Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

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.

Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats

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.

Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

The structure and function of brain networks have been altered in patients with end-stage renal disease(ESRD).Manifold regularization(MR)only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions.To solve this issue,we developed a method to construct a dynamic brain functional network(DBFN)based on dynamic hypergraph MR(DHMR)and applied it to the classification of ESRD associated with mild cognitive impairment(ESRDaMCI).The construction of DBFN with Pearson’s correlation(PC)was transformed into an optimization model.Node convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure,and then got the dynamic hypergraph to form the manifold regular terms of the dynamic hypergraph.The DHMR and L_(1) norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN(DDBFN).Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction methods.Our work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI,providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.

Effect of cognitive training on brain dynamics

The human brain is highly plastic.Cognitive training is usually used to modify functional connectivity of brain networks.Moreover,the structures of brain networks may determine its dynamic behavior which is related to human cognitive abilities.To study the effect of functional connectivity on the brain dynamics,the dynamic model based on functional connections of the brain and the Hindmarsh–Rose model is utilized in this work.The resting-state fMRI data from the experimental group undergoing abacus-based mental calculation(AMC)training and from the control group are used to construct the functional brain networks.The dynamic behavior of brain at the resting and task states for the AMC group and the control group are simulated with the above-mentioned dynamic model.In the resting state,there are the differences of brain activation between the AMC group and the control group,and more brain regions are inspired in the AMC group.A stimulus with sinusoidal signals to brain networks is introduced to simulate the brain dynamics in the task states.The dynamic characteristics are extracted by the excitation rates,the response intensities and the state distributions.The change in the functional connectivity of brain networks with the AMC training would in turn improve the brain response to external stimulus,and make the brain more efficient in processing tasks.

Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders.However,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia diagnosis.In this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven models.Specifically,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation robust.Also,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions.The typical topological properties and discriminative features can be preserved entirely.Furthermore,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive diseases.Attempts on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional networks.The classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related augmentedmodels.Overall,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks.

The development of brain functional connectivity networks revealed by resting-state functional magnetic resonance imaging

Previous studies on brain functional connectivity networks in children have mainly focused on changes in function in specific brain regions, as opposed to whole brain connectivity in healthy children. By analyzing the independent components of activation and network connectivity between brain regions, we examined brain activity status and development trends in children aged 3 and 5 years. These data could provide a reference for brain function rehabilitation in children with illness or abnormal function. We acquired functional magnetic resonance images from 15 3-year-old children and 15 5-year-old children under natural sleep cond让ions. The participants were recruited from five kindergartens in the Nanshan District of Shenzhen City, China. The parents of the participants signed an informed consent form with the premise that they had been fully informed regarding the experimental protocol. We used masked independent component analysis and BrainNet Viewer software to explore the independent components of the brain and correlation connections between brain regions. We identified seven independent components in the two groups of children, including the executive control network, the dorsal attention network, the default mode network, the left frontoparietal network, the right frontoparietal network, the salience network, and the motor network. In the default mode network, the posterior cingulate cortex, medial frontal gyrus, and inferior parietal lobule were activated in both 3- and 5-year-old children, supporting the "three-brain region theory” of the default mode network. In the frontoparietal network, the frontal and parietal gyri were activated in the two groups of children, and functional connectivity was strengthened in 5-year-olds compared with 3-year-olds, although the nodes and network connections were not yet mature. The high-correlation network connections in the default mode networks and dorsal attention networks had been significantly strengthened in 5-year-olds vs. 3-year-olds. Further, the salience network in the 3-year-old children included an activated insula/inferior frontal gyrus-anterior cingulate cortex network circu让 and an activated thalamus-parahippocampal-posterior cingulate cortex-subcortical regions network circuit. By the age of 5 years, no des and high-correlation network connections (edges) were reduced in the salience network. Overall, activation of the dorsal attention network, default mode network, left frontoparietal network, and right frontoparietal network increased (the volume of activation increased, the signals strengthened, and the high-correlation connections increased and strengthened) in 5-year-olds compared with 3-year-olds, but activation in some brain nodes weakened or disappeared in the salience network, and the network connections (edges) were reduced. Between the ages of 3 and 5 years, we observed a tendency for function in some brain regions to be strengthened and for the generalization of activation to be reduced, indicating that specialization begins to develop at this time. The study protocol was approved by the local ethics committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences in China with approval No. SIAT-IRB- 131115-H0075 on November 15, 2013.

Changes in brain functional network connectivity after stroke

Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.

Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state

It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we inves-tigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state.Z-values in the vision-related brain regions were calculated, conifrming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental ifndings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

Abnormal characterization of dynamic functional connectivity in Alzheimer’s disease

Numerous studies have shown abnormal brain functional connectivity in individuals with Alzheimer’s disease(AD)or amnestic mild cognitive impairment(aMCI).However,most studies examined traditional resting state functional connections,ignoring the instantaneous connection mode of the whole brain.In this case-control study,we used a new method called dynamic functional connectivity(DFC)to look for abnormalities in patients with AD and aMCI.We calculated dynamic functional connectivity strength from functional magnetic resonance imaging data for each participant,and then used a support vector machine to classify AD patients and normal controls.Finally,we highlighted brain regions and brain networks that made the largest contributions to the classification.We found differences in dynamic function connectivity strength in the left precuneus,default mode network,and dorsal attention network among normal controls,aMCI patients,and AD patients.These abnormalities are potential imaging markers for the early diagnosis of AD.

Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization

Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.

Sex Differences in Reconstructed Resting-State Functional Brain Networks for Children

Neuroscience studies have demonstrated that functional differences in human brains between males and females might result in their cognitive and psychological distinctions. To investigate sex differences in resting-state functional networks for children, the functional brain networks of two groups including boys and girls were reconstructed by functional connectivity with significant between-group differences respectively based on two brain atlases, and then the reconstructed functional networks were compared from the viewpoint of small-world properties. The functional brain networks of the two groups both displayed topological properties of the small-world network based on different brain atlases but exhibited some sex differences in certain measures. Specifically, for the automated anatomical labeling atlas, compared with girls, boys showed stronger small-world properties and higher ability of local information processing in brain networks;for the Harvard Oxford Atlas, the shortest path length of boys increased, indicating poorer performance in both global information transmission and resistance to the random attack.

Estimating Functional Brain Network with Low-Rank Structure via Matrix Factorization for MCI/ASD Identification

Functional brain networks (FBNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Due to its importance, many FBN construction methods have been proposed currently, including the low-order Pearson’s correlation (PC) and sparse representation (SR), as well as the high-order functional connection (HoFC). However, most existing methods usually ignore the information of topological structures of FBN, such as low-rank structure which can reduce the noise and improve modularity to enhance the stability of networks. In this paper, we propose a novel method for improving the estimated FBNs utilizing matrix factorization (MF). More specifically, we firstly construct FBNs based on three traditional methods, including PC, SR, and HoFC. Then, we reduce the rank of these FBNs via MF model for estimating FBN with low-rank structure. Finally, to evaluate the effectiveness of the proposed method, experiments have been conducted to identify the subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) from norm controls (NCs) using the estimated FBNs. The results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate that the classification performances achieved by our proposed method are better than the selected baseline methods.

NP-13 Impact of 36 hours of Total Sleep Deprivation on Large-Scale Functional Brain Network Interactions

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.

Age-related changes in resting-state functional connectivity in older adults

Age-related changes in the brain connectivity of healthy older adults have been widely studied in recent years,with some differences in the obtained results.Most of these studies showed decreases in general functional connectivity,but they also found increases in some particular regions and areas.Frequently,these studies compared young individuals with older subjects,but few studies compared different age groups only in older populations.The purpose of this study is to analyze whole-brain functional connectivity in healthy older adult groups and its network characteristics through functional segregation.A total of 114 individuals,48 to 89 years old,were scanned using resting-state functional magnetic resonance imaging in a resting state paradigm and were divided into six different age groups(<60,60–64,65–69,70–74,75–79,≥80 years old).A partial correlation analysis,a pooled correlation analysis and a study of 3-cycle regions with prominent connectivity were conducted.Our results showed progressive diminution in the functional connectivity among different age groups and this was particularly pronounced between 75 and 79 years old.The oldest group(≥80 years old)showed a slight increase in functional connectivity compared to the other groups.This occurred possibly because of compensatory mechanism in brain functioning.This study provides information on the brain functional characteristics of every age group,with more specific information on the functional progressive decline,and supplies methodological tools to study functional connectivity characteristics.Approval for the study was obtained from the ethics committee of the Comision de Bioetica de la Universidad de Barcelona(approval No.PSI2012-38257)on June 5,2012,and from the ethics committee of the Barcelona’s Hospital Clinic(approval No.2009-5306 and 2011-6604)on October 22,2009 and April 7,2011 respectively.

