The total effect of HI can be studied by comparing the left ipsilateral (injured) hemisphere of HI-exposed mice with the left hemisphere of sham-operated animals (**Figure 3**, red marking compared to the zero level). Total mRNA for Best3 increased transiently at 6 h after HI, and then remained at control level

FIGURE 3 | The expression of Best3 mRNA in mouse brain tissue at different time points after HI. Figures show relative expression of the studied mRNAs in uninjured (R, blue) and injured (L, red) hemispheres in relation to the average expression in corresponding hemispheres of sham animals at the respective time points (sham group mean set to zero in the figure). Statistical analysis was performed by mixed-model three-way ANOVA on Cp values; main effects were based on subsequently estimated marginal means. Symbols above graphs indicate significant difference between uninjured and sham (R vs. sham group at zero, ), between injured and sham (L vs. sham group at zero, ), and between uninjured and injured (R vs. L, ×), all symbols indicate P < 0.05. The total mRNA for Best3 increased in both hemispheres at early time points after HI, but returned to control values already 24 h after HI, and even showed a transient decrease at 72 h (A). The long "+6" splice variant increased only in the uninjured hemisphere, while in the injured hemisphere it decreased and stayed decreased through all time point except 24 h (B). The short "–6" splice variant transiently increased at 6 and 12 h, and in the injured hemisphere also again at 72 h (C). Expression of mRNA for nestin (D) decreased at early time points in non-injured hemisphere, but returned to control values at 72 h after HI, while it increased in the injured hemisphere during this period to return to control values at 7 days after injury. The ER-stress marker CHOP slightly decreased during the early period after HI in the non-injured hemisphere, but was elevated throughout the studied period in the injured hemisphere (E). A total of 41 pups (26 HI, 15 sham) were used; for details see section "Materials and Methods." Detailed statistical results are given in the (Supplementary Table S1).

(**Figure 3A**). The long and the short splice variants of Best3 showed opposite changes: the "+6" variant was downregulated (**Figure 3B**), and "−6" was markedly upregulated (**Figure 3C**). At 24 h after injury the expression of both splice variants was close to the control values (**Figures 3B,C**).

Both nestin (**Figure 3D**) and ER-stress marker CHOP (**Figure 3E**) were upregulated already at 6 h and stayed upregulated through most of the investigated period. Nestin developed its maximal expression between 12 and 72 h, while CHOP showed maximum expression at 6 h after HI, after which its expression decreased over time toward baseline.

#### Time Course of Injury-Related Changes in Best3 mRNA Expression in the Brain After HI

By comparing the left ipsilateral (injured; **Figure 3**, red marking) with the right contralateral (non-injured; **Figure 3**, blue marking) hemisphere in the same animal the changes produced by the development of tissue injury can be estimated. **Figure 3** shows that injury reduced the expression of total Best3 mRNA at 12 h only (**Figure 3A**). Best3 splice variants changed in opposite directions: the long "+6" splice variant was downregulated in a biphasic manner with a transient return to control values at 24 h after HI (**Figure 3B**), while the expression of the short "−6" splice variant, on the other hand, slightly increased at 6 h and from 24 h onwards compared to the non-injured hemisphere (**Figure 3C**).

Nestin gradually increased in response to injury, reaching maximal expression between 12 and 24 h, and returning to its control values by 7 days after HI (**Figure 3D**). Injury was associated with ER stress as indicated by CHOP expression, which was maximally increased at 6 h, and then gradually decreased, returning to its control values at 7 days after HI (**Figure 3E**). Details of the statistical analysis are given in the **Supplementary Table S1**.

#### ER Stress Alters Expression of Best3 mRNA in Mouse Cultured Astrocytes

The results of the experiments are presented in **Figure 4**. To confirm that Best3 is altered in astrocytes after injury we investigated mRNA expression for Best3 in cultured astrocytes after inducing ER stress by TG. Before inducing ER stress the "−6" variant was dominating. ER stress caused an increase in expression of total Best3 and both its splice variants (**Figures 4A–C**). The increase in the long "+6" splice variant was the most pronounced (P < 0.01; **Figure 4B**). Nestin also increased after TG (P < 0.01; **Figure 4D**), and CHOP, as an ER-stress marker, was dramatically upregulated (P < 0.001; **Figure 4E**).

