Study on curve optimization and structural safety effect of south roof of solar greenhouse in China.

To maximize the use of solar energy and increase the building area of solar greenhouses in China, a light radiation model for solar greenhouses is established. This model integrates previous research results with the solar motion principle, meteorological data and the optical properties of materials. The results indicate that optimizing the structural curve of the south roof of the greenhouse improves both internal land utilization and solar capture. After optimization, the internal land utilization rate of the solar greenhouse increased by 42 m2, with a respective 15.2 and 0.78% increase in lighting on the southern roof and ground. The light interception by the back wall of the greenhouse was reduced by 0.67%, while the total light interception increased by 2.22%. The research results identify the optimal shoulder height (0.7 m) and overall height (2 m) for the second-generation solar greenhouse in Liaoshen. The optimal curve functions Y 1 and Y 2 for the south roofs of greenhouses are calculated according to the actual construction requirements. This article verifies the structural safety of the solar greenhouse after renovation and shows that optimizing the shoulder height increases the structural stability and safety of the greenhouse.

Advancing insights into in vivo meningeal lymphatic vessels with stereoscopic wide-field photoacoustic microscopy.

Meningeal lymphatic vessels (mLVs) play a pivotal role in regulating metabolic waste from cerebrospinal fluid (CSF). However, the current limitations in field of view and resolution of existing imaging techniques impede understanding the stereoscopic morphology and dynamic behavior of mLVs in vivo. Here, we utilized dual-contrast functional photoacoustic microscopy to achieve wide-field intravital imaging of the lymphatic system, including mLVs and glymphatic pathways. The stereoscopic photoacoustic microscopy based on opto-acoustic confocal features has a depth imaging capability of 3.75 mm, facilitating differentiation between mLVs on the meninges and glymphatic pathways within the brain parenchyma. Subsequently, using this imaging technique, we were able to visualize the dynamic drainage of mLVs and identify a peak drainage period occurring around 20-40 min after injection, along with determining the flow direction from CSF to lymph nodes. Inspiringly, in the Alzheimer's disease (AD) mouse model, we observed that AD mice exhibit a ~ 70% reduction in drainage volume of mLVs compared to wild-type mice. With the development of AD, there is be continued decline in mLVs drainage volume. This finding clearly demonstrates that the AD mouse model has impaired CSF drainage. Our study opens up a horizon for understanding the brain's drainage mechanism and dissecting mLVs-associated neurological disorders.

Auditory cues modulate the short timescale dynamics of STN activity during stepping in Parkinson's disease.

BACKGROUND: Gait impairment has a major impact on quality of life in patients with Parkinson's disease (PD). It is believed that basal ganglia oscillatory activity at ß frequencies (15-30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies. OBJECTIVE: We aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait. METHODS: We studied STN local field potentials (LFPs) in eight PD patients while they performed stepping movements. Hidden Markov Models (HMMs) were used to discover transient states of spectral activity that occurred during stepping with and without auditory cues. RESULTS: The occurrence of low and high ß bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues. CONCLUSIONS: We show that STN oscillatory activity in the α and ß frequency bands are differentially modulated by gait-promoting oscillatory cues. These findings suggest that the enhancement of α rhythms may be an approach for ameliorating gait impairments in PD.

A pilot study on a patient with refractory headache: Personalized deep brain stimulation through stereoelectroencephalography.

The integration of stereoelectroencephalography with therapeutic deep brain stimulation (DBS) holds immense promise as a viable approach for precise treatment of refractory disorders, yet it has not been explored in the domain of headache or pain management. Here, we implanted 14 electrodes in a patient with refractory migraine and integrated clinical assessment and electrophysiological data to investigate personalized targets for refractory headache treatment. Using statistical analyses and cross-validated machine-learning models, we identified high-frequency oscillations in the right nucleus accumbens as a critical headache-related biomarker. Through a systematic bipolar stimulation approach and blinded sham-controlled survey, combined with real-time electrophysiological data, we successfully identified the left dorsal anterior cingulate cortex as the optimal target for the best potential treatment. In this pilot study, the concept of the herein-proposed data-driven approach to optimizing precise and personalized treatment strategies for DBS may create a new frontier in the field of refractory headache and even pain disorders.

Inflammatory cytokines and their potential role in Sjogren's syndrome risk: insights from a mendelian randomization study.

