL models provide a scalable and effective methodology for individual detection and prediction in brain disorders.Despite current limitations in interpretability and data heterogeneity,the potential of SSL for future clinical applications,particularly in the areas of transdiagnostic psychosis subtyping and decoding task-based brain functional recordings,is substantial.
