It has been argued that the human brain,as an information-processing machine,operates near a phase transition point in a non-equilibrium state,where it violates detailed balance leading to entropy production.Thus,the ...It has been argued that the human brain,as an information-processing machine,operates near a phase transition point in a non-equilibrium state,where it violates detailed balance leading to entropy production.Thus,the assessment of irreversibility in brain networks can provide valuable insights into their non-equilibrium properties.In this study,we utilized an open-source whole-brain functional magnetic resonance imaging(fMRI)dataset from both resting and task states to evaluate the irreversibility of large-scale human brain networks.Our analysis revealed that the brain networks exhibited significant irreversibility,violating detailed balance,and generating entropy.Notably,both physical and cognitive tasks increased the extent of this violation compared to the resting state.Regardless of the state(rest or task),interactions between pairs of brain regions were the primary contributors to this irreversibility.Moreover,we observed that as global synchrony increased within brain networks,so did irreversibility.The first derivative of irreversibility with respect to synchronization peaked near the phase transition point,characterized by the moderate mean synchronization and maximized synchronization entropy of blood oxygenation level-dependent(BOLD)signals.These findings deepen our understanding of the non-equilibrium dynamics of large-scale brain networks,particularly in relation to their phase transition behaviors,and may have potential clinical applications for brain disorders.展开更多
The dynamic behaviors of a large-scale ring neural network with a triangular coupling structure are investigated.The characteristic equation of the high-dimensional system using Coate’s flow graph method is calculate...The dynamic behaviors of a large-scale ring neural network with a triangular coupling structure are investigated.The characteristic equation of the high-dimensional system using Coate’s flow graph method is calculated.Time delay is selected as the bifurcation parameter,and sufficient conditions for stability and Hopf bifurcation are derived.It is found that the connection coefficient and time delay play a crucial role in the dynamic behaviors of the model.Furthermore,a phase diagram of multiple equilibrium points with one saddle point and two stable nodes is presented.Finally,the effectiveness of the theory is verified through simulation results.展开更多
Emotion and executive control are often conceptualized as two distinct modes of human brain functioning.Little,however,is known about how the dynamic organization of large-scale functional brain networks that support ...Emotion and executive control are often conceptualized as two distinct modes of human brain functioning.Little,however,is known about how the dynamic organization of large-scale functional brain networks that support flexible emotion processing and executive control,especially their interactions.The amygdala and prefrontal systems have long been thought to play crucial roles in these processes.Recent advances in human neuroimaging studies have begun to delineate functional organization principles among the large-scale brain networks underlying emotion,executive control,and their interactions.Here,we propose a dynamic brain network model to account for interactive competition between emotion and executive control by reviewing recent resting-state and task-related neuroimaging studies using network-based approaches.In this model,dynamic interactions among the executive control network,the salience network,the default mode network,and sensorimotor networks enable dynamic processes of emotion and support flexible executive control of multiple processes;neural oscillations across multiple frequency bands and the locus coeruleus−norepinephrine pathway serve as communicational mechanisms underlying dynamic synergy among large-scale functional brain networks.This model has important implications for understanding how the dynamic organization of complex brain systems and networks empowers flexible cognitive and affective functions.展开更多
Transcranial direct current stimulation(tDCS)is a noninvasive technique that uses constant,low-intensity direct current to regulate brain activities.Clinical studies have shown that cathode-tDCS(c-tDCS)is effective in...Transcranial direct current stimulation(tDCS)is a noninvasive technique that uses constant,low-intensity direct current to regulate brain activities.Clinical studies have shown that cathode-tDCS(c-tDCS)is effective in reducing seizure frequency in patients with epilepsy.Due to the heterogeneity and patient specificity of seizures,patient-specific epilepsy networks are increasingly important in exploring the regulatory role of c-tDCS.In this study,we first set the left hippocampus,parahippocampus,and amygdala as the epileptogenic zone(EZ),and the left inferior temporal cortex and ventral temporal cortex as the initial propagation zone(PZ)to establish a large-scale epilepsy network model.Then we set tDCS cathode locations according to the maximum average energy of the simulated EEG signals and systematically study c-tDCS inhibitory effects on the propagation of epileptic activity.The results show that c-tDCS is effective in suppressing the propagation of epileptic activity.Further,to consider the patient specificity,we set specific EZ and PZ according to the clinical diagnosis of 6 patients and establish patient-specific epileptic networks.We find that c-tDCS can suppress the propagation of abnormal activity in most patient-specific epileptic networks.However,when the PZ is widely distributed in both hemispheres,the treatment effect of c-tDCS is not satisfactory.Hence,we propose dual-cathode tDCS.For epilepsy models with a wide distribution of PZ,it can inhibit the propagation of epileptiform activity in other nodes except EZ and PZ without increasing the tDCS current strength.Our results provide theoretical support for the treatment of epilepsy with tDCS.展开更多