AIM: This study aimed to investigate the causal impact of inflammatory cytokines on Sjogren's Syndrome (SS) and to identify potential biomarkers for SS clinical management using Mendelian Randomization (MR). MATERIALS AND METHODS: Leveraging GWAS summary data of inflammatory cytokines and SS, we executed the first two-sample MR analysis. Genetic variants from prior GWASs associated with circulating inflammatory cytokines served as instrumental variables (IVs). Data regarding cytokines were analyzed using the Olink Target-96 Inflammation panel, synthesizing data from 14,824 participants. GWAS summary statistics for SS were procured from the UK Biobank, focusing on samples of European ancestry. To discern the causal relationship between inflammatory cytokines and SS, several MR methodologies, including inverse variance weighted (IVW) and MR-Egger regression, were applied. RESULTS: After rigorous IV quality control, 91 cytokines were incorporated into the MR analysis. The IVW analysis identified 8 cytokines with a positive association to SS: Axin-1 (OR 2.56, 95% CI 1.07-6.10), T-cell surface glycoprotein CD5 (OR 1.81, 95% CI 1.08-3.02), CUDP1 (OR 1.61, 95% CI 1.00-2.58), CXCL10 (OR 1.92, 95% CI 1.25-2.95), IL-4 (OR 2.18, 95% CI 1.22-3.91), IL-7 (OR 2.35, 95% CI 1.27-4.33), MCP-2 (OR 1.27, 95% CI 1.05-1.54), and TNFRSF9 (OR 1.83, 95% CI 1.03-3.24), suggesting their potential in increasing SS risk. CONCLUSION: Our study conducted through MR, identified various inflammatory cytokines associated with SS risk, validating some previous research results and offering some new potential biomarkers for SS. However, these findings necessitate further research for validation and exploration of their precise role in the onset and progression of SS.

Quantifying Sharpness and Nonlinearity in Neonatal Seizure Dynamics.

The integration of multiple electrophysiological biomarkers is crucial for monitoring neonatal seizure dynamics. The present study aimed to characterize the temporal dynamics of neonatal seizures by analyzing intrinsic waveforms of epileptic electroencephalogram (EEG) signals. We proposed a complementary set of methods considering envelope power, focal sharpness changes, and nonlinear patterns of EEG signals of 79 neonates with seizures. Features derived from EEG signals were used as input to the machine learning classifier. All three characteristics were significantly elevated during seizure events, as agreed upon by all viewers (P < 0.0001). Envelope power was elevated in the entire seizure period, and the degree of nonlinearity rose at the termination of a seizure event. Epileptic sharpness effectively characterizes an entire seizure event, complementing the role of envelope power in identifying its onset. However, the degree of nonlinearity showed superior discriminability for the termination of a seizure event. The proposed computational methods for intrinsic sharp or nonlinear EEG patterns evolving during neonatal seizure could share some features with envelope power. Current findings may be helpful in developing strategies to improve neonatal seizure monitoring.

Spatiotemporal convolution sleep network based on graph attention mechanism with automatic feature extraction.

BACKGROUND AND OBJECTIVE: Graph neural networks (GNNs) are widely used for automatic sleep staging. However, the majority of GNNs are based on spectral approaches, as far as we know, which heavily depend on the Laplacian eigenbasis determined by the graph structure with a large computing cost. METHODS: We introduced a non-spectral approach named graph attention networks v2 (GATv2) as the core of our network to extract spatial information (S-GATv2 in our work), which is more flexible and intuitive than the routined spectral method. Meanwhile, to resolve the issue of weak generalization of using traditional feature extraction, the multi-convolutional layers are implemented to automatically extract features. In this work, the proposed spatiotemporal convolution sleep network (ST-GATv2) consists of multi-convolution layers and a GATv2 block. Of note, the graph attention technique to the time domain was applied to construct temporal GATv2 (T-GATv2), which intends to capture the connection between two channels in the adjacent sleep stages. Besides, the modified function is further proposed to capture the hidden changing trend information by the difference in the feature's value of the two adjacent stages. RESULTS: In our experiment, we used the SS3 datasets in the MASS as our test datasets to compare with other advanced models. Our result reveals our model achieves the highest accuracy at 89.0 %. Besides, the proposed T-GATv2 block and modified function bring an approximate 0.5 % improvement in Kappa and F1-score. CONCLUSIONS: Our results support the potential of graph attention mechanisms and creative blocks (T-GATv2 and modified function) in sleep classification. We suggest the proposed ST-GATv2 model as an effective tool in sleep staging in either healthy or diseased states.

