Abnormal caudate nucleus activity in patients with depressive disorder: Meta-analysis of task-based functional magnetic resonance imaging studies with behavioral domain

During task-based functional magnetic resonance imaging (t-fMRI) patients with depressive disorder (DD) have shown abnormal caudate nucleus activation. There have been no meta-analyses that are conducted on the caudate nucleus using Activation Likelihood Estimation (ALE) in patients with DD, and the relationships between abnormal caudate activity and different behavior domains in patients with DD remain unclear. There were 24 previously published t-fMRI studies included in the study with the caudate nucleus as the region of interest. Meta-analyses were performed using the method of ALE. Included five ALE meta-analyses: (1) the hypoactivated caudate nucleus relative to healthy controls (HCs); (2) the hyper-activated caudate nucleus; (3) the abnormal activation in the caudate nucleus in the emotion domain; (4) the abnormal activation in cognition domain; (5) the abnormal activation in the affective cognition domain. Results revealed that the hypo-/hyper-activity in the caudate subregions is mainly located in the caudate body and head, while the relationships between abnormal caudate subregions and different behavior domains are complex. The hypoactivation of the caudate body and head plays a key role in the emotions which indicates there is a positive relationship between the decreased caudate activity and depressed emotional behaviors in patients with DD.

The Ca2+ -MdCRF4-MdWRKY9 module negatively affects apple fruit watercore formation by suppressing the transcription of MdSOT2

Watercore is a common physiological disease of Rosaceae plants, such as apples (Malus domestica), usually occurring during fruit ripening. Apple fruit with watercore symptoms is prone to browning and rotting, thus losing commercial viability. Sorbitol and calcium ions are considered key factors affecting watercore occurrence in apples. However, the mechanism by which they affect the occurrence of watercore remains unclear. Here, we identified that the transcription factor MdWRKY9 directly binds to the promoter of MdSOT2, positively regulates the transcription of MdSOT2, increases sorbitol content in fruit, and promotes watercore occurrence. Additionally, MdCRF4 can directly bind to MdWRKY9 and MdSOT2 promoters, positively regulating their expression. Since calcium ions can induce the ubiquitination and degradation of the transcription factor MdCRF4, they can inhibit the transcription of MdWRKY9 and MdSOT2 by degrading MdCRF4, thereby reducing the sorbitol content in fruit and inhibiting the occurrence of fruit watercore disease. Our data sheds light on how calcium ions mitigate watercore in fruit, providing molecular-level insights to enhance fruit quality artificially.

Feedback-related brain activity in individual decision: evidence from a gambling EEG study

In this study, based on scalp electroencephalogram (EEG), we conducted cortical source localization and functional network analyses to investigate the underlying mechanism explaining the decision processes when individuals anticipate maximizing gambling benefits, particularly in situations where the decision outcomes are inconsistent with the profit goals. The findings shed light on the feedback monitoring process, wherein incongruity between outcomes and gambling goals triggers a more pronounced medial frontal negativity and activates the frontal lobe. Moreover, long-range theta connectivity is implicated in processing surprise and uncertainty caused by inconsistent feedback conditions, while middle-range delta coupling reflects a more intricate evaluation of feedback outcomes, which subsequently modifies individual decision-making for optimizing future rewards. Collectively, these findings deepen our comprehension of decision-making under circumstances where the profit goals are compromised by decision outcomes and provide electrophysiological evidence supporting adaptive adjustments in individual decision strategies to achieve maximum benefit.

The long-term effect of modulated acoustic stimulation on alteration in EEG brain network of chronic tinnitus patients: An exploratory study

Acoustic stimulation is one of the most influential techniques for distressing tinnitus, while how it functions to reverse neural changes associated with tinnitus remains undisclosed. In this study, our objective is to investigate alterations in brain networks to shed light on the enigma of acoustic intervention for tinnitus. We designed a 75-day long-term acoustic intervention experiment, during which chronic tinnitus patients received daily modulated acoustic stimulation with each session lasting 15 days. Every 15 days, professional tinnitus assessments were conducted, collecting both electroencephalogram (EEG) and tinnitus handicap inventory (THI) data from the patients. Thereafter, we investigated the changes in EEG network organizations during continuous acoustic stimulation and their progressive evolution throughout long-term therapy, alongside exploring the associations between the evolving changes of the network alterations and THI. Our current study findings reveal reorganization in alpha/beta long-range frontal-parietal-occipital connections as well as local frontal and parietal-occipital regions induced by acoustic stimulation. Furthermore, we observed a decrease in modulation effects as therapy sessions progressed. These alterations in brain networks reflect the reversal of tinnitus-related neural activities, particularly distress and perception; thus contributing to tinnitus rehabilitation through long-term modulation effects. This study provides unique insights into how long-term acoustic intervention affects the network organizations of tinnitus patients and deepens our understanding of the pathophysiological mechanisms underlying tinnitus rehabilitation.

