Main Body: Increasing evidence of the cardioprotective effects of both invasive and non-invasive vagal nerve stimulation (VNS) suggests that these may be feasible methods to treat myocardial ischemia/reperfusion injury via anti-inflammatory regulation. The mechanisms through which auricular VNS controls inflammation are yet to be explored. In this review, we discuss the potential of autonomic nervous system modulation, particularly via the parasympathetic branch, in ameliorating MI. Novel insights are provided about the activation of the cholinergic anti-inflammatory pathway on cardiac macrophages. Acetylcholine binding to the α7 nicotinic acetylcholine receptor (α7nAChR) expressed on macrophages polarizes the pro-inflammatory into anti-inflammatory subtypes. Activation of the α7nAChR stimulates the signal transducer and activator of transcription 3 (STAT3) signaling pathway. This inhibits the secretion of pro-inflammatory cytokines, limiting ischemic injury in the myocardium and initiating efficient reparative mechanisms. We highlight recent developments in the controversial auricular vagal neuro-circuitry and how they may relate to activation of the cholinergic anti-inflammatory pathway.
Conclusion: Emerging published data suggest that auricular VNS is an inexpensive healthcare modality, mediating the dynamic balance between pro- and anti-inflammatory responses in cardiac macrophages and ameliorating cardiac ischemia/reperfusion injury.
METHODS: The Bruce protocol was used as the experimental exercise model with a self-controlled experimental design. Thirty males performed incremental load exercise tests on treadmill until exhaustion. EEG signal acquisition was completed before and after exercise. EEG microstates and resting-state cortical rhythm techniques were used to analyze the EEG signal.
RESULTS: The microstate results showed that the duration, occurrence, and contribution of Microstate C were significantly higher after exhaustive exercise (p's < 0.01). There was a significantly lower contribution of Microstate D (p < 0.05), a significant increase in transition probabilities between Microstate A and C (p < 0.05), and a significant decrease in transition probabilities between Microstate B and D (p < 0.05). The results of EEG rhythm energy on the large-scale brain network showed that the energy in the high-frequency β band was significantly higher in the visual network (p < 0.05).
DISCUSSION: Our results suggest that frequently Microstate C associated with the convexity network are important for the organism to respond to internal and external information stimuli and thus regulate motor behavior in time to protect organism integrity. The decreases in Microstate D parameters, associated with the attentional network, are an important neural mechanism explaining the decrease in attention-related cognitive or behavioral performance due to acute exercise fatigue. The high energy in the high-frequency β band on the visual network can be explained in the sense of the neural efficiency hypothesis, which indicates a decrease in neural efficiency.
METHODS: In contrast, ViTs have demonstrated proficiency in capturing global signal patterns. In light of these observations, we propose a novel approach to enhance AD risk assessment. Our proposition involves a hybrid architecture, merging the strengths of CNNs and ViTs to compensate for their respective feature extraction limitations. Our proposed Dual-Branch Feature Fusion Network (DBN) leverages both CNN and ViT components to acquire texture features and global semantic information from EEG signals. These elements are pivotal in capturing dynamic electrical signal changes in the cerebral cortex. Additionally, we introduce Spatial Attention (SA) and Channel Attention (CA) blocks within the network architecture. These attention mechanisms bolster the model's capacity to discern abnormal EEG signal patterns from the amalgamated features. To make well-informed predictions, we employ a two-factor decision-making mechanism. Specifically, we conduct correlation analysis on predicted EEG signals from the same subject to establish consistency.
RESULTS: This is then combined with results from the Clinical Neuropsychological Scale (MMSE) assessment to comprehensively evaluate the subject's susceptibility to AD. Our experimental validation on the publicly available OpenNeuro database underscores the efficacy of our approach. Notably, our proposed method attains an impressive 80.23% classification accuracy in distinguishing between AD, Frontotemporal dementia (FTD), and Normal Control (NC) subjects.
DISCUSSION: This outcome outperforms prevailing state-of-the-art methodologies in EEG-based AD prediction. Furthermore, our methodology enables the visualization of salient regions within pathological images, providing invaluable insights for interpreting and analyzing AD predictions.