A novel dynamic cardiorespiratory coupling quantification method reveals the effect of aging on the autonomic nervous system.

Traditional cardiopulmonary coupling (CPC) based on the Fourier transform shares an inherent trade-off between temporal and frequency resolutions with fixed window designs. Therefore, a cross-wavelet cardiorespiratory coupling (CRC) method was developed to highlight interwave cardiorespiratory dynamics and applied to evaluate the age effect on the autonomic regulation of cardiorespiratory function. The cross-wavelet CRC visualization successfully reflected dynamic alignments between R-wave interval signal (RR intervals) and respiration. Strong and continuous CRC was shown if there was perfect temporal coordination between consecutive R waves and respiration, while CRC becomes weaker and intermittent without such coordination. Using real data collected on electrocardiogram (ECG) and respiratory signals, the heart rate variability (HRV) and CRC were calculated. Subsequently, comparisons were conducted between young and elderly individuals. Young individuals had significantly higher partial time and frequency HRV indices than elderly individuals, indicating stronger control of parasympathetic regulation. The overall coupling strength of the CRC of young individuals was higher than that of elderly individuals, especially in high-frequency power, which was significantly lower in the elderly group than in the young group, achieving better results than the HRV indices in terms of statistical significance. Further analyses of the time-frequency dynamics of CRC indices revealed that the coupling strength was consistently higher in the high-frequency (HF) band (0.15-0.4 Hz) in young participants compared to elderly individuals. The dynamic CRC between respiration and HRV indices was accessible by integrating the cross-wavelet spectrum and coherence. Young participants had a significantly higher level of CRC in the HF band, indicating that aging reduces vagus nerve modulation.

A Novel Sleep Staging Method Based on EEG and ECG Multimodal Features Combination.

Accurate sleep staging evaluates the quality of sleep, supporting the clinical diagnosis and intervention of sleep disorders and related diseases. Although previous attempts to classify sleep stages have achieved high classification performance, little attention has been paid to integrating the rich information in brain and heart dynamics during sleep for sleep staging. In this study, we propose a generalized EEG and ECG multimodal feature combination to classify sleep stages with high efficiency and accuracy. Briefly, a hybrid features combination in terms of multiscale entropy and intrinsic mode function are used to reflect nonlinear dynamics in multichannel EEGs, along with heart rate variability measures over time/frequency domains, and sample entropy across scales are applied for ECGs. For both the max-relevance and min-redundancy method and principal component analysis were used for dimensionality reduction. The selected features were classified by four traditional machine learning classifiers. Macro-F1 score, macro-geometric mean, and Cohen kappa value are adopted to evaluate the classification performance of each class in an imbalanced dataset. Experimental results show that EEG features contribute more to wake stage classification while ECG features contribute more to deep sleep stages. The proposed combination achieves the highest accuracy of 84.3% and the highest kappa value of 0.794 on the support vector machine in the ISRUC-S3 dataset, suggesting the proposed multimodal features combination is promising in accuracy and efficiency compared to other state-of-the-art methods.

Cross-Frequency Coupling and Intelligent Neuromodulation.

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.

Monitoring the perivascular cerebrospinal fluid dynamics of the glymphatic pathway using co-localized photoacoustic microscopy.

In vivo imaging plays an important role in investigating how the glymphatic system drains metabolic waste and pathological proteins from the central nervous system. However, the spatial resolutions and imaging specificities of the available preclinical imaging methods for the glymphatic system are insufficient, and they cannot simultaneously locate the cerebrovascular and glymphatic pathways to enable the monitoring of the perivascular cerebrospinal fluid dynamics. This Letter proposes an imaging strategy for the in vivo monitoring of cerebrospinal fluid flow using co-localized photoacoustic volumetric microscopy. Imaging results showed that the glymphatic pathway is one of the crucial pathways for the drainage of cerebrospinal fluid, and it mainly enters the brain parenchyma along periarterial routes. Continuous intravital imaging enables the monitoring of the cerebrospinal fluid flow as well as the drainage and clearance from the glymphatic system after the tracer has entered the cerebrospinal fluid. The technique can enhance understanding of the cerebrospinal fluid circulation and open up new insights into neurodegenerative brain diseases.

