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نتیجه جستجو - Functional brain network

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 The release of exosomes in the medial prefrontal cortex and nucleus accumbens brain regions of chronic constriction injury (CCI) model mice could elevate the pain sensation
انتشار اگزوزومها در قشر جلوی مغز داخلی و هسته مجذور مناطق مغزی از آسیب مدل انقباضی مزمن (CCI) موش مدل می تواند احساس درد را بالا ببرد-2020
Background: Brain function relies on the capacity of neurons to locally modulate each other at the level of synapses. Therefore, the exosomal pathway may constitute a well-designed mechanism for local and systemic interneuronal transfer of information within functional brain networks. Exosomes bind to and are endocytosed by neurons of different brain regions to play a definite role. The medial prefrontal cortex (mPFC) and nucleus accumbens (NAc) brain regions are known to involve in pain modulation. Our study observes the roles of exosomal activity in these two dominant regions of the pain-related pathway, and there influence on the analgesic effects in CCI mice. Methods: We induced pain exosomes in the mPFC and NAc in the mice of chronic constriction injury of the sciatic nerve model to produce neuropathic pain, and assessed changes that might affect analgesic behaviors. These changes were measured through a combination of behavioral, surgical, and other cellular testings. Results: Our study found that pain expression was elevated in mice given exogenous exosomes isolated from CCI mice, especially at the 2 h and 4 h time interval, in mice given exosomes at the mPFC and NAc, respectively. We also found that inhibiting formation of pain exosomes through GW4869 within the mPFC and NAc can elevate the pain threshold. Conclusion: Results from our study supported the idea that the release of mPFC and NAc exosomes of CCI model has elevated the pain sensations in the subjected mice. This study will further help in designing new clinical trials, and will revolutionize the drug-induced anesthetic responses.
مقاله انگلیسی
2 EEG intentions recognition in dynamic complex object control task byfunctional brain networks and regularized discriminant analysis
تشخیص اهداف EEG در شبکه کنترل عملکردی پیچیده پویا شبکه های مغزی و عملکرد تجزیه و تحلیل تبعیض آمیز منظم-2020
Most of the tasks involved in neurological rehabilitation are generally constrained. However the complex-ity of experimental paradigms with constraints for acquiring electroencephalography (EEG) are relativelylow. In order to improve the level of arousal in the brain, we propose an energy-constrained dynamicand complex experimental paradigm. In this novel experimental paradigm, EEG signals collected underthe novel paradigm have different motor intentions. In this study, we combine the phase synchroniza-tion method and common spatial pattern (CSP) to build functional brain networks of subjects. Based onthis method, we investigate the correlation of the global and local features of the functional brain net-works and use regularized discriminant analysis (RDA) to recognize features. The accuracy obtained bythe proposed method for EEG intention recognition can reach 93.47% (p < 0.01). The results show that theproposed method can decode EEG intention with high recognition performance during the manipulationof complex objects, which lays a foundation for the study of neurological rehabilitation.
Keywords:Dynamic complex model | EEG | Regularized discriminant analysis | Functional brain network
مقاله انگلیسی
3 Exploring the fatigue affecting electroencephalography based functional brain networks during real driving in young males
بررسی خستگی مؤثر بر شبکه های عملکردی مغزی مبتنی بر الکترونسفالوگرافی در هنگام رانندگی واقعی در مردان جوان-2019
In recent years, a large proportion of traffic accidents are caused by driver fatigue. The brain has been conceived as a complex network, whose function can be assessed with EEG. Hence, in this research, fourteen subjects participated in the real driving experiments, and a comprehensive EEG-based expert system was designed for detecting driver fatigue. Collected EEG signals were first decomposed into delta-range, theta-range, alpha-range and beta-range by wavelet packet transform (WPT). Unlike other approaches, a multi-channel network construction method based on Phase Lag Index (PLI) was then proposed in this paper. Finally, the functional connectivity between alert state (at the beginning of the drive) and fatigue state (at the end of the drive) in multiple frequency bands were analyzed. The results indicate that functional connectivity of the brain area was significantly different between alert and fatigue states, especially in alpha-range and beta-range. Particularly, the frontal-to-parietal functional connectivity was weakened. Meanwhile, lower clustering coefficient (C) values and higher characteristic path length (L) values were observed in fatigue state in comparison with alert state. Based on this, two new EEG feature selection approaches, C and L in the corresponding sub-frequency range were applied to feature recognition and classification system. Using a support vector machine (SVM) machine learning algorithm, these features were combined to distinguish between alert and fatigue states, achieving an accuracy of 94.4%, precision of 94.3%, sensitivity of 94.6% and false alarm rate of 5.7%. The results suggest that brain network analysis approaches combined with SVM are helpful to alert drivers while being sleepy or even fatigue.
Keywords: Electroencephalography (EEG) | Driver fatigue | Phase lag index | Graph theory | Functional connectivity | Brain network
مقاله انگلیسی
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