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Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network. 前运动皮层的反应抑制与中观信息网络的复杂重组相对应。
IF 3.6 3区 医学 Q2 NEUROSCIENCES Pub Date : 2024-07-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00365
Giampiero Bardella, Valentina Giuffrida, Franco Giarrocco, Emiliano Brunamonti, Pierpaolo Pani, Stefano Ferraina

Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using transfer entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.

最近的研究探索了动物模型中的功能和有效神经网络;然而,认知控制下功能模块之间的信息传播动力学在很大程度上仍是未知的。在这里,我们利用转移熵和图论方法,对记录在恒河猴背侧前运动皮层的中观神经活动进行了研究。我们将研究重点放在 "停止信号 "任务的决策时间上,以寻找在 "停止信号 "提供时可能影响运动计划成熟的网络配置模式。将成功抑制的试验与产生运动的试验进行比较,结果发现网络节点分为四个簇,呈分层排列,并明显参与信息传递。有趣的是,在整个任务过程中,簇之间的层次结构和信息传递强度各不相同,可以区分产生的运动和取消的运动,并与可测量的网络复杂性水平相对应。我们的研究结果表明了前运动皮层运动抑制的一种假定机制:神经元群之间信息交换的拓扑重组。
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引用次数: 0
Measures of the coupling between fluctuating brain network organization and heartbeat dynamics. 波动的大脑网络组织与心跳动态之间的耦合测量。
IF 3.6 3区 医学 Q2 NEUROSCIENCES Pub Date : 2024-07-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00369
Diego Candia-Rivera, Mario Chavez, Fabrizio De Vico Fallani

In recent years, there has been an increasing interest in studying brain-heart interactions. Methodological advancements have been proposed to investigate how the brain and the heart communicate, leading to new insights into some neural functions. However, most frameworks look at the interaction of only one brain region with heartbeat dynamics, overlooking that the brain has functional networks that change dynamically in response to internal and external demands. We propose a new framework for assessing the functional interplay between cortical networks and cardiac dynamics from noninvasive electrophysiological recordings. We focused on fluctuating network metrics obtained from connectivity matrices of EEG data. Specifically, we quantified the coupling between cardiac sympathetic-vagal activity and brain network metrics of clustering, efficiency, assortativity, and modularity. We validate our proposal using open-source datasets: one that involves emotion elicitation in healthy individuals, and another with resting-state data from patients with Parkinson's disease. Our results suggest that the connection between cortical network segregation and cardiac dynamics may offer valuable insights into the affective state of healthy participants, and alterations in the network physiology of Parkinson's disease. By considering multiple network properties, this framework may offer a more comprehensive understanding of brain-heart interactions. Our findings hold promise in the development of biomarkers for diagnostic and cognitive/motor function evaluation.

近年来,人们对研究大脑与心脏的相互作用越来越感兴趣。人们提出了一些先进的方法来研究大脑和心脏如何交流,从而对一些神经功能有了新的认识。然而,大多数框架只关注一个大脑区域与心跳动态的相互作用,忽略了大脑的功能网络会随着内部和外部需求的变化而动态变化。我们提出了一个新的框架,通过无创电生理记录评估大脑皮层网络与心脏动态之间的功能相互作用。我们重点研究了从脑电图数据的连接矩阵中获得的波动网络指标。具体来说,我们量化了心脏交感-迷走神经活动与大脑网络的聚类、效率、同类性和模块性指标之间的耦合。我们使用开源数据集验证了我们的建议:一个数据集涉及健康人的情绪诱发,另一个数据集涉及帕金森病患者的静息状态数据。我们的研究结果表明,皮层网络分离与心脏动力学之间的联系可以为了解健康参与者的情绪状态以及帕金森病网络生理学的改变提供有价值的见解。通过考虑多种网络特性,这一框架可以更全面地了解大脑与心脏之间的相互作用。我们的研究结果为开发用于诊断和认知/运动功能评估的生物标记物带来了希望。
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引用次数: 0
Altered topological structure of the brain white matter in maltreated children through topological data analysis. 通过拓扑数据分析研究受虐待儿童脑白质拓扑结构的改变。
IF 4.7 3区 医学 Q2 NEUROSCIENCES Pub Date : 2024-04-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_a_00355
Moo K Chung, Tahmineh Azizi, Jamie L Hanson, Andrew L Alexander, Seth D Pollak, Richard J Davidson