Differentiating Between Alzheimer's Disease and Frontotemporal Dementia Based on the Resting-State Multilayer EEG Network

Frontotemporal dementia (FTD) is frequently misdiagnosed as Alzheimer's disease (AD) due to similar clinical symptoms. In this study, we constructed frequency-based multilayer resting-state electroencephalogram (EEG) networks and extracted representative network features to improve the differentiation between AD and FTD. When compared with healthy controls (HC), AD showed primarily stronger delta-alpha cross-couplings and weaker theta-sigma cross-couplings. Notably, when comparing the AD and FTD groups, we found that the AD exhibited stronger delta-alpha and delta-beta connectivity than the FTD. Thereafter, by extracting the representative network features and then applying these features in the classification between AD and FTD, an accuracy of 81.1% was achieved. Finally, a multivariable linear regressive model was built, based on the differential topologies, and then adopted to predict the scores of the Mini-Mental State Examination (MMSE) scale. Accordingly, the predicted and actual measured scores were indeed significantly correlated with each other ( r = 0.274, p = 0.036). These findings consistently suggest that frequency-based multilayer resting-state networks can be utilized for classifying AD and FTD and have potential applications for clinical diagnosis.

Resting-state network predicts the decision-making behaviors of the proposer during the ultimatum game

Objective. The decision-making behavior of the proposer is a key factor in achieving effective and equitable maintenance of social resources, particularly in economic interactions, and thus understanding the neurocognitive basis of the proposer's decision-making is a crucial issue. Yet the neural substrate of the proposer's decision behavior, especially from the resting-state network perspective, remains unclear.Approach. In this study, we investigated the relationship between the resting-state network and decision proposals and further established a multivariable model to predict the proposers' unfair offer rates in the ultimatum game.Main results.The results indicated the unfair offer rates of proposers are significantly related to the resting-state frontal-occipital and frontal-parietal connectivity in the delta band, as well as the network properties. And compared to the conservative decision group (low unfair offer rate), the risk decision group (high unfair offer rate) exhibited stronger resting-state long-range linkages. Finally, the established multivariable model did accurately predict the unfair offer rates of the proposers, along with a correlation coefficient of 0.466 between the actual and predicted behaviors.Significance. Together, these findings demonstrated that related resting-state frontal-occipital and frontal-parietal connectivity may serve as a dispositional indicator of the risky behaviors for the proposers and subsequently predict a highly complex decision-making behavior, which contributed to the development of artificial intelligence decision-making system with biological characteristics as well.

Allisartan Isoproxil Promotes Uric Acid Excretion by Interacting with Intestinal Urate Transporters in Hyperuricemic Zebrafish (Danio rerio)

To evaluate the urate-lowering effect and potential drug targets of antihypertensive agent allisartan isoproxil (ALI) and its bioactive metabolite EXP3174, we developed an acute hyperuricemic zebrafish model using potassium oxonate and xanthine sodium salt. Losartan potassium served as the positive control (reference drug). In this model, ALI and losartan potassium exerted a greater urate-lowering effect than EXP3174 indicating that the latter is not the critical substance for elimination of uric acid. The quantitative real-time PCR showed that ALI upregulates the expression of intestinal urate transporters genes ABCG2, PDZK1, and SLC2A9 (p<0.01). Thus, we can suggest that this substance promotes uric acid excretion mainly by interacting with intestinal urate transporters.

Cueing effect of attention among nurses with different anxiety levels: an EEG study

The attention to cueing among nurses with anxiety affects their nursing quality seriously. Nevertheless, the neural mechanism of attention under anxiety among nurses has not been revealed. In this study, we utilized the event-related potential (ERP) and functional brain networks to investigate the neural mechanism of the cueing attention differences between anxiety and non-anxiety nurse groups (AG-20 nurses; NAG-20 nurses) in the spatial cueing task. The results revealed that in the invalid cues (144 trials), longer reaction times, larger P2 amplitudes, and more linkages between the right frontal and parietal areas were found in AG compared to NAG. In the valid cues (288 trials), there were no significant behavioral and neural differences between the two groups. The AG in the invalid cues showed slower response times, larger P2 and N5 amplitudes, and denser linkages originating from the occipital cortex than those in the valid cues. The convolutional neural network was trained for discriminating between the anxiety nurses and the normal ones, with the average accuracy being 0.76. The findings provided a potential physiological biomarker to predict the anxiety group who need to give more psychological attention. Nurse leaders maybe get more information for offering solutions to retain mental health among nurses.

Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification

Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.

The electrophysiology and structural difference between humans with distinct risky preference: a study based on EEG and MRI

Risky decision-making is affected by past feedback, especially after encountering the beneficial loss in the past decision-making round, yet little is known about the mechanism accounting for the distinctive decision-making that different individuals may make under the past loss context. We extracted decision functional medial frontal negative (MFN) and the cortical thickness (CT) from multi-modality electroencephalography (EEG) and T1-weighted structural MRI (sMRI) datasets to assess the individual risky decision under the past loss context. First, concerning the MFN, the low-risk group (LRG) exhibits larger MFN amplitude and longer reaction time than the high-risk group (HRG) when making risky decisions under the loss context. Subsequently, the sMRI analysis reveals a greater CT in the left anterior insula (AI) for HRG compared with LRG, and a greater CT in AI is associated with a high level of impulsivity, driving individuals to make risky choices under the past loss context. Furthermore, for all participants, the corresponding risky decision behavior could be exactly predicted as a correlation coefficient of 0.523 was acquired, and the classification by combing the MFN amplitude and the CT of the left AI also achieves an accuracy of 90.48% to differentiate the two groups. This study may offer new insight into understanding the mechanism that accounts for the inter-individual variability of risky decisions under the loss context and denotes new indices for the prediction of the risky participants.

Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular-Structural-Functional Co-alterations

Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain regions. Owing to lacking an effective approach to quantifying the covarying of structure and functional responses, how the structural-functional circuits interact and how genes encode the relationships, to deepen our knowledge of human cognition and disease, are still unclear. Here, we propose a multimodal covariance network (MCN) construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single individual. We further explored the potential association between brain-wide gene expression patterns and structural-functional covarying in individuals involved in a gambling task and individuals with major depression disorder (MDD), adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent cohorts. MCN analysis showed a replicable cortical structural-functional fine map in healthy individuals, and the expression of cognition- and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN differences. Further analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences. In contrast, changes in MCN of MDD patients were enriched for biological processes related to synapse function and neuroinflammation in astrocytes, microglia, and neurons, suggesting its promising application in developing targeted therapies for MDD patients. Collectively, these findings confirmed the correlations of MCN-related differences with brain-wide gene expression patterns, which captured genetically validated structural-functional differences at the cellular level in specific cognitive processes and psychiatric patients.

Edge-centric functional network predicts risk propensity in economic decision-making: evidence from a resting-state fMRI study

Despite node-centric studies revealing an association between resting-state functional connectivity and individual risk propensity, the prediction of future risk decisions remains undetermined. Herein, we applied a recently emerging edge-centric method, the edge community similarity network (ECSN), to alternatively describe the community structure of resting-state brain activity and to probe its contribution to predicting risk propensity during gambling. Results demonstrated that inter-individual variability of risk decisions correlates with the inter-subnetwork couplings spanning the visual network (VN) and default mode network (DMN), cingulo-opercular task control network, and sensory/somatomotor hand network (SSHN). Particularly, participants who have higher community similarity of these subnetworks during the resting state tend to choose riskier and higher yielding bets. And in contrast to low-risk propensity participants, those who behave high-risky show stronger couplings spanning the VN and SSHN/DMN. Eventually, based on the resting-state ECSN properties, the risk rate during the gambling task is effectively predicted by the multivariable linear regression model at the individual level. These findings provide new insights into the neural substrates of the inter-individual variability in risk propensity and new neuroimaging metrics to predict individual risk decisions in advance.

Information transmission velocity-based dynamic hierarchical brain networks

The brain functions as an accurate circuit that regulates information to be sequentially propagated and processed in a hierarchical manner. However, it is still unknown how the brain is hierarchically organized and how information is dynamically propagated during high-level cognition. In this study, we developed a new scheme for quantifying the information transmission velocity (ITV) by combining electroencephalogram (EEG) and diffusion tensor imaging (DTI), and then mapped the cortical ITV network (ITVN) to explore the information transmission mechanism of the human brain. The application in MRI-EEG data of P300 revealed bottom-up and top-down ITVN interactions subserving P300 generation, which was comprised of four hierarchical modules. Among these four modules, information exchange between visual- and attention-activated regions occurred at a high velocity, related cognitive processes could thus be efficiently accomplished due to the heavy myelination of these regions. Moreover, inter-individual variability in P300 was probed to be attributed to the difference in information transmission efficiency of the brain, which may provide new insight into the cognitive degenerations in clinical neurodegenerative disorders, such as Alzheimer's disease, from the transmission velocity perspective. Together, these findings confirm the capacity of ITV to effectively determine the efficiency of information propagation in the brain.

Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and increase the stability of emotion recognition, we propose an effective emotion recognition model that performs multicategory emotion recognition with multiple emotion-related spatial network topology patterns (MESNPs) by learning discriminative graph topologies in EEG brain networks. To evaluate the performance of our proposed MESNP model, we conducted single-subject and multisubject four-class classification experiments on two public datasets, MAHNOB-HCI and DEAP. Compared with existing feature extraction methods, the MESNP model significantly enhances the multiclass emotional classification performance in the single-subject and multisubject conditions. To evaluate the online version of the proposed MESNP model, we designed an online emotion monitoring system. We recruited 14 participants to conduct the online emotion decoding experiments. The average online experimental accuracy of the 14 participants was 84.56%, indicating that our model can be applied in affective brain-computer interface (aBCI) systems. The offline and online experimental results demonstrate that the proposed MESNP model effectively captures discriminative graph topology patterns and significantly improves emotion classification performance. Moreover, the proposed MESNP model provides a new scheme for extracting features from strongly coupled array signals.

Antipsychotics-induced improvement of cool executive function in individuals living with schizophrenia

Cool executive dysfunction is a crucial feature in people living with schizophrenia which is related to cognition impairment and the severity of the clinical symptoms. Based on electroencephalogram (EEG), our current study explored the change of brain network under the cool executive tasks in individuals living with schizophrenia before and after atypical antipsychotic treatment (before_TR vs. after_TR). 21 patients with schizophrenia and 24 healthy controls completed the cool executive tasks, involving the Tower of Hanoi Task (THT) and Trail-Marking Test A-B (TMT A-B). The results of this study uncovered that the reaction time of the after_TR group was much shorter than that of the before_TR group in the TMT-A and TMT-B. And the after_TR group showed fewer error numbers in the TMT-B than those of the before_TR group. Concerning the functional network, stronger DMN-like linkages were found in the before_TR group compared to the control group. Finally, we adopted a multiple linear regression model based on the change network properties to predict the patient's PANSS change ratio. Together, the findings deepened our understanding of cool executive function in individuals living with schizophrenia and might provide physiological information to reliably predict the clinical efficacy of schizophrenia after atypical antipsychotic treatment.

