Aiming at the problem that existing emotion recognition methods fail to make full use of the information in the time, frequency, and spatial domains in the EEG signals, which leads to the low accuracy of EEG emotion classification, this paper proposes a multi-feature, multi-frequency band-based cross-scale attention convolutional model (CATM). The model is mainly composed of a cross-scale attention module, a frequency-space attention module, a feature transition module, a temporal feature extraction module, and a depth classification module. First, the cross-scale attentional convolution module extracts spatial features at different scales for the preprocessed EEG signals; then, the frequency-space attention module assigns higher weights to important channels and spatial locations; next, the temporal feature extraction module extracts temporal features of the EEG signals; and, finally, the depth classification module categorizes the EEG signals into emotions. We evaluated the proposed method on the DEAP dataset with accuracies of 99.70% and 99.74% in the valence and arousal binary classification experiments, respectively; the accuracy in the valence-arousal four-classification experiment was 97.27%. In addition, considering the application of fewer channels, we also conducted 5-channel experiments, and the binary classification accuracies of valence and arousal were 97.96% and 98.11%, respectively. The valence-arousal four-classification accuracy was 92.86%. The experimental results show that the method proposed in this paper exhibits better results compared to other recent methods, and also achieves better results in few-channel experiments.

Subjective estimates of duration are affected by emotional expectations about the future. For example, temporal intervals preceding a threatening event such as an electric shock are estimated as longer than intervals preceding a non-threatening event. However, it has not been unequivocally shown that such temporal overestimation occurs also when anticipating a similarly arousing but appealing event. In this study, we examined how anticipation of visual erotic material influenced perceived duration. Participants did a temporal bisection task, where they estimated durations of visual cues relative to previously learned short and long standard durations. The color of the to-be-timed visual cue signalled either a chance of seeing a preferred erotic picture at the end of the interval or certainty of seeing a neutral grey bar instead. The results showed that anticipating an appealing event increased the likelihood of estimating the cue duration as long as compared to the anticipation of a grey bar. Further analyses showed that this temporal overestimation effect was stronger for those who rated the anticipated erotic pictures as more sexually arousing. The results thus indicate that anticipation of appealing events has a similar dilating effect on perceived duration as does the anticipation of aversive events.

Objective: To investigate the relationship between arousal threshold (ArTH) and hypertension in patients with obstructive sleep apnea hypopnea syndrome (OSA). Methods: This study recruited 648 patients diagnosed with OSA at the Sleep Center of the Second Affiliated Hospital of Soochow University from January 2020 to August 2021, including 569 males and 79 females, aged 42(35,52) years. The basic demographic information and clinical data of all patients were collected, including blood pressure measurement, and relevant questionnaire scores, and nocturnal polysomnography (PSG) parameters. A clinical predictive model based on sleep apnea hypopnea index (AHI), lowest pulse oxygen saturation (LSpaO2) and hypopnea ratio (FHypopneas) was used to access the arousal threshold of OSA patients. Patients were divided into OSA group and OSA with hypertension group according to whether they were combined with hypertension. The differences in the above indexes between the two groups were analyzed to explore the relationship between arousal threshold and hypertension in OSA patients, using a binary logistic stepwise regression analysis. Results: A total of 648 OSA patients were enrolled, including 415 in the OSA with hypertension group and 233 in the OSA group. Compared with OSA group, OSA with hypertension group had older age, higher body mass index (BMI), higher blood pressure at bedtime and at awakening, higher AHI and lower proportion of hypopnea (all P<0.05). There were no significant differences between other general data and PSG parameters (all P>0.05). The proportion of patients with low arousal threshold (AHI<30 events per hour, LSpO2>82.5%, Fhypopneas>58.3%) in OSA with hypertension group was lower, and the proportion of phenotypic patients with low arousal threshold was significantly lower (30.1% vs. 52.4% P<0.001). Binary logistic stepwise regression analysis showed that the high arousal threshold (OR=1.930, 95%CI:1.326-2.808, P=0.001) was an independent risk factor for OSA complicated with hypertension. Conclusion: The arousal threshold is associated with the development of hypertension in OSA patients, and OSA patients with a high arousal threshold have a higher risk of developing hypertension.
目的： 探讨觉醒阈值（ArTH）与阻塞性睡眠呼吸暂停（OSA）患者高血压发生的相关性。 方法： 回顾性收集2020年1月至 2021年8月于苏州大学附属第二医院睡眠中心确诊为OSA的患者648例，其中男569例，女79例，年龄42（35，52）岁。收集所有患者的基本信息、临床资料，完成血压测量、相关量表评估，进行夜间多导睡眠监测（PSG）。采用以睡眠呼吸暂停低通气指数（AHI）、最低脉搏血氧饱和度（LSpO2）及低通气呼吸事件比例（FHypopneas）作为评分标准的临床预测模型来判断OSA患者的觉醒阈值。根据是否合并高血压将所有患者分为单纯OSA组和OSA合并高血压组，比较上述指标在两组间的差异，并通过二元logistic逐步回归分析探讨OSA患者觉醒阈值与合并高血压的关系。 结果： 648例OSA患者中，OSA合并高血压组415例，单纯OSA组233例。与单纯OSA组相比，OSA合并高血压组的年龄、体重指数、睡前及醒后血压值更大，AHI更高，FHypopneas更低，差异均有统计学意义（均P<0.05），其他一般资料、PSG参数差异均无统计学意义（均P>0.05）；OSA合并高血压组中低觉醒阈值的三条评分标准，即AHI<30次/h、LSpO2>82.5%、FHypopneas>58.3%的患者比例均更低（均P<0.05），低觉醒阈值患者比例明显更少（30.1%比52.4%，P<0.001）；经二元logistic逐步回归分析显示，高觉醒阈值（OR=1.930，95%CI：1.326~2.808，P=0.001）是OSA合并高血压的独立危险因素。 结论： 觉醒阈值在OSA发生高血压中起一定作用，高觉醒阈值的OSA患者发生高血压的风险增高。.