## Best3 Protein Expression Analysis by Western Blot

Protein analysis by western blot was performed in brain tissue using ARP50108\_P050 antibody and in astrocyte culture by Bst-301AP. It revealed Best3-related protein bands in the range of 55–80 kDa (**Figure 5**). In homogenates of parts of cortex from each hemisphere, typically Best3 staining revealed a pair of bands with molecular weight differing by approximately 2–3 kDa, which might correspond to the predicted difference between "+6" and "−6" splice variants of Best3. As there is no antibody specifically recognizing splice variants of Best3, we calculated the density of both bands together in our analysis.

Lighter pairs of bands at about 65 kDa could often be seen. The appearance of these additional bands likely might be related to the homogenization protocol, by which possibly posttranslational modifications or tightly bound proteins, for example a protein phosphatase (Marmorstein et al., 2002) could be partially removed. It is interesting to note that in cultured astrocytes the Best3-related band had a weight of approximately 52–55 kDa, closer to the theoretically predicted weight for "+6" and "−6" splice variants of Best3. Whether the lower molecular weight in cultured cells relates to a lack of modifications/associations remains to be determined.

Quantification of western blot results showed downregulation of Best3 protein expression in brain cortex and hippocampus after HI (P < 0.05) and in cultured mouse astrocytes in ER stress (P < 0.001; **Figure 5**), although this was not apparent in the cytoplasmic fraction of the brain tissue after HI injury (P > 0.05). The same result was observed whether Best3-related protein bands were normalized to total protein (**Figure 5**) or to GAPDH (data not shown).

# DISCUSSION

Best3 protein has previously not been described in the brain, and there are only a few reports where Best3 mRNA was detected in the whole adult mouse brain, although without identification of the cells expressing it (Krämer et al., 2004; Srivastava et al., 2008), and only weakly detected in the normal adult human brain (Stöhr et al., 2002). We show for the first time that Best3 protein and mRNA are expressed in normal and injured brain in newborn mouse pups and in a term infant with white matter gliosis.

Our main focus in this study was to investigate Best3 in cell injury as recent studies suggest a novel role for Best3 in apoptosis and ER-stress. In our mouse experiments we describe for the first time a subpopulation of nestin-positive astrocytes appearing after the HI injury, which expresses Best3 and can be visualized primarily in the penumbra-like area (**Figure 1a**). These cells have an astrocyte morphology, are positive for GFAP, a classic marker of astrocytes (**Figures 1b,d**) and for nestin (**Figures 1a,c**), and do not co-express neuronal or microglial markers. Under normal conditions nestin expression in the brain is more characteristic for progenitor cells than for astrocytes. We cannot exclude the possibility that some of the Best3-positive cells were neural progenitor cells, yet we did not see Best3 expression in the uninjured brain. However, the possibility that progenitor cell proliferation, triggered by injury, contributes to the Best3 expression cannot be ruled out. After injury a subpopulation of activated astrocytes start expressing nestin (Gilyarov, 2008), and these cells have been suggested to be in an early stage of activation preceding hypertrophic changes (Cho et al., 2013). Functionally, these cells have been shown to be proliferating astrocytes that have a positive influence on tissue recovery (Suzuki et al., 2012). The appearance of nestin+/GFAP+ cells has been described in the neonatal rat brain after HI, and these cells are suggested to be

in a transition state from nestin-positive radial glia into GFAPexpressing mature astrocytes (Sizonenko et al., 2008). Mouse astrocytes in primary culture in the present study showed a similar pattern of Best3 protein expression as after HI in vivo: partial overlap with GFAP and high degree of co-expression with nestin (**Figures 1e,f**). These results confirm the presence of Best3 in astrocytes, and the pronounced expression of both nestin and Best3 in the majority of cells suggests that astrocytes in culture are to a large extent in an activated state.

We also show that Best3 is expressed in astrocytes in the human term neonatal brain with neuropathology as a result of an acute event leading to intrauterine death (**Figures 1g,h**).

#### FIGURE 5 | Continued

fphys-10-00023 January 25, 2019 Time: 17:50 # 12

sham-operated animals (A). Two neighboring R and L lanes belong to the same animal. In total protein samples from brain (A) at approximately 75–80 kDa two bands with close molecular weights could be detected; their density was summarized in the analysis. These bands were reduced in injury. Additional lighter pairs of bands could be observed at approximately 65 kDa. The result of subcellular fractionation of brain tissue is shown in (B), where two bands of approximately 75–80 kDa were detected in the cytoplasmic fraction as well as in the total protein fraction. The content of Best3 in the cytoplasm did not change significantly in injury, while Best3 in the total protein fraction was downregulated. In cultured astrocytes (C) treatment with thapsigargin for 48 h (TG 48 h) also caused downregulation of Best3. <sup>∗</sup>P < 0.05 by two-way ANOVA and estimated marginal means; ∗∗∗P < 0.001 t-test R vs. L or treated vs. untreated cells. A total of 18 pups (14 HI, 4 sham) were used; for details see section "Materials and Methods."

