Nonlinear transfer entropy to assess the neurovascular coupling in premature neonates

Dries Hendrikx, Liesbeth Thewissen, Anne Smits, Gunnar Naulaers, Karel Allegaert, Sabine Van Huffel, Alexander Caicedo

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In the adult brain, it is well known that increases in local neural activity trigger changes in regional blood flow and, thus, changes in cerebral energy metabolism. This regulation mechanism is called neurovascular coupling (NVC). It is not yet clear to what extent this mechanism is present in the premature brain. In this study, we explore the use of transfer entropy (TE) in order to compute the nonlinear coupling between changes in brain function, assessed by means of EEG, and changes in brain oxygenation, assessed by means of near-infrared spectroscopy (NIRS). In a previous study, we measured the coupling between both variables using a linear model to compute TE. The results indicated that changes in brain oxygenation were likely to precede changes in EEG activity. However, using a nonlinear and nonparametric approach to compute TE, the results indicate an opposite directionality of this coupling. The source of the different results provided by the linear and nonlinear TE is unclear and needs further research. In this study, we present the results from a cohort of 21 premature neonates. Results indicate that TE values computed using the nonlinear approach are able to discriminate between neonates with brain abnormalities and healthy neonates, indicating a less functional NVC in neonates with brain abnormalities.

Original languageEnglish (US)
Title of host publicationAdvances in Experimental Medicine and Biology
PublisherSpringer
Pages11-17
Number of pages7
DOIs
StatePublished - 2020
Externally publishedYes

Publication series

NameAdvances in Experimental Medicine and Biology
Volume1232
ISSN (Print)0065-2598
ISSN (Electronic)2214-8019

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)

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