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Complexity
Volume 2017, Article ID 9078541, 13 pages
https://doi.org/10.1155/2017/9078541
Research Article

Monitoring Effective Connectivity in the Preterm Brain: A Graph Approach to Study Maturation

1Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
2imec, Leuven, Belgium
3Department of Development and Regeneration, Neonatal Intensive Care Unit, UZ Leuven, Leuven, Belgium
4Department of Development and Regeneration, Child Neurology, UZ Leuven, Leuven, Belgium

Correspondence should be addressed to M. Lavanga; eb.nevueluk.tase@agnavalm

Received 12 May 2017; Revised 28 July 2017; Accepted 6 September 2017; Published 17 October 2017

Academic Editor: Mirjana Popović

Copyright © 2017 M. Lavanga et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Goldenberg and A. Galván, “The use of functional and effective connectivity techniques to understand the developing brain,” Developmental Cognitive Neuroscience, vol. 12, article no. 265, pp. 155–164, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. E. W. Lang, A. M. Tomé, I. R. Keck, J. M. Górriz-Sáez, and C. G. Puntonet, “Brain connectivity analysis: A short survey,” Computational Intelligence and Neuroscience, vol. 2012, Article ID 412512, 21 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. J. L. Vincent, G. H. Patel, M. D. Fox et al., “Intrinsic functional architecture in the anaesthetized monkey brain,” Nature, vol. 447, no. 7140, pp. 83–86, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. J. D. Power, D. A. Fair, B. L. Schlaggar, and S. E. Petersen, “The development of human functional brain networks,” Neuron, vol. 67, no. 5, pp. 735–748, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Batalle, E. J. Hughes, H. Zhang et al., “Early development of structural networks and the impact of prematurity on brain connectivity,” NeuroImage, vol. 149, pp. 379–392, 2017. View at Publisher · View at Google Scholar · View at Scopus
  6. M. André, M.-D. Lamblin, A. M. d'Allest et al., “Electroencephalography in premature and full-term infants. Developmental features and glossary,” Neurophysiologie Clinique, vol. 40, no. 2, pp. 59–124, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. E. Takahashi, R. D. Folkerth, A. M. Galaburda, and P. E. Grant, “Emerging cerebral connectivity in the human fetal Brain: an MR tractography study,” Cerebral Cortex, vol. 22, no. 2, pp. 455–464, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Dubois, M. Benders, A. Cachia et al., “Mapping the early cortical folding process in the preterm newborn brain,” Cerebral Cortex, vol. 18, no. 6, pp. 1444–1454, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. P. S. Hüppi and J. Dubois, “Diffusion tensor imaging of brain development,” Seminars in Fetal and Neonatal Medicine, vol. 11, no. 6, pp. 489–497, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. M. C. Stevens, “The developmental cognitive neuroscience of functional connectivity,” Brain and Cognition, vol. 70, no. 1, pp. 1–12, 2009. View at Publisher · View at Google Scholar
  11. E. Bullmore and O. Sporns, “Complex brain networks: graph theoretical analysis of structural and functional systems,” Nature Reviews Neuroscience, vol. 10, no. 3, pp. 186–198, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. K. J. Friston, “Functional and effective connectivity in neuroimaging: a synthesis,” Human Brain Mapping, vol. 2, no. 1-2, pp. 56–78, 1994. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Omidvarnia, P. Fransson, M. Metsaranta, and S. Vanhatalo, “Functional bimodality in the brain networks of preterm and term human newborns,” Cerebral Cortex, vol. 24, no. 10, pp. 2657–2668, 2014. View at Publisher · View at Google Scholar
  14. E. M. Schumacher, T. A. Stiris, and P. G. Larsson, “Effective connectivity in long-term EEG monitoring in preterm infants,” Clinical Neurophysiology, vol. 126, no. 12, pp. 2261–2268, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Lavanga, O. De Wel, A. Caicedo Dorado, K. Jansen, A. Dereymaeker, G. Naulaers et al., “Linear and nonlinear functional connectivity methods to predict brain maturation in preterm babies,” in Proceedings of the 8th International Workshop on Biosignal Interpretation, pp. 37–40, 2016.
  16. T. Schreiber, “Measuring information transfer,” Physical Review Letters, vol. 85, no. 2, pp. 461–464, 2000. View at Publisher · View at Google Scholar · View at Scopus
  17. C. W. J. Granger, “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica, vol. 37, no. 3, pp. 424–238, 1969. View at Publisher · View at Google Scholar
  18. A. Montalto, L. Faes, and D. Marinazzo, “MuTE: a MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy,” PLoS ONE, vol. 9, no. 10, Article ID e109462, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. E. Florin, J. Gross, J. Pfeifer, G. R. Fink, and L. Timmermann, “The effect of filtering on Granger causality based multivariate causality measures,” NeuroImage, vol. 50, no. 2, pp. 577–588, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Dereymaeker, K. Pillay, J. Vervisch et al., “An automated quiet sleep detection approach in preterm infants as a gateway to assess brain maturation,” International Journal of Neural Systems, vol. 27, no. 06, p. 1750023, 2017. View at Publisher · View at Google Scholar
