Brain connectivity最新文献

{"title":"<i>Call for Papers for Special Issue on Brain-Computer Interfaces</i>.","authors":"Jianjun Meng, Christoph Guger","doi":"10.1089/brain.2024.14072.cfp","DOIUrl":"https://doi.org/10.1089/brain.2024.14072.cfp","url":null,"abstract":"","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142131859","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":"Acute Exercise Improves Large-Scale Brain Network Segregation in Healthy Older Adults.","authors":"Yash Kommula, Daniel D Callow, Jeremy J Purcell, J Carson Smith","doi":"10.1089/brain.2024.0003","DOIUrl":"10.1089/brain.2024.0003","url":null,"abstract":"<p><p><b><i>Introduction</i></b>: Age-related cognitive decline and mental health problems are accompanied by changes in resting-state functional connectivity (rsFC) indices, such as reduced brain network segregation. Meanwhile, exercise can improve cognition, mood, and neural network function in older adults. Studies on effects of exercise on rsFC outcomes in older adults have chiefly focused on changes after exercise training and suggest improved network segregation through enhanced within-network connectivity. However, effects of acute exercise on rsFC measures of neural network integrity in older adults, which presumably underlie changes observed after exercise training, have received less attention. In this study, we hypothesized that acute exercise in older adults would improve functional segregation of major cognition and affect-related brain networks. <b><i>Methods:</i></b> To test this, we analyzed rsFC data from 37 healthy and physically active older adults after they completed 30 min of moderate-to-vigorous intensity cycling and after they completed a seated rest control condition. Conditions were performed in a counterbalanced order across separate days in a within-subject crossover design. We considered large-scale brain networks associated with cognition and affect, including the frontoparietal network (FPN), salience network (SAL), default mode network (DMN), and affect-reward network (ARN). <b><i>Results:</i></b> We observed that after acute exercise, there was greater segregation between SAL and DMN, as well as greater segregation between SAL and ARN. <b><i>Conclusion:</i></b> These findings indicate that acute exercise in active older adults alters rsFC measures in key cognition and affect-related networks in a manner that opposes age-related dedifferentiation of neural networks that may be detrimental to cognition and mental health.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141417731","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":"Differences Between Resting-State fMRI BOLD Variability and Default Mode Network Connectivity in Healthy Older and Younger Adults.","authors":"Vanessa Scarapicchia, Heather Kwan, Alexis Czippel, Jodie R Gawryluk","doi":"10.1089/brain.2023.0078","DOIUrl":"10.1089/brain.2023.0078","url":null,"abstract":"<p><p><b><i>Background:</i></b> Resting-state fMRI analyses have been used to examine functional connectivity in the aging brain. Recently, fluctuations in the fMRI BOLD signal have been used as a potential marker of integrity in neural systems. Despite its increasing popularity, the results of BOLD variability analyses and traditional seed-based functional connectivity analyses have rarely been compared. The current study examined fMRI BOLD signal variability and default mode network seed-based analyses in healthy older and younger adults to better understand the unique contributions of these methodological approaches. <b><i>Methods:</i></b> Thirty-four healthy participants were separated into a younger adult group (age 25-35, <i>n</i> = 17) and an older adult group (age 65+, <i>n</i> = 17). For each participant, a map of the standard deviation of the BOLD signal (SDBOLD) was derived. Group comparisons examined differences in resting-state SDBOLD in younger versus older adults. Seed-based analyses were used to examine differences between younger and older adults in the default mode network. <b><i>Results:</i></b> Between-group comparisons revealed significantly greater BOLD variability in widespread brain regions in older relative to younger adults. There were no significant differences between younger and older adults in the default mode network connectivity. <b><i>Conclusion:</i></b> The current findings align with an increasing number of studies reporting greater BOLD variability in older relative to younger adults. The current results also suggest that the traditional resting state examination methods may not detect nuanced age-related differences. Further large-scale studies in an adult lifespan sample are needed to better understand the functional relevance of the BOLD variability in normative aging.