A Novel Method for Constructing EEG Large-Scale Cortical Dynamical Functional Network Connectivity (dFNC): WTCS

As a kind of biological network, the brain network conduces to understanding the mystery of high-efficiency information processing in the brain, which will provide instructions to develop efficient brain-like neural networks. Large-scale dynamical functional network connectivity (dFNC) provides a more context-sensitive, dynamical, and straightforward sight at a higher network level. Nevertheless, dFNC analysis needs good enough resolution in both temporal and spatial domains, and the construction of dFNC needs to capture the time-varying correlations between two multivariate time series with unmatched spatial dimensions. Effective methods still lack. With well-developed source imaging techniques, electroencephalogram (EEG) has the potential to possess both high temporal and spatial resolutions. Therefore, we proposed to construct the EEG large-scale cortical dFNC based on brain atlas to probe the subtle dynamic activities in the brain and developed a novel method, that is, wavelet coherence-S estimator (WTCS), to assess the dynamic couplings among functional subnetworks with different spatial dimensions. The simulation study demonstrated its robustness and availability of applying to dFNC. The application in real EEG data revealed the appealing "Primary peak" and "P3-like peak" in dFNC network properties and meaningful evolutions in dFNC network topology for P300. Our study brings new insights for probing brain activities at a more dynamical and higher hierarchical level and pushing forward the development of brain-inspired artificial neural networks. The proposed WTCS not only benefits the dFNC studies but also gives a new solution to capture the time-varying couplings between the multivariate time series that is often encountered in signal processing disciplines.

Dynamic networks of P300-related process

P300 as an effective biomarker to index attention and memory has been widely used for brain-computer interface, cognitive evaluation, and clinical diagnosis. To evoke clear P300, an oddball paradigm consisting of two types of stimuli, i.e., infrequent target stimuli and frequent standard stimuli, is usually used. However, to simply and quickly explore the P300-related process, previous studies predominately focused on the target condition but ignored the fusion of target and standard conditions, as well as the difference of brain networks between them. Therefore, in this study, we used the hidden Markov model to investigate the fused multi-conditional electroencephalogram dataset of P300, aiming to effectively identify the underlying brain networks and explore the difference between conditions. Specifically, the inferred networks, including their transition sequences and spatial distributions, were scrutinized first. Then, we found that the difference between target and standard conditions was mainly concentrated in two phases. One was the stimulation phase that mainly related to the cortical activities of the postcentral gyrus and superior parietal lobule, and the other corresponded to the response phase that involved the activities of superior and medial frontal gyri. This might be attributed to distinct cognitive functions, as the stimulation phase is associated with visual information integration whereas the response phase involves stimulus discrimination and behavior control. Taken together, the current work explored dynamic networks underlying the P300-related process and provided a complementary understanding of distinct P300 conditions, which may contribute to the design of P300-related brain-machine systems.

The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS

Human language units are hierarchical, and reading acquisition involves integrating multisensory information (typically from auditory and visual modalities) to access meaning. However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in auditory and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory, visual, or combined audio-visual modalities while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e. characters/monosyllabic words) and higher-level linguistic structures (i.e. phrases and sentences) across the 3 modalities separately. We found that audio-visual integration occurs in all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.

The molecular mechanism on suppression of climacteric fruit ripening with postharvest wax coating treatment via transcriptome

Wax coating is an important means to maintain fruit quality and extend fruit shelf life, especially for climacteric fruits, such as apples (Malus domestica). Here, we found that wax coating could inhibit ethylene production, chlorophyll degradation, and carotenoid synthesis, but the molecular mechanism remains unclear. The regulatory mechanism of wax coating on apple fruit ripening was determined by subjecting wax-treated apple fruits to transcriptome analysis. RNA-seq revealed that 1,137 and 1,398 genes were upregulated and downregulated, respectively. These differentially expressed genes (DEGs) were shown to be related to plant hormones, such as ethylene, auxin, abscisic acid, and gibberellin, as well as genes involved in chlorophyll degradation and carotenoid biosynthesis. Moreover, we found that some genes related to the wax synthesis process also showed differential expression after the wax coating treatment. Among the DEGs obtained from RNA-seq analysis, 15 were validated by quantitative RT-PCR, confirming the results from RNA-seq analysis. RNA-seq and qRT-PCR of pear (Pyrus ussuriensis) showed similar changes after wax treatment. Our data suggest that wax coating treatment inhibits fruit ripening through ethylene synthesis and signal transduction, chlorophyll metabolism, and carotenoid synthesis pathways and that waxing inhibits endogenous wax production. These results provide new insights into the inhibition of fruit ripening by wax coating.

