For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
12
views
37
references
 Top references
cited by
9
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      193
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Functional Cortical Hubs in the Eyes-Closed Resting Human Brain from an Electrophysiological Perspective Using Magnetoencephalography

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          It is not clear whether specific brain areas act as hubs in the eyes-closed (EC) resting state, which is an unconstrained state free from any passive or active tasks. Here, we used electrophysiological magnetoencephalography (MEG) signals to study functional cortical hubs in 88 participants. We identified several multispectral cortical hubs. Although cortical hubs vary slightly with different applied measures and frequency bands, the most consistent hubs were observed in the medial and posterior cingulate cortex, the left dorsolateral superior frontal cortex, and the left pole of the middle temporal cortex. Hubs were characterized as connector nodes integrating EC resting state functional networks. Hubs in the gamma band were more likely to include midline structures. Our results confirm the existence of multispectral cortical cores in EC resting state functional networks based on MEG and imply the existence of optimized functional networks in the resting brain.

          Related collections

          Most cited references37

          • Record: found
          • Abstract: found
          • Article: not found

          Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization.

          Koene R. A. Van Dijk, Trey Hedden, Archana Venkataraman … (2010)
          Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.
          Article 0 comments Cited 595 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Efficient Behavior of Small-World Networks

            Massimo Marchiori, Vito Latora (2001)
            We introduce the concept of efficiency of a network, measuring how efficiently it exchanges information. By using this simple measure small-world networks are seen as systems that are both globally and locally efficient. This allows to give a clear physical meaning to the concept of small-world, and also to perform a precise quantitative a nalysis of both weighted and unweighted networks. We study neural networks and man-made communication and transportation systems and we show that the underlying general principle of their construction is in fact a small-world principle of high efficiency.
            Article 0 comments Cited 509 times – based on 0 reviews
            Preprint
                 Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Functional cartography of complex metabolic networks

              , (2005)
              High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that one can (i) find functional modules in complex networks, and (ii) classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a ``cartographic representation'' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with more potential for immediate applicability. We use our method to analyze the metabolic networks of twelve organisms from three different super-kingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that low-degree metabolites that connect different modules are more conserved than hubs whose links are mostly within a single module.
              Article 0 comments Cited 460 times – based on 0 reviews
              Preprint
                   Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Yong He: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2013
                Publication date (Electronic): 9 July 2013
                Volume: 8
                Issue: 7
                Electronic Location Identifier: e68192
                Affiliations
                [1 ]MEG center, Department of Neurosurgery, Seoul National University Hospital, Seoul, Korea
                [2 ]Department of Neurosurgery, Seoul National University Hospital, Seoul, Korea
                [3 ]Neuroscience Research Institute, Seoul National University Medical Research Center, Seoul, Korea
                [4 ]Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Korea
                [5 ]Seoul National University College of Medicine, Seoul, Korea
                Beijing Normal University, Beijing, China
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SHJ CKC. Performed the experiments: SHJ. Analyzed the data: SHJ WJ JS JK. Contributed reagents/materials/analysis tools: SHJ JS. Wrote the paper: SHJ CKC.

                Article
                Publisher ID: PONE-D-13-11176
                DOI: 10.1371/journal.pone.0068192
                PMC ID: 3706585
                PubMed ID: 23874535
                SO-VID: 30c102bc-d2b8-401d-891e-7894f7c2f637
                Copyright statement: Copyright @ 2013
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 17 March 2013
                Date accepted : 27 May 2013
                Page count
                Pages: 13
                Funding
                This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (No. 2012R1A13007555). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Biology
                Subject: Anatomy and Physiology
                Subject: Neurological System
                Subject: Central Nervous System
                Subject: Neuroanatomy
                Subject: Electrophysiology
                Subject: Neuroscience
                Subject: Neuroanatomy
                Subject: Connectomics
                Subject: Neurophysiology
                Subject: Central Nervous System
                Subject: Neural Networks
                Subject: Neuroimaging
                Subject: Medicine
                Subject: Anatomy and Physiology
                Subject: Neurological System
                Subject: Central Nervous System
                Subject: Neuroanatomy
                Subject: Electrophysiology
                Subject: Neurology
                Subject: Neuroimaging

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

                Comments

                Comment on this article

                scite_

                Similar content193

                See all similar

                Cited by8

                See all cited by