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Node Accessibility in Cortical Networks During Motor Tasks

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An Erratum to this article was published on 02 April 2014

Abstract

Recent findings suggest that the preparation and execution of voluntary self-paced movements are accompanied by the coordination of the oscillatory activities of distributed brain regions. Here, we use electroencephalographic source imaging methods to estimate the cortical movement-related oscillatory activity during finger extension movements. Then, we apply network theory to investigate changes (expressed as differences from the baseline) in the connectivity structure of cortical networks related to the preparation and execution of the movement. We compute the topological accessibility of different cortical areas, measuring how well an area can be reached by the rest of the network. Analysis of cortical networks reveals specific agglomerates of cortical sources that become less accessible during the preparation and the execution of the finger movements. The observed changes neither could be explained by other measures based on geodesics or on multiple paths, nor by power changes in the cortical oscillations.

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References

  • Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3, e17.

    Article  PubMed Central  PubMed  Google Scholar 

  • Ansmann, G., & Lehnertz, K. (2011). Constrained randomization of weighted networks. Physical Review E, 84, 026103.

    Article  Google Scholar 

  • Astolfi, L., Cincotti, F., Mattia, D., de Vico Fallani, F., Lai, M., Baccala, L., Salinari, S., Ursino, M., Zavaglia, M., Babiloni, F. (2005). Comparison of different multivariate methods for the estimation of cortical connectivity: Simulations and applications to EEG data. In Conference proceedings of the IEEE engineering in medicine and biology society (pp. 4484–4487).

  • Bai, O., Mari, Z., Vorbach, S., Hallet, M. (2005). Asymmetric spatio-temporal patterns of event-related desynchronization preceding voluntary sequential finger movements: a high-resolution EEG study. Clinical Neurophysiology, 116, 1213–1221.

    Article  PubMed  Google Scholar 

  • Baillet, S., Mosher, J., Leahy, R. (2001). Electromagnetic brain mapping. IEEE Signal Processing Magazine, 18, 14–30.

    Article  Google Scholar 

  • Baker, K.S., Mattingley, J.B., Chambers, C.D., Cunnington, R. (2011). Attention and the readiness for action. Neuropsychologia, 49(12), 3303–3313.

    Article  PubMed  Google Scholar 

  • Bassett, D.S., Meyer-Lindenberg, A., Achard, S., Duke, T., Bullmore, E. (2006). Adaptive reconfiguration of fractal small-world human brain functional networks. Proceedings of the National Academy of Sciences of the United States of America, 103, 19518–19523.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57, 289–300.

    Google Scholar 

  • Benjamini, Y., & Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165–1188.

    Article  Google Scholar 

  • Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.-U. (2006). Complex networks: structure and dynamics. Physics Reports, 424, 175–308.

    Article  Google Scholar 

  • Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2, 113–120.

    Article  Google Scholar 

  • Borgatti, S.P. (2005). Centrality and network flow. Social Networks, 27, 55–71.

    Article  Google Scholar 

  • Brillinger, D.R. (2001). Time series: Data analysis and theory. Philadelphia: SIAM.

    Book  Google Scholar 

  • Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 1–13.

    Article  Google Scholar 

  • Bullmore, E.T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E., Bramme, M.J. (1999). Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Transactions on Medical Imaging, 18, 32–42.

    Article  CAS  PubMed  Google Scholar 

  • Cassidy, M., Mazzone, P., Oliviero, A., Insola, A., Tonali, P., Di Lazzaro, V., Brown, P. (2002). Movement-related changes in synchronization in the human basal ganglia. Brain, 125, 1235–1246.

    Article  PubMed  Google Scholar 

  • Cheyne, D., Bells, S., Ferrari, P., Gaetz, W., Bostan, A.C. (2008). Self-paced movements induce high-frequency gamma oscillations in primary motor cortex. Neuroimage, 42(1), 332–342.

    Article  PubMed  Google Scholar 

  • Chung, F.R.K. (1997). Spectral graph theory. Providence: American Mathematical Society.

    Google Scholar 

  • Cincotti, F., Mattia, D., Aloise, F., Bufalari, S., Astolfi, L., De Vico Fallani, F., Tocci, A., Bianchi, L., Marciani, M.G., Gao, S., Millan, J., Babiloni, F. (2008). High-resolution EEG techniques for brain-computer interface applications. Journal of Neuroscience Methods, 167, 31–42.

    Article  PubMed  Google Scholar 

  • Crofts, J.J., & Higham, D.J. (2009). A weighted communicability measure applied to complex networks. Journal of the Royal Society Interface, 6, 411–414.