The brain is organized as a complex network architecture, which can be mapped into structural(SC) and functional connectivity(FC) by advanced neuroimaging techniques. Achievements in brain network research have reveal...The brain is organized as a complex network architecture, which can be mapped into structural(SC) and functional connectivity(FC) by advanced neuroimaging techniques. Achievements in brain network research have revealed that modularity is a universal trait in brain networks and may be vital for cognitive segregation and integration. Large-scale brain network modeling is a promising computational approach to combine neuroimaging data with generative rules for brain dynamics. Recently, it has been proposed that chimera states, a type of dynamics referring to the coexistence of coherent and incoherent participants, have traits in common with cognitive functions like segregated and integrated brain processing. Previous studies have reported the existence of chimera-like dynamics in large-scale brain network models, whereas they did not account for the relationship between chimeralike dynamics and corresponding functional modular organizations of the brain network. By specifying qualitatively different network dynamics in an anatomically-constrained brain network model, we compare the different modular organizations of FC unfolded by network dynamics. Our simulations reveal that chimera-like dynamics support a meaningful pattern of functional modular organization, which promotes a diversity of node roles with a distributed pattern of functional cartography. The distinct node roles in modular FC are also found to occur with a spatial preference in speciflc brain regions, and, to some extent, reflect the underlying structure constraints. Our results support the view that chimera-like dynamics is a functionally meaningful scenario that may play a fundamental role in the segregation and integration of brain functioning.展开更多
Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network lev...Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.展开更多
Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,n...Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.展开更多
Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuro...Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuronal damage,it is crucial to find a biomarker to distinguish individuals with these diseases from healthy people.In this study,we construct a brain function network based on electroencephalography data to study changes in AD and MCI patients.Using a graph-theoretical approach,we examine connectivity features and explore their contributions to dementia recognition at edge,node,and network levels.We find that connectivity is reduced in AD and MCI patients compared with healthy controls.We also find that the edge-level features give the best performance when machine learning models are used to recognize dementia.The results of feature selection identify the top 50 ranked edge-level features constituting an optimal subset,which is mainly connected with the frontal nodes.A threshold analysis reveals that the performance of edge-level features is more sensitive to the threshold for the connection strength than that of node-and network-level features.In addition,edge-level features with a threshold of 0 provide the most effective dementia recognition.The K-nearest neighbors(KNN)machine learning model achieves the highest accuracy of 0.978 with the optimal subset when the threshold is 0.Visualization of edge-level features suggests that there are more long connections linking the frontal region with the occipital and parietal regions in AD and MCI patients compared with healthy controls.Our codes are publicly available at https://github.com/Debbie-85/eeg-connectivity.展开更多
The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors po...The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors possess high changes in terms of size,shape,and amount,and hence the classification process acts as a more difficult research problem.This paper suggests a deep learning model using the magnetic resonance imaging technique that overcomes the limitations associated with the existing classification methods.The effectiveness of the suggested method depends on the coyote optimization algorithm,also known as the LOBO algorithm,which optimizes the weights of the deep-convolutional neural network classifier.The accuracy,sensitivity,and specificity indices,which are obtained to be 92.40%,94.15%,and 91.92%,respectively,are used to validate the effectiveness of the suggested method.The result suggests that the suggested strategy is superior for effectively classifying brain tumors.展开更多
BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving SA.AIM To explo...BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving SA.AIM To explore the potential characteristics of the brain network and functional connectivity(FC)of SA.METHODS Twenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly,which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination(MMSE).The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf,and the brain functional network was conducted by DPABINet.The support vector machine model was constructed with altered functional connectivities to evaluate the identification value of SA.RESULTS The results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE performance.The FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score,and the FC of the right supramarginal gyrus and right temporal pole:Middle temporal gyrus was the only one decreased and negatively related to MMSE score.All 17 significantly altered FCs of SA were taken into the support vector machine model,and the area under the curve was 0.895.CONCLUSION The identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.展开更多
With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that consid...With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.展开更多
Background:Sleep deprivation(SD)can potentially lead to deficits in many cognitive capacities,suggesting that sleep pressure represented a basic physiological constraint of brain function.However,the neural mechanism ...Background:Sleep deprivation(SD)can potentially lead to deficits in many cognitive capacities,suggesting that sleep pressure represented a basic physiological constraint of brain function.However,the neural mechanism underlying the decline awareness and cognition induced by SD is far from clear.Methods:Thirty-seven healthy male adults were recruited in this within-subjects,repeat-measure,counterbalanced study.These individuals were both examined during a state of rested wakefulness(RW)state and after 36 hours of total SD.Using functional connectivity magnetic resonance imaging(fcMRI),we investigated the specifi c effect of SD on static functional connectivity density,sparse representation of resting-state fMRI signal,and dynamic connectivity pattern.Results:Our analysis based on fcMRI revealed that multiple functional networks involved in memory,emotion,attention,and vigilance processing were impaired by SD.Of particular interest,the thalamus was observed to contribute to multiple functional networks in which differentiated response patterns were exhibited.We also detect robust changes in the temporal properties of specifi c connectivity states,such as the occurrence frequencies,dwell times and transition probabilities that were likely associated with the vigilance loss induced by SD.These changes led to differentiation of these states with the RW-dominant states characterized by anti-correlation between the default mode network and other cortices and the SD-dominant states marked by significantly decreased thalamocortical connectivity.Conclusion:These fi ndings suggest specifi c patterns of the large-scale functional brain network changes after SD,which are important for understanding of the impacts of SD on brain function and developing effective intervention strategy against SD.展开更多
OBJECTIVE: To analyze part of the mechanism of electroacupuncture on Sishencong(EX-HN1) for strokerelated sleep disorders(SSD) and post-stroke cognitive impairment(PSCI). METHODS: Using a randomized controlled trial(R...OBJECTIVE: To analyze part of the mechanism of electroacupuncture on Sishencong(EX-HN1) for strokerelated sleep disorders(SSD) and post-stroke cognitive impairment(PSCI). METHODS: Using a randomized controlled trial(RCT) design, 72 patients were assigned to the electroacupuncture(EA) group or the sham acupuncture(SA) group. A healthy control(HC) group was also included. Both groups were given routine rehabilitation treatment. Then, patients in the EA group were given additional electroacupuncture at Sishencong(EX_HN1). Meanwhile, patients in the SA group were given a flat-head needle sham/placebo treatment placed at the bilateral Jianyu (LI15) and Binao(LI14) line midpoints and the Jianyu(LI15) and Jianzhen(SI9) line midpoints. Before and after treatment, scales were collected and analyzed. In the second phase of the study, some subjects from the EA group were selected for functional magnetic resonance imaging(f MRI) data acquisition and comparative analysis with the HC group using a non-RCT design. RESULTS: The EA group performed better than the SA group on the Pittsburgh sleep quality index(PSQI), Montreal cognitive assessment basic(Mo CA_B), selfrating anxiety scale(SAS), and self-rating depression scale(SDS). Analysis of the f MRI showed that lowfrequency(2 Hz) electroacupuncture stimulation at Sishencong(EX_HN1) can restrain frontal sup medial right(SFGmed.R), precuneus right(PCUN.R), and posterior cingulate cortex right(PCC.R) and enhance angular left(ANG.L), parietal inf left(IPL.L) and occipital mid left(MOG.L). The functional connectivity(FC) of SFGmed.R was positively correlated with PSQI. Electroacupuncture stimulation at Sishencong(EX_HN1) can reduce the side efficiency of the whole brain connection with the Thalamus.L, Hippocampus.L, and Occipital.Mid.L. CONCLUSIONS: Low frequency(2 Hz) electroacupuncture stimulation at Sishencong(EX_HN1) can simultaneously improve sleep quality, negative emotions, and cognitive functions, the first two of which may be related to SFGmed.R restraint. Electroacupuncture can make some brain areas approach the physiological bias state, which is characterized by dominant hemispheric enhancement and non-dominant hemispheric weakening. The reduced whole brain connection side efficiency with some key nodes of the brain net may relate to sleep quality improvements in SSD patients.展开更多
Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal d...Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.展开更多
Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely u...Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury.These findings highlight the importance of integrating structural and functional connectivity in unde rstanding the occurrence and evolution of mild traumatic brain injury.Additionally,exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.展开更多
Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture t...Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture therapy. Methods: Twenty-two PSCI patients who underwent acupuncture therapy in our hospital were enrolled as research subjects. Another 14 people matched for age, sex, and education level were included in the normal control (HC) group. All the subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans;the PSCI patients underwent one scan before acupuncture therapy and another after. The network metric difference between PSCI patients and HCs was analyzed via the independent-sample t test, whereas the paired-sample t test was employed to analyze the network metric changes in PSCI patients before vs. after treatment. Results: Small-world network attributes were observed in both groups for sparsities between 0.1 and 0.28. Compared with the HC group, the PSCI group presented significantly lower values for the global topological properties (γ, Cp, and Eloc) of the brain;significantly greater values for the nodal attributes of betweenness centrality in the CUN. L and the HES. R, degree centrality in the SFGdor. L, PCG. L, IPL. L, and HES. R, and nodal local efficiency in the ORBsup. R, ORBsupmed. R, DCG. L, SMG. R, and TPOsup. L;and decreased degree centrality in the MFG. R, IFGoperc. R, and SOG. R. After treatment, PSCI patients presented increased degree centrality in the LING.L, LING.R, and IOG. L and nodal local efficiency in PHG. L, IOG. R, FFG. L, and the HES. L, and decreased betweenness centrality in the PCG. L and CUN. L, degree centrality in the ORBsupmed. R, and nodal local efficiency in ANG. R. Conclusion: Cognitive decline in PSCI patients may be related to BFN disorders;acupuncture therapy may modulate the topological properties of the BFNs of PSCI patients.展开更多
Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve ...Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.展开更多
A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a force...A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.展开更多
As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerou...As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.展开更多
Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization.Compared with other studies which focus on designing heurist...Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization.Compared with other studies which focus on designing heuristic algorithms to reduce the hardness of the NP-hard problem we propose a robust VNE algorithm based on component connectivity in large-scale network.We distinguish the different components and embed VN requests onto them respectively.And k-core is applied to identify different VN topologies so that the VN request can be embedded onto its corresponding component.On the other hand,load balancing is also considered in this paper.It could avoid blocked or bottlenecked area of substrate network.Simulation experiments show that compared with other algorithms in large-scale network,acceptance ratio,average revenue and robustness can be obviously improved by our algorithm and average cost can be reduced.It also shows the relationship between the component connectivity including giant component and small components and the performance metrics.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(Grant Nos.lzujbky-2021-62 and lzujbky-2024-jdzx06)the National Natural Science Foundation of China(Grant No.12247101)+1 种基金the Natural Science Foundation of Gansu Province,China(Grant Nos.22JR5RA389 and 23JRRA1740)the‘111 Center’Fund(Grant No.B20063).
文摘It has been argued that the human brain,as an information-processing machine,operates near a phase transition point in a non-equilibrium state,where it violates detailed balance leading to entropy production.Thus,the assessment of irreversibility in brain networks can provide valuable insights into their non-equilibrium properties.In this study,we utilized an open-source whole-brain functional magnetic resonance imaging(fMRI)dataset from both resting and task states to evaluate the irreversibility of large-scale human brain networks.Our analysis revealed that the brain networks exhibited significant irreversibility,violating detailed balance,and generating entropy.Notably,both physical and cognitive tasks increased the extent of this violation compared to the resting state.Regardless of the state(rest or task),interactions between pairs of brain regions were the primary contributors to this irreversibility.Moreover,we observed that as global synchrony increased within brain networks,so did irreversibility.The first derivative of irreversibility with respect to synchronization peaked near the phase transition point,characterized by the moderate mean synchronization and maximized synchronization entropy of blood oxygenation level-dependent(BOLD)signals.These findings deepen our understanding of the non-equilibrium dynamics of large-scale brain networks,particularly in relation to their phase transition behaviors,and may have potential clinical applications for brain disorders.
基金Supported by Natural Science Foundation of Shandong Province of China(Grant Nos.ZR2020MF080 and ZR2020MF065).