The rewarding compensatory mechanism of music enhances the sense of fairness

Whether music can influence moral judgment is controversial in the aesthetics and philosophy of music. Aesthetic Autonomy pointed out that music had a morally educational function because of its lyrics or a particular context. The key to resolving the divergence is to select absolute music without lyrics or specific context as the eliciting material. In this study, 84 participants were recruited and randomly divided into three groups to complete the Ultimatum Game (UG) after listening to different stimuli: absolute music, white noise, and no sound. Behavioral results indicated that the participants’ acceptance of unfair offers was significantly lower in the music group. Also, participants in the music group have a shorter reaction time for rejecting an unfair offer than other unfair conditions. However, ERP comparison showed no significant difference in medial frontal negativity (MFN) amplitude, which reflects fairness levels, between the music group and the no sound group for either accepting or rejecting the moderately unfair offer. Brain network analyses revealed that participants in the music group showed stronger activation of rewarding circuits, including the ventral striatum, during the decision-making process of rejecting unfair offers, before the decision especially, compared to the no sound group. These results suggest that absolute music can influence fair decision-making. The reward activated by music compensates participants vicariously for the reward they receive for choosing self-interest in an unfair offer, participants no longer have to choose between self-interest and fairness norms, so the participants reject the unfair offer due to the negative emotions induced by the unfair offer.

NUCLEOCYTOPLASMIC shuttling of ETHYLENE RESPONSE FACTOR 5 mediated by nitric oxide suppresses ethylene biosynthesis in apple fruit

Nitric oxide (NO) is known to modulate the action of several phytohormones. This includes the gaseous hormone ethylene, but the molecular mechanisms underlying the effect of NO on ethylene biosynthesis are unclear. Here, we observed a decrease in endogenous NO abundance during apple (Malus domestica) fruit development and exogenous treatment of apple fruit with a NO donor suppressed ethylene production, suggesting that NO is a ripening suppressor. Expression of the transcription factor MdERF5 was activated by NO donor treatment. NO induced the nucleocytoplasmic shuttling of MdERF5 by modulating its interaction with the protein phosphatase, MdPP2C57. MdPP2C57-induced dephosphorylation of MdERF5 at Ser260 is sufficient to promote nuclear export of MdERF5. As a consequence of this export, MdERF5 proteins in the cytoplasm interacted with and suppressed the activity of MdACO1, an enzyme that converts 1-aminocyclopropane-1-carboxylic acid (ACC) to ethylene. The NO-activated MdERF5 was observed to increase in abundance in the nucleus and bind to the promoter of the ACC synthase gene MdACS1 and directly suppress its transcription. Together, these results suggest that NO-activated nucleocytoplasmic MdERF5 suppresses the action of ethylene biosynthetic genes, thereby suppressing ethylene biosynthesis and limiting fruit ripening.

[Otologic disorders and management strategies in Turner syndrome]

Objective: To analyze the incidence and risk factors of otologic disorders in patients with Turner syndrome (TS), so as to provide management strategies for ear health. Methods: This study is a prospective study based on questionnaires and a cross-sectional study. The TS patients who visited our hospital from 2010 January to 2021 March were included (A total of 71 patients with TS were included in this study. the age of TS diagnosed was 3- to 11-year-old, age of visiting ENT department was 4- to 27-year-old) and the incidence of otologic diseases in different age groups was investigated by questionnaires. The cross-sectional study included ear morphology and auditory function assessment, and further analysis of the risk factors that related to ear disease. Prism was used for data analysis. Results: The investigation found that the incidence of acute otitis media in patients aged 3-6 and 7-12 years was higher than that of patients over 12 years old, which was 33.8%(24/71), 42.9%(30/70)and 23.5%(8/34), respectively; 21.1% (15/71) of patients were recurrent acute otitis media in patients aged 3-6 years, and about 46.6% (7/15)of them persisted beyond 6-year. The prevalence of otitis media with effusion in the three groups was 32.4%(23/71), 34.3%(24/70)and 38.2%(13/34), respectively; the recurrence rate of tympanocentesis was 100%(7/7), 42.9%(3/7)and 50.0%(1/2), which was significantly higher than that of grommet insertion. For age groups of 3-6 and 7-12 years, the prevalence of acute otitis media and secretory otitis media was lower in the X chromosome structure abnormal patients; while for patients older than 12 years, otitis media with effusion was the highest prevalence in Y-chromosome-containing karyotypes. In addition, the prevalence of acute otitis media and otitis media with effusion in patients with other system diseases were increased significantly. A cross-sectional study found that 7.0% (5/71)of the lower auricular, 4.2% (3/71)of the external auditory canal narrow, and 38.0% (27/71)of the tympanic membrane abnormality. 35.2%(25/71) had abnormal hearing, including 17 cases of conductive deafness, 6 cases of sensorineural hearing loss, and 2 cases of mixed deafness. The rest of the patients had normal hearing, but 6 of them had abnormalities in otoacoustic emission. Eustachian tube function assessment found that the eustachian tube dysfunction accounted for 38%(27/71). Hearing loss and abnormal Eustachian tube function were not significantly related to karyotype(Chi-square 2.83 and 2.84,P value 0.418 and 0.417), but significantly related to other system diseases(Chi-square 13.43 and 7.53,P value<0.001). Conclusions: The incidence of TS-related otitis media and auditory dysfunction is significantly higher than that of the general population. It not only occurs in preschool girls, but also persists or develops after school age. Accompanied by other system diseases are risk factors for ear diseases. Clinicians should raise their awareness of TS-related ear diseases and incorporate ear health monitoring into routine diagnosis and treatment.

Iodine Maps from Dual-Energy CT to Predict Extrathyroidal Extension and Recurrence in Papillary Thyroid Cancer Based on a Radiomics Approach

BACKGROUND AND PURPOSE

Accurate prediction of extrathyroidal extension and subsequent recurrence is crucial in papillary thyroid cancer clinical management. Our aim was to conduct iodine map-based radiomics to predict extrathyroidal extension and to explore its prognostic value for recurrence-free survival in papillary thyroid cancer.