    Article  PubMed Central  Google Scholar 

  • da Fontoura Costa, L., Rodrigues, F.A., Travieso, G., Boas, P.R.V. (2002). Characterization of complex networks: a survey of measurements. Advances in Physics, 56, 167–242.

    Article  Google Scholar 

  • da Fontoura Costa, L., Batista, J.L.B., Ascoli, G.A. (2011). Communication structure of cortical networks. Frontiers in Computational Neuroscience, 5, 6.

    PubMed Central  PubMed  Google Scholar 

  • Dale, A.M., & Sereno, M.I. (1993). Improved localisation of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. Journal of Cognitive Neuroscience, 5, 162–176.

    Article  CAS  PubMed  Google Scholar 

  • De Vico Fallani, F., Astolfi, L., Cincotti, F., Mattia, D., Marciani, M.G., Tocci, A., Salinari, S., Witte, H., Hesse, W., Gao, S., Colosimo, A., Babiloni, F. (2008). Cortical network dynamics during foot movements. Neuroinformatics, 6(1), 23–34.

    Article  PubMed  Google Scholar 

  • De Vico Fallani, F., Rodrigues, F.A., da Fontoura Costa, L., Astolfi, L., Cincotti, F., Mattia, D., Salinari, S., Babiloni, F. (2011). Multiple pathways analysis of brain functional networks from EEG signals: an application to real data. Brain Topography, 23, 344–354.

    Article  PubMed  Google Scholar 

  • Doyle, P.G., & Snell, L. (1984). Random walks and electric networks,. Washington: The Mathematical Association of America.

    Google Scholar 

  • Eguíluz, V.M., Chialvo, D.R., Cecchi, G.A., Baliki, M., Apkarian, A.V. (2005). Scale-free brain functional networks. Physical Review Letters, 94, 018102.

    Article  PubMed  Google Scholar 

  • Estrada, E., & Hatano, N. (2008). Communicability in complex networks. Physical Review E, 77, 036111.

    Article  Google Scholar 

  • Estrada, E., Higham, D.J., Hatano, N. (2009). Communicability betweenness in complex networks. Physica A, 388, 764–774.

    Article  Google Scholar 

  • Fogassi, L., & Luppino, G. (2005). Motor functions of the parietal lobe. Current Opinion in Neurobiology, 15, 626–631.

    Article  CAS  PubMed  Google Scholar 

  • Freeman, L.C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41.

    Article  Google Scholar 

  • Gross, J., Timmermann, L., Kujala, J., Dirks, M., Schmitz, F., Salmelin, R., Schnitzler, A. (2002). The neural basis of intermittent motor control in humans. Proceedings of the National Academy of Sciences of the United States of America, 99, 2299–2302.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Gross, J., Schmitz, F., Schnitzler, I., Kessler, K., Shapiro, K., Hommel, B., Schnitzler, A. (2004). Modulation of long-range neural synchrony reflects temporal limitations of visual attention in humans. Proceedings of the National Academy of Sciences of the United States of America, 101, 13050–13055.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Hayasaka, S., & Nichols, T.E. (2003). Validating cluster size inference: random field and permutation methods. Neuroimage, 20, 2343–2356.

    Article  PubMed  Google Scholar 

  • He, B. (1998). High-resolution source imaging of brain electrical activity. IEEE Engineering in Medicine and Biology Magazine, 17, 123–129.

    Article  CAS  PubMed  Google Scholar 

  • He, B., Wang, Y., Wu, D. (1999). Estimating cortical potentials from scalp EEG’s in a realistically shaped inhomogeneous head model by means of the boundary element method. IEEE Transactions on Biomedical Engineering, 46, 1264–1268.

    Article  CAS  PubMed  Google Scholar 

  • Horwitz, B. (1994). The elusive concept of brain connectivity. Neuroimage, 19, 466–470.

    Article  Google Scholar 

  • Ikeda, A., Lüders, H.O., Burgess, R.C., Shibasaki, H. (1992). Movement-related potentials recorded from supplementary motor area and primary motor area. Role of supplementary motor area in voluntary movements. Brain, 115, 1017–1043.

    Article  PubMed  Google Scholar 

  • Imamoglu, F., Kahnt, T., Koch, C., Haynes, J.-D. (2012). Changes in functional connectivity support conscious object recognition. Neuroimage, 63, 1909–1917.