This indicates the relevance of our observations in mice to situations with brain injury in humans. This term neonate very likely had injury to brain cells as a result of different traumatic events leading to death. As a result, the brain tissue shows signs of pronounced gliosis, in particular strong activation of astrocytes, and it was interesting to see expression of Best3 in this clinical case. Best3-positive cells were reminiscent of activated astrocytes by their morphology and developed cell processes. Based on the morphology we can suggest that Best3 can be expressed in a subpopulation of activated astrocytes in situations of neuropathology in neonates. The specific relevance of this discovery to hypoxic-ischemic brain injury in neonates, however, is not determined, and a more detailed study of the colocalization of Best3 with various cell markers is required to characterize the Best3-positive human cells.

In the HI model exposure to hypoxia alone does not induce brain injury, while its combination with ischemia causes appearance of an infarct area and cell apoptosis (Hedtjärn et al., 2002). Total Best3 mRNA was upregulated early after HI in both hemispheres. This change thus is related either to hypoxemia or to some influence of the injury on both hemispheres, whether neural or humoral. Interestingly, the rise in Best3 mRNA lasted longer in the uninjured hemisphere. Whether this relates to that hemisphere coping with the hypoxemia, and the other one succumbing, remains to be investigated.

The alternative splicing of Best3 mRNA after the HI injury in mouse brain was also analyzed. In mouse Best3 mRNA exons 2, 3, and 6 can be spliced out in different combinations in different organs, and the whole brain homogenate from the adult mouse was reported to have two splice variants "−2−3+6" and "−2−3−6", but not the full-length mRNA (Krämer et al., 2004). Similar results were obtained in our PCR analysis of Best3 splicing in the brains of neonatal mice (**Figure 2**), and the composition of splice variants was similar in contralateral non-injured and ipsilateral injured brain hemispheres. However, there is also recent data on possible splicing of the C-terminal region of Best3 mRNA and protein in mouse myoblasts (Wu et al., 2016). If this is the case also in brain, the "+6" and "−6" variants seen in our experiments could represent more than one transcript each. A more detailed analysis of alternative splicing of Best3 in situations with brain injury may therefore require RNA sequencing.

In contrast to total Best3, expression of the long "+6" splice variant changed in an injury-related manner, it was downregulated in the injured hemisphere in parallel with developing ER stress, but it was upregulated on the noninjured side (**Figure 3**). The short "−6" was less injurydependent and was mostly upregulated in both hemispheres. These changes in splicing started already at 6 h after HI, and it is interesting to note that the changes in expression of both splice variants showed biphasic dynamics: the values always tended to transiently return to control levels 24 h after the injury.

Increased expression of the transcription factor CHOP and nestin mRNA was present in the injured hemisphere only (**Figure 3**) suggesting pro-apoptotic ER stress in this tissue (Xu et al., 2005), although the degree of apoptosis was not examined in the present study. In non-injured brain tissue CHOP and nestin were actually downregulated after HI, which might be related to compensatory changes in blood flow and is an interesting observation for future investigation.

In mouse astrocyte cell culture (**Figure 4**) direct induction of strong ER stress by blocking the SERCA pump showed, together with a dramatic increase in CHOP expression, also a tendency for total Best3 mRNA to increase as well as mRNA for both its splice variants, with the most pronounced change in "+6" splice variant expression in contrast to the injury in the brain tissue, where "+6" variant was downregulated. This may indicate that different origins of ER stress can influence Best3 expression differently, but it could also show that cultured astrocytes, being already in an activated state, respond to ER stress differently from astrocytes in vivo.

Our results of Best3 mRNA expression show that it is important to study not only the total expression of the gene, but also to analyze individual splice variants, as such proteins can have different functions. Alternative splicing of mRNA can be changed by cell injury and inflammation, and this phenomenon is well described for the protein family Bcl2 (Bcl2, Bcl2L1, Bax, and other), which can act as pro- or antiapoptotic proteins depending on their splicing (Glasgow et al., 2001; Akgul et al., 2004; Miura et al., 2012). A change in the ratio between the antiapoptotic long Bcl-xL and the proapoptotic short Bcl-xS splice variants of one of the members of this family, Bcl2L1 (also called BclX), was also described in neonatal HI injury (Xiao et al., 2012). The results of the present work and of our previous studies (Golubinskaya et al., 2015) reveal a new protein, Best3, alternative splicing of which changes in the situation of tissue injury.

