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IntroductionThis study aimed to evaluate morphological changes in cortical and subcortical regions and their asymmetrical differences in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). These morphological changes may provide valuable insights into the early diagnosis and treatment of Alzheimer's disease (AD).MethodsWe conducted structural MRI scans on a cohort comprising 62 SCD patients, 97 MCI patients, and 70 age-, sex-, and years of education-matched healthy controls (HC). Using Freesurfer, we quantified surface area, thickness, the local gyrification index (LGI) of cortical regions, and the volume of subcortical nuclei. Asymmetry measures were also calculated. Additionally, we explored the correlation between morphological changes and clinical variables related to cognitive decline.ResultsCompared to HC, patients with MCI exhibited predominantly left-sided surface morphological changes in various brain regions, including the transverse temporal gyrus, superior temporal gyrus, insula, and pars opercularis. SCD patients showed relatively minor surface morphological changes, primarily in the insula and pars triangularis. Furthermore, MCI patients demonstrated reduced volumes in the anterior-superior region of the right hypothalamus, the fimbria of the bilateral hippocampus, and the anterior region of the left thalamus. These observed morphological changes were significantly associated with clinical ratings of cognitive decline.ConclusionThe findings of this study suggest that cortical and subcortical morphometric changes may contribute to cognitive impairment in MCI, while compensatory mechanisms may be at play in SCD to preserve cognitive function. These insights have the potential to aid in the early diagnosis and treatment of AD.

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue. However, the most commonly employed sparse regularization models in motor imagery decoding, such as the least absolute shrinkage and selection operator (LASSO), is a biased estimation method and leads to the loss of target feature information.MethodsIn this paper, we propose a non-convex sparse regularization model that employs the Cauchy function. By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing sparse models. Therefore, it can learn more accurate, discriminative, and effective feature information. Additionally, the proposed method can perform feature selection and classification simultaneously, without requiring additional classifiers.ResultsWe conducted experiments on two publicly available motor imagery EEG datasets. The proposed method achieved an average classification accuracy of 82.98% and 64.45% in subject-dependent and subject-independent decoding assessment methods, respectively.ConclusionThe experimental results show that the proposed method can significantly improve the performance of motor imagery decoding, with better classification performance than existing feature selection and deep learning methods. Furthermore, the proposed model shows better generalization capability, with parameter consistency over different datasets and robust classification across different training sample sizes. Compared with existing sparse regularization methods, the proposed method converges faster, and with shorter model training time.

BackgroundThe government’s COVID-19 pandemic response lockdown strategy had a negative psychological and physical impact on individuals, which necessitated special care to pregnant women’s mental health. There has been no large-scale research on the underlying relationship between perceived stress and insomnia symptoms in pregnant Chinese women up to this point. During the COVID-19 pandemic, we wanted to see if there was an association between perceived stress and insomnia symptoms, as well as the moderating impact of resilience for Chinese pregnant women.MethodsThis cross-sectional study examined 2115 pregnant women from central and western China using multi-stage sampling methodologies. A systematic questionnaire was used to collect information on sleep quality, perceived stress, and resilience using the Insomnia Severity Index, Perceptual Stress Scale, and Connor and Davidson Resilience Scale. To assess the moderating influence of resilience, hierarchical regressions were used.ResultsDuring the COVID-19 pandemic, 18.53% of respondents (N = 2115) reported experiencing sleeplessness. In pregnant women, perceived stress was positively linked with insomnia symptoms (p < 0.001). Furthermore, resilience significantly attenuated the influence of perceived stress on insomnia symptoms in Chinese expectant mother (βinteraction = −0.0126, p < 0.001).ConclusionPregnant women with strong resilience were less influenced by perceived stress than those with poor resilience. The findings of this study might give empirical proof that health care professionals should identify the relevance of reducing perceived stress in pregnant women with poor resilience and provide better treatment and support when necessary.