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

儿童时期的虐待可能会对大脑发育产生不利影响,进而影响成年后的行为、情绪和心理模式。在本研究中,我们提出了一个分析管道,用于模拟受虐待儿童和发育正常儿童大脑白质拓扑结构的改变。我们通过拓扑数据分析(TDA)来评估儿童大脑白质结构协方差网络的全局拓扑结构的改变。我们使用拓扑数据分析中的一种代数技术--持久同源性来分析由结构磁共振成像和扩散张量成像构建的大脑协方差网络中的拓扑特征。我们开发了一种基于 Wasserstein 距离的新型统计推断框架,用于评估观察到的拓扑差异的显著性。使用这些方法比较受虐待儿童和发育正常的对照组,我们发现虐待可能会增加白质结构的同质性,从而导致结构协方差的相关性增加;这反映在拓扑特征上。我们的研究结果有力地表明,TDA 可以成为大脑拓扑结构变化建模的重要框架。本研究使用的 MATLAB 代码和处理过的数据可在 https://github.com/laplcebeltrami/maltreated 上找到。
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引用次数: 0
Erratum: Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication. 勘误:从慢波睡眠中醒来时大脑网络的重新配置:对神经交流的干预和影响
IF 4.7 3区 医学 Q2 NEUROSCIENCES Pub Date : 2024-04-01 eCollection Date: 2024-01-01 DOI: 10.1162/netn_x_00359
Cassie J Hilditch, Kanika Bansal, Ravi Chachad, Lily R Wong, Nicholas G Bathurst, Nathan H Feick, Amanda Santamaria, Nita L Shattuck, Javier O Garcia, Erin E Flynn-Evans

[This corrects the article DOI: 10.1162/netn_a_00272.].

[This corrects the article DOI: 10.1162/netn_a_00272.].
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引用次数: 0
Modeling the cell-type-specific mesoscale murine connectome with anterograde tracing experiments. 通过逆行追踪实验建立细胞类型特异性中尺度鼠类连接体模型
IF 4.7 3区 医学 Q2 NEUROSCIENCES Pub Date : 2023-12-22 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00337
Samson Koelle, Dana Mastrovito, Jennifer D Whitesell, Karla E Hirokawa, Hongkui Zeng, Marina Meila, Julie A Harris, Stefan Mihalas

The Allen Mouse Brain Connectivity Atlas consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild-type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class-specific whole-brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how much we can fill in these gaps and estimate the cell-class-specific connectivity function given the simplifying assumptions that nearby voxels have smoothly varying projections, but that these projection tensors can change sharply depending on the region and class of the projecting cells. This paper describes the conversion of Cre-line tracer experiments into class-specific connectivity matrices representing the connection strengths between source and target structures. We introduce and validate a novel statistical model for creation of connectivity matrices. We extend the Nadaraya-Watson kernel learning method that we previously used to fill in spatial gaps to also fill in gaps in cell-class connectivity information. To do this, we construct a "cell-class space" based on class-specific averaged regionalized projections and combine smoothing in 3D space as well as in this abstract space to share information between similar neuron classes. Using this method, we construct a set of connectivity matrices using multiple levels of resolution at which discontinuities in connectivity are assumed. We show that the connectivities obtained from this model display expected cell-type- and structure-specific connectivities. We also show that the wild-type connectivity matrix can be factored using a sparse set of factors, and analyze the informativeness of this latent variable model.