Analysis of Best3 protein expression by western blot showed that the general expression of Best3 protein in brain cortex and hippocampus was reduced after HI injury, and yet in our IHC experiments Best3 appeared in a specific cell population around the injured area. This data suggests that the western blot shows the overall expression in all cell types of the brain and cannot detect an increase in a small sub-population of

Best3-positive astrocytes. At the same time expression of Best3 protein was strongly reduced in cultured astrocytes after 48 h with thapsigargin treatment. Both HI injury and thapsigargin treatment caused ER stress, judging from the increase in CHOP. The Best3 protein was downregulated in cultured cells during ER stress, which calls into question whether Best3 is actually involved in resolving the ER-stress response in astrocytes. On the other hand, the severity of ER stress may differ between the brain astrocytes in HI and thapsigargin-treated cultured astrocytes, where thapsigargin presumably is a much stronger inducer of ER stress or induces ER stress by different mechanisms. Another reason may be that ER stress as such may cause downregulation of Best3 protein, whereas signals related to tissue injury and coming from other types of the cells cause upregulation of Best3 in astrocytes close to the injured area. It is also interesting to note that Best3 protein levels did not change significantly in the cytoplasmic fraction of brain cortex, suggesting the reduction in protein must occur in other compartments of the cells.

In summary, our study demonstrates expression of Best3 in a nestin-positive subpopulation of astrocytes in neonatal mouse brain after hypoxic-ischemic injury, with a general suppression of its expression in the cortex as a whole. Best3 also shows localized expression in astrocyte-like cells in human neonatal brain with pathologies. Analysis of alternative splicing for Best3 mRNA in mouse brain after HI injury showed that even if total expression of Best3 mRNA may not show much change, its splice variants respond differently and thus might have different roles in development of injury in brain tissue. The shorter splice variants are unlikely to function as ion channels (Srivastava et al., 2008) although they may have both intracellular and membrane localization. Their function is so far unknown. If Best3 actually functions as a regulator of apoptosis, it is possible that this property is associated with one variant as it is with Bcl-X, however, this is highly speculative and needs further investigation. Best3 expression in astrocytes near injury in the neonatal brain is a novel and important finding. As a marker for a certain cell population it can help understanding mechanisms of brain injury, where glial cells play an important role. A definitive determination of the role for Best3 in pathophysiology hinges upon the development of a knock-out model.

#### REFERENCES

## AUTHOR CONTRIBUTIONS

VG, HG, CM, and HN designed the study. VG, RV, and JW-A collected the data. VG, RV, JW-A, CM, and HN analyzed the data. VG, CM, and HN drafted the manuscript. All authors revised and approved the manuscript.

### FUNDING

This study was supported by grants from Vetenskapsrådet (2012-2992), CM, http://www.vr.se, Swedish state under the agreement between the Swedish Government and the county councils, the ALF-agreement (ALFGBG 432291), CM, http: //www.fou.nu/is/alfgbg, Leducq Foundation (DSRR\_P34404), CM, https://www.fondationleducq.org, Swedish Brain Foundation (FO2014-008), CM, http://www.hjarnfonden.se, Torsten Söderberg Foundation (M98/15), CM, http://www. torstensoderbergsstiftelse.se, Åhlén Foundation (CM and HN) http://www.ahlen-stiftelsen.se/index.html, Stiftelsen Wilhelm och Martina Lundgrens Vetenskapsfond (VG, CM, and HN) http: //www.wmlundgren.se, Stiftelsen Fru Mary von Sydows, född Wijk, donationsfond (VG) http://www.maryvonsydowstiftelsen. se, Svenska Frimurare Barnhusdirektionen i Göteborg (VG) https://www.frimurarorden.se/loger/tredje-fordelningen/gotaprovinsialloge/caritas. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

#### ACKNOWLEDGMENTS

We thank the families who consented to the "Postmortem MRI study" and our colleagues in the Perinatal Pathology Services at St Thomas' Hospital.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys. 2019.00023/full#supplementary-material

transient forebrain ischemia. Anat. Cell Biol. 46, 131–140. doi: 10.5115/acb. 2<IP_ADDRESS>

