Decoding Human Somatosensory Sensitivity Through Resting EEG and Behavioral Analysis: A Multimodal Fusion Approach.

In precision medicine and clinical pain management, the creation of quantitative, objective indicators to assess somatosensory sensitivity was essential. This study proposed a fusion approach for decoding human somatosensory sensitivity, which combined multimodal (quantitative sensory test and neurophysiology) features to classify the dataset on individual somatosensory sensitivity and reveal distinct types of brain activation patterns. Sixty healthy participants took part in the experiment on somatosensory sensitivity that implemented cold, heat, mechanical punctate, and pressure stimuli, and the resting-state electroencephalography (EEG) was collected using BrainVision. The quantitative sensory testing (QST) scores of the participants were clustered using the unsupervised k-means algorithm into four subgroups: generally hypersensitive (HS), generally non-sensitive (NS), predominantly thermally sensitive (TS), and predominantly mechanically sensitive (MS). Furthermore, two types of power spectral density (PSD), band-based PSD (BB-PSD) and frequency-based PSD (FB-PSD), and two types of inter-electrode connectivity (IEC), band-based connectivity (BBC) and frequency-based connectivity (FBC), derived from resting-state EEG were subjected to feature selection with a proposed prior-compared minimum-redundancy maximum-relevance (PCMRMR) protocol. Their effectiveness was then tested by the supervised classification tasks using support vector machine (SVM), k-nearest neighbor (kNN), random forest (RF), and Gaussian classifier (GC). Brain networks of four somatosensory types were revealed by decoding fused multimodal data, namely type-averaged connectivity. The data from sixty healthy individuals were divided into training (n =59) and validation (n =1) datasets according to leave-one-subject-out (LOSO) criteria. The FBC was identified, which can serve as better brain signatures than BB-PSD, FB-PSD, and BBC to classify subjects as HS, NS, TS, or MS groups. Using the SVM, kNN, RF, and GC models, the best accuracy of 87% was obtained when classifying participants into HS, NS, TS, or MS groups. Moreover, the brain networks were decoded from HS, NS, TS, and MS groups by decoding the type-averaged connectivity fused from somatosensory phenotypes and selected FBC. It indicated that quantified multi-parameter somatosensory sensitivity could be achieved with acceptable accuracy, leading to considerable possibilities for using objective pain perception evaluation in clinical practice.

Extracting Stress-Related EEG Patterns From Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency.

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution.

Flexible graphene/GO electrode for gel-free EEG.

Objective.Developments in electroencephalography (EEG) technology have allowed the use of the brain-computer interface (BCI) outside dedicated labratories. In order to achieve long-term monitoring and detection of EEG signals for BCI application, dry electrodes with good signal quality and high bio compatibility are essential. In 2016, we proposed a flexible dry electrode made of silicone gel and Ag flakes, which showed good signal quality and mechanical robustness. However, the Ag components used in our previous design made the electrode too expensive for commercial adaptation.Approach.In this study, we developed an affordable dry electrode made of silicone gel, metal flakes and graphene/GO based on our previous design. Two types of electrodes with different graphene/GO proportions were produced to explore how the amount of graphene/GO affects the electrode.Main results.During our tests, the electrodes showed low impedance and had good signal correlation to conventional wet electrodes in both the time and frequency domains. The graphene/GO electrode also showed good signal quality in eyes-open EEG recording. We also found that the electrode with more graphene/GO had an uneven surface and worse signal quality. This suggests that adding too much graphene/GO may reduce the electrods' performance. Furthermore, we tested the proposed dry electrodes' capability in detecting steady state visually evoked potential. We found that the dry electrodes can reliably detect evoked potential changes even in the hairy occipital area.Significance.Our results showed that the proposed electrode has good signal quality and is ready for BCI applications.

A pilot study on essential oil aroma stimulation for enhancing slow-wave EEG in sleeping brain.

Sleep quality is important to health and life quality. Lack of sleep can lead to a variety of health issues and reduce in daytime function. Recent study by Fultz et al. also indicated that sleep is crucial to brain metabolism. Delta power in sleep EEG often indicates good sleep quality while alpha power usually indicates sleep interruptions and poor sleep quality. Essential oil has been speculated to improve sleep quality. Previous studies also suggest essential oil aroma may affect human brain activity when applied awake. However, those studies were often not blinded, which makes the effectiveness and mechanism of aroma a heavily debated topic. In this study, we aim to explore the effect of essential oil aroma on human sleep quality and sleep EEG in a single-blinded setup. The aroma was released when the participants are asleep, which kept the influence of psychological expectation to the minimum. We recruited nine young, healthy participants with regular lifestyle and no sleep problem. All participants reported better sleep quality and more daytime vigorous after exposing to lavender aroma in sleep. We also observed that upon lavender aroma releases, alpha wave in wake stage was reduced while delta wave in slow-wave sleep (SWS) was increased. Lastly, we found that lavender oil promote occurrence of SWS. Overall, our study results show that essential oil aroma can be used to promote both subjective and objective sleep quality in healthy human subjects. This makes aroma intervention a potential solution for poor sleep quality and insomnia.

Physical exercise prevents mice from L-Kynurenine-induced depression-like behavior.

BACKGROUND: Depression is a wide-spread disease that affects millions of people worldwide. Recent studies in neuroinflammation suggested that increased plasma kynurenine (KYN) level was related to depressive symptoms, while animal studies indicated that KYN increase could be caused by environmental stressor. Recent study reported that exercise may prevent stress-induced depression by enhancing KYN metabolism in muscle. This study seeks to test the effect of voluntary exercise on depressive-like behavior induced by stress and KYN in mice. HYPOTHESIS: Exercise prevents depressive-like behavior induced by KYN. RESULTS: Our study found that two weeks of voluntary exercise greatly reduced stress-induced helplessness in mice. On non-stressed mice, naïve mice injected with KYN showed increased immobile time in the TST (214 ± 30 s for KYN vs. 181 ± 33 s for saline; p < 0.05) and higher failure rate in the escape test (39 ± 31 % for KYN vs. 16 ± 13 % for saline; p < 0.05), while exercised mice were not affected by KYN injection in neither test. We also observed that exercised mice's plasma KYN concentration (3.29 ± 1.09 u M) was as low as a quarter of that of control (12.95 ± 3.44 u M) (P < 0.01) after KYN injection. Finally, we found that exercised mice expressed more kynurenine aminotransferase III (KAT3) in the muscle than control mice (1.62 ± 0.60 folds for exercise vs. 1.00 ± 0.22 folds for control; p = 0.005) CONCLUSION: Exercise promotes KAT3 expression, enhances KYN metabolism, and consequently prevents mice from stress or KYN-induced depressive-like behavior.