The effect of early procedural pain in preterm infants on the maturation of electroencephalogram and heart rate variability

Lavanga, Mario; Bollen, Bieke; Caicedo, Alexander; Dereymaeker, A; Jansen, Katrien; Ortibus, Els; Huffel, Sabine Van; Naulaers, Gunnar

doi:10.1097/j.pain.0000000000002125

Cited by 13 publications

(11 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…S1 for the distribution of stressful procedures. The distribution of NICU-related invasive procedures observed in the current study are is in line with previously reported numbers (e.g., skin-breaking procedures [ 33 , 34 ], days of mechanical ventilation [ 31 , 35 , 36 ]). For subsequent analyses, infants were grouped into low (lowest 50%) or high (highest 50%) stress exposure (for cut-off see Table 1 ).…”

Section: Methodssupporting

confidence: 92%

Early-life stress exposure and large-scale covariance brain networks in extremely preterm-born infants

et al. 2022

View full text Add to dashboard Cite

The stressful extrauterine environment following premature birth likely has far-reaching and persistent adverse consequences. The effects of early “third-trimester” ex utero stress on large-scale brain networks’ covariance patterns may provide a potential avenue to understand how early-life stress following premature birth increases risk or resilience. We evaluated the impact of early-life stress exposure (e.g., quantification of invasive procedures) on maturational covariance networks (MCNs) between 30 and 40 weeks of gestational age in 180 extremely preterm-born infants (<28 weeks of gestation; 43.3% female). We constructed MCNs using covariance of gray matter volumes between key nodes of three large-scale brain networks: the default mode network (DMN), executive control network (ECN), and salience network (SN). Maturational coupling was quantified by summating the number of within- and between-network connections. Infants exposed to high stress showed significantly higher SN but lower DMN maturational coupling, accompanied by DMN-SN decoupling. Within the SN, the insula, amygdala, and subthalamic nucleus all showed higher maturational covariance at the nodal level. In contrast, within the DMN, the hippocampus, parahippocampal gyrus, and fusiform showed lower coupling following stress. The decoupling between DMN-SN was observed between the insula/anterior cingulate cortex and posterior parahippocampal gyrus. Early-life stress showed longitudinal network-specific maturational covariance patterns, leading to a reprioritization of developmental trajectories of the SN at the cost of the DMN. These alterations may enhance the ability to cope with adverse stimuli in the short term but simultaneously render preterm-born individuals at a higher risk for stress-related psychopathology later in life.

show abstract

Section: Methodssupporting

confidence: 92%

Early-life stress exposure and large-scale covariance brain networks in extremely preterm-born infants

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In comparison to the neurobehavioral results from healthy full-term infants at birth (Provenzi et al, 2018), our findings showed that preterm infants had higher NNNS scores than full term infants in stress/abstinence (0.18 vs. 0.11) and handling responses (0.56 vs. 0.38), and lower quality of movement (3.97 vs. 4.71). The negative impact of higher acute painful/stressful events during the NICU stay on worse neurobehavioral outcomes is congruent with previous studies (Swamy et al, 2008; Lavanga et al, 2021; Russell et al, 2021). We identified eight OTUs of gut microbiome that were significantly associated with infant neurobehavioral profiles in early life.…”

Section: Discussionsupporting

confidence: 91%

The impact of early life experiences and gut microbiota on neurobehavioral development among preterm infants: A longitudinal cohort study

Chen

Zhao

et al. 2023

Preprint

View full text Add to dashboard Cite

Objectives: To investigate the impact of early life experiences and gut microbiota on neurobehavioral development among preterm infants during neonatal intensive care unit (NICU) hospitalization. Methods: Preterm infants were followed from the NICU admission until their 28th postnatal day or until discharge. Daily stool samples, painful/stressful experiences, feeding patterns, and other clinical and demographic data were collected. Gut microbiota was profiled using 16S rRNA sequencing, and operational taxonomic units (OTUs) were selected to predict the neurobehaviors. The neurobehavioral development was assessed by the Neonatal Neurobehavioral Scale (NNNS) at 36 to 38 weeks of post-menstrual age (PMA). Fifty-five infants who had NNNS measurements were included in the sparse log-contrast regression analysis. Results: Preterm infants who experienced high level of pain/stress during the NICU hospitalization that were associated with higher NNNS stress/abstinence scores. Eight operational taxonomic units (OTUs) were identified to be associated with of NNNS subscales after controlling demographic and clinical features, feeding patterns, and painful/stressful experiences. These OTUs, taxa belong to seven genera including Enterobacteriaceae_unclassified, Escherichia-Shigella, Incertae_Sedis, Veillonella, Enterococcus, Clostridium_sensu_stricto_1, and Streptococcus with five belonging to Firmicutes and two belonging to Proteobacteria phylum. The enriched abundance of Enterobacteriaceae_unclassified (OTU17) and Streptococcus (OTU28) were consistently associated with less optimal neurobehavioral outcomes. The other six OTUs were also associated with infant neurobehavioral responses depending on days at NICU stay. Conclusions: This study explored the dynamic impact of specific OTUs on neurobehavioral development among preterm infants after controlling for early life experiences, i.e., acute and chronic pain/stress, and feeding in the NICU.

show abstract

“…The reason is two-fold: (1) irregularity as assessed by ApEn-based algorithms (including SampEn, FuzzyEn, and CE) is not equivalent to complexity. It is believed that complex systems are neither too random nor too regular [50][51][52] but are at a critical state of 'complex'. The irregularity of the system increases with the degree of randomness whereas complexity does not.…”

Section: Discussionmentioning

confidence: 99%

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Yan

Yang

et al. 2022

Entropy

View full text Add to dashboard Cite

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.

show abstract

The effect of early procedural pain in preterm infants on the maturation of electroencephalogram and heart rate variability

Abstract: Stress exposure and early-pain procedure might increase the level of EEG and HRV dismaturity, which can ultimately impact the development outcome of the infant.

Cited by 13 publications

References 59 publications

Early-life stress exposure and large-scale covariance brain networks in extremely preterm-born infants

Early-life stress exposure and large-scale covariance brain networks in extremely preterm-born infants

The impact of early life experiences and gut microbiota on neurobehavioral development among preterm infants: A longitudinal cohort study

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Contact Info

Product

Resources

About