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Golubinskaya, Vontell, Supramaniam, Wyatt-Ashmead, Gustafsson, Mallard and Nilsson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Relationship Between Clinical Imaging and Neurobehavioral Assessment in Posthemorrhagic Ventricular Dilation of Prematurity

Rebecca A. Dorner 1,2 \*, Bruno P. Soares 2,3, Shenandoah Robinson2,4, Marilee C. Allen1,2 , Jamie Perin<sup>5</sup> and Vera Joanna Burton6,7

*<sup>1</sup> Neonatology, Johns Hopkins Hospital, Baltimore, MD, United States, <sup>2</sup> Neurosciences Intensive Care Nursery, Johns Hopkins Hospital, Baltimore, MD, United States, <sup>3</sup> Pediatric Radiology and Pediatric Neuroradiology, Johns Hopkins Hospital, Baltimore, MD, United States, <sup>4</sup> Pediatric Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States, <sup>5</sup> Biostatistics, Epidemiology, and Data Management Core, Johns Hopkins Hospital, Baltimore, MD, United States, <sup>6</sup> Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, MD, United States, <sup>7</sup> Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, United States*

Introduction: Neonatal intraventricular hemorrhage (IVH) and subsequent posthemorrhagic ventricular dilation and hydrocephalus of prematurity are associated with brain injury and neurodevelopmental impairment in the preterm population. Neuroimaging assesses cerebral injury and guides neurosurgical intervention; however, the relationship of head ultrasound (HUS) and magnetic resonance imaging (MRI) parameters to neonatal exams in this group has not been well described. The NICU Network Neurobehavioral Scale (NNNS) is a reproducible, highly reliable battery with motor and cognitive domain scores.

#### Edited by:

*Mary Tolcos, RMIT University, Australia*

#### Reviewed by:

MRI.

*Angela Leigh Cumberland, RMIT University, Australia Sandrine de Ribaupierre, University of Western Ontario, Canada*

\*Correspondence:

*Rebecca A. Dorner<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Embryonic and Developmental Physiology, a section of the journal Frontiers in Physiology*

Received: *15 October 2018* Accepted: *21 January 2019* Published: *11 February 2019*

#### Citation:

*Dorner RA, Soares BP, Robinson S, Allen MC, Perin J and Burton VJ (2019) The Relationship Between Clinical Imaging and Neurobehavioral Assessment in Posthemorrhagic Ventricular Dilation of Prematurity. Front. Physiol. 10:64. doi: 10.3389/fphys.2019.00064* Objective: To evaluate the relationship between neonatal neurobehavioral findings on the NNNS and measures of ventricular dilation and associated brain injury on HUS and

Materials and Methods: Neonates with IVH and ventricular dilatation with and without posthemorrhagic hydrocephalus were enrolled. NNNS exams were performed at approximately term age equivalent. HUS indices were measured on the last HUS before initial neurosurgical procedure or that with worst ventriculomegaly if no intervention. The posterior fossa was assessed with MRI at term. Descriptive statistics including medians, interquartile ranges, means, and percentages were performed. Correlations were estimated using Pearson's method.

Results: 28 patients had NNNS and HUS, and 18 patients also had an MRI. Ventricle size measures for the cohort were significantly above normal. Motor and cognitive subscores on the NNNS exam varied from established baseline scores for postmenstrual age. Children who required neurosurgical intervention had higher ventricle/brain ratios and worse NNNS habituation scores. Ventricle sizes were modestly correlated with motor abnormalities (0.24–0.59); larger anterior horn width correlated with nonoptimal reflexes, hypertonicity and hypotonicity. Ventricle sizes were modestly correlated with cognitive scores (−0.44 to 0.27); larger ventricular index correlated with worse attention. Periventricular hemorrhagic infarction correlated with worse habituation.

**99**

Conclusion: For this cohort of preterm infants with IVH, surgical intervention for posthemorrhagic hydrocephalus correlated with both larger degrees of ventriculomegaly and worse NNNS exams. Findings on both HUS and MRI correlated with motor and cognitive abnormalities on neonatal neurobehavioral exam, suggesting that larger neonatal ventricle sizes and white matter injury have detectable correlates on exam. The NNNS exam provides important additional information when assessing posthemorrhagic ventricular dilation and hydrocephalus of prematurity.