艾伦小鼠脑连接图谱包括针对不同结构和类别的投射神经元的逆行追踪实验。除了在 C57BL/6 野生型小鼠中进行区域性顺行追踪外,还有很大一部分实验是使用转基因 Cre 线粒体进行的。这样就能获得特定细胞类别的全脑连接信息,而类别是由转基因品系定义的。然而,尽管实验数量庞大,但仍无法覆盖现有细胞类别存在的所有区域。在此,我们研究了我们能在多大程度上填补这些空白,并估算出细胞类别特异性连接功能,前提是简化假设,即附近体素具有平滑变化的投射,但这些投射张量会根据投射细胞的区域和类别发生急剧变化。本文介绍了如何将 Cre 线追踪实验转化为代表源结构和目标结构之间连接强度的特定类别连接矩阵。我们引入并验证了一种用于创建连接矩阵的新型统计模型。我们将之前用于填补空间空白的 Nadaraya-Watson 核学习方法扩展到了填补细胞类连接信息的空白。为此,我们根据特定类别的平均区域化投影构建了一个 "细胞类别空间",并在三维空间和这个抽象空间中结合平滑处理,以共享类似神经元类别之间的信息。利用这种方法,我们使用多级分辨率构建了一组连通性矩阵,其中假定了连通性的不连续性。我们发现,从该模型中获得的连通性显示了预期的细胞类型和结构特异性连通性。我们还表明,野生型连通性矩阵可以使用一组稀疏因子进行分解,并分析了这种潜在变量模型的信息量。
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引用次数: 0
Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks. 通过静息状态网络之间的高度隔离,可以预测疼痛感知中的自上而下威胁偏见
IF 4.7 3区 医学 Q2 NEUROSCIENCES Pub Date : 2023-12-22 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00328
Veronika Pak, Javeria Ali Hashmi

Top-down processes such as expectations have a strong influence on pain perception. Predicted threat of impending pain can affect perceived pain even more than the actual intensity of a noxious event. This type of threat bias in pain perception is associated with fear of pain and low pain tolerance, and hence the extent of bias varies between individuals. Large-scale patterns of functional brain connectivity are important for integrating expectations with sensory data. Greater integration is necessary for sensory integration; therefore, here we investigate the association between system segregation and top-down threat bias in healthy individuals. We show that top-down threat bias is predicted by less functional connectivity between resting-state networks. This effect was significant at a wide range of network thresholds and specifically in predefined parcellations of resting-state networks. Greater system segregation in brain networks also predicted higher anxiety and pain catastrophizing. These findings highlight the role of integration in brain networks in mediating threat bias in pain perception.

