No abstract

Background Sleep problem or disturbance often exists in pain or neurological/psychiatric diseases. However, sleep scoring is a time-consuming tedious labor. Very few studies discuss the 5-stage (wake/NREM1/NREM2/transition sleep/REM) automatic fine analysis of wake–sleep stages in rodent models. The present study aimed to develop and validate an automatic rule-based classification of 5-stage wake–sleep pattern in acid-induced widespread hyperalgesia model of the rat. Results The overall agreement between two experts’ consensus and automatic scoring in the 5-stage and 3-stage analyses were 92.32% ( κ = 0.88) and 94.97% ( κ = 0.91), respectively. Standard deviation of the accuracy among all rats was only 2.93%. Both frontal–occipital EEG and parietal EEG data showed comparable accuracies. The results demonstrated the performance of the proposed method with high accuracy and reliability. Subtle changes exhibited in the 5-stage wake–sleep analysis but not in the 3-stage analysis during hyperalgesia development of the acid-induced pain model. Compared with existing methods, our method can automatically classify vigilance states into 5-stage or 3-stage wake–sleep pattern with a promising high agreement with sleep experts. Conclusions In this study, we have performed and validated a reliable automated sleep scoring system in rats. The classification algorithm is less computation power, a high robustness, and consistency of results. The algorithm can be implanted into a versatile wireless portable monitoring system for real-time analysis in the future.

No abstract

The main purpose of this study was to investigate the effects of neurofeedback training (NFT) of theta activity on working memory (WM) and episodic memory (EM) in healthy participants via a systematic review and meta-analysis. A total of 337 articles obtained from electronic databases were assessed; however, only 11 articles met the criteria for meta-analysis after manually screening and eliminating unnecessary studies. A meta-analysis calculating the Hedges’ g effect size metric with 95% confidence intervals using random effects models was employed. Heterogeneity was estimated using I2 statistics. Theta NFT is effective in improving memory outcomes, including WM with a Hedges’ g of 0.56 [0.10; 1.02] (I2 = 62.9% and p = 0.02), and EM with a Hedges’ g of 0.62 [0.13; 1.10] (I2 = 42.04% and p = 0.01). Overall, the results suggest that theta NFT seems to be useful as nonpharmacological/adjunct training to improve WM and EM in healthy participants.

No abstract