No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
Distinguishing between direct and indirect directional couplings in large oscillator networks: Partial or non-partial phase analyses?
A. Barrat, M. Barthélemy, and A. Vespignani, Dynamical Processes on Complex Networks ( Cambridge University Press, New York, USA, 2008).
S. Boccaletti, G. Bianconi, R. Criado, C. I. del Genio, J. Gomez-Gardees, M. Romance, I. Sendina-Nadal, Z. Wang, and M. Zanin, “ The structure and dynamics of multilayer networks,” Phys. Rep. 544, 1–122 (2014).
A. S. Pikovsky, M. G. Rosenblum, and J. Kurths, Synchronization: A Universal Concept in Nonlinear Sciences ( Cambridge University Press, Cambridge, UK, 2001).
B. Gourevitch, R. Le Bouquin-Jeannes, and G. Faucon, “ Linear and nonlinear causality between signals: Methods, examples and neurophysiological applications,” Biol. Cybern. 95, 349–369 (2006).
K. Hlaváčková-Schindler, M. Paluš, M. Vejmelka, and J. Bhattacharya, “ Causality detection based on information-theoretic approaches in time series analysis,” Phys. Rep. 441, 1–46 (2007).
K. Lehnertz, S. Bialonski, M.-T. Horstmann, D. Krug, A. Rothkegel, M. Staniek, and T. Wagner, “ Synchronization phenomena in human epileptic brain networks,” J. Neurosci. Methods 183, 42–48 (2009).
L. A. Baccalá and K. Sameshima, “ Partial directed coherence: a new concept in neural structure determination,” Biol. Cybern. 84, 463–474 (2001).
B. Schelter, M. Winterhalder, M. Eichler, M. Peifer, B. Hellwig, B. Guschlbauer, C. H. Lücking, R. Dahlhaus, and J. Timmer, “ Testing for directed influences among neural signals using partial directed coherence,” J. Neurosci. Methods 152, 210–219 (2006).
J. Nawrath, M. C. Romano, M. Thiel, I. Z. Kiss, M. Wickramasinghe, J. Timmer, J. Kurths, and B. Schelter, “ Distinguishing direct from indirect interactions in oscillatory networks with multiple time scales,” Phys. Rev. Lett. 104, 038701 (2010).
Y. Zou, M. C. Romano, M. Thiel, N. Marwan, and J. Kurths, “ Inferring indirect coupling by means of recurrences,” Int. J. Bifurcation Chaos Appl. Sci. Eng. 21, 1099–1111 (2011).
S. Stramaglia, G.-R. Wu, M. Pellicoro, and D. Marinazzo, “ Expanding the transfer entropy to identify information circuits in complex systems,” Phys. Rev. E 86, 066211 (2012).
L. Baccalá, C. De Brito, D. Takahashi, and K. Sameshima, “ Unified asymptotic theory for all partial directed coherence forms,” Philos. Trans. R. Soc., A 371, 20120158 (2013).
L. Leistritz, B. Pester, A. Doering, K. Schiecke, F. Babiloni, L. Astolfi, and H. Witte, “ Time-variant partial directed coherence for analysing connectivity: A methodological study,” Philos. Trans. R. Soc., A 371, 20110616 (2013).
A. Papana, C. Kyrtsou, D. Kugiumtzis, and C. Diks, “ Simulation study of direct causality measures in multivariate time series,” Entropy 15, 2635–2661 (2013).
R. Ramb, M. Eichler, A. Ing, M. Thiel, C. Weiller, C. Grebogi, C. Schwarzbauer, J. Timmer, and B. Schelter, “ The impact of latent confounders in directed network analysis in neuroscience,” Philos. Trans. R. Soc., A 371, 20110612 (2013).
H. Elsegai, H. Shiells, M. Thiel, and B. Schelter, “ Network inference in the presence of latent confounders: The role of instantaneous causalities,” J. Neurosci. Methods 245, 91–106 (2015).
L. Faes, D. Kugiumtzis, G. Nollo, F. Jurysta, and D. Marinazzo, “ Estimating the decomposition of predictive information in multivariate systems,” Phys. Rev. E 91, 032904 (2015).