A novel measure of cardiopulmonary coupling during sleep based on the synchrosqueezing transform algorithm.

OBJECTIVE: This paper presents a novel method to quantify cardiopulmonary dynamics for automatic sleep apnea detection by integrating the synchrosqueezing transform (SST) algorithm with the standard cardiopulmonary coupling (CPC) method. METHODS: Simulated data were designed to validate the reliability of the proposed method, with varying levels of signal bandwidth and noise contamination. Real data were collected from the Physionet sleep apnea database, consisting of 70 single-lead ECGs with expert-labeled apnea annotations on a minute-by-minute basis. Three different signal processing techniques applied to sinus interbeat interval and respiratory time series include short-time Fourier transform, continuous Wavelet transform, and synchrosqueezing transform, respectively. Subsequently, the CPC index was computed to construct sleep spectrograms. Features derived from such spectrogram were used as input to five machine- learning-based classifiers including decision trees, support vector machines, k-nearest neighbors, etc. Results: The simulation results showed that the SST-CPC method is robust to both noise level and signal bandwidth, outperforming Fourier-based and Wavelet-based approaches. Meanwhile, the SST-CPC spectrogram exhibited relatively explicit temporal-frequency biomarkers compared with the rest. Furthermore, by integrating SST-CPC features with common-used heart rate and respiratory features, accuracies for per-minute apnea detection improved from 72% to 83%, validating the added value of CPC biomarkers in sleep apnea detection. CONCLUSION: The SST-CPC method improves the accuracy of automatic sleep apnea detection and presents comparable performances with those automated algorithms reported in the literature. SIGNIFICANCE: The proposed SST-CPC method enhances sleep diagnostic capabilities, and may serve as a complementary tool to the routine diagnosis of sleep respiratory events.

Variational Phase-Amplitude Coupling Characterizes Signatures of Anterior Cortex under Emotional Processing.

Emotion, an essential aspect in inferring human psychological states, is featured by entangled oscillators operating at multiple frequencies and montages. However, the dynamics of mutual interactions among rhythmic activities in EEGs under various emotional expressions are unclear. To this end, a novel method named variational phase-amplitude coupling is proposed to quantify the rhythmic nesting structure in EEGs under emotional processing. The proposed algorithm lies in variational mode decomposition, featured by its robustness to noise artifacts and its merit in avoiding the mode-mixing problem. This novel method reduces the risk of spurious coupling compared to that with ensemble empirical mode decomposition or iterative filter when evaluated by simulations. An atlas of cross-couplings in EEGs under eight emotional processing is established. Mainly, α activity in the anterior frontal region serves as a critical sign for neutral emotional state, whereas γ amplitude seems to be linked with both positive and negative emotional states. Moreover, for those γ-amplitude-related couplings under neutral emotional state, the frontal lobe is associated with lower phase-given frequencies while the central lobe is attached to higher ones. The γ-amplitude-related coupling in EEGs is a promising biomarker for recognizing mental states. We recommend our method as an effective tool in characterizing the entangled multifrequency rhythms in brain signals for emotion neuromodulation.

Composite nanoarchitectonics with TiO2 nanocrystals and superior thin Ti3C2Tx nanosheets towards efficient NO removal.

Accordion-like Ti3C2Tx MXene supplied a possibility to construct two-dimensional composites with novel performance. In this paper, few-layered Ti3C2Tx MXene was created via a chemical etching strategy. The oxidation in-situ using a powerful alkaline solution resulted successfully in TiO2 nanocrystals grown on Ti3C2Tx nanosheets. The alkaline treatment adjusted terminations of the Ti3C2Tx MXene and controlled the oxidation degree by changing temperature. The ratio of Ti3C2Tx and TiO2 was finally optimized. Because of Ti3C2Tx nanosheets with well conductivity and excellent light absorption as well as TiO2 nanocrystal arrays on Ti3C2Tx nanosheets with a high specific surface area and more active sites, TiO2/Ti3C2Tx composites revealed excellent photocatalystic activity, especially for NO removal. The improvement of separation and transfer efficiency of phootogenerated carriers is ascribed to the microstructure of TiO2/Ti3C2Tx composites. The composite sample synthesized at 75 °C revealed the best NO removal efficiency, in which 70% of NO was removed at a concentration of 600 ppb. This study offers a new thought for preparing high performance MXene-based photocatalysts.