Age-related hearing loss accelerates the decline in fast speech comprehension and the decompensation of cortical network connections

Patients with age-related hearing loss face hearing difficulties in daily life.The causes of age-related hearing loss are complex and include changes in peripheral hearing,central processing,and cognitive-related abilities.Furthermore,the factors by which aging relates to hearing loss via changes in audito ry processing ability are still unclear.In this cross-sectional study,we evaluated 27 older adults(over 60 years old) with age-related hearing loss,21 older adults(over 60years old) with normal hearing,and 30 younger subjects(18-30 years old) with normal hearing.We used the outcome of the uppe r-threshold test,including the time-compressed thres h old and the speech recognition threshold in noisy conditions,as a behavioral indicator of auditory processing ability.We also used electroencephalogra p hy to identify presbycusis-related abnormalities in the brain while the participants were in a spontaneous resting state.The timecompressed threshold and speech recognition threshold data indicated significant diffe rences among the groups.In patients with age-related hearing loss,information masking(babble noise) had a greater effect than energy masking(speech-shaped noise) on processing difficulties.In terms of resting-state electroencephalography signals,we observed enhanced fro ntal lobe(Brodmann’s area,BA11) activation in the older adults with normal hearing compared with the younger participants with normal hearing,and greater activation in the parietal(BA7) and occipital(BA19) lobes in the individuals with age-related hearing loss compared with the younger adults.Our functional connection analysis suggested that compared with younger people,the older adults with normal hearing exhibited enhanced connections among networks,including the default mode network,sensorimotor network,cingulo-opercular network,occipital network,and frontoparietal network.These results suggest that both normal aging and the development of age-related hearing loss have a negative effect on advanced audito ry processing capabilities and that hearing loss accele rates the decline in speech comprehension,especially in speech competition situations.Older adults with normal hearing may have increased compensatory attentional resource recruitment represented by the to p-down active listening mechanism,while those with age-related hearing loss exhibit decompensation of network connections involving multisensory integration.

职业噪声暴露人群额叶脑网络功能连通性研究

目的 采用功能性近红外线光谱技术(functional near-infrared spectroscopy,fNIRS)探讨健听的职业噪声暴露人群额叶脑网络功能连通性(functional connectivity,FC)的特征。方法 对2023年6月~2023年11月中国某运输公司符合纳入标准的受试者进行脑网络FC、纯音听阈、声阻抗、血糖、血压及焦虑抑郁进行测试。结果 噪声组和对照组性别、年龄、血压、血糖、医院焦虑抑郁量表-抑郁分量表(hospital anxiety and depression scale-depression subscale,HADS-D)评分无差异;噪声组医院焦虑抑郁量表-焦虑分量表(hospital anxiety and depression scale-anxiety subscale,HADS-A)评分高于对照组。噪声组的HADS-A主要集中在正常状态和临界程度,而对照组主要集中在正常状态。噪声组的额叶FC高于对照组,噪声组的额叶静息态HBO高于对照组。噪声暴露时间与额叶FC呈正相关。噪声组额叶FC与医院焦虑抑郁量表-焦虑分量表(hospital anxiety and depression scale-anxiety subscale,HADS-A)得分呈正相关。结论 职业噪声暴露人群在听力出现异常前已经出现了脑功能改变和临床症状;随着噪声暴露时间的增加,脑网络功能改变更加明显;职业噪声暴露人群焦虑与脑网络FC的变化有一定相关性;基于fNIRS的额叶脑网络研究可为职业噪声暴露人群全脑网络的研究提供理论基础,为职业噪声人群的非听觉扩大预防工作提供依据。

基于独立成分分析的终末期肾病患者静态及动态功能网络连接研究

目的:探索终末期肾病(ESRD)患者功能网络连接(FNC)的静态及动态变化特点。方法:收集33例ESRD患者及34例健康对照组作为研究对象。首先基于独立成分分析识别到6个静息态功能网络,即听觉网络、凸显网络、视觉网络、感觉运动网络(SMN)、执行控制网络(ECN)及默认网络。然后比较两组静态功能网络连接(sFNC)和动态功能网络连接(dFNC)相关参数的差异,并与神经心理测试进行相关分析。结果:sFNC分析显示ESRD组ECN与SMN的sFNC强度显著高于健康对照组(P<0.05,FDR校正),而且与执行功能评分[连线追踪测试A(TMT-A)]呈显著正相关(r=0.429,P=0.018)。dFNC分析显示ESRD组状态3的时间分数和平均驻留时间显著低于健康对照组(P<0.05);状态2的时间分数(r=0.503,P=0.005)和平均驻留时间(r=0.412,P=0.024)与TMT-A评分呈显著正相关;状态4的时间分数与焦虑评分呈显著负相关(r=-0.372,P=0.043)。结论:本研究采用独立成分分析的方法揭示了ESRD患者静态及动态功能网络连接的特点,为深入理解ESRD患者神经病理损害机制提供了新视角。