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141544497","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":"Resting-State Network Analysis Reveals Altered Functional Brain Connectivity in Essential Tremor.","authors":"Sheng-Min Huang, Cheung-Ter Ong, Yu-Ching Huang, Nan-Hao Chen, Ting-Kai Leung, Chun-Ying Shen, Li-Wei Kuo","doi":"10.1089/brain.2024.0004","DOIUrl":"10.1089/brain.2024.0004","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Essential tremor (ET) comprises motor and non-motor-related features, whereas the current neuro-pathogenetic basis is still insufficient to explain the etiologies of ET. Although cerebellum-associated circuits have been discovered, the large-scale cerebral network connectivity in ET remains unclear. This study aimed to characterize the ET in terms of functional connectivity as well as network. We hypothesized that the resting-state network (RSN) within cerebrum could be altered in patients with ET. <b><i>Methods:</i></b> Resting-state functional magnetic resonance imaging (fMRI) was used to evaluate the inter- and intra-network connectivity as well as the functional activity in ET and normal control. Correlation analysis was performed to explore the relationship between RSN metrics and tremor features. <b><i>Results:</i></b> Comparison of inter-network connectivity indicated a decreased connectivity between default mode network and ventral attention network in the ET group (<i>p</i> < 0.05). Differences in functional activity (assessed by amplitude of low-frequency fluctuation, ALFF) were found in several brain regions participating in various RSNs (<i>p</i> < 0.05). The ET group generally has higher degree centrality over normal control. Correlation analysis has revealed that tremor features are associated with inter-network connectivity (|r| = 0.135-0.506), ALFF (|r| = 0.313-0.766), and degree centrality (|r| = 0.523-0.710). <b><i>Conclusion:</i></b> Alterations in the cerebral network of ET were detected by using resting-state fMRI, demonstrating a potentially useful approach to explore the cerebral alterations in ET.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315973","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":"The Interest and Usefulness of Resting State fMRI in Brain Connectivity Research.","authors":"Roxane Hoyer, Steven Laureys","doi":"10.1089/brain.2024.0052","DOIUrl":"10.1089/brain.2024.0052","url":null,"abstract":"","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141791949","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":"Rosalind Franklin Society Proudly Announces the 2023 Award Recipient for <i>Brain Connectivity</i>.","authors":"Liara Rizzi","doi":"10.1089/brain.2024.59245.rfs2023","DOIUrl":"https://doi.org/10.1089/brain.2024.59245.rfs2023","url":null,"abstract":"","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142131860","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":"Editorial Commentary: Bridging Neuroscience and Clinical Innovation.","authors":"Rajanikant Panda, Edward Schiettecatte, Steven Laureys","doi":"10.1089/brain.2024.0065","DOIUrl":"10.1089/brain.2024.0065","url":null,"abstract":"","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142003611","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":"Comparison of Resting-State Functional Connectivity Between Generalized Anxiety Disorder and Social Anxiety Disorder: Differences in the Nucleus Accumbens and Thalamus Network.","authors":"Tomomi Nagano, Kohei Kurita, Tokiko Yoshida, Koji Matsumoto, Junko Ota, Ritu Bhusal Chhatkuli, Eiji Shimizu, Yoshiyuki Hirano","doi":"10.1089/brain.2024.0034","DOIUrl":"10.1089/brain.2024.0034","url":null,"abstract":"<p><p><b><i>Background:</i></b> Generalized anxiety disorder (GAD) and social anxiety disorder (SAD) are distinguished by whether anxiety is limited to social situations. However, reports on the differences in brain functional networks between GAD and SAD are few. Our objective is to understand the pathogenesis of GAD and SAD by examining the differences in resting brain function between patients with GAD and SAD and healthy controls (HCs). <b><i>Methods:</i></b> This study included 21 patients with SAD, 17 patients with GAD, and 30 HCs. Participants underwent psychological assessments and resting-state functional magnetic resonance imaging. Whole-brain analyses were performed to compare resting-state functional connectivity (rsFC) among the groups. In addition, logistic regression analysis was conducted on the rsFC to identify significant differences between GAD and SAD. <b><i>Results:</i></b> Patients with SAD and GAD had significantly higher rsFC between the bilateral postcentral gyri and bilateral amygdalae/thalami than HCs. Compared with patients with SAD, those with GAD had significantly higher rsFC between the right nucleus accumbens and bilateral thalami and between the left nucleus accumbens and right thalamus. rsFC between the left nucleus accumbens and right thalamus positively correlated with state anxiety in patients with SAD and GAD, respectively. In addition, logistic regression analysis revealed that the right nucleus accumbens and the right thalamus connectivity could distinguish SAD from GAD. <b><i>Conclusions:</i></b> GAD and SAD were distinguished by the right nucleus accumbens and the right thalamus connectivity. Our findings offer insights into the disease-specific neural basis of SAD and GAD. Clinical Trial Registration Number: M10545. Impact Statement This study is the first to identify a resting state functional connectivity that distinguishes social anxiety disorder (SAD) from generalized anxiety disorder (GAD) and to clarify a common connectivity in both disorders. We found that the connectivity between the right nucleus accumbens and the right thalamus differentiated SAD from GAD. Furthermore, these rsFC differences suggest an underlying basis for fear overgeneralization. Our findings shed light on the pathophysiology of these conditions and could be used as a basis for further studies to improve outcomes for such patients.