MATERIALS AND METHODS

A total of 452 patients with papillary thyroid cancer were retrospectively recruited between June 2017 and June 2020. Radiomics features were extracted from noncontrast images, dual-phase mixed images, and iodine maps, respectively. Random forest and least absolute shrinkage and selection operator (LASSO) were applied to build 6 radiomics scores (noncontrast radiomics score_random forest; noncontrast rad-score_LASSO; mixed rad-score_random forest; mixed rad-score_LASSO; iodine radiomics score_random forest; iodine radiomics score_LASSO) respectively. Logistic regression was used to construct 6 radiomics models incorporating 6 radiomics scores with clinical risk factors and to compare them with the clinical model. A radiomics model that achieved the highest performance was presented as a nomogram and assessed by discrimination, calibration, clinical usefulness, and prognosis evaluation.

RESULTS

Iodine radiomics scores performed significantly better than mixed radiomics scores. Both of them outperformed noncontrast radiomics scores. Iodine map-based radiomics models significantly surpassed the clinical model. A radiomics nomogram incorporating size, capsule contact, and iodine radiomics score_random forest was built with the highest performance (training set, area under the curve = 0.78; validation set, area under the curve  = 0.84). Stratified analysis confirmed the nomogram stability, especially in group negative for CT-reported extrathyroidal extension (area under the curve  = 0.69). Nomogram-predicted extrathyroidal extension risk was an independent predictor of recurrence-free survival. A high risk for extrathyroidal extension portended significantly lower recurrence-free survival than low risk (P < .001).

CONCLUSIONS

Iodine map-based radiomics might be a supporting tool for predicting extrathyroidal extension and subsequent recurrence risk in patients with papillary thyroid cancer, thus facilitating clinical decision-making.

L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery

OBJECTIVE

EEG-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent.

APPROACH

In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers (ADMM), which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments.

MAIN RESULTS

A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved.

SIGNIFICANCE

The L1-ADTF may not only be capable of tracking time-varying brain network state drifts robustly but may also be useful in solving a wide range of dynamic systems such as trajectory tracking problems and dynamic neural networks.

Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study

Objective.Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.Approach.In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300).Main results.The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.Significance.This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.

Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

Ethnic Disparities in Dental Caries among Adolescents in China

Comprehensive research on ethnic disparities in dental caries in China is limited. The aims of this cross-sectional study were to compare the levels of dental caries in adolescents between the Han ethnic group and ethnic minority groups in China and to explore the risk indicators for dental caries within ethnic subgroups. Data from the Fourth National Oral Health Survey in 2015, which covered all 31 province-level administrative divisions in mainland China, were used. The dental caries status in the permanent dentition of adolescents aged 12, 13, 14, and 15 y was measured using the decayed, missing, and filled teeth (DFMT) score, and sociodemographic characteristics and oral health-related behaviors were also collected. A total of 118,601 adolescents were included, with ethnic minority groups accounting for 13.15%. Of the Han and minority groups, the standardized prevalence of dental caries experience was 40.58% and 47.67%, and the mean DMFT scores were 0.97 and 1.28, respectively. According to the multivariate zero-inflated negative binomial regression analysis, the caries status of minorities was more severe than Han adolescents (adjusted prevalence rate ratio [PRR], 1.14; 95% confidence interval [CI], 1.10-1.18). This disparity was greater among adolescents who lived in rural areas, had mid-level economic status, and frequently consumed sugary beverages. After propensity score matchings, Uygur (PRR, 1.44; 95% CI, 1.25-1.67), Tibetan (PRR, 1.39; 95% CI, 1.3-1.48), and Yi (PRR, 1.24; 95% CI, 1.04-1.48) adolescents were significantly more likely to have caries than Han adolescents. Subgroup analyses revealed that gender, age, location of residence, economic status, region, consumption of sweet snacks and sugary beverages, and dental visit pattern were significantly associated with dental caries within ethnic minorities.

[Countermeasures and treatment for aortic acute syndrome with novel coronavirus pneumonia]

The novel coronavirus pneumonia (NCP) has cost a great loss to the health and economic property of Chines people. Under such a special circumstance, how to deal with such patients with acute aortic syndrome has become a serious challenge. Rapid diagnosis of concomitant NCP, safe and effective transportation, implementation of the interventional procedure, protection of vascular surgical team and postoperative management and follow-up of such patients have become urgent problems for us. Combined with the latest novel government documents, the literature and the experiences from Wuhan, we answered the above questions briefly and plainly. It also hopes to inspire the national vascular surgeons to manage critical emergencies in vascular surgery and even routine vascular diseases with NCP, as a final point to limit the severe epidemic situation, and minimize the damage of NCP.

[Biomechanical changes of sheep cervical spine after unilateral hemilaminectomy and different degrees of facetectomy]

OBJECTIVE

To establish animal models and investigate the impact of unilateral hemilaminectomy (ULHL) and different degrees of facetectomy (FT) on the cervical spinal biomechanics.