Impacts of synoptic circulation on surface ozone pollution in a coastal eco-city in Southeastern China during 2014-2019.

The coastal eco-city of Fuzhou in Southeastern China has experienced severe ozone (O3) episodes at times in recent years. In this study, three typical synoptic circulations types (CTs) that influenced more than 80% of O3 polluted days in Fuzhou during 2014-2019 were identified using a subjective approach. The characteristics of meteorological conditions linked to photochemical formation and transport of O3 under the three CTs were summarized. Comprehensive Air Quality Model with extensions was applied to simulate O3 episodes and to quantify O3 sources from different regions in Fuzhou. When Fuzhou was located to the west of a high-pressure system (classified as "East-ridge"), more warm southwesterly currents flowed to Fuzhou, and the effects of cross-regional transport from Guangdong province and high local production promoted the occurrence of O3 episodes. Under a uniform pressure field with a low-pressure system occurring to the east of Fuzhou (defined as "East-low"), stagnant weather conditions caused the strongest local production of O3 in the atmospheric boundary layer. Controlled by high-pressure systems over the mainland (categorized as "Inland-high"), northerly airflows enhanced the contribution of cross-regional transport to O3 in Fuzhou. The abnormal increases of the "East-ridge" and "Inland-high" were closely related to O3 pollution in Fuzhou in April and May 2018, resulting in the annual maximum number of O3 polluted days during recent years. Furthermore, the rising number of autumn O3 episodes in 2017-2019 was mainly related to the "Inland-high", indicating the aggravation of cross-regional transport and highlighting the necessity of enhanced regional collaboration and efforts in combating O3 pollution.

Brain Complexity Predicts Response to Adrenocorticotropic Hormone in Infantile Epileptic Spasms Syndrome: A Retrospective Study.

INTRODUCTION: Infantile epileptic spasms syndrome (IESS) is an age-specific and severe epileptic encephalopathy. Although adrenocorticotropic hormone (ACTH) is currently considered the preferred first-line treatment, it is not always effective and may cause side effects. Therefore, seeking a reliable biomarker to predict the treatment response could benefit clinicians in modifying treatment options. METHODS: In this study, the complexities of electroencephalogram (EEG) recordings from 15 control subjects and 40 patients with IESS before and after ACTH therapy were retrospectively reviewed using multiscale entropy (MSE). These 40 patients were divided into responders and nonresponders according to their responses to ACTH. RESULTS: The EEG complexities of the patients with IESS were significantly lower than those of the healthy controls. A favorable response to treatment showed increasing complexity in the γ band but exhibited a reduction in the ß/α-frequency band, and again significantly elevated in the δ band, wherein the latter was prominent in the parieto-occipital regions in particular. Greater reduction in complexity was significantly linked with poorer prognosis in general. Occipital EEG complexities in the γ band revealed optimized performance in recognizing response to the treatment, corresponding to the area under the receiver operating characteristic curves as 0.8621, while complexities of the δ band served as a fair predictor of unfavorable outcomes globally. CONCLUSION: We suggest that optimizing frequency-specific complexities over critical brain regions may be a promising strategy to facilitate predicting treatment response in IESS.

Study of a novel front-roof-back natural ventilation system for Chinese solar greenhouses.

A Chinese solar greenhouse (CSG) is a highly efficient and energy-saving horticultural facility. Ventilation is significantly important for crop production in the greenhouse, and the vent configuration is the basis of the greenhouse design. Current CSG ventilation structures mostly include front bottom vents and top vents to create a suitable temperature environment for the normal development of crops. However, the ventilation capacity and efficiency are limited. In the present study, we proposed a comprehensive front bottom + top + back roof (FTB) ventilation configuration. The greenhouse ventilation was investigated during the summer season by means of field testing and simulation, and the performance of three ventilation structures-front bottom + top (FT), front bottom + back roof (FB) and FTB-was compared. The results showed that FTB stabilized the greenhouse temperature for 20 s less time than FT and FB. The cooling rate of FTB showed a 24.84% and 5.52% improvement over FT and FB, respectively, and the average temperature showed a 13.81% and 3.65% decrease, respectively. Moreover, the ventilation performance of the side walls was investigated in order to determine if they might serve as auxiliary structures for FTB ventilation. Nevertheless, the improvements of cooling rate, wind speed and average temperature were only 0.52%, 2.09% and 0.11%, respectively. The results demonstrated that the novel FTB ventilation proposed in the present study significantly improved ventilation efficiency and uniformity compared with conventional ventilation structures. The results presented herein provide theoretical support for the use and design of greenhouses suitable for China's special climate.