文摘The dynamic behaviors of a large-scale ring neural network with a triangular coupling structure are investigated.The characteristic equation of the high-dimensional system using Coate’s flow graph method is calculated.Time delay is selected as the bifurcation parameter,and sufficient conditions for stability and Hopf bifurcation are derived.It is found that the connection coefficient and time delay play a crucial role in the dynamic behaviors of the model.Furthermore,a phase diagram of multiple equilibrium points with one saddle point and two stable nodes is presented.Finally,the effectiveness of the theory is verified through simulation results.
基金supported by the National Natural Science Foundation of China(31920103009,32371104,and 32130045)the Major Project of National Social Science Foundation(20&ZD153)the Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions(2023SHIBS0003).
文摘Emotion and executive control are often conceptualized as two distinct modes of human brain functioning.Little,however,is known about how the dynamic organization of large-scale functional brain networks that support flexible emotion processing and executive control,especially their interactions.The amygdala and prefrontal systems have long been thought to play crucial roles in these processes.Recent advances in human neuroimaging studies have begun to delineate functional organization principles among the large-scale brain networks underlying emotion,executive control,and their interactions.Here,we propose a dynamic brain network model to account for interactive competition between emotion and executive control by reviewing recent resting-state and task-related neuroimaging studies using network-based approaches.In this model,dynamic interactions among the executive control network,the salience network,the default mode network,and sensorimotor networks enable dynamic processes of emotion and support flexible executive control of multiple processes;neural oscillations across multiple frequency bands and the locus coeruleus−norepinephrine pathway serve as communicational mechanisms underlying dynamic synergy among large-scale functional brain networks.This model has important implications for understanding how the dynamic organization of complex brain systems and networks empowers flexible cognitive and affective functions.
基金supported by the National Natural Science Foundation of China (Grant Nos.12202027,11932003,12272092,11972115)the China Postdoctoral Science Foundation (Grant No.2021TQ0025)。
文摘Transcranial direct current stimulation(tDCS)is a noninvasive technique that uses constant,low-intensity direct current to regulate brain activities.Clinical studies have shown that cathode-tDCS(c-tDCS)is effective in reducing seizure frequency in patients with epilepsy.Due to the heterogeneity and patient specificity of seizures,patient-specific epilepsy networks are increasingly important in exploring the regulatory role of c-tDCS.In this study,we first set the left hippocampus,parahippocampus,and amygdala as the epileptogenic zone(EZ),and the left inferior temporal cortex and ventral temporal cortex as the initial propagation zone(PZ)to establish a large-scale epilepsy network model.Then we set tDCS cathode locations according to the maximum average energy of the simulated EEG signals and systematically study c-tDCS inhibitory effects on the propagation of epileptic activity.The results show that c-tDCS is effective in suppressing the propagation of epileptic activity.Further,to consider the patient specificity,we set specific EZ and PZ according to the clinical diagnosis of 6 patients and establish patient-specific epileptic networks.We find that c-tDCS can suppress the propagation of abnormal activity in most patient-specific epileptic networks.However,when the PZ is widely distributed in both hemispheres,the treatment effect of c-tDCS is not satisfactory.Hence,we propose dual-cathode tDCS.For epilepsy models with a wide distribution of PZ,it can inhibit the propagation of epileptiform activity in other nodes except EZ and PZ without increasing the tDCS current strength.Our results provide theoretical support for the treatment of epilepsy with tDCS.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11932003 and 11972115)。
文摘The brain is organized as a complex network architecture, which can be mapped into structural(SC) and functional connectivity(FC) by advanced neuroimaging techniques. Achievements in brain network research have revealed that modularity is a universal trait in brain networks and may be vital for cognitive segregation and integration. Large-scale brain network modeling is a promising computational approach to combine neuroimaging data with generative rules for brain dynamics. Recently, it has been proposed that chimera states, a type of dynamics referring to the coexistence of coherent and incoherent participants, have traits in common with cognitive functions like segregated and integrated brain processing. Previous studies have reported the existence of chimera-like dynamics in large-scale brain network models, whereas they did not account for the relationship between chimeralike dynamics and corresponding functional modular organizations of the brain network. By specifying qualitatively different network dynamics in an anatomically-constrained brain network model, we compare the different modular organizations of FC unfolded by network dynamics. Our simulations reveal that chimera-like dynamics support a meaningful pattern of functional modular organization, which promotes a diversity of node roles with a distributed pattern of functional cartography. The distinct node roles in modular FC are also found to occur with a spatial preference in speciflc brain regions, and, to some extent, reflect the underlying structure constraints. Our results support the view that chimera-like dynamics is a functionally meaningful scenario that may play a fundamental role in the segregation and integration of brain functioning.