自上而下的过程,如期望,对疼痛感知有很强的影响。预测即将到来的疼痛威胁对感知疼痛的影响甚至比有害事件的实际强度更大。这种类型的疼痛感知中的威胁偏见与对疼痛的恐惧和较低的疼痛耐受性有关,因此偏见的程度因人而异。脑功能连接的大规模模式对于整合预期与感觉数据是重要的。因此,我们在健康个体中研究了系统隔离与自上而下威胁偏见之间的关系。我们表明,自上而下的威胁偏差是通过静息状态网络之间较少的功能连接来预测的。这种效应在广泛的网络阈值范围内是显著的,特别是在静息状态网络的预定义分组中。大脑网络中更大的系统隔离也预示着更高的焦虑和疼痛灾难化。这些发现强调了大脑网络整合在疼痛感知中介导威胁偏见的作用。
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引用次数: 0
Genome-wide association study of brain functional and structural networks 大脑功能和结构网络的全基因组关联研究
IF 4.7 3区 医学 Q2 NEUROSCIENCES Pub Date : 2023-12-14 DOI: 10.1162/netn_a_00356
Ruonan Cheng, Ruochen Yin, Xiaoyu Zhao, Wei Wang, Gaolang Gong, Chuansheng Chen, Gui Xue, Q. Dong, Chunhui Chen
Imaging genetics studies with large samples have identified many genes associated with brain functions and structures, but little is known about genes associated with brain functional and structural network properties. The current genome-wide association study (GWAS) examined graph theory measures of brain structural and functional networks with 497 healthy Chinese participants (17-28 years). Four genes (TGFB3, LGI1, TSPAN18 and FAM155A) were identified significantly associated with functional network global efficiency, two (NLRP6 and ICE2) with structural network global efficiency. Meta-analysis of structural and functional brain network property confirmed the 4 functional related genes and revealed two more (RBFOX1 and WWOX). They were reported significantly associated with regional brain structural or functional measurements in the UK Biobank project; and showed differential gene expression level between low and high structure-function coupling regions according to Allen Human Brain Atlas gene expression data. Taken together, our results suggest that brain structural and functional networks had shared and unique genetic bases, consistent with the notion of many-to-many structure-function coupling of the brain.
大样本成像遗传学研究发现了许多与大脑功能和结构相关的基因,但对与大脑功能和结构网络特性相关的基因却知之甚少。目前的全基因组关联研究(GWAS)对 497 名健康的中国参与者(17-28 岁)进行了大脑结构和功能网络的图论测量。研究发现四个基因(TGFB3、LGI1、TSPAN18 和 FAM155A)与功能网络的全局效率显著相关,两个基因(NLRP6 和 ICE2)与结构网络的全局效率显著相关。对大脑结构和功能网络特性的元分析证实了 4 个功能相关基因,并发现了另外两个基因(RBFOX1 和 WWOX)。据报道,在英国生物库项目中,这两个基因与区域大脑结构或功能测量结果明显相关;根据艾伦人类大脑图谱基因表达数据,低结构-功能耦合区域和高结构-功能耦合区域的基因表达水平存在差异。综上所述,我们的研究结果表明,大脑结构和功能网络具有共享和独特的遗传基础,这与大脑多对多结构-功能耦合的概念是一致的。
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引用次数: 0
Similarity in evoked responses does not imply similarity in macroscopic network states 诱发反应的相似性并不意味着宏观网络状态的相似性
IF 4.7 3区 医学 Q2 NEUROSCIENCES Pub Date : 2023-12-14 DOI: 10.1162/netn_a_00354
J. Rasero, Richard F. Betzel, Amy Isabella Sentis, Thomas E. Kraynak, P. Gianaros, Timothy D. Verstynen
It is commonplace in neuroscience to assume that if two tasks activate the same brain areas in the same way, then they are recruiting the same underlying networks. Yet computational theory has shown that the same pattern of activity can emerge from many different underlying network representations. Here we evaluated whether similarity in activation necessarily implies similarity in network architecture by comparing region-wise activation patterns and functional correlation profiles from a large sample of healthy subjects (N=242) that performed two executive control tasks known to recruit nearly identical brain areas, the color-word Stroop task and the Multi-Source Interference Task (MSIT). Using a measure of instantaneous functional correlations, based on edge time series, we estimated the task-related networks that differed between incongruent and congruent conditions. We found that the two tasks were much more different in their network profiles than in their evoked activity patterns at different analytical levels, as well as for a wide range of methodological pipelines. Our results reject the notion that having the same activation patterns means two tasks engage the same underlying representations, suggesting that task representations should be independently evaluated at both node and edge (connectivity) levels.
神经科学界通常认为,如果两项任务以相同的方式激活了相同的脑区,那么它们招募的是相同的底层网络。然而,计算理论表明,相同的活动模式可能来自许多不同的底层网络表征。在这里,我们通过比较大样本健康受试者(N=242)在执行两项已知会招募几乎相同脑区的执行控制任务(颜色词 Stroop 任务和多源干扰任务 (MSIT))时的区域激活模式和功能相关曲线,来评估激活的相似性是否必然意味着网络结构的相似性。利用基于边缘时间序列的瞬时功能相关性测量,我们估算出了在不一致和一致条件下存在差异的任务相关网络。我们发现,在不同的分析水平和各种方法管道下,这两种任务在网络概况上的差异远远大于其诱发活动模式的差异。我们的研究结果否定了激活模式相同意味着两个任务使用相同的基本表征的观点,这表明任务表征应在节点和边缘(连通性)层面进行独立评估。
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引用次数: 0
