Network Neuroscience最新文献_第10页

Sleep spindles in primates: Modeling the effects of distinct laminar thalamocortical connectivity in core, matrix, and reticular thalamic circuits. 灵长类动物的睡眠纺锤体：模拟核心、基质和网状丘脑回路中不同的丘脑皮层层流连接的影响。

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-06-30 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00311

Arash Yazdanbakhsh, Helen Barbas, Basilis Zikopoulos

{"title":"Sleep spindles in primates: Modeling the effects of distinct laminar thalamocortical connectivity in core, matrix, and reticular thalamic circuits.","authors":"Arash Yazdanbakhsh, Helen Barbas, Basilis Zikopoulos","doi":"10.1162/netn_a_00311","DOIUrl":"10.1162/netn_a_00311","url":null,"abstract":"Sleep spindles are associated with the beginning of deep sleep and memory consolidation and are disrupted in schizophrenia and autism. In primates, distinct core and matrix thalamocortical (TC) circuits regulate sleep spindle activity through communications that are filtered by the inhibitory thalamic reticular nucleus (TRN); however, little is known about typical TC network interactions and the mechanisms that are disrupted in brain disorders. We developed a primate-specific, circuit-based TC computational model with distinct core and matrix loops that can simulate sleep spindles. We implemented novel multilevel cortical and thalamic mixing, and included local thalamic inhibitory interneurons, and direct layer 5 projections of variable density to TRN and thalamus to investigate the functional consequences of different ratios of core and matrix node connectivity contribution to spindle dynamics. Our simulations showed that spindle power in primates can be modulated based on the level of cortical feedback, thalamic inhibition, and engagement of model core versus matrix, with the latter having a greater role in spindle dynamics. The study of the distinct spatial and temporal dynamics of core-, matrix-, and mix-generated sleep spindles establishes a framework to study disruption of TC circuit balance underlying deficits in sleep and attentional gating seen in autism and schizophrenia.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9737078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes. 描述青少年早期奖赏网络的异质性以及与行为和临床结果的个性化关联。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2023-06-30 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00306

Matthew Mattoni, David V Smith, Thomas M Olino

{"title":"Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes.","authors":"Matthew Mattoni, David V Smith, Thomas M Olino","doi":"10.1162/netn_a_00306","DOIUrl":"10.1162/netn_a_00306","url":null,"abstract":"Associations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qualitatively distinct processes across individuals may be obscured in group averages. This study characterizes the heterogeneity of effective connectivity reward networks among 103 early adolescents and examines associations between individualized features and multiple behavioral and clinical outcomes. To characterize network heterogeneity, we used extended unified structural equation modeling to identify effective connectivity networks for each individual and an aggregate network. We found that an aggregate reward network was a poor representation of individuals, with most individual-level networks sharing less than 50% of the group-level network paths. We then used Group Iterative Multiple Model Estimation to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks. We identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Finally, we found numerous associations between individual-specific connectivity features and behavioral reward functioning and risk for substance use disorders. We suggest that accounting for heterogeneity is necessary to use connectivity networks for inferences precise to the individual.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9745793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information. 运动皮层神经元协同作用的动态结构蕴含着与行为相关的信息。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2023-06-30 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00298

Marina Sundiang, Nicholas G Hatsopoulos, Jason N MacLean

{"title":"Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information.","authors":"Marina Sundiang, Nicholas G Hatsopoulos, Jason N MacLean","doi":"10.1162/netn_a_00298","DOIUrl":"10.1162/netn_a_00298","url":null,"abstract":"Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of coactivity between neurons. Using pairwise spike time statistics, coactivity can be summarized as a functional network (FN). Here, we show that the structure of FNs constructed from an instructed-delay reach task in nonhuman primates is behaviorally specific: Low-dimensional embedding and graph alignment scores show that FNs constructed from closer target reach directions are also closer in network space. Using short intervals across a trial, we constructed temporal FNs and found that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly decodable shortly after the Instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the Instruction cue, consistent with the hypothesis that information external to the recorded population temporarily alters the structure of the network at this moment.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9743490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Age differences in functional brain networks associated with loneliness and empathy. 与孤独和移情相关的大脑功能网络的年龄差异

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-06-30 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00293

Laetitia Mwilambwe-Tshilobo, Roni Setton, Danilo Bzdok, Gary R Turner, R Nathan Spreng

{"title":"Age differences in functional brain networks associated with loneliness and empathy.","authors":"Laetitia Mwilambwe-Tshilobo, Roni Setton, Danilo Bzdok, Gary R Turner, R Nathan Spreng","doi":"10.1162/netn_a_00293","DOIUrl":"10.1162/netn_a_00293","url":null,"abstract":"Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks in early- and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality-loneliness and empathic responding-and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9745792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast-local and slow-global neural ensembles in the mouse brain. 小鼠大脑中的快速局部和慢速全局神经组合

IF 3.6 3区医学

Network Neuroscience Pub Date : 2023-06-30 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00309

