Network Neuroscience最新文献

{"title":"Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network.","authors":"Giampiero Bardella, Valentina Giuffrida, Franco Giarrocco, Emiliano Brunamonti, Pierpaolo Pani, Stefano Ferraina","doi":"10.1162/netn_a_00365","DOIUrl":"10.1162/netn_a_00365","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 2","pages":"597-622"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477717","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}

{"title":"Measures of the coupling between fluctuating brain network organization and heartbeat dynamics.","authors":"Diego Candia-Rivera, Mario Chavez, Fabrizio De Vico Fallani","doi":"10.1162/netn_a_00369","DOIUrl":"10.1162/netn_a_00369","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 2","pages":"557-575"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477688","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}

{"title":"Altered topological structure of the brain white matter in maltreated children through topological data analysis.","authors":"Moo K Chung, Tahmineh Azizi, Jamie L Hanson, Andrew L Alexander, Seth D Pollak, Richard J Davidson","doi":"10.1162/netn_a_00355","DOIUrl":"10.1162/netn_a_00355","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 1","pages":"355-376"},"PeriodicalIF":4.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11073548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140853282","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}

{"title":"Erratum: Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication.","authors":"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","doi":"10.1162/netn_x_00359","DOIUrl":"https://doi.org/10.1162/netn_x_00359","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1162/netn_a_00272.].</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 1","pages":"i-ii"},"PeriodicalIF":4.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10974861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337332","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}

{"title":"Modeling the cell-type-specific mesoscale murine connectome with anterograde tracing experiments.","authors":"Samson Koelle, Dana Mastrovito, Jennifer D Whitesell, Karla E Hirokawa, Hongkui Zeng, Marina Meila, Julie A Harris, Stefan Mihalas","doi":"10.1162/netn_a_00337","DOIUrl":"10.1162/netn_a_00337","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 4","pages":"1497-1512"},"PeriodicalIF":4.7,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10745083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139032788","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}

{"title":"Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks.","authors":"Veronika Pak, Javeria Ali Hashmi","doi":"10.1162/netn_a_00328","DOIUrl":"10.1162/netn_a_00328","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"1 1","pages":"1248-1265"},"PeriodicalIF":4.7,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41881498","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}

{"title":"Genome-wide association study of brain functional and structural networks","authors":"Ruonan Cheng, Ruochen Yin, Xiaoyu Zhao, Wei Wang, Gaolang Gong, Chuansheng Chen, Gui Xue, Q. Dong, Chunhui Chen","doi":"10.1162/netn_a_00356","DOIUrl":"https://doi.org/10.1162/netn_a_00356","url":null,"abstract":"\u0000 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.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"20 17","pages":""},"PeriodicalIF":4.7,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138974933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Similarity in evoked responses does not imply similarity in macroscopic network states","authors":"J. Rasero, Richard F. Betzel, Amy Isabella Sentis, Thomas E. Kraynak, P. Gianaros, Timothy D. Verstynen","doi":"10.1162/netn_a_00354","DOIUrl":"https://doi.org/10.1162/netn_a_00354","url":null,"abstract":"\u0000 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.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"13 2","pages":""},"PeriodicalIF":4.7,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138975010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weak coupling of neurons enables very high-frequency and ultra-fast oscillations through the interplay of synchronized phase-shifts","authors":"Lenka Přibylová, Jan Ševčík, V. Eclerová, Petr Klimeš, M. Brázdil, Hil Meijer","doi":"10.1162/netn_a_00351","DOIUrl":"https://doi.org/10.1162/netn_a_00351","url":null,"abstract":"\u0000 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.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"124 18","pages":""},"PeriodicalIF":4.7,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstructing brain functional networks through identifiability and Deep Learning","authors":"Massimiliano Zanin, Tuba Aktürk, E. Yıldırım, D. Yerlikaya, G. Yener, B. Güntekin","doi":"10.1162/netn_a_00353","DOIUrl":"https://doi.org/10.1162/netn_a_00353","url":null,"abstract":"\u0000 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.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"85 22","pages":""},"PeriodicalIF":4.7,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}