A Novel Framework in Quantifying Oscillatory Coupling to Gait Disturbance in Parkinson's Disease.

Phase-amplitude coupling (PAC) based on the uniform phase empirical mode decomposition (UPEMD) is proposed to improve the accuracy of PAC assessment. The framework is applied to investigate the mechanism and improvement measure of gait disturbance for Parkinson's disease (PD). Hß modulation is suppressed at the time of contralateral heel strikes and rebounds when the contralateral foot rests on the ground and the ipsilateral foot is raised. Prominent PACs exist between δ and Lß/Hß activities. Auditory cue improves the gait; meanwhile, it enhances the Hß modulation, and suppresses the δ-Lß/Hß PACs, which may rebound toward the before-cue stage afterward. Our findings suggest the proposed UPEMD-PAC is a useful framework in quantifying PAC with pre-determined frequencies, whereas the δ-Lß/Hß PACs in the subthalamic nucleus serve as potential biomarkers for gait disturbance in PD. Clinical Relevance- This manifests the efficacy of auditory cues on gait disturbance. The proposed framework may be useful in diagnosing the severity of motor impairment.

Sleep Dynamic Analysis Technology Based on Cross-Phase-Amplitude Transfer Entropy in Multiple Brain Regions.

Information flow existed across brain regions, and varies dynamically during sleep. In evaluating brain communication and neural-oscillation connectivity across spatiotemporal scales, the phase-amplitude coupling (PAC) is well-explored. However, the directional connectivity is still a deficiency. In this work, we propose a cross-phase-amplitude transfer entropy method in quantifying the characteristics of multi-regional sleep dynamics. The simulation of multivariate nonlinear and nonstationary signals verifies both effectiveness and veracity of the proposed algorithm. The results achieved in sleep EEG of healthy adults indicate that the direction of PAC is from the occipital lobe to the frontal lobe in the Awake and N1 sleep stages. And the flow of PAC turns to the opposite direction for the other sleep stages, i.e., frontal-to-occipital lobe. Besides, the δ-θ/α PAC gradually strengthens with the deepening of the sleep. Of note, the PAC results in the REM sleep stage vary across different frequency pairs. The obtained results support the proposed method as a reliable tool in evaluating brain functions during sleep with brain signals. Clinical Relevance- This manifests the brain communication and neuron-oscillation connectivity across spatiotemporal scales. The proposed framework may be useful in identifying multi-regional sleep dynamics.

A Pursuit of the Degree of Nonlinearity for ß Oscillations under Motor Imagery.

The power of ß oscillations is an essential pathological biomarker for movement disorders, parkinsonism in particular. Motor imagery training was reported to support self-regulate such ß oscillations. Past studies had focused on the modulation of ß oscillatory power per se, ignoring the intrinsic oscillatory characteristics-the nonlinearity of the waveform. This work applied ensemble empirical mode decomposition to decompose neural activities in multiple frequency bands without destroying the temporal characteristics of the raw signal at all scales. We explored the dynamics of the degree of nonlinearity plus the averaged power across all periods and frequency bands of interest and tested how motor imagery may or may not induce nonlinearities under various frequency bands. With motor imagery, the degree of nonlinearity for the ß activity is significantly suppressed referenced to that without, of note, and the average power fails to present significant differences between segments with and without motor imagery training. Our results indicate that the degree of nonlinearity is a complementary and vital biomarker as the average power for ß oscillations, thereby providing theoretical support for the possible application in motor imagery therapy. Clinical Relevance- This suggests that motor imagery can suppress irregular patterns of ß oscillations for healthy, and the degree of nonlinearity is an effective feature in improving classification in training states for the MI-neurofeedback paradigm compared to that of the averaged power.