Paper Info
Title
Monitoring Effective Connectivity in the Preterm Brain: A Graph Approach to Study Maturation.
Abstract
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 graphtheory 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 (root MSE) equal to 2.11 weeks. These results are similar to the ones reported in the literature for age prediction in preterm babies.
Year
DOI
Venue
2017
10.1155/2017/9078541
COMPLEXITY
Field
DocType
Volume
Graph,Pregnancy,Postmenstrual Age,Artificial intelligence,Audiology,Clustering coefficient,Mathematics,Machine learning,Electroencephalography,Anatomical connectivity
Journal
2017
ISSN
Citations 
PageRank 
1076-2787
1
0.38
References 
Authors
10
7
Name
Order
Citations
PageRank
Mario Lavanga132.23
Ofelie De Wel232.23
Alexander Caicedo3375.40
Katrien Jansen4207.25
Anneleen Dereymaeker593.24
Gunnar Naulaers6187.52
S. Van Huffel726032.75