There are mutual neural projections between the ventral tegmental area (VTA) and the medial prefrontal cortex (mPFC),which form a circuit.Recent studies have shown that this circuit is vital in regulating arousal from sleep and general anesthesia.This paper introduces the anatomical structures of VTA and mPFC and the roles of various neurons and projection pathways in the regulation of arousal,aiming to provide new ideas for further research on the mechanism of arousal from sleep and general anesthesia.
腹侧被盖区(VTA)与内侧前额叶皮层(mPFC)之间存在相互神经投射,并形成环路,近年来的研究显示该环路在睡眠与全身麻醉的觉醒调控中发挥着重要的作用。本文通过对VTA与mPFC的解剖结构、二者中的各种神经元及投射通路在觉醒调控过程中的作用进行综述,期望为睡眠觉醒与全身麻醉机制研究提供新的思路。.

Long-range thalamocortical communication is central to anesthesia-induced loss of consciousness and its reversal. However, isolating the specific neural networks connecting thalamic nuclei with various cortical regions for state-specific anesthesia regulation is challenging, with the biological underpinnings still largely unknown. Here, simultaneous electroencephalogram-fuctional magnetic resonance imaging (EEG-fMRI) and deep brain stimulation are applied to the intralaminar thalamus in macaques under finely-tuned propofol anesthesia. This approach led to the identification of an intralaminar-driven network responsible for rapid arousal during slow-wave oscillations. A network-based RNA-sequencing analysis is conducted of region-, layer-, and cell-specific gene expression data from independent transcriptomic atlases and identifies 2489 genes preferentially expressed within this arousal network, notably enriched in potassium channels and excitatory, parvalbumin-expressing neurons, and oligodendrocytes. Comparison with human RNA-sequencing data highlights conserved molecular and cellular architectures that enable the matching of homologous genes, protein interactions, and cell types across primates, providing novel insight into network-focused transcriptional signatures of arousal.

Emotion recognition based on Electroencephalogram (EEG) has been applied in various fields, including human-computer interaction and healthcare. However, for the popular Valence-Arousal-Dominance emotion model, researchers often classify the dimensions into high and low categories, which cannot reflect subtle changes in emotion. Furthermore, there are issues with the design of EEG features and the efficiency of transformer. To address these issues, we have designed TPRO-NET, a neural network that takes differential entropy and enhanced differential entropy features as input and outputs emotion categories through convolutional layers and improved transformer encoders. For our experiments, we categorized the emotions in the DEAP dataset into 8 classes and those in the DREAMER dataset into 5 classes. On the DEAP and the DREAMER datasets, TPRO-NET achieved average accuracy rates of 97.63%/97.47%/97.88% and 98.18%/98.37%/98.40%, respectively, on the Valence/Arousal/Dominance dimension for the subject-dependent experiments. Compared to other advanced methods, TPRO-NET demonstrates superior performance.