Characterization and prediction of individual difference of pain sensitivity are of great importance in clinical practice. MRI techniques, such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), have been popularly used to predict an individual’s pain sensitivity, but existing studies are limited by using one single imaging modality (fMRI or DTI) and/or using one type of metrics (regional or connectivity features). As a result, pain-relevant information in MRI has not been fully revealed and the associations among different imaging modalities and different features have not been fully explored for elucidating pain sensitivity. In this study, we investigated the predictive capability of multi-features (regional and connectivity metrics) of multimodal MRI (fMRI and DTI) in the prediction of pain sensitivity using data from 210 healthy subjects. We found that fusing fMRI-DTI and regional-connectivity features are capable of more accurately predicting an individual’s pain sensitivity than only using one type of feature or using one imaging modality. These results revealed rich information regarding individual pain sensitivity from the brain’s both structural and functional perspectives as well as from both regional and connectivity metrics. Hence, this study provided a more comprehensive characterization of the neural correlates of individual pain sensitivity, which holds a great potential for clinical pain management.

Electroencephalography (EEG) microstate analysis is a powerful tool to study the spatial and temporal dynamics of human brain activity, through analyzing the quasi-stable states in EEG signals. However, current studies mainly focus on rest-state EEG recordings, microstate analysis for the recording of EEG signals during naturalistic tasks is limited. It remains an open question whether current topographical clustering strategies for rest-state microstate analysis could be directly applied to task-state EEG data under the natural and dynamic conditions and whether stable and reliable results could still be achieved. It is necessary to answer the question and explore whether the topographical clustering strategies would affect the performance of microstate detection in task-state EEG microstate analysis. If it exists differences in microstate detection performance when different topographical clustering strategies are adopted, then we want to know how the alternations of the topographical clustering strategies are associated with the naturalistic task. To answer these questions, we work on a public emotion database using naturalistic and dynamic music videos as the stimulation to evaluate the effects of different topographical clustering strategies for task-state EEG microstate analysis. The performance results are systematically examined and compared in terms of microstate quality, task efficacy, and computational efficiency, and the impact of topographical clustering strategies on microstate analysis for naturalistic task data is discussed. The results reveal that a single-trial-based bottom-up topographical clustering strategy (bottom-up) achieves comparable results with the task-driven-based top-down topographical clustering (top-down). It suggests that, when task information is unknown, the single-trial-based topographical clustering could be a good choice for microstate analysis and neural activity study on naturalistic EEG data.

Electroencephalographic (EEG) neurofeedback (NFB) is a popular neuromodulation method to help one selectively enhance or inhibit his/her brain activities by means of real-time visual or auditory feedback of EEG signals. Sensory motor rhythm (SMR) NFB protocol has been applied to improve cognitive performance, but a large proportion of participants failed to self-regulate their brain activities and could not benefit from NFB training. Therefore, it is important to identify the neural predictors of SMR up-regulation NFB training performance for a better understanding the mechanisms of individual difference in SMR NFB. Twenty-seven healthy participants (12 males, age: 23.1 ± 2.36) were enrolled to complete three sessions of SMR up-regulation NFB training and collection of multimodal neuroimaging data [resting-state EEG, structural magnetic resonance imaging (MRI), and resting-state functional MRI (fMRI)]. Correlation analyses were performed between within-session NFB learning index and anatomical and functional brain features extracted from multimodal neuroimaging data, in order to identify the neuroanatomical and neurophysiological predictors for NFB learning performance. Lastly, machine learning models were trained to predict NFB learning performance using features from each modality as well as multimodal features. According to our results, most participants were able to successfully increase the SMR power and the NFB learning performance was significantly correlated with a set of neuroimaging features, including resting-state EEG powers, gray/white matter volumes from MRI, regional and functional connectivity (FC) of resting-state fMRI. Importantly, results of prediction analysis indicate that NFB learning index can be better predicted using multimodal features compared with features of single modality. In conclusion, this study highlights the importance of multimodal neuroimaging technique as a tool to explain the individual difference in within-session NFB learning performance, and could provide a theoretical framework for early identification of individuals who cannot benefit from NFB training.

