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Volume 2017, Article ID 9078541, 13 pages
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.


In recent years, functional connectivity in the developmental science received increasing attention. Although it has been reported that the anatomical connectivity in the preterm brain develops dramatically during the last months of pregnancy, little is known about how functional and effective connectivity change with maturation. The present study investigated how effective connectivity in premature infants evolves. To assess it, we use EEG measurements and graph-theory methodologies. We recorded data from 25 preterm babies, who underwent long-EEG monitoring at least twice during their stay in the NICU. The recordings took place from 27 weeks postmenstrual age (PMA) until 42 weeks PMA. Results showed that the EEG-connectivity, assessed using graph-theory indices, moved from a small-world network to a random one, since the clustering coefficient increases and the path length decreases. This shift can be due to the development of the thalamocortical connections and long-range cortical connections. Based on the network indices, we developed different age-prediction models. The best result showed that it is possible to predict the age of the infant with a root mean-squared error () equal to 2.11 weeks. These results are similar to the ones reported in the literature for age prediction in preterm babies.