Keywords: intraventricular hemorrhage, prematurity, ventriculomegaly, hydrocephalus, development, neuroimaging

#### BACKGROUND

Despite advances in neonatal care, intraventricular hemorrhage (IVH) remains a serious complication of prematurity occurring in up to 30% of premature infants. It is estimated that one-third to half of infants with severe IVH (grades 3 or 4) develop posthemorrhagic ventricular dilatation (Alan et al., 2012; Robinson, 2012). Approximately 10% of all neonates with IVH and 20% of infants with severe IVH will need surgical intervention due to posthemorrhagic hydrocephalus (Alan et al., 2012; Robinson, 2012). Preterm children with posthemorrhagic ventricular dilatation, and especially posthemorrhagic hydrocephalus requiring surgical intervention, are at high risk for future neurodevelopmental challenges. Abnormal motor outcomes include a spectrum from cerebral palsy to minor neuromotor dysfunction/developmental coordination disorder (Spittle and Orton, 2014). Other areas of neurodevelopment impacted include: intellectual disability, fine motor coordination problems, memory and executive function deficits, chronic pain, behavior problems, depression and anxiety, attention deficit/hyperactivity disorder and cortical visual impairment (Ment et al., 1999; Brouwer et al., 2008; Roze et al., 2009; Goldstein et al., 2013; Guzzetta et al., 2013; Tsai et al., 2014; Holwerda et al., 2016). Despite these risks, limited data is available regarding observable neurologic abnormalities in the neonatal period in children with ventriculomegaly and hydrocephalus.

The NNNS, or NICU Network Neurobehavioral Scale, is a standardized assessment tool used to measure neurologic integrity and organization (i.e., active and passive tone, reflexes), behavioral and state regulation, and stress/abstinence (Lester et al., 2004). It has strong psychometric qualities and good validity in the assessment of motor and cognitive deficits in the newborn period (Noble and Boyd, 2012) and normative values are available (Tronick et al., 2004). Administration of the exam requires certification after formal instruction and reliability testing. The examination consists of 45 administration and 70 observation items. Summary scores are created for 13 neurobehavioral domains including: habituation, attention, handling, quality of movement, regulation, nonoptimal reflexes, asymmetrical reflexes, stress/abstinence, arousal, hypertonicity, hypotonicity, excitability, and lethargy (Tronick et al., 2004). See **Table 1** for descriptions of each summary score (Tronick et al., 2004). NNNS performance at various time points including birth, term age equivalent, and time of neonatal admission discharge, have been independently correlated with later developmental outcomes in children with a variety of highrisk conditions, including neonatal abstinence syndrome(for which it was originally developed) (Lester et al., 2004), premature and low birthweight babies (Lester et al., 2011; El-Dib et al., 2012), term babies with fetal risk factors (Appleton et al., 2016) and more recently children with hypoxic ischemic encephalopathy (Massaro et al., 2015). Importantly, the NNNS is especially equipped to assess early cognitive function and state regulation. Regulation and adaptation to negative stimuli in the NNNS can begin to predict behavioral regulation, "a higher function that would be missed by other routine neurological examinations" (Lester et al., 2011).

There are no studies in the literature on NNNS exams in children with posthemorrhagic hydrocephalus of prematurity. The NNNS may help caregivers better understand the infant by providing information about early cognitive and motor function. Additionally, exams can be used serially to recognize subtle changes indicative of worsening neurologic status to guide neuroprotective and rehabilitative interventions.

The aim of our study was to evaluate the association between quantitative measures of neurobehavioral performance in the neonatal period with current gold standard measures of cerebral injury, head ultrasound imaging (HUS) and adjunctive magnetic resonance imaging (MRI). We hypothesized that children with more ventricular dilation, white matter injury, and those requiring intervention for posthemorrhagic hydrocephalus would have worse neonatal neurobehavioral assessments in cognitive and motor domains. Neonatal neurobehavioral exams, if associated with both imaging and later developmental milestones, could function as a bridge between commonly-performed imaging and early milestone attainment.

**Abbreviations:** AHW, anterior horn width; BPD, Bronchopulmonary dysplasia; HUS, Head ultrasound; IVH, Intraventricular hemorrhage; MRI, Magnetic Resonance Imaging; NEC, Necrotizing Enterocolitis; NICU, Neonatal Intensive Care Unit; NNNS, NICU Network Neurobehavioral Scale; PVHI, Periventricular hemorrhagic infarction; RI, Resistive Index; ROP, Retinopathy of Prematurity; TOD, Thalamooccipital Distance; V/B Ratio, Ventricular/Brain Ratio; VI, Ventricular Index; VPS, Ventriculoperitoneal shunt; VSGS, Ventriculosubgaleal shunt.