基金supported by the National Natural Science Foundation of China,Nos.81871836(to MZ),82172554(to XH),and 81802249(to XH),81902301(to JW)the National Key R&D Program of China,Nos.2018YFC2001600(to JX)and 2018YFC2001604(to JX)+3 种基金Shanghai Rising Star Program,No.19QA1409000(to MZ)Shanghai Municipal Commission of Health and Family Planning,No.2018YQ02(to MZ)Shanghai Youth Top Talent Development PlanShanghai“Rising Stars of Medical Talent”Youth Development Program,No.RY411.19.01.10(to XH)。
文摘Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.
文摘Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.
基金supported by the National Natural Science Foundation of China(Grant Nos.62071451,62331025,and U21A20447)the National Key Research and Development Project(Grant No.2021YFC3002204)the CAMS Innovation Fund for Medical Sciences(Grant No.2019-I2M-5-019).
文摘Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuronal damage,it is crucial to find a biomarker to distinguish individuals with these diseases from healthy people.In this study,we construct a brain function network based on electroencephalography data to study changes in AD and MCI patients.Using a graph-theoretical approach,we examine connectivity features and explore their contributions to dementia recognition at edge,node,and network levels.We find that connectivity is reduced in AD and MCI patients compared with healthy controls.We also find that the edge-level features give the best performance when machine learning models are used to recognize dementia.The results of feature selection identify the top 50 ranked edge-level features constituting an optimal subset,which is mainly connected with the frontal nodes.A threshold analysis reveals that the performance of edge-level features is more sensitive to the threshold for the connection strength than that of node-and network-level features.In addition,edge-level features with a threshold of 0 provide the most effective dementia recognition.The K-nearest neighbors(KNN)machine learning model achieves the highest accuracy of 0.978 with the optimal subset when the threshold is 0.Visualization of edge-level features suggests that there are more long connections linking the frontal region with the occipital and parietal regions in AD and MCI patients compared with healthy controls.Our codes are publicly available at https://github.com/Debbie-85/eeg-connectivity.
文摘The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors possess high changes in terms of size,shape,and amount,and hence the classification process acts as a more difficult research problem.This paper suggests a deep learning model using the magnetic resonance imaging technique that overcomes the limitations associated with the existing classification methods.The effectiveness of the suggested method depends on the coyote optimization algorithm,also known as the LOBO algorithm,which optimizes the weights of the deep-convolutional neural network classifier.The accuracy,sensitivity,and specificity indices,which are obtained to be 92.40%,94.15%,and 91.92%,respectively,are used to validate the effectiveness of the suggested method.The result suggests that the suggested strategy is superior for effectively classifying brain tumors.
基金Supported by the Wuxi Municipal Health Commission Major Project,No.Z202107。
文摘BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving SA.AIM To explore the potential characteristics of the brain network and functional connectivity(FC)of SA.METHODS Twenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly,which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination(MMSE).The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf,and the brain functional network was conducted by DPABINet.The support vector machine model was constructed with altered functional connectivities to evaluate the identification value of SA.RESULTS The results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE performance.The FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score,and the FC of the right supramarginal gyrus and right temporal pole:Middle temporal gyrus was the only one decreased and negatively related to MMSE score.All 17 significantly altered FCs of SA were taken into the support vector machine model,and the area under the curve was 0.895.CONCLUSION The identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.
基金The work was supported by Humanities and Social Sciences Fund of the Ministry of Education(No.22YJA630119)the National Natural Science Foundation of China(No.71971051)Natural Science Foundation of Hebei Province(No.G2021501004).
文摘With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.