    Article  PubMed  Google Scholar 

  • Jin, S.H., Lin, P., Hallett, M. (2012). Reorganization of brain functional small-world networks during finger movements. Human Brain Mapping, 115, 861–872.

    Article  Google Scholar 

  • Langer, N., Peroni, A., Jäncke, L. (2013). The problem of thresholding in small-world network analysis. PLoS ONE, 8, e53199.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Leocani, L., Toro, C., Manganotti, P., Zhuang, P., Hallett, M. (1997). Event-related coherence and event-related desynchronization/synchronization in the 10 hz and 20 Hz EEG during self-paced movements. Clinical Neurophysiology, 104, 199–206.

    CAS  Google Scholar 

  • Lovász, L. (1993). Random walks on graphs: A survey. In D. Miklos, V.T. Sos, T. Szonyi (Eds.), Combinatorics, Paul Erdõs is eighty (Vol. 2, pp. 353–398). Budapest: János Bolyai Mathematical Society.

    Google Scholar 

  • Mattia, D., Cincotti, F., Astolfi, L., De Vico Fallani, F., Scivoletto, G., Marciani, M.G., Babiloni, F. (2009). Motor cortical responsiveness to attempted movements in tetraplegia: evidence from neuroelectrical imaging. Clinical Neurophysiology, 119, 2231–2237.

    Google Scholar 

  • Middleton, F.A., & Strick, P.L. (2000). Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Research. Brain Research Reviews, 31, 236–250.

    Article  CAS  PubMed  Google Scholar 

  • Moretti, D.V., Babiloni, F., Carducci, F., Cincotti, F., Remondini, E., Rossigni, P.M., Salinari, S., Babiloni, C. (2003). Computerized processing of EEG-EOG-EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials. International Journal of Psychophysiology, 47, 199–216.

    Article  CAS  PubMed  Google Scholar 

  • Muthukumaraswamy, S.D. (2010). Functional properties of human primary motor cortex gamma oscillations. Journal of Neurophysiology, 104(5), 2873–2885.

    Article  PubMed  Google Scholar 

  • Newman, M.E.J. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256.

    Article  Google Scholar 

  • Newman, M.E.J. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27, 39–54.

    Article  Google Scholar 

  • Noh, J.D., & Rieger, H. (2004). Random walks in complex networks. Physical Review Letters, 92, 118701.

    Article  PubMed  Google Scholar 

  • Nolte, G., Bai, U., Weathon, L., Mari, Z., Vorbach, S., Hallet, M. (2004). Identifying true brain interaction from EEG data using the imaginary part of coherency. Clinical Neurophysiology, 115, 2294–2307.

    Article  Google Scholar 

  • Nunez, P.L., Srinivasan, R., Westdorp, A.F., Wijesinghe, R.S., Tucker, D.M., Silberstein, R.B., Cadusch, P.J. (1997). EEG coherency I: statistics, reference electrode, volume conduction, laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalography and Clinical Neurophysiology, 103, 499–515.

    Article  CAS  PubMed  Google Scholar 

  • Ohara, S., Mima, T., Baba, K., Ikeda, A., Kunieda, T., Matsumoto, R., Yamamoto, J., Matsuhashi, M., Nagamine, T., Hirasawa, K., Hori, T., Mihara, T., Hashimoto, N., Salenius, S., Shibasaki, H. (2001). Increased synchronization of cortical oscillatory activities between human supplementary motor and primary sensorimotor areas during voluntary movements. Journal of Neuroscience, 21(23), 9377–9386.

    CAS  PubMed  Google Scholar 

  • Pantazis, D., Nichols, T.E., Baillet, S., Leahy, R.M. (2005). A comparison of random field theory and permutation methods for the statistical analysis of MEG data. Neuroimage, 25, 383–394.

    Article  PubMed  Google Scholar 

  • Pfurtscheller, G., & Lopes da Silva, F.H. (1999). Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 11, 1842–1857.

    Article  Google Scholar 

  • Pollok, B., Gross, J., Schnitzler, A. (2002). Human cortical EEG rhythms during the observation of simple aimless movements. A high resolution EEG study. Neuroimage, 17, 559–572.

    Article  Google Scholar 

  • Pollok, B., Gross, J., Schnitzler, A. (2006). How the brain controls repetitive finger movements. Journal of Physiology - Paris, 99, 8–13.

    Article  Google Scholar 

  • Reijneveld, J.C., Ponten, S.C., Berendse, H.W., Stam, C.J. (2007). The application of graph theoretical analysis to complex networks in the brain. Clinical Neurophysiology, 118, 2317–2331.