TABLE 1 | NNNS summary score descriptions (Tronick et al., 2004).

# MATERIALS AND METHODS

Recruitment for this prospective cohort study was conducted from July 1, 2016–July 31, 2018. Eligible patients were preterm infants born in this time frame with IVH and ventricular dilatation, with or without posthemorrhagic hydrocephalus. Babies were either in-born or referred to the Johns Hopkins Hospital Level IIIB and IIIC neonatal intensive care units. Infants were excluded if they had suspected or confirmed genetic anomalies. Infants were identified via neurodevelopmental consultation from the Neurosciences Intensive Care Nursery or by request of the clinical team as part of our routine practice for infants with IVH and ventricular dilatation. After NNNS examination was performed for clinical purposes, parents were approached to consent to join the research cohort. Clinically obtained standard-of-care neuroimaging were recorded in addition to NNNS exam results (see **Figure 1**). Twenty-Eight eligible infants were enrolled in this time frame, including 88% (16/18) of infants admitted with posthemorrhagic hydrocephalus.

Baseline demographics and comorbidities, including IVH grade, sepsis, necrotizing enterocolitis (NEC), bronchopulmonary dysplasia (BPD) severity (Jobe and Bancalari, 2001) and retinopathy of prematurity (ROP), were recorded (**Table 2**). Covariates included postmenstrual age at time of NNNS, HUS, and MRI.

NNNS exams were performed at the closest time point to term age equivalent, ideally no sooner than 34 weeks or less than 10 days from any surgical procedure. Although NNNS exams can be performed in infants from 30 to 48 weeks, we wanted to compare our infant exams to wellestablished norms at term age equivalent. We chose no sooner than 10 days after a procedure, neurosurgical or otherwise, as we found that with any less time infants were excessively irritable. Per exam criteria, NNNS scores were only performed in medically stable infants with the agreement of the treating medical team. NNNS summary scores were grouped into motor and cognitive categories. Motor subscores include hypertonicity, hypotonicity, excitability, lethargy, nonoptimal reflexes, asymmetrical reflexes, and quality of movement. Cognitive subscores include habituation, attention, handling, regulation, stress/abstinence, and arousal.

The last HUS prior to surgical intervention was selected for measurements as it typically represents the most severe ventricular dilation. If not requiring surgical intervention, the HUS with worst ventriculomegaly was selected. A pediatric neuroradiologist (B.P.S) blinded to clinical course measured the following HUS indices of ventricular size: left and right ventricular index (VI), anterior horn width (AHW), thalamooccipital distance (TOD), and ventricle/brain (V/B) ratio. The left (L) is recorded for each sided measurement for brevity as there was no significant difference amongst right- and left- sided values. Doppler resistive indices (RI) in the anterior cerebral artery, both with and without gentle manual pressure on the transducer, as well as presence of periventricular hemorrhagic infarction (PVHI) and cystic changes were evaluated. The posterior fossa was assessed with MRI; the degree of fourth ventricle dilatation was qualitatively scored (0- none; 1-mild, fourth ventricle compresses vermis only; 2-moderate, compresses dorsal brainstem; 3-severe, compresses ventral brainstem against clivus; 4- massive, fourth ventricle extends superiorly to supratentorial compartment), and the anterior-posterior diameter of the cerebellum from the fastigial point to the posterior vermis (pyramid) was measured. Due to the anatomical distortion of brain parenchyma in the majority of the patients, we were not able to complete typical preterm MRI scoring systems such as the Kidokoro system (Kidokoro et al., 2013). For example, due to the severity of fourth ventricle dilation, cerebellar height and transcerebellar diameter were unable to be completed for the majority of patients. Additionally, HUS measurements of ventricular size agreed with MRI measures, and the sample size of MRIs was more limited than HUS. For this reason, the majority of our statistics analyze HUS measures, with the exception of the fourth ventricle size and anterior-posterior diameter of the cerebellum by MRI. Descriptive statistics including medians, interquartile ranges (IQRs), means, and percentages were used to summarize the cohort. Correlations were estimated using Pearson's method. All analyses were conducted in R version 3.5.0 (R Core Development Team, 2018).