文摘Background:Sleep deprivation(SD)can potentially lead to deficits in many cognitive capacities,suggesting that sleep pressure represented a basic physiological constraint of brain function.However,the neural mechanism underlying the decline awareness and cognition induced by SD is far from clear.Methods:Thirty-seven healthy male adults were recruited in this within-subjects,repeat-measure,counterbalanced study.These individuals were both examined during a state of rested wakefulness(RW)state and after 36 hours of total SD.Using functional connectivity magnetic resonance imaging(fcMRI),we investigated the specifi c effect of SD on static functional connectivity density,sparse representation of resting-state fMRI signal,and dynamic connectivity pattern.Results:Our analysis based on fcMRI revealed that multiple functional networks involved in memory,emotion,attention,and vigilance processing were impaired by SD.Of particular interest,the thalamus was observed to contribute to multiple functional networks in which differentiated response patterns were exhibited.We also detect robust changes in the temporal properties of specifi c connectivity states,such as the occurrence frequencies,dwell times and transition probabilities that were likely associated with the vigilance loss induced by SD.These changes led to differentiation of these states with the RW-dominant states characterized by anti-correlation between the default mode network and other cortices and the SD-dominant states marked by significantly decreased thalamocortical connectivity.Conclusion:These fi ndings suggest specifi c patterns of the large-scale functional brain network changes after SD,which are important for understanding of the impacts of SD on brain function and developing effective intervention strategy against SD.
基金the National Natural Science Foundation of China Project:the Study of Central Mechanism of Electroacupuncture on Sishencong (EX-HN1) Improving Sleep Architecture and Neurocognitive Function Using Electroencephalogram-Functional Magnetic Resonance Imaging (No. 81774426)Major Scientific Research Project of Wuxi Health Commission:Clinical Study of Intelligent Hand Rehabilitation Training System for Nerve Function Reconstruction of Patients with Hand Dysfunction after Cerebral Infarction (No. Z202121)。
文摘OBJECTIVE: To analyze part of the mechanism of electroacupuncture on Sishencong(EX-HN1) for strokerelated sleep disorders(SSD) and post-stroke cognitive impairment(PSCI). METHODS: Using a randomized controlled trial(RCT) design, 72 patients were assigned to the electroacupuncture(EA) group or the sham acupuncture(SA) group. A healthy control(HC) group was also included. Both groups were given routine rehabilitation treatment. Then, patients in the EA group were given additional electroacupuncture at Sishencong(EX_HN1). Meanwhile, patients in the SA group were given a flat-head needle sham/placebo treatment placed at the bilateral Jianyu (LI15) and Binao(LI14) line midpoints and the Jianyu(LI15) and Jianzhen(SI9) line midpoints. Before and after treatment, scales were collected and analyzed. In the second phase of the study, some subjects from the EA group were selected for functional magnetic resonance imaging(f MRI) data acquisition and comparative analysis with the HC group using a non-RCT design. RESULTS: The EA group performed better than the SA group on the Pittsburgh sleep quality index(PSQI), Montreal cognitive assessment basic(Mo CA_B), selfrating anxiety scale(SAS), and self-rating depression scale(SDS). Analysis of the f MRI showed that lowfrequency(2 Hz) electroacupuncture stimulation at Sishencong(EX_HN1) can restrain frontal sup medial right(SFGmed.R), precuneus right(PCUN.R), and posterior cingulate cortex right(PCC.R) and enhance angular left(ANG.L), parietal inf left(IPL.L) and occipital mid left(MOG.L). The functional connectivity(FC) of SFGmed.R was positively correlated with PSQI. Electroacupuncture stimulation at Sishencong(EX_HN1) can reduce the side efficiency of the whole brain connection with the Thalamus.L, Hippocampus.L, and Occipital.Mid.L. CONCLUSIONS: Low frequency(2 Hz) electroacupuncture stimulation at Sishencong(EX_HN1) can simultaneously improve sleep quality, negative emotions, and cognitive functions, the first two of which may be related to SFGmed.R restraint. Electroacupuncture can make some brain areas approach the physiological bias state, which is characterized by dominant hemispheric enhancement and non-dominant hemispheric weakening. The reduced whole brain connection side efficiency with some key nodes of the brain net may relate to sleep quality improvements in SSD patients.
基金supported by the Natural Science Foundation of Sichuan Province of China,Nos.2022NSFSC1545 (to YG),2022NSFSC1387 (to ZF)the Natural Science Foundation of Chongqing of China,Nos.CSTB2022NSCQ-LZX0038,cstc2021ycjh-bgzxm0035 (both to XT)+3 种基金the National Natural Science Foundation of China,No.82001378 (to XT)the Joint Project of Chongqing Health Commission and Science and Technology Bureau,No.2023QNXM009 (to XT)the Science and Technology Research Program of Chongqing Education Commission of China,No.KJQN202200435 (to XT)the Chongqing Talents:Exceptional Young Talents Project,No.CQYC202005014 (to XT)。
文摘Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.