The existence of nociceptive-specific brain regions has been a controversial issue for decades. Multisensory fMRI studies, which examine fMRI activities in response to various types of sensory stimulation, could help identify nociceptive-specific brain regions, but previous studies are limited by sample size and they did not differentiate nociceptive-specific regions and nociceptive-preferential regions, which have significantly larger responses to nociceptive input. In this study, we conducted a multisensory fMRI experiment on 80 healthy participants, with the aim to determine whether there are certain brain regions that specifically or preferentially respond to nociceptive stimulation. By comparing the evoked fMRI responses across four sensory modalities, we found a series of brain regions specifically or preferentially involved in nociceptive sensory input. Particularly, we found different parts of some cortical regions, such as insula and cingulate gyrus, play different functional roles in the processing of nociceptive stimulation. Hence, this multisensory study improves our understanding of the functional integrations and segregations of the nociceptive-related regions.

Pain sensitivity is highly variable among individuals, and it is clinically important to predict an individual’s pain sensitivity for individualized diagnosis and management of pain. Literature has shown that pain sensitivity is associated with regional structural features of the brain, but it remains unclear whether pain sensitivity is also related to structural brain connectivity. In the present study, we investigated the relationship between pain thresholds and morphological connectivity (MC) inferred from structural MRI based on data of 221 healthy participants. We found that MC was highly predictive of an individual’s pain thresholds and, importantly, it had a better prediction performance than regional structural features. We also identified a number of most predictive MC features and confirmed the crucial role of the prefrontal cortex in the determination of pain sensitivity. These results suggest the potential of using structural MRI-based MC to predict an individual’s pain sensitivity in clinical settings, and hence this study has important implications for diagnosis and treatment of pain.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are a novel class of glucose-lowering agents that significantly improve the prognosis of patients with type 2 diabetes (T2D) and heart failure. SGLT2i has recently been implicated in the treatment of atrial fibrillation (AF) with clinical data demonstrating that these agents decrease the incidence of AF events in patients with T2D. Fundamental findings have suggested that SGLT2i may alleviate atrial electrical and structural remodeling. The underlying mechanisms of SGLT2i are likely associated with balancing the sodium and calcium handling disorders and mitigating the mitochondrial dysfunction in atrial myocytes. This review illustrates the advances in understanding the underlying mechanisms of SGLT2i as an evolving treatment modality for AF.

Background: “Pill-in-the-pocket” (PIP) treatment with type IC drugs for cardioversion of recent-onset atrial fibrillation (AF) has been recommended in guidelines. Major adverse effects have been often reported, and the underlying mechanisms are proposed to be associated with the genetic backgrounds.Methods and Results: A male patient was treated with PIP approach (propafenone 600 mg.po) for the conversion of new onset AF. His symptoms got worse and referred to emergency room; ECG showed a typical Brugada syndrome (BrS) type I ECG pattern with sinus rhythm. Genetic screening identified a common SCN5A polymorphism R1193Q. Propafenone blockade of INa was studied in HEK293 cells expressed SCN5A R1193Q channel and WT channel using patch clamp techniques. There was no significant difference in peak current and steady-state gating parameters between R1193Q and WT at baseline. At clinically relevant concentration of 2 μmol/L propafenone, use-dependent block (UDB) of INa was more pronounced in R1193Q versus WT (44.2 ± 7.2 versus 24.8 ± 5.7% at the frequency of 2 Hz, P < 0.05); IC50 of UDB was 2.9 ± 0.7 μmol/L for R1193Q and 8.1 ± 1.8 μmol/L for WT, respectively. Propafenone produced more left shift of steady-state inactivation and slower recovery from inactivation in R1193Q compared with WT.Conclusion: A common SCN5A polymorphism R1193Q enhances UDB by propafenone and predisposes the patients to drug-induced BrS with PIP treatment. Our data suggest that R1193Q polymorphism is likely to be a genetic marker for the major adverse effects associated with propafenone PIP approach for AF patients’ management. Ajmaline challenge to rule out the presence of BrS should be considered prior to propafenone PIP therapy in AF patients who are identified to have R1193Q polymorphism.