  21. N. Koolen, A. Dereymaeker, O. Rasanen et al., “Data-driven metric representing the maturation of preterm EEG,” in Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC '15, pp. 1492–1495, IEEE, Milan, Italy, August 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. K. J. Blinowska, “Review of the methods of determination of directed connectivity from multichannel data,” Medical Biological Engineering Computing, vol. 49, no. 5, pp. 521–529, 2011. View at Publisher · View at Google Scholar
  23. L. A. Baccalá and K. Sameshima, “Causality and influentiability: the need for distinct neural connectivity concepts,” Journal of Chemistry, pp. 424–435, 2014. View at Google Scholar
  24. L. Barnett and A. K. Seth, “The MVGC multivariate granger causality toolbox: a new approach to granger-causal inference,” Journal of Neuroscience Methods, vol. 223, pp. 50–68, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. K. Sameshima, D. Y. Takahashi, and L. A. Baccalá, “On the statistical performance of Granger-causal connectivity estimators,” Brain Informatics, vol. 2, no. 2, pp. 119–133, 2015. View at Publisher · View at Google Scholar
  26. R. Vicente, M. Wibral, M. Lindner, and G. Pipa, “Transfer entropy—a model-free measure of effective connectivity for the neurosciences,” Journal of Computational Neuroscience, vol. 30, no. 1, pp. 45–67, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  27. A. K. Seth, “Causal connectivity of evolved neural networks during behavior,” Network: Computation in Neural Systems, vol. 16, no. 1, pp. 35–54, 2015. View at Publisher · View at Google Scholar
  28. F. D. Fallani and F. Babiloni, “The graph theoretical approach in brain functional networks: theory and applications,” Synthesis Lectures on Biomedical Engineering, vol. 5, no. 1, pp. 1–92, 2010. View at Publisher · View at Google Scholar
  29. G. Fagiolo, “Clustering in complex directed networks,” Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, vol. 76, no. 2, part 2, Article ID 026107, 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. J. Bondy and U. Murty, Graph Theory with Applications, Springer, Berlin, Heidelberg, Germany, 1976.
  31. M. Rubinov and O. Sporns, “Complex network measures of brain connectivity: uses and interpretations,” NeuroImage, vol. 52, no. 3, pp. 1059–1069, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. A. B. Barrett, L. Barnett, and A. K. Seth, “Multivariate Granger causality and generalized variance,” Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, vol. 81, no. 4, 041907, 14 pages, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  33. A. Yu, M. Lu, and F. Tian, “On the spectral radius of graphs,” Linear Algebra and its Applications, vol. 387, pp. 41–49, 2004. View at Google Scholar
  34. E. Estrada and N. Hatano, “Communicability angle and the spatial efficiency of networks,” SIAM Review, vol. 58, no. 4, pp. 692–715, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  35. F. Chung, “Laplacians and the Cheeger inequality for directed graphs,” Annals of Combinatorics, vol. 9, no. 1, pp. 1–19, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  36. L. Barnett and A. K. Seth, “Behaviour of Granger causality under filtering: theoretical invariance and practical application,” Journal of Neuroscience Methods, vol. 201, no. 2, pp. 404–419, 2011. View at Publisher · View at Google Scholar · View at Scopus
  37. W. DeClercq, A. Vergult, B. Vanrumste, W. van Paesschen, and S. van Huffel, “Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, pp. 2583–2587, 2006. View at Publisher · View at Google Scholar
  38. H. Peng, K. Li, B. Li, H. Ling, W. Xiong, and W. Hu, “Predicting image memorability by multi-view adaptive regression,” in Proceedings of the the 23rd ACM international conference, pp. 1147–1150, Brisbane, Australia, October 2015. View at Publisher · View at Google Scholar
  39. J. M. O'Toole, G. B. Boylan, S. Vanhatalo, and N. J. Stevenson, “Estimating functional brain maturity in very and extremely preterm neonates using automated analysis of the electroencephalogram,” Clinical Neurophysiology, vol. 127, no. 8, pp. 2910–2918, 2016. View at Publisher · View at Google Scholar · View at Scopus
  40. I. Kostović and M. Judaš, “The development of the subplate and thalamocortical connections in the human foetal brain,” Acta Paediatrica, vol. 99, no. 8, pp. 1119–1127, 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. E. J. Meijer, K. H. M. Hermans, A. Zwanenburg et al., “Functional connectivity in preterm infants derived from EEG coherence analysis,” European Journal of Paediatric Neurology, vol. 18, no. 6, pp. 780–789, 2014. View at Publisher · View at Google Scholar · View at Scopus
  42. D. A. Fair, N. U. Dosenbach, J. A. Church, A. L. Cohen, S. Brahmbhatt, F. M. Miezin et al., “Development of distinct control networks through segregation and integration,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 33, pp. 13507–13512, 2007. View at Publisher · View at Google Scholar