Obstructive sleep apnea (OSA) is primarily characterized by intermittent nocturnal hypoxia and sleep fragmentation. Arousals interrupt sleep continuity and lead to sleep fragmentation, which can lead to cognitive dysfunction, excessive daytime sleepiness, and adverse cardiovascular outcome events, making arousals important for diagnosing OSA and reducing the risk of complications, including heart disease and cognitive impairment. Traditional arousal interpretation requires sleep specialists to manually score PSG recordings throughout the night, which is time consuming and has low inter-specialist agreement, so the search for simple, efficient, and reliable arousal detection methods can be a powerful tool to clinicians. In this paper, we systematically reviewed different methods for recognizing arousal in OSA patients, including autonomic markers (pulse conduction time, pulse wave amplitude, peripheral arterial tone, heart rate, etc.) and machine learning-based automated arousal detection systems, and found that autonomic markers may be more beneficial in certain subgroups, and that deep artificial networks will remain the main research method for automated arousal detection in the future.
阻塞性睡眠呼吸暂停（OSA）的主要特点是夜间间歇低氧及睡眠片段化。觉醒中断睡眠连续性，导致睡眠破碎，这可能会导致认知功能障碍、白天过度嗜睡以及心血管不良结局事件的发生，觉醒对于诊断OSA和降低并发症（包括心脏病和认知障碍）的发生风险非常重要。传统的觉醒判读需要睡眠专家对整夜的PSG记录进行手动评分，耗时长且专家之间的一致性较低，因此寻找简便、高效、可靠的觉醒检测方法，可为临床医生提供有力的帮助。本文系统综述识别OSA患者觉醒的不同方法，包括自主神经标志物（脉搏传导时间、脉搏波波幅、外周动脉张力、心率等）及基于机器学习的自动觉醒检测系统，发现自主神经标志物可能在特定的亚群中更有益，深度人工网络仍然是未来自动检测觉醒的主要研究方法。.

Sleep disturbance usually accompanies anxiety disorders and exacerbates their incidence rates. The precise circuit mechanisms remain poorly understood. Here, we found that glutamatergic neurons in the posteroventral medial amygdala (MePVGlu neurons) are involved in arousal and anxiety-like behaviors. Excitation of MePVGlu neurons not only promoted wakefulness but also increased anxiety-like behaviors. Different projections of MePVGlu neurons played various roles in regulating anxiety-like behaviors and sleep-wakefulness. MePVGlu neurons promoted wakefulness through the MePVGlu/posteromedial cortical amygdaloid area (PMCo) pathway and the MePVGlu/bed nucleus of the stria terminals (BNST) pathway. In contrast, MePVGlu neurons increased anxiety-like behaviors through the MePVGlu/ventromedial hypothalamus (VMH) pathway. Chronic sleep disturbance increased anxiety levels and reduced reparative sleep, accompanied by the enhanced excitability of MePVGlu/PMCo and MePVGlu/VMH circuits but suppressed responses of glutamatergic neurons in the BNST. Inhibition of the MePVGlu neurons could rescue chronic sleep deprivation-induced phenotypes. Our findings provide important circuit mechanisms for chronic sleep disturbance-induced hyperarousal response and obsessive anxiety-like behavior and are expected to provide a promising strategy for treating sleep-related psychiatric disorders and insomnia.

Does the brain track how fast our blood glucose is changing? Knowing such a rate of change would enable the prediction of an upcoming state and a timelier response to this new state. Hypothalamic arousal-orchestrating hypocretin/orexin neurons (HONs) have been proposed to be glucose sensors, yet whether they track glucose concentration (proportional tracking) or rate of change (derivative tracking) is unknown. Using simultaneous recordings of HONs and blood glucose in behaving male mice, we found that maximal HON responses occur in considerable temporal anticipation (minutes) of glucose peaks due to derivative tracking. Analysis of >900 individual HONs revealed glucose tracking in most HONs (98%), with derivative and proportional trackers working in parallel, and many (65%) HONs multiplexed glucose and locomotion information. Finally, we found that HON activity is important for glucose-evoked locomotor suppression. These findings reveal a temporal dimension of brain glucose sensing and link neurobiological and algorithmic views of blood glucose perception in the brain\'s arousal orchestrators.

Arterial spin-labeled perfusion and blood oxygenation level-dependent functional MRI are indispensable tools for noninvasive human brain imaging in clinical and cognitive neuroscience, yet concerns persist regarding the reliability and reproducibility of functional MRI findings. The circadian rhythm is known to play a significant role in physiological and psychological responses, leading to variability in brain function at different times of the day. Despite this, test-retest reliability of brain function across different times of the day remains poorly understood. This study examined the test-retest reliability of six repeated cerebral blood flow measurements using arterial spin-labeled perfusion imaging both at resting-state and during the psychomotor vigilance test, as well as task-induced cerebral blood flow changes in a cohort of 38 healthy participants over a full day. The results demonstrated excellent test-retest reliability for absolute cerebral blood flow measurements at rest and during the psychomotor vigilance test throughout the day. However, task-induced cerebral blood flow changes exhibited poor reliability across various brain regions and networks. Furthermore, reliability declined over longer time intervals within the day, particularly during nighttime scans compared to daytime scans. These findings highlight the superior reliability of absolute cerebral blood flow compared to task-induced cerebral blood flow changes and emphasize the importance of controlling time-of-day effects to enhance the reliability and reproducibility of future brain imaging studies.