Maturational Changes in the Interdependencies between Cortical Brain Areas of Neonates during Sleep

Unit ofPediatrics, University Hospital Nuestra Sen˜ora de laCandelaria, Tenerife, SpainThis work aims at assessing the maturational changes in the inter-dependence between the activities of different cortical areas inneonates during active sleep (AS) and quiet sleep (QS). Eightelectroencephalography (EEG) channels were recorded in 3 groupsof neonates of increasing postmenstrual age. The average linear(AVL) and average nonlinear (AVN) interdependencies of eachelectrode region with the remaining ones were calculated using thecoherence function and a recently developed index of nonlinearcoupling between 2 signals in their state spaces, respectively. Intheta band, AVL increased with neonate’s age for central and tem-poral regions during QS. In beta band, AVL increased for mostcortical regions during QS and a parallel decrease of AVL withneonate’s age was found during AS. For all regions, beta AVL wasgreater in AS than in QS in preterm neonates but the reverse hap-pened in older term neonates. Contrarily to AVL, AVN decreasedwith age during QS for most cortical regions. Surrogate data testshowed that the interdependencies were nonlinear in preterm andyounger term neonates but in older term both linear and nonlinearinterdependencies coexisted. It is concluded that neonatal matu-ration is associated with changes in the magnitude and character ofthe EEG interdependencies during sleep.Keywords: coherence, interdependence, neonates, nonlinear analysis,sleep electroencephalogramIntroductionDuring a neonate’s first months of life, the electroencephalog-raphy (EEG) constitutes a valuable tool for the evaluation of thedegree of brain maturity because in this ontogenical period thesignal presents major changes (Mizrahi and others 2004). Thequantification of such changes is relevant from the diagnosticpoint of view, because they are characteristic of the process ofcerebral maturation, and hence, can provide information aboutits alterations. Quantitative EEG analysis based on spectral mea-sures is a good candidate for such aim, as it has proved useful asa noninvasive method to assess cerebral damage (Thakor andTong 2004). In addition, recent results suggest that quantitativeEEG may be more effective than some methods of diagnosis forimage in the detection of anomalies in the cerebral function ofneonates (Mandelbaum and others 2000). As a result, severalfrequency domain measures have been used to assess thedegree of neonate’s maturation from the EEG. For instance,a recent work (Burdjalov and others 2003) proposes a methodbased on the integrated EEG amplitude of the parietal electro-des P1 and P3. Other authors (Holthausen and others 2000)reported an index of neonatal maturation based on the spectralamplitudes of various frequency bands (d, h, and b/h ratios),whereas Scher and others (2003) have used the power in thesebands as well as spectral correlations between central andparietal regions to establish differences between premature andfull-term neonates.Despite their usefulness, all these methods present thedrawback of assuming a linear behavior of the EEG activityand hence, they take into account only its more regularvariations. But the EEG is a complex, irregular signal, and otherprocedures of analysis that take into account these character-istics might be used to better characterize it (Quian Quirogaand others 2002; David and others 2004). In this line, theapplication of nonlinear time series analysis has been useful tostudy neonate’s maturation from EEG records. For instance, thecorrelation dimension (a measure of the signal complexity orirregularity) calculated from the waking EEG, increased withage from neonates to adults (Meyer-Lindenberg 1996) andwithin neonates of increasing age during active sleep (AS) andquiet sleep (QS) (Scher and others 2005; Pereda and others2006). As far as multivariate analysis concerns, nonlinear me-thods have been useful to assess the interdependence betweentheEEGsignals ofdifferent brainareasinbothneonates(Peredaand others 2003) and adult subjects (Pereda and others 2001;Terry and others 2004) during sleep, which suggests the use-fulness of these methods to study changes in cortical connec-tivity from this signal.AstheEEGisasignalthatreflectstheintegratedbrainactivity,it seems sensible to use multivariate analysis techniques toestablish how the interdependence between different brainareas changes during maturation. On the other hand, neonatalEEG records are quite irregular during both AS and QS andexhibit,duringQSandintheperiodofpostmenstrualage(PMA)ranging from 36 to 38 weeks until about 44 weeks, the patternof relatively short-duration called ‘‘trace´ alternant’’ (see, e.g.,Mizrahi and others 2004). In addition, neonate EEG changes itsmorphology from one electrode to another as well as frompremature to full-term neonates (Mandelbaum and others 2000;Pereda and others 2003, 2006; Scher and others 2003). Inconsequence,ourobjectiveis2-fold:ontheonehand,weaimatdetermining whether neonate’s maturation is accompanied bychanges in the magnitude and nature of the global interdepen-dence between the activities of each cortical area and theremainder during sleep; on the other hand, we try to get insightinto the usefulness of multivariate techniques to assess thematurational process in neonates as a method that might helpin the detection of neonatal neurological disorders. We dem-onstrate that the simultaneous use of multivariate linear andnonlinear interdependence measures is relevant to detectdynamical changes in the structure of the sleep EEG activityduring neonate’s maturation.