W. Mader, M. Mader, J. Timmer, M. Thiel, and B. Schelter, “ Networks: On the relation of bi-and multivariate measures,” Sci. Rep. 5, 10805 (2015).
O. Sporns, Networks of the Brain ( MIT Press, Cambridge, MA, 2011).
D. Yao, L. Wang, R. Oostenveld, K. Dremstrup Nielsen, L. Arendt-Nielsen, and A. C. N. Chen, “ A comparative study of different references for EEG spectral mapping: The issue of the neutral reference and the use of the infinity reference,” Physiol. Meas. 26, 173–184 (2005).
M. G. Rosenblum, A. S. Pikovsky, J. Kurths, C. Schaefer, and P. A. Tass, “ Phase synchronization: From theory to data analysis,” in Handbook of Biological Physics, edited by F. Moss and S. Gielen ( Elsevier Science, Amsterdam, 2001), pp. 297–321.
B. Kralemann, L. Cimponeriu, M. G. Rosenblum, A. S. Pikovsky, and R. Mrowka, “ Phase dynamics of coupled oscillators reconstructed from data,” Phys. Rev. E 77, 066205 (2008).
Y. Kuramoto, Chemical Oscillations, Waves and Turbulence ( Springer Verlag, Berlin, 1984).
J. Waddell, R. Dzakpasu, V. Booth, B. Riley, J. Reasor, G. Poe, and M. Żochowski, “ Causal entropies–A measure for determining changes in the temporal organization of neural systems,” J. Neurosci. Methods 162, 320–332 (2007).
H. Osterhage, F. Mormann, T. Wagner, and K. Lehnertz, “ Detecting directional coupling in the human epileptic brain: Limitations and potential pitfalls,” Phys. Rev. E 77, 011914 (2008).
K. Lehnertz and H. Dickten, “ Assessing directionality and strength of coupling through symbolic analysis: An application to epilepsy patients,” Philos. Trans. R. Soc., A 373, 20140094 (2015).
F. Mormann, K. Lehnertz, P. David, and C. E. Elger, “ Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients,” Physica D 144, 358–369 (2000).
W. Mader, D. Feess, R. Lange, D. Saur, V. Glauche, C. Weiller, J. Timmer, and B. Schelter, “ On the detection of direct directed information flow in fMRI,” IEEE J. Sel. Top. Signal Process. 2, 965–974 (2008).
T. Zerenner, P. Friederichs, K. Lehnertz, and A. Hense, “ A Gaussian graphical model approach to climate networks,” Chaos 24, 023103 (2014).
N. Rubido, A. C. Marti, E. Bianco-Martinez, C. Grebogi, M. S. Baptista, and C. Masoller, “ Exact detection of direct links in networks of interacting dynamical units,” New J. Phys. 16, 093010 (2014).
T. Stankovski, P. V. E. McClintock, and A. Stefanovska, “ Dynamical inference: Where phase synchronization and generalized synchronization meet,” Phys. Rev. E 89, 062909 (2014).
Z. Shen, W.-X. Wang, Y. Fan, Z. Di, and Y.-C. Lai, “ Reconstructing propagation networks with natural diversity and identifying hidden sources,” Nat. Commun. 5, 4323 (2014).
Y. V. Zaytsev, A. Morrison, and M. Deger, “ Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity,” J. Comput. Neurosci. 39, 77–103 (2015).
Article metrics loading...
We investigate the relative merit of phase-based methods for inferring directional couplings in complex networks of weakly interacting dynamical systems from multivariate time-series data. We compare the evolution map approach and its partialized extension to each other with respect to their ability to correctly infer the network topology in the presence of indirect directional couplings for various simulated experimental situations using coupled model systems. In addition, we investigate whether the partialized approach allows for additional or complementary indications of directional interactions in evolving epileptic brain
networks using intracranial electroencephalographic recordings from an epilepsy patient. For such networks, both direct and indirect directional couplings can be expected, given the brain's connection structure and effects that may arise from limitations inherent to the recording technique. Our findings indicate that particularly in larger networks (number of nodes ), the partialized approach does not provide information about directional couplings extending the information gained with the evolution map approach.
Full text loading...
Most read this month