METHODS

Twenty sheep were randomly and evenly divided into 4 groups. No operation was performed for group A, right C4-C6 ULHL was performed for group B, right C4-C6 ULHL and 50% ipsilateral C4-C5 FT was performed for group C, right C4-C6 ULHL and 100% ipsilateral C4-C5 FT was performed for group D. Animals of group A, B, C and D were sacrificed 24 weeks after operating and fresh cervical spine specimens were acquired, biomechanically tested and these data were compared to determine whether ULHL and different degrees of FT led to long-term differences in range of motion.

RESULTS

(1) Changes of the total range of motion of cervical spine 24 weeks after surgery: the total range of motion of group D (60.2°±8.6°) was significantly greater than group A (40.7°±6.4°) and group B (41.2°±13.1°) under flexion-extension station, the total range of motion of group D (81.5°±15.7°) was significantly greater than that of group A (56.7°±12.2°) and group B (57.7°±12.8°) under lateral bending station, and the total range of motion of group D (38.5°±17.5°) had no obvious increase compared with group A (26.4°±9.9°) and group B (27.1°±10.9°) under axial rotation station. The total range of motion of group C had no obvious increase compared with group A and group B under flexion-extension station (44.1°±11.7°), lateral bending station (73.6°±11.4°) and axial rotation station (31.3°±11.5°). (2) Changes of the intersegmental motion 24 weeks after surgery: the intersegmental motion of group D (20.3°±4.6°) at C4-C5 was significantly greater than that of group A (11.7°±3.4°) and group B (11.9°±2.1°) under flexion-extension station, the intersegmental motion of group D (26.8°±3.5°) at C4-C5 was significantly greater than that of group A (15.2°±3.1°) and group B (16.2°±3.2°) under lateral bending station, the intersegmental motion of group D (15.2°±3.5°) at C4-C5 was significantly greater than that of group A (6.6°±2.3°) and group B (7.1°±1.9°) under axial rotation station. The intersegmental motion of group C (21.2°±4.1°) at C4-C5 was significantly greater than that of group A and group B under lateral bending station, the intersegmental motion of group C at C4-C5 had no obvious increase compared with group A and group B under flexion-extension station (15.7°±3.7°) and axial rotation station (10.3°±3.1°).

CONCLUSION

ULHL does not affect cervical stability, ULHL and 50% ipsilateral FT does not affect the long-term cervical stability, ULHL and 100% ipsilateral FT can lead to long-term instability under lateral bending and flexion-extension station.

Clinical, neuroimaging and prognostic study of 127 cases with infarction of the corpus callosum

Background and purpose

The aim of this study was to retrospectively investigate clinical and neuroimaging characteristics in the largest sample size of patients with corpus callosum infarction to date and then to follow up these patients for 1 year to clarify the prognosis of this rare stroke entity.

Methods

A total of 127 patients with acute callosal infarction out of 5584 acute ischaemic stroke patients were included in this study. The recruited patients were divided into a pure callosal infarction group and a complex callosal infarction group (coupled with other infarct locations simultaneously), and clinical and neuroimaging features were analyzed. Some of the patients were followed up for 1 year to evaluate recurrence rate and mortality.

Results

The incidence of acute callosal infarction was 2.3%. Most patients presented with advanced neurological dysfunction with or without mild to moderate motor or sensory disorders on admission. The negative rate of computed tomography scan was still 76.4% even at >24 h after onset. Large‐artery atherosclerosis was the most common etiological type. Compared with complex callosal infarction, the pure callosal infarction group had more mental disorders (P = 0.030). Compared with common basal ganglia infarction, the pure callosal infarction group had better short‐term recovery (P = 0.016) but higher 1‐year mortality (P = 0.037). Age and mental disorders were independent risk factors for death in callosal infarction.

Conclusions

Callosal infarction is a white matter stroke that occurs with low incidence. Elderly patients with vascular risk factors showed sudden mental or cognitive disorders and callosal infarction could not be excluded. More attention should be paid to the early diagnosis and secondary prevention of callosal infarction because of its poor long‐term outcome.

EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations

OBJECTIVE

Spectral power analysis plays a predominant role in electroencephalogram-based emotional recognition. It can reflect activity differences among multiple brain regions. In addition to activation difference, different emotions also involve different large-scale network during related information processing. In this paper, both information propagation patterns and activation difference in the brain were fused to improve the performance of emotional recognition.

METHODS

We constructed emotion-related brain networks with phase locking value and adopted a multiple feature fusion approach to combine the compensative activation and connection information for emotion recognition.

RESULTS

Recognition results on three public emotional databases demonstrated that the combined features are superior to either single feature based on power distribution or network character. Furthermore, the conducted feature fusion analysis revealed the common characters between activation and connection patterns involved in the positive, neutral, and negative emotions for information processing.

SIGNIFICANCE

The proposed feasible combination of both information propagation patterns and activation difference in the brain is meaningful for developing the effective human-computer interaction systems by adapting to human emotions in the real world applications.