基金supported by the National Natural Science Foundation of China,Nos.81671671(to JL),61971451(to JL),U22A2034(to XK),62177047(to XK)the National Defense Science and Technology Collaborative Innovation Major Project of Central South University,No.2021gfcx05(to JL)+6 种基金Clinical Research Cen terfor Medical Imaging of Hunan Province,No.2020SK4001(to JL)Key Emergency Project of Pneumonia Epidemic of Novel Coronavirus Infection of Hu nan Province,No.2020SK3006(to JL)Innovative Special Construction Foundation of Hunan Province,No.2019SK2131(to JL)the Science and Technology lnnovation Program of Hunan Province,Nos.2021RC4016(to JL),2021SK53503(to ML)Scientific Research Program of Hunan Commission of Health,No.202209044797(to JL)Central South University Research Program of Advanced Interdisciplinary Studies,No.2023Q YJC020(to XK)the Natural Science Foundation of Hunan Province,No.2022JJ30814(to ML)。
文摘Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury.These findings highlight the importance of integrating structural and functional connectivity in unde rstanding the occurrence and evolution of mild traumatic brain injury.Additionally,exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.
文摘Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture therapy. Methods: Twenty-two PSCI patients who underwent acupuncture therapy in our hospital were enrolled as research subjects. Another 14 people matched for age, sex, and education level were included in the normal control (HC) group. All the subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans;the PSCI patients underwent one scan before acupuncture therapy and another after. The network metric difference between PSCI patients and HCs was analyzed via the independent-sample t test, whereas the paired-sample t test was employed to analyze the network metric changes in PSCI patients before vs. after treatment. Results: Small-world network attributes were observed in both groups for sparsities between 0.1 and 0.28. Compared with the HC group, the PSCI group presented significantly lower values for the global topological properties (γ, Cp, and Eloc) of the brain;significantly greater values for the nodal attributes of betweenness centrality in the CUN. L and the HES. R, degree centrality in the SFGdor. L, PCG. L, IPL. L, and HES. R, and nodal local efficiency in the ORBsup. R, ORBsupmed. R, DCG. L, SMG. R, and TPOsup. L;and decreased degree centrality in the MFG. R, IFGoperc. R, and SOG. R. After treatment, PSCI patients presented increased degree centrality in the LING.L, LING.R, and IOG. L and nodal local efficiency in PHG. L, IOG. R, FFG. L, and the HES. L, and decreased betweenness centrality in the PCG. L and CUN. L, degree centrality in the ORBsupmed. R, and nodal local efficiency in ANG. R. Conclusion: Cognitive decline in PSCI patients may be related to BFN disorders;acupuncture therapy may modulate the topological properties of the BFNs of PSCI patients.
基金National Natural Science Foundation of China(82274265 and 82274588)Hunan University of Traditional Chinese Medicine Research Unveiled Marshal Programs(2022XJJB003).
文摘Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.
基金supported by the Ministry of Trade,Industry & Energy(MOTIE,Korea) under Industrial Technology Innovation Program (No.10063424,'development of distant speech recognition and multi-task dialog processing technologies for in-door conversational robots')
文摘A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.
基金This work was supported by National Key R&D Program of China under Grant 2018YFB1800802in part by the National Natural Science Foundation of China under Grant No.61771488,No.61631020 and No.61827801+1 种基金in part by State Key Laboratory of Air Traffic Management System and Technology under Grant No.SKLATM201808in part by Postgraduate Research and Practice Innovation Program of Jiangsu Province under No.KYCX190188.
文摘As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.
基金supported in part by the National Natural Science Foundation of China under Grant No.61471055
文摘Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization.Compared with other studies which focus on designing heuristic algorithms to reduce the hardness of the NP-hard problem we propose a robust VNE algorithm based on component connectivity in large-scale network.We distinguish the different components and embed VN requests onto them respectively.And k-core is applied to identify different VN topologies so that the VN request can be embedded onto its corresponding component.On the other hand,load balancing is also considered in this paper.It could avoid blocked or bottlenecked area of substrate network.Simulation experiments show that compared with other algorithms in large-scale network,acceptance ratio,average revenue and robustness can be obviously improved by our algorithm and average cost can be reduced.It also shows the relationship between the component connectivity including giant component and small components and the performance metrics.
