Network neuroscience (Cambridge, Mass.)最新文献_第2页

Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times. 使用微分协方差的fMRI功能连通性预测结构连通性和行为反应时间。

IF 4.7

Network neuroscience (Cambridge, Mass.) Pub Date : 2022-06-01 DOI: 10.1162/netn_a_00239

Yusi Chen, Qasim Bukhari, Tiger W Lin, Terrence J Sejnowski

{"title":"Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times.","authors":"Yusi Chen, Qasim Bukhari, Tiger W Lin, Terrence J Sejnowski","doi":"10.1162/netn_a_00239","DOIUrl":"https://doi.org/10.1162/netn_a_00239","url":null,"abstract":"Recordings from resting-state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of \"ground truth\" has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. When we applied dCov to rs-fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion magnetic resonance imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose dCov-FCs were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration significantly correlated with behavior.","PeriodicalId":520719,"journal":{"name":"Network neuroscience (Cambridge, Mass.)","volume":" ","pages":"614-633"},"PeriodicalIF":4.7,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9207998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40223585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Cooperative contributions of structural and functional connectivity to successful memory in aging. 结构和功能连接对衰老成功记忆的合作贡献。

IF 3.1

Network neuroscience (Cambridge, Mass.) Pub Date : 2018-12-01 eCollection Date: 2019-01-01 DOI: 10.1162/netn_a_00064

Simon W Davis, Amanda Szymanski, Homa Boms, Thomas Fink, Roberto Cabeza

{"title":"Cooperative contributions of structural and functional connectivity to successful memory in aging.","authors":"Simon W Davis, Amanda Szymanski, Homa Boms, Thomas Fink, Roberto Cabeza","doi":"10.1162/netn_a_00064","DOIUrl":"10.1162/netn_a_00064","url":null,"abstract":"Understanding the precise relation between functional connectivity and structural (white matter) connectivity and how these relationships account for cognitive changes in older adults are major challenges for neuroscience. We investigate these issues using an approach in which structural equation modeling (SEM) is employed to integrate functional and structural connectivity data from younger and older adults (n = 62), analyzed with a common framework based on regions connected by canonical tract groups (CTGs). CTGs (e.g., uncinate fasciculus) serve as a common currency between functional and structural connectivity matrices, and ensure equivalent sparsity in connectome information. We used this approach to investigate the neural mechanisms supporting memory for items and memory for associations, and how they are affected by healthy aging. We found that different structural and functional CTGs made independent contributions to source and item memory performance, suggesting that both forms of connectivity underlie age-related differences in specific forms of memory. Furthermore, the relationship between functional and structural connectivity was best explained by a general relationship between latent constructs-a relationship absent in any specific CTG group. These results provide insights into the relationship between structural and functional connectivity patterns, and elucidate their relative contribution to age-related differences in source memory performance.","PeriodicalId":520719,"journal":{"name":"Network neuroscience (Cambridge, Mass.)","volume":" ","pages":"173-194"},"PeriodicalIF":3.1,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6516741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37281467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dissociating tinnitus patients from healthy controls using resting-state cyclicity analysis and clustering. 利用静息状态循环分析和聚类分析将耳鸣患者与健康对照分离。

IF 4.7

Network neuroscience (Cambridge, Mass.) Pub Date : 2018-10-01 eCollection Date: 2019-01-01 DOI: 10.1162/netn_a_00053

Benjamin J Zimmerman, Ivan Abraham, Sara A Schmidt, Yuliy Baryshnikov, Fatima T Husain

{"title":"Dissociating tinnitus patients from healthy controls using resting-state cyclicity analysis and clustering.","authors":"Benjamin J Zimmerman, Ivan Abraham, Sara A Schmidt, Yuliy Baryshnikov, Fatima T Husain","doi":"10.1162/netn_a_00053","DOIUrl":"https://doi.org/10.1162/netn_a_00053","url":null,"abstract":"Chronic tinnitus is a common and sometimes debilitating condition that lacks scientific consensus on physiological models of how the condition arises as well as any known cure. In this study, we applied a novel cyclicity analysis, which studies patterns of leader-follower relationships between two signals, to resting-state functional magnetic resonance imaging (rs-fMRI) data of brain regions acquired from subjects with and without tinnitus. Using the output from the cyclicity analysis, we were able to differentiate between these two groups with 58-67% accuracy by using a partial least squares discriminant analysis. Stability testing yielded a 70% classification accuracy for identifying individual subjects' data across sessions 1 week apart. Additional analysis revealed that the pairs of brain regions that contributed most to the dissociation between tinnitus and controls were those connected to the amygdala. In the controls, there were consistent temporal patterns across frontal, parietal, and limbic regions and amygdalar activity, whereas in tinnitus subjects, this pattern was much more variable. Our findings demonstrate a proof-of-principle for the use of cyclicity analysis of rs-fMRI data to better understand functional brain connectivity and to use it as a tool for the differentiation of patients and controls who may differ on specific traits.","PeriodicalId":520719,"journal":{"name":"Network neuroscience (Cambridge, Mass.)","volume":" ","pages":"67-89"},"PeriodicalIF":4.7,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/netn_a_00053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36988517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Global connectivity of the fronto-parietal cognitive control network is related to depression symptoms in the general population. 额顶叶认知控制网络的全球连通性与一般人群的抑郁症状有关。

IF 4.7

Network neuroscience (Cambridge, Mass.) Pub Date : 2018-10-01 eCollection Date: 2019-01-01 DOI: 10.1162/netn_a_00056

Douglas H Schultz, Takuya Ito, Levi I Solomyak, Richard H Chen, Ravi D Mill, Alan Anticevic, Michael W Cole

{"title":"Global connectivity of the fronto-parietal cognitive control network is related to depression symptoms in the general population.","authors":"Douglas H Schultz, Takuya Ito, Levi I Solomyak, Richard H Chen, Ravi D Mill, Alan Anticevic, Michael W Cole","doi":"10.1162/netn_a_00056","DOIUrl":"https://doi.org/10.1162/netn_a_00056","url":null,"abstract":"We all vary in our mental health, even among people not meeting diagnostic criteria for mental illness. Understanding this individual variability may reveal factors driving the risk for mental illness, as well as factors driving subclinical problems that still adversely affect quality of life. To better understand the large-scale brain network mechanisms underlying this variability, we examined the relationship between mental health symptoms and resting-state functional connectivity patterns in cognitive control systems. One such system is the fronto-parietal cognitive control network (FPN). Changes in FPN connectivity may impact mental health by disrupting the ability to regulate symptoms in a goal-directed manner. Here we test the hypothesis that FPN dysconnectivity relates to mental health symptoms even among individuals who do not meet formal diagnostic criteria but may exhibit meaningful symptom variation. We found that depression symptoms severity negatively correlated with between-network global connectivity (BGC) of the FPN. This suggests that decreased connectivity between the FPN and the rest of the brain is related to increased depression symptoms in the general population. These findings complement previous clinical studies to support the hypothesis that global FPN connectivity contributes to the regulation of mental health symptoms across both health and disease.","PeriodicalId":520719,"journal":{"name":"Network neuroscience (Cambridge, Mass.)","volume":" ","pages":"107-123"},"PeriodicalIF":4.7,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/netn_a_00056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36988519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 45