Objective: Pain and affective disorders have clear clinical relevance; however, very few studies have investigated the association between pain and bipolar disorder. This study investigated the brain activity of patients with bipolar disorder (BPs) undergoing tonic pain and assessed the interaction between pain and emotion.Methods: Ten BPs and ten healthy controls (HCs) were exposed to emotional pictures (positive, neutral, or negative), tonic pain only (pain session), and emotional pictures along with tonic pain (combined session). A moderate tonic pain was induced by the infusion of hypertonic saline (5% NaCl) into the right masseter muscle with a computer-controlled system. Whole-brain blood oxygenation level dependent (BOLD) signals were acquired using 3T functional resonance imaging (fMRI).Results: Ten BPs and ten healthy participants were included in the final analysis. During the pain session, BPs accepted more saline, but showed lower pain rating scores than HCs. When experiencing pain, BPs showed a significant decrease in the BOLD signal in the bilateral insula, left inferior frontal gyrus (IFG), and left cerebellum as compared with HCs. In the combined session, the activated regions for positive mood (pain with positive mood > baseline) in BPs were the left cerebellum, right temporal gyrus, and left occipital gyrus; the activated regions for negative mood (pain with negative mood > baseline) were the right occipital gyrus, left insula, left IFG, and bilateral precentral gyrus.Conclusions: This study presents the preliminary finding of the interaction between pain and emotion in BPs. BPs exhibited lower sensitivity to pain, and the activation of insula and IFG may reflect the interaction between emotion and pain stimulus.

Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still remains unclear how and to what extent these individual differences affect the performance of between-individual fMRI-based pain prediction. The present study is aimed to examine the relationship between individual differences in pain prediction models and between-individual prediction error, and, further, to identify brain regions that contribute to between-individual prediction error. To this end, we collected and analyzed fMRI data and pain ratings in a laser-evoked pain experiment. By correlating different types of individual difference metrics with between-individual prediction error, we are able to quantify the influence of these individual differences on prediction performance and reveal a set of brain regions whose activities are related to prediction error. Interestingly, we found that the precuneus, which does not have predictive capability to pain, could also affect the prediction error. This study elucidates the influence of interindividual variability in pain on the between-individual prediction performance, and the results will be useful for the design of more accurate and robust fMRI-based pain prediction models.

Anthracyclines are effective agents generally used to treat solid-tumor and hematologic malignancies. The use of anthracyclines for over 40 years has improved cancer survival statistics. Nevertheless, the clinical utility of anthracyclines is limited by its dose-dependent cardiotoxicity that adversely affects 10–30% of patients. Anthracycline-induced cardiotoxicity may be classified as acute/subacute or chronic/late toxicity and leads to devastating adverse effects resulting in poor quality of life, morbidity, and premature mortality. Traditional Chinese medicine has a history of over 2,000 years, involving both unique theories and substantial experience. Several studies have investigated the potential of natural products to decrease the cardiotoxic effects of chemotherapeutic agents on healthy cells, without negatively affecting their antineoplastic activity. This article discusses the mechanism of anthracycline-induced cardiotoxicity, and summarizes traditional Chinese medicine treatment for anthracycline-induced heart failure (HF), cardiac arrhythmia, cardiomyopathy, and myocardial ischemia in recent years, in order to provide a reference for the clinical prevention and treatment of cardiac toxicity.

The level of pain perception is correlated with the magnitude of pain-evoked brain responses, such as laser-evoked potentials (LEP), across trials. The positive LEP-pain relationship lays the foundation for pain prediction based on single-trial LEP, but cross-individual pain prediction does not have a good performance because the LEP-pain relationship exhibits substantial cross-individual difference. In this study, we aim to explain the cross-individual difference in the LEP-pain relationship using inter-stimulus EEG (isEEG) features. The isEEG features (root mean square as magnitude and mean square successive difference as temporal variability) were estimated from isEEG data (at full band and five frequency bands) recorded between painful stimuli. A linear model was fitted to investigate the relationship between pain ratings and LEP response for fast-pain trials on a trial-by-trial basis. Then the correlation between isEEG features and the parameters of LEP-pain model (slope and intercept) was evaluated. We found that the magnitude and temporal variability of isEEG could modulate the parameters of an individual's linear LEP-pain model for fast-pain trials. Based on this, we further developed a new individualized fast-pain prediction scheme, which only used training individuals with similar isEEG features as the test individual to train the fast-pain prediction model, and obtained improved accuracy in cross-individual fast-pain prediction. The findings could help elucidate the neural mechanism of cross-individual difference in pain experience and the proposed fast-pain prediction scheme could be potentially used as a practical and feasible pain prediction method in clinical practice.