Weak coupling of neurons enables very high-frequency and ultra-fast oscillations through the interplay of synchronized phase-shifts 通过同步相移的相互作用,神经元的弱耦合实现了极高频率和超快速振荡
IF 4.7 3区 医学 Q2 NEUROSCIENCES Pub Date : 2023-12-05 DOI: 10.1162/netn_a_00351
Lenka Přibylová, Jan Ševčík, V. Eclerová, Petr Klimeš, M. Brázdil, Hil Meijer
Recently, in the past decade, high-frequency oscillations (HFOs), very high-frequency oscillations (VHFOs), and ultra-fast oscillations (UFOs) were reported in epileptic patients with drug-resistant epilepsy. However, to this day, the physiological origin of these events has yet to be understood. Our study establishes a mathematical framework based on bifurcation theory for investigating the occurrence of VHFOs and UFOs in depth EEG signals of patients with focal epilepsy, focusing on the potential role of reduced connection strength between neurons in an epileptic focus. We demonstrate that synchronization of a weakly coupled network can generate very and ultra high-frequency signals detectable by nearby microelectrodes. In particular, we show that a bistability region enables the persistence of phase-shift synchronized clusters of neurons. This phenomenon is observed for different hippocampal neuron models, including Morris-Lecar, Destexhe-Paré, and an interneuron model. The mechanism seems to be robust for small coupling, and it also persists with random noise affecting the external current. Our findings suggest that weakened neuronal connections could contribute to the production of oscillations with frequencies above 1000Hz, which could advance our understanding of epilepsy pathology and potentially improve treatment strategies. However, further exploration of various coupling types and complex network models is needed.
近十年来,在耐药癫痫患者中报道了高频振荡(HFOs)、甚高频振荡(VHFOs)和超快振荡(UFOs)。然而,直到今天,这些事件的生理起源还没有被理解。本研究建立了一个基于分岔理论的数学框架,用于研究局灶性癫痫患者深度脑电图信号中VHFOs和UFOs的发生,重点研究癫痫灶中神经元连接强度降低的潜在作用。我们证明了弱耦合网络的同步可以产生被附近的微电极检测到的甚高频和超高频信号。特别地,我们证明了双稳区使相移同步神经元簇的持久性。这一现象在不同的海马神经元模型中被观察到,包括Morris-Lecar、destexhe - par和中间神经元模型。该机制似乎对小耦合具有鲁棒性,并且在随机噪声影响外部电流的情况下仍然存在。我们的研究结果表明,减弱的神经元连接可能有助于产生频率高于1000Hz的振荡,这可以促进我们对癫痫病理的理解,并有可能改善治疗策略。然而,需要对各种耦合类型和复杂的网络模型进行进一步的探索。
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引用次数: 0
Reconstructing brain functional networks through identifiability and Deep Learning 通过可识别性和深度学习重建大脑功能网络
IF 4.7 3区 医学 Q2 NEUROSCIENCES Pub Date : 2023-12-05 DOI: 10.1162/netn_a_00353
Massimiliano Zanin, Tuba Aktürk, E. Yıldırım, D. Yerlikaya, G. Yener, B. Güntekin
We propose a novel approach for the reconstruction of functional networks representing brain dynamics based on the idea that the co-participation of two brain regions in a common cognitive task should result in a drop in their identifiability, or in the uniqueness of their dynamics. This identifiability is estimated through the score obtained by Deep Learning models in supervised classification tasks; and therefore requires no a priori assumptions about the nature of such co-participation. The method is tested on EEG recordings obtained from Alzheimer‘s and Parkinson‘s Disease patients, and matched healthy volunteers, for eyes-open and eyes-closed resting state conditions; and the resulting functional networks are analysed through standard topological metrics. Both groups of patients are characterised by a reduction in the identifiability of the corresponding EEG signals, and by differences in the patterns that support such identifiability. Resulting functional networks are similar, but not identical to those reconstructed by using a correlation metric. Differences between control subjects and patients can be observed in network metrics like the clustering coefficient and the assortativity, in different frequency bands. Differences are also observed between eyes-open and closed conditions, especially for Parkinson‘s Disease patients.
我们提出了一种新的方法来重建代表大脑动力学的功能网络,该方法基于两个大脑区域在共同认知任务中的共同参与应该导致其可识别性下降或其动力学的独特性。这种可识别性是通过深度学习模型在监督分类任务中获得的分数来估计的;因此,不需要对这种共同参与的性质进行先验假设。该方法在阿尔茨海默氏症和帕金森病患者以及匹配的健康志愿者的脑电图记录上进行了测试,测试了眼睛睁开和闭上的静息状态;并通过标准拓扑度量对得到的功能网络进行了分析。两组患者的特点是相应脑电图信号的可识别性降低,以及支持这种可识别性的模式的差异。得到的功能网络与使用关联度量重建的网络相似,但不完全相同。在不同频带的聚类系数和分类度等网络指标上,可以观察到对照组和患者之间的差异。在睁眼和闭眼的情况下也观察到差异,特别是对于帕金森病患者。
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引用次数: 0
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Network Neuroscience
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