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141970619","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":"Atlas-Based Labeling of Resting-State fMRI.","authors":"Hrishikesh Kambli, Alberto Santamaria-Pang, Ivan Tarapov, Elham Beheshtian, Licia P Luna, Haris Sair, Craig Jones","doi":"10.1089/brain.2023.0080","DOIUrl":"10.1089/brain.2023.0080","url":null,"abstract":"<p><p><b><i>Background:</i></b> Functional magnetic resonance imaging (fMRI) has the potential to provide noninvasive functional mapping of the brain with high spatial and temporal resolution. However, fMRI independent components (ICs) must be manually inspected, selected, and interpreted, requiring time and expertise. We propose a novel approach for automated labeling of fMRI ICs by establishing their characteristic spatio-functional relationship. <b><i>Methods:</i></b> The approach identifies 9 resting-state networks and 45 ICs and generates a functional activation feature map that quantifies the spatial distribution, relative to an anatomical labeled atlas, of the z-scores of each IC across a cohort of 176 subjects. The cosine-similarity metric was used to classify unlabeled ICs based on the similarity to the spatial distribution of activation with the pregenerated feature map. The approach was tested on three fMRI datasets from the 1000 functional connectome projects, consisting of 280 subjects, that were not included in feature map generation. <b><i>Results:</i></b> The results demonstrate the effectiveness of the approach in classifying ICs based on their spatial features with an accuracy of better than 95%. <b><i>Conclusions:</i></b> The approach significantly reduces expert time and computation time required for labeling ICs, while improving reliability and accuracy. The spatio-functional relationship also provides an explainable relationship between the functional activation and the anatomically defined regions.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141178938","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":"Disrupted Dynamic Network Attribution Associated with Gait Disorder in Cerebral Small Vessel Disease.","authors":"Xia Zhou, Chaojuan Huang, Zhiwei Li, Mingxu Li, Wenwen Yin, Mengmeng Ren, Yating Tang, Jiabin Yin, Wenhui Zheng, Chao Zhang, Xueying Li, Ke Wan, Xiaoqun Zhu, Zhongwu Sun","doi":"10.1089/brain.2023.0092","DOIUrl":"10.1089/brain.2023.0092","url":null,"abstract":"<p><p><b><i>Background and Aims:</i></b> Previous research has focused on static functional connectivity in gait disorders caused by cerebral small vessel disease (CSVD), neglecting dynamic functional connections and network attribution. This study aims to investigate alterations in dynamic functional network connectivity (dFNC) and topological organization variance in CSVD-related gait disorders. <b><i>Methods:</i></b> A total of 85 patients with CSVD, including 41 patients with CSVD and gait disorders (CSVD-GD), 44 patients with CSVD and non-gait disorders (CSVD-NGD), and 32 healthy controls (HC), were enrolled in this study. Five networks composed of 10 independent components were selected using independent component analysis. Sliding time window and <i>k</i>-means clustering methods were used for dFNC analysis. The relationship between alterations in the dFNC properties and gait metrics was further assessed. <b><i>Results:</i></b> Three reproducible dFNC states were determined (State 1: sparsely connected, State 2: intermediate pattern, and State 3: strongly connected). CSVD-GD showed significantly higher fractional windows (FW) and mean dwell time (MDT) in State 1 compared with CSVD-NGD. Higher local efficiency variance was observed in the CSVD-GD group compared with HC, but no differences were found in the global efficiency comparison. Both the FW and MDT in State 1 were negatively correlated with gait speed and step length, and the relationship between MDT of State 1 and gait speed was mediated by overall cognition, information processing speed, and executive function. <b><i>Conclusions:</i></b> Our study uncovered abnormal dFNC indicators and variations in topological organization in CSVD-GD, offering potential early prediction indicators and freshening insights into the underlying pathogenesis of gait disturbances in CSVD.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315971","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}