Topography of EEG complexity in human neonates: Effect of the postmenstrual age and the sleep state

Pereda, Ernesto; Cruz, Dulce M A de La; Sangworasil, Manas; Garrido, José M.; López, S.; González, José M.

doi:10.1016/j.neulet.2005.10.036

Cited by 23 publications

(25 citation statements)

References 27 publications

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“…For other spectral power measures, the correlation was less clear. Topographic mapping of changes in spectral power measures is an interesting technique to localize brain activity but is beyond the scope of this article (39).…”

Section: Discussionmentioning

confidence: 99%

Maturational Changes in Automated EEG Spectral Power Analysis in Preterm Infants

et al. 2011

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Our study aimed at automated power spectral analysis of the EEG in preterm infants to identify changes of spectral measures with maturation. Weekly (10 -20 montage) 4-h EEG recordings were performed in 18 preterm infants with GA Ͻ32 wk and normal neurological follow-up at 2 y, resulting in 79 recordings studied from 27 ϩ4 to 36 ϩ3 wk of postmenstrual age (PMA, GA ϩ postnatal age). Automated spectral analysis was performed on 4-h EEG recordings. The frequency spectrum was divided in delta 1 (0.5-1 Hz), delta 2 (1-4 Hz), theta (4 -8 Hz), alpha (8 -13 Hz), and beta (13-30 Hz) band. Absolute and relative power of each frequency band and spectral edge frequency were calculated. Maturational changes in spectral measures were observed most clearly in the centrotemporal channels. With advancing PMA, absolute powers of delta 1 to 2 and theta decreased. With advancing PMA, relative power of delta 1 decreased and relative powers of alpha and beta increased, respectively. In conclusion, with maturation, spectral analysis of the EEG showed a significant shift from the lower to the higher frequencies. Computer analysis of EEG will allow an objective and reproducible analysis for long-term prognosis and/or stratification of clinical treatment. (Pediatr Res 70: 529-534, 2011) A dvances in the care of very preterm infants have led to an increased survival (1). However, a considerable number of these infants experience neurological deficits later in life, even in the absence of neuroimaging abnormalities (2,3). The exact etiology of these developmental deficits remains to be clarified, but it is suggested that medical, environmental, and iatrogenic conditions may interfere with white matter development of the vulnerable preterm brain (4). Therefore, brain function monitoring in preterm infants during their stay in the NICU may be valuable in detecting conditions that interfere with brain development (5). It is a challenge to develop effective monitoring and therapeutic strategies to protect the preterm brain.The EEG is regarded as the gold standard in the assessment of cerebral function. Assessing changes in EEG are useful in the prediction of long-term outcome (6). Although the acute and chronic EEG changes are mainly nonspecific regarding type of damage, they correlate with later neurological and cognitive function (7). In preterm infants developing white matter damage, acute EEG findings include decreased continuity, lower amplitude of background activity, and epileptic seizure activity (8). The chronic EEG changes associated with white matter injury and abnormal neurological development include delayed maturation and disorganized pattern with the presence of abundant positive Rolandic sharp waves (9,10). In addition, EEG patterns of preterm infants change with postmenstrual age (PMA) (11,12). In the very preterm infant, the EEG background activity is characterized by discontinuity, instability, and fragmentation (13). The greater the prematurity, the more marked are these EEG aspects. These characteristics make the ...

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Section: Discussionmentioning

confidence: 99%

Maturational Changes in Automated EEG Spectral Power Analysis in Preterm Infants

et al. 2011

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“…Spectral analyses of EEG [65,82,94,131] , cardiorespiratory behavior [65,80] , arousal behavior [21,66] , and REMs [21,65,131] established that there are important physiologic differences between healthy preterm and full-term cohorts involving sleep-state-specific neuronal network expression throughout the neuroaxis. Nonlinear computations for feature extraction of EEG signals [132,133] , arousals [66] , and state/outcome prediction [134,135] have recently been added to linear analysis as part of a comprehensive computational strategy.…”

Section: Computer-assisted Analyses Of Neonatal Eeg/sleep Represent Amentioning

confidence: 99%

Neonatal EEG/Sleep State Analyses: A Complex Phenotype of Developmental Neural Plasticity

Scher

Loparo

2009

Dev Neurosci

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Computer analyses of EEG/sleep states can be used as physiologic biomarkers of developmental neural plasticity. Frequency- and time-dependent signal processing strategies of cerebral and noncerebral measures can help test current theories of neuronal network maturation in terms of segregation and integration of short-distance versus long-distance neuronal connections throughout the neuroaxis. Specific phenotypic expressions of adaptive or maladaptive neuronal connectivity are proposed based on comparisons of whole-brain EEG/sleep resting states between preterm and full-term cohorts when developmental outcome measures are applied. Combined use of neurophysiological datasets with neuroimaging and genetic methodologies define endophenotypes that will more accurately diagnose children at risk for developmental disorders, as well as design appropriate neuroprotective interventions for the individual’s age and disease progress.

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“…There are surely complex relations within these patterns that may be best explored using alternative analytic approaches. Some authors recommend nonlinear analyses to better explore the multi-level signals that make up neonatal brain function (27, 41, 42). …”

Section: Discussionmentioning

confidence: 99%

Quantification of neonatal amplitude-integrated EEG patterns

Thorngate

Foreman

Thomas

2013

Early Human Development

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Background Amplitude-integrated EEG (aEEG) is increasingly used in research with premature infants; however, comprehensive interpretation is limited by the lack of simple approaches for reliably quantifying and summarizing the data. Aim Explore operational measures for quantifying continuity and discontinuity, measured by aEEG as components of infant brain function. Study design An exploratory naturalistic study of neonates while in the Neonatal Intensive Care Unit (NICU). One single channel aEEG recording per infant was obtained without disruption of nursing care practices. Subjects 24 infants with mean postmenstrual age (PMA) 33.11 weeks (SD 3.49), average age 2.62 weeks (SD 1.35) and mean birth weights 1.39kg (SD 0.73). Outcome Measures Quantification of continuity and discontinuity included bandwidth and lower border of aEEG, calculated proportion of time with signal amplitude below 10μV, and peak counts. Variance of bandwidth and lower border denoted cycling. Results Group mean bandwidth was 52.98 μV (SD 27.62). Median peak count in 60 second epochs averaged 3.63 (SD 1.74), while median proportion <10 μV was 22% (SD 0.20). The group mean of lower border within-subject aggregated medians was 6.20μV (SD 2.13). Group mean lower border standard deviation was 3.96μV. Proportion <10μV showed a strong negative correlation with the natural log of the lower border median (r = −0.906, p<.0001) after controlling for PMA. Conclusions This study introduces a novel quantification process by counting peaks and proportion of time <10 μV. Expanded definitions and analytic techniques will serve to strengthen the application of existing scoring systems for use in naturalistic research settings and clinical practice.

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Topography of EEG complexity in human neonates: Effect of the postmenstrual age and the sleep state

Cited by 23 publications

References 27 publications

Maturational Changes in Automated EEG Spectral Power Analysis in Preterm Infants

Maturational Changes in Automated EEG Spectral Power Analysis in Preterm Infants

Neonatal EEG/Sleep State Analyses: A Complex Phenotype of Developmental Neural Plasticity

Quantification of neonatal amplitude-integrated EEG patterns

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