    Article  PubMed  Google Scholar 

  • Rodrigues, F.A., & da Fontoura Costa, L. (2010). Generalized connectivity between any two nodes in a complex network. Physical Review E, 81, 036113.

    Article  Google Scholar 

  • Schlögel, A., & Supp, G. (2006). Analyzing event-related EEG data with multivariate autoregressive parameters. In C. Neuper, & W. Klimesh (Eds.), Progress in brain research (Vol. 159, pp. 135–147). The Netherlands: Elsevier.

  • Schoffelen, J.M., & Gross, J. (2009). Source connectivity analysis with MEG and EEG. Human Brain Mapping, 30, 1857–1865.

    Article  PubMed  Google Scholar 

  • Sporns, O., Chialvo, D.R., Kaiser, M., Hilgetag, C.C. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8, 418–425.

    Article  PubMed  Google Scholar 

  • Srinivasan, R., Nunez, P.L., Silberstein, R.B. (1998). Spatial filtering and neocortical dynamics: estimates of EEG coherence. IEEE Transactions on Biomedical Engineering, 45, 814–826.

    Article  CAS  PubMed  Google Scholar 

  • Stephenson, K., & Zelen, M. (1989). Rethinking centrality: methods and examples. Social Networks, 11, 1–37.

    Article  Google Scholar 

  • Toppi, J., De Vico Fallani, F., Vecchiato, G., Maglione, A.G., Cincotti, F., Mattia, D., Salinari, S., Babiloni, F., Astolfi, L. (2012). How the statistical validation of functional connectivity patterns can prevent erroneous definition of small-world properties of a brain connectivity network.Computational and Mathematical Methods in Medicine, 2012, 130985.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Valencia, M., Pastor, M.A., Fernández-Seara, M.A., Artieda, J., Martinerie, J., Chavez, M. (2009). Complex modular structure of large-scale brain networks. Chaos, 19, 02311.

    Article  Google Scholar 

  • van Wijk, B.C.M., Stam, C.J., Daffertshofer, A. (2010). Comparing brain networks of different size and connectivity density using graph theory. PLoS ONE, 5, e13701.

    Article  PubMed Central  PubMed  Google Scholar 

  • Varela, F., Lachaux, J.-P., Rodriguez, E., Martinerie, J. (2001). The brainweb: phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2, 229–239.

    Article  CAS  PubMed  Google Scholar 

  • Wolpert, D.M., Ghahramani, Z., Jordan, M.I. (1995). An internal model for sensorimotor integration. Science, 269, 1880–1882.

    Article  CAS  PubMed  Google Scholar 

  • Zamora-López, G., Zhou, C., Kurths, J. (2009). Graph analysis of cortical networks reveals complex anatomical communication substrate. Chaos, 19, 015117.

    Article  PubMed  Google Scholar 

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Acknowledgments

This study was supported in part by Cochlear Inc. and by a grant of “Ministero dell’Istruzione, dell’Universita e della Ricerca”, Direzione Generale per l’ Internazionalizzazione della Ricerca, in a bilateral project between Italy and Hungary. M. V. acknowledges financial support from the Spanish Ministry of Science and Innovation; Juan de la Cierva Programme Ref. JCI-2010-07876. F. D. V. F. is founded by the French program “Investissements d’avenir” ANR-10-IAIHU-06. M.V. and M.C. acknowledge financial support from the Gobierno de Navarra, Education Department, Jerónimo de Ayanz Programme M. C. thanks to the CIMA and University of Navarra, for their kind hospitality during the different visits for the preparation of this work.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. CNRS UMR-7225, Hôpital de la Salpêtrière, Paris, France

    Mario Chavez & Fabrizio De Vico Fallani

  2. Department of Human Physiology and Pharmacology, University “La Sapienza”, Rome, Italy

    Fabrizio De Vico Fallani & Fabio Babiloni

  3. Neuroelectrical Imaging and BCI Lab, IRCCS “Fondazione Santa Lucia”, Rome, Italy

    Fabrizio De Vico Fallani, Donatella Mattia & Fabio Babiloni

  4. Neurophysiology Laboratory, Division of Neurosciences, CIMA & University of Navarra, Pamplona, Spain

    Miguel Valencia & Julio Artieda

  5. School of Mathematical Sciences, Queen Mary, University of London, London, UK

    Vito Latora

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  1. Mario Chavez
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Chavez, M., De Vico Fallani, F., Valencia, M. et al. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinform 11, 355–366 (2013). https://doi.org/10.1007/s12021-013-9185-2

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