Thomas J Delaney, Cian O'Donnell

{"title":"Fast-local and slow-global neural ensembles in the mouse brain.","authors":"Thomas J Delaney, Cian O'Donnell","doi":"10.1162/netn_a_00309","DOIUrl":"10.1162/netn_a_00309","url":null,"abstract":"Ensembles of neurons are thought to be coactive when participating in brain computations. However, it is unclear what principles determine whether an ensemble remains localised within a single brain region, or spans multiple brain regions. To address this, we analysed electrophysiological neural population data from hundreds of neurons recorded simultaneously across nine brain regions in awake mice. At fast subsecond timescales, spike count correlations between pairs of neurons in the same brain region were stronger than for pairs of neurons spread across different brain regions. In contrast at slower timescales, within- and between-region spike count correlations were similar. Correlations between high-firing-rate neuron pairs showed a stronger dependence on timescale than low-firing-rate neuron pairs. We applied an ensemble detection algorithm to the neural correlation data and found that at fast timescales each ensemble was mostly contained within a single brain region, whereas at slower timescales ensembles spanned multiple brain regions. These results suggest that the mouse brain may perform fast-local and slow-global computations in parallel.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9917861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trade-offs among cost, integration, and segregation in the human connectome. 人类连接组中成本、整合和分离之间的权衡。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2023-06-30 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00291

Junji Ma, Xitian Chen, Yue Gu, Liangfang Li, Ying Lin, Zhengjia Dai

{"title":"Trade-offs among cost, integration, and segregation in the human connectome.","authors":"Junji Ma, Xitian Chen, Yue Gu, Liangfang Li, Ying Lin, Zhengjia Dai","doi":"10.1162/netn_a_00291","DOIUrl":"10.1162/netn_a_00291","url":null,"abstract":"The human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-offs among cost, integration, and segregation shape the human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used a multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity; Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity (Tri-factor model [Q]) showed the best performance. They had a high recovery rate of structural connections and optimal performance in most network features, especially in segregated processing capacity and network robustness. Morphospace of this trade-off model could further capture the variation of individual behavioral/demographic characteristics in a domain-specific manner. Overall, our results highlight the importance of modularity in the formation of the human brain structural network and provide new insights into the original cost-efficiency trade-off hypothesis.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9745789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pattern forming mechanisms of color vision. 色觉的模式形成机制

IF 3.6 3区医学

Network Neuroscience Pub Date : 2023-06-30 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00294

Zily Burstein, David D Reid, Peter J Thomas, Jack D Cowan

引用次数: 0

Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity. 多发性硬化症患者的病变体积增大与大脑状态之间的转换能量增加和大脑活动熵减小有关。

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-06-30 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00292

Ceren Tozlu, Sophie Card, Keith Jamison, Susan A Gauthier, Amy Kuceyeski

{"title":"Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity.","authors":"Ceren Tozlu, Sophie Card, Keith Jamison, Susan A Gauthier, Amy Kuceyeski","doi":"10.1162/netn_a_00292","DOIUrl":"10.1162/netn_a_00292","url":null,"abstract":"Quantifying the relationship between the brain's functional activity patterns and its structural backbone is crucial when relating the severity of brain pathology to disability in multiple sclerosis (MS). Network control theory (NCT) characterizes the brain's energetic landscape using the structural connectome and patterns of brain activity over time. We applied NCT to investigate brain-state dynamics and energy landscapes in controls and people with MS (pwMS). We also computed entropy of brain activity and investigated its association with the dynamic landscape's transition energy and lesion volume. Brain states were identified by clustering regional brain activity vectors, and NCT was applied to compute the energy required to transition between these brain states. We found that entropy was negatively correlated with lesion volume and transition energy, and that larger transition energies were associated with pwMS with disability. This work supports the notion that shifts in the pattern of brain activity in pwMS without disability results in decreased transition energies compared to controls, but, as this shift evolves over the disease, transition energies increase beyond controls and disability occurs. Our results provide the first evidence in pwMS that larger lesion volumes result in greater transition energy between brain states and decreased entropy of brain activity.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9737083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes. 双向图匹配改进了连接组中双侧同源神经元的自动配对。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2023-06-30 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00287

Benjamin D Pedigo, Michael Winding, Carey E Priebe, Joshua T Vogelstein

{"title":"Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes.","authors":"Benjamin D Pedigo, Michael Winding, Carey E Priebe, Joshua T Vogelstein","doi":"10.1162/netn_a_00287","DOIUrl":"10.1162/netn_a_00287","url":null,"abstract":"Graph matching algorithms attempt to find the best correspondence between the nodes of two networks. These techniques have been used to match individual neurons in nanoscale connectomes-in particular, to find pairings of neurons across hemispheres. However, since graph matching techniques deal with two isolated networks, they have only utilized the ipsilateral (same hemisphere) subgraphs when performing the matching. Here, we present a modification to a state-of-the-art graph matching algorithm that allows it to solve what we call the bisected graph matching problem. This modification allows us to leverage the connections between the brain hemispheres when predicting neuron pairs. Via simulations and experiments on real connectome datasets, we show that this approach improves matching accuracy when sufficient edge correlation is present between the contralateral (between hemisphere) subgraphs. We also show how matching accuracy can be further improved by combining our approach with previously proposed extensions to graph matching, which utilize edge types and previously known neuron pairings. We expect that our proposed method will improve future endeavors to accurately match neurons across hemispheres in connectomes, and be useful in other applications where the bisected graph matching problem arises.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9807867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Arithmetic Study about Efficiency in Network Topologies for Data Centers 数据中心网络拓扑效率的算法研究

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-06-26 DOI: 10.3390/network3030015

P. Roig, S. Alcaraz, K. Gilly, Cristina Bernad, C. Juiz

引用次数: 1