The Dynamic Brain Networks of Motor Imagery: Time-Varying Causality Analysis of Scalp EEG

Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which involves a large-scale network that spans multiple brain areas. The corresponding cortical activity reflected on the scalp is characterized by event-related desynchronization (ERD) and then by event-related synchronization (ERS). However, the network mechanisms that account for the dynamic information processing of MI during the ERD and ERS periods remain unknown. Here, we combined ERD/ERS analysis with the dynamic networks in different MI stages (i.e. motor preparation, ERD and ERS) to probe the dynamic processing of MI information. Our results show that specific dynamic network structures correspond to the ERD/ERS evolution patterns. Specifically, ERD mainly shows the contralateral networks, while ERS has the symmetric networks. Moreover, different dynamic network patterns are also revealed between the two types of MIs, in which the left-hand MIs exhibit a relatively less sustained contralateral network, which may be the network mechanism that accounts for the bilateral ERD/ERS observed for the left-hand MIs. Similar to the network topologies, the three MI stages also appear to be characterized by different network properties. The above findings all demonstrate that different MI stages that involve specific brain networks for dynamically processing the MI information.

Different Contexts in the Oddball Paradigm Induce Distinct Brain Networks in Generating the P300

Despite the P300 event-related potential (ERP) differences between distinct stimulus sequences, the effect of stimulus sequence on the brain network is still left unveiled. To uncover the corresponding effect of stimulus sequence, we thus investigated the differences of functional brain networks, when a target (T) or standard (S) stimulus was presented preceding another T as background context. Results of this study demonstrated that, when an S was first presented preceding a T (i.e., ST sequence), the P300 experiencing large amplitude was evoked by the T, along with strong network architecture. In contrast, if a T was presented in advance [i.e., target-to-target (TT) sequence], decreased P300 amplitude and attenuated network efficiency were demonstrated. Additionally, decreased activations in regions, such as inferior frontal gyrus and superior frontal gyrus were also revealed in TT sequence. Particularly, the effect of stimulus sequence on P300 network could be quantitatively measured by brain network properties, the increase in network efficiency corresponded to large P300 amplitude evoked in P300 task.

Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis

Epilepsy is a neurological disorder in the brain that is characterized by unprovoked seizures. Epileptic seizures are attributed to abnormal synchronous neuronal activity in the brain. To detect the seizure as early as possible, the identification of specific electroencephalogram (EEG) dynamics is of great importance in investigating the transition of brain activity as the epileptic seizure approaches. In this study, we investigated the transition of brain activity from interictal to preictal states preceding a seizure by combining EEG network and clustering analyses together in different frequency bands. The findings of this study demonstrated the best clustering performance of k-medoids in the beta band; in addition, compared to the interictal state, the preictal state experienced increased synchronization of EEG network connectivity, characterized by relatively higher network properties. These findings can provide helpful insight into the mechanism of epilepsy, which can also be used in the prediction of epileptic seizures and subsequent intervention.

Different Decision-Making Responses Occupy Different Brain Networks for Information Processing: A Study Based on EEG and TMS

This study used large-scale time-varying network analysis to reveal the diverse network patterns during the different decision stages and found that the responses of rejection and acceptance involved different network structures. When participants accept unfair offers, the brain recruits a more bottom-up mechanism with a much stronger information flow from the visual cortex (O2) to the frontal area, but when they reject unfair offers, it displayed a more top-down flow derived from the frontal cortex (Fz) to the parietal and occipital cortices. Furthermore, we performed 2 additional studies to validate the above network models: one was to identify the 2 responses based on the out-degree information of network hub nodes, which results in 70% accuracy, and the other utilized theta burst stimulation (TBS) of transcranial magnetic stimulation (TMS) to modulate the frontal area before the decision-making tasks. We found that the intermittent TBS group demonstrated lower acceptance rates and that the continuous TBS group showed higher acceptance rates compared with the sham group. Similar effects were not observed after TBS of a control site. These results suggest that the revealed decision-making network model can serve as a potential intervention model to alter decision responses.

Dexmedetomidine attenuation of renal ischaemia-reperfusion injury requires sirtuin 3 activation

BACKGROUND

Dexmedetomidine attenuates renal ischaemia and reperfusion (I/R) injury, but its mechanism of action is unclear. As sirtuin 3 (SIRT3) activation can alleviate acute kidney injury, we investigated whether dexmedetomidine acts through SIRT3 to reduce renal I/R injury.

METHODS

The potential involvement of SIRT3 in dexmedetomidine attenuation of renal I/R injury was tested in HK2 cells subjected to hypoxia/reoxygenation and C57BL/6J mice subjected to renal I/R. A short interfering RNA targeting SIRT3 was used in some experiments to examine the potential role of SIRT3. Cell death and mitochondrial membrane potential (Δψm) were analysed in cultured cells. Mitochondrial damage in mice was assessed using electron microscopy and markers for renal function. Expression of cyclophilin D, cytochrome c, and SIRT3, and the level of cyclophilin D acetylation were determined.

RESULTS

Hypoxia/reoxygenation of HK2 cells increased cell death, cytochrome C expression, and cyclophilin D acetylation, and decreased Δψm and SIRT3 expression (P<0.05). Dexmedetomidine attenuated these changes. The dexmedetomidine effects were enhanced by SIRT3 overexpression and eliminated by SIRT3 knockdown. I/R in mice damaged renal function, and increased histological lesions, mitochondrial damage, cytochrome c expression, and cyclophilin D acetylation, while SIRT3 activity was decreased by 51% (P<0.05). Dexmedetomidine inhibited these changes in mice expressing normal levels of SIRT3, but not in SIRT3-knockdown mice.

CONCLUSIONS

Dexmedetomidine appears to act, at least in part, by up-regulating SIRT3 to inhibit mitochondrial damage and cell apoptosis and thereby protect against renal I/R injury.