针刺阳陵泉对中风偏瘫患者脑网络功能连接的即刻效应

目的 基于功能磁共振成像(fMRI)技术,对比针刺阳陵泉真穴/假穴对中风偏瘫患者感觉运动网络(SMN)、背侧注意网络(DAN)功能连接的即刻效应,探讨针刺对中风偏瘫患者中枢调节机制及经穴特异性。方法 纳入中风偏瘫患者20例,间隔2周分别进行1次针刺阳陵泉真穴与假穴的fMRI扫描,应用独立成分分析提取运动相关的SMN、DAN,比较功能连接差异。结果 在SMN内,针刺阳陵泉真穴后较针刺前功能连接增强,增强的脑区有右侧中央前回、颞上回、额下回、楔叶、楔前叶,左侧颞中回、枕中回、颞上回、海马旁回、额下回、颞上回;针刺阳陵泉假穴后较针刺前功能连接增强,增强的脑区有右侧中央前回、额上回、额中回、扣带回,左侧额内侧回、前扣带回、豆状核、尾状核。在DAN中,针刺阳陵泉真穴后较针刺前功能连接增强,增强的脑区包括右侧大脑楔前叶、颞上回、颞中回、枕中回,左侧扣带回、后扣带回、楔前叶;针刺阳陵泉假穴后较针刺前功能连接增强,增强的脑区包括右侧前扣带回,左侧前扣带回、额内侧回。结论 针刺阳陵泉能激活中风偏瘫患者SMN、DAN双侧相关脑区,可能通过调节运动的启动及执行促进患者运动功能的恢复,且相较假穴更具经穴特异性。

基于典型相关分析的脑网络研究方法综述

脑网络分析在研究大脑的认知活动、探究大脑的信息处理模式和辅助精神类疾病的诊断等方面都起着重要作用。近年来,基于多变量数据集的脑网络研究方法得到了普遍关注。典型相关分析(CCA)作为一种基于数据驱动的多元统计方法,能够有效捕捉多变量数据间的隐含关系,被广泛地应用于脑网络研究。综述CCA在脑网络研究中的作用、具体应用模式、存在的优势和局限性。首先,对传统的CCA其及常见变体的算法原理进行归纳总结;然后,阐述基于CCA分析方法在脑网络构建、脑网络分析、脑网络标记物识别方面的研究现状;最后,对基于CCA的脑网络研究方法进行总结并探讨未来研究的方向。

基于rs-fMRI的2型糖尿病患者动态功能网络连接研究

目的 应用动态功能网络连接(dynamic functional network connectivity, dFNC)分析技术探讨2型糖尿病(type 2 diabetes mellitus, T2DM)患者脑功能连接时变性及其动态功能指标与临床指标的相关性。材料与方法 前瞻性纳入31例T2DM患者的临床和影像资料,并记录患者的糖尿病相关生化指标和神经心理学测试得分。同期招募32名年龄、性别及受教育年限相匹配的健康对照(healthy control, HC)。使用滑动时间窗技术进行dFNC分析得到4个功能连接状态及dFNC指标(平均停留时间、时间分数、转换次数)。采用两独立样本t检验计算不同状态内FNC矩阵以及dFNC指标的组间差异,采用Spearman相关分析计算T2DM组dFNC指标与临床指标和认知评分的相关性。结果 在状态1弱连接中,T2DM组与HC组相比,平均停留时间增多(t=2.086,P<0.05)。在状态3局部强连接中,T2DM组与HC组相比,平均停留时间减少(t=-2.250,P<0.05),时间分数减小(t=-2.582,P<0.05),默认模式网络(default mode network, DMN)与视觉网络(visual network, VIS)之间的功能连接减弱(t=-4.875,P<0.05,FDR校正)。T2DM患者的病程与状态1弱连接状态的平均停留时间呈正相关(r=0.42,P<0.05),其他糖尿病相关生化指标和认知功能评分与dFNC指标无相关性(P>0.05)。结论 dFNC分析能捕获更多有关T2DM患者脑网络连接改变的潜在信息,揭示大脑网络复杂多变的时变特征及活动形式,有望为探究T2DM相关认知障碍的神经生物学机制提供新的见解。