Placebo exhibits beneficial effects on pain perception in human experimental studies. Most of these studies demonstrate that placebo significantly decreased neural activities in pain modulatory brain regions and pain-evoked potentials. This study examined placebo analgesia-related effects on spontaneous brain oscillations. We examined placebo effects on four order-fixed 20-minute conditions in two sessions: isotonic saline-induced control conditions (with/without placebo) followed by hypertonic saline-induced tonic muscle pain conditions (with/without placebo) in 19 subjects using continuous electroencephalography recording. Placebo treatment exerted significant analgesic effects in 14 placebo responders, as subjective intensity of pain perception decreased. Frequency analyses were performed on whole continuous electroencephalography data, data during pain perception rating and data after rating. The results in the first two cases revealed that placebo induced significant increases and a trend toward significant increases in the amplitude of alpha oscillation during tonic muscle pain compared to control conditions in frontal-central regions of the brain, respectively. Placebo-induced decreases in the subjective intensity of pain perception significantly and positively correlated with the increases in the amplitude of alpha oscillations during pain conditions. In conclusion, the modulation effect of placebo treatment was captured when the pain perception evaluating period was included. The strong correlation between the placebo effect on reported pain perception and alpha amplitude suggest that alpha oscillations in frontal-central regions serve as a cortical oscillatory basis of the placebo effect on tonic muscle pain. These results provide important evidence for the investigation of objective indicators of the placebo effect.

This study aimed to examine the factors that potentially impact the self-directed use of mobile English learning resources (MELR). The participants were 206 Chinese undergraduate EFL learners at Yangzhou University in Mainland China. Applying and modifying the Unified Theory of Acceptance and Use of Technology (UTAUT), this study involved six constructs, including students’ performance expectancy, effort expectancy, social influence, facilitating conditions, perceived playfulness, and behavioral intention to use MELR. The structural equation modeling (SEM) technique was adopted to analyze the data collected from the questionnaire. The findings showed that facilitating conditions acted as the most significant predictor of behavioral intention to adopt MELR, followed by effort expectancy, perceived playfulness, and performance expectancy. However, social influence did not have significant effects on students’ use of MELR. Pedagogical implications for teachers and students were also presented in the end.

Inflammatory immune response plays a key role in exercise-induced injury and healing; however, the relevant regulatory mechanisms of immune infiltration in exercise-induced injuries remain less studied. In the present study, a highly efficient system for screening immunity-related biomarkers and immunomodulatory ability of natural nutritional supplements was developed by integrating intelligent data acquisition, data mining, network pharmacology, and computer-assisted target fishing. The findings demonstrated that resting natural killer cells showed a higher rate of infiltration after exercise, whereas naive B cells and activated dendritic cells showed higher rate of infiltration before exercise. Four key genes, namely PRF1, GZMB, CCL4, and FASLG, were associated with exercise-induced injuries and inflammatory immune response. In total, 26 natural compounds including echinacoside, eugenol, tocopherol, and casuariin were predicted by using the HERB databases. Molecular docking analysis showed that GZMB, FASLG, and CCL4 bound to echinacoside. In vivo experiments in mice showed that after 30 min swimming, natural killer (NK) cells showed high infiltration rates, and the key genes (GZMB, PRF1, FASLG, and CCL4) were highly expressed; however, echinocandin significantly reduced the level of NK cells and decreased the expression of the four key genes post exercise. This natural nutritional supplement may act to protect against inflammatory injury after exercise by suppressing specific immune infiltration.