# RESULTS

In total, 28 patients were consented and had the NNNS and HUS performed. Of these 28, 18 patients had the NNNS, HUS and MRI performed. Average gestational age at birth was 27.1(range 23.4– 34.2) weeks and average birthweight 1,115 (range 500–3816) g (**Table 2**). Mean postmenstrual age (PMA) at the time of the HUS was 32.2 weeks, NNNS exam was 37.2 weeks, and MRI was 39.5 weeks (**Table 2**). Ventricle size measures for the cohort were significantly above normal (**Table 3**). Motor and cognitive subscores on the NNNS exam (**Table 4**) varied from established baseline scores for term-corrected age (Lester and Tronick, 2004; Lester et al., 2011; Appleton et al., 2016). The only significant difference between infants with and without MRI was that babies with MRIs were more likely to have a 5 min APGAR score less than 7 (59 vs. 10%, p = 0.018).

Significant associations between imaging parameters and NNNS motor and cognitive subscores are summarized in **Figure 2**.

Abnormal radiology measures were associated with other abnormal radiology markers. Ventricle size measures (VI, AHW, TOD, and V/B ratio) were positively associated with one another (**Figure 2**). This is important as many studies have used different measurements to define ventriculomegaly (Sondhi et al., 2008; Maunu et al., 2011; Brouwer et al., 2012; Dorner et al., 2018). Larger AHW (but not other ventricle size measures) and larger fourth ventricle sizes were associated with smaller anteriorposterior diameter of the cerebellum (**Figure 2**). PVHI and cystic changes were associated; the odds ratio between PVHI and cystic changes was infinite (95% CI: 7.22—Infinity, p = < 0.001).

Features on both the HUS and NNNS exam were associated with receipt of neurosurgical intervention, where intervention was defined as either ventriculosubgaleal shunt (VSGS) placement or ventriculoperitoneal shunt (VPS) placement (**Table 5**). Fifty-Seven Percent of the cohort (16/28) required shunt placement; 2 patients required a VSGS, 11 had an initial VSGS followed by a VPS, and 3 patients had a VPS alone. Children receiving neurosurgical intervention were more likely to have larger ventricle sizes; means of ventricular size for those receiving neurosurgical intervention versus those not receiving intervention were statistically different as measured by: TOD (45.42 mm vs. 27.67, respectively, p = < 0.001), AHW (23.85 vs. 11.53 mm, respectively, p = 0.001), VI (24.62 vs. 14.20, respectively, p = 0.001), and V/B Ratio (0.615 vs. 0.378, respectively, p = 0.022) (**Table 5**). Neurosurgical intervention was most strongly associated with V/B ratio, with a correlation coefficient of 0.78 (**Figure 2**). The NNNS habituation subscore (response decrement to repeated auditory and visual stimuli) was the only additional parameter that differed between those infants receiving intervention and not receiving intervention (**Table 5**). Doppler RI values, with and without compression, did not correlate with receiving surgery.

Ventricle sizes were modestly correlated with motor abnormalities on NNNS exam (estimated correlations −0.72 to 0.39, **Figure 2**). Larger AHW sizes on HUS were associated with higher scores for nonoptimal reflexes (number of nonoptimal responses to reflex elicitation, such as excessive clonus), hypotonicity (number of hypotonic responses in arm, legs, trunk, or general tone) and hypertonicity (number of hypertonic responses in arm, legs, trunk, or general tone). Correlations between AHW and hypertonicity, hypotonicity, and nonoptimal reflexes subscores from the NNNS exam were: 0.59 (95% CI: 0.09–0.70, p = 0.018), 0.24 (95% CI: 0.08–0.70, p = 0.018), and 0.44 (95% CI: 0.08–0.70, p = 0.018), respectively (**Figure 2**).

Ventricle sizes were modestly inversely correlated with cognitive scores on NNNS exam as well (estimated correlations from −0.44 to 0.27, **Figure 2**). NNNS attention was negatively correlated with VI, where a larger VI was related to a lower average NNNS attention score (−0.06, 95% CI: −0.70 to −0.04, p = 0.034).

Of the 16 children in the cohort requiring intervention for posthemorrhagic hydrocephalus, 8 had antecedent Grade 3 IVH and 8 had PVHI (formerly called Grade 4 IVH). Presence of PVHI was associated with decreased or inadequate habituation to stimuli; the mean (standard deviation) for habituation for those with PVHI was 5.41 (1.75) and for no PVHI was 7.73 (1.18), p = 0.012.

*PMA, Postmenstrual Age.*

\**Completed 24 h in-utero after second dose.*

<sup>+</sup>*Per NICHD criteria (Jobe and Bancalari, 2001); moderate BPD as O<sup>2</sup> for* ≥*28 days plus treatment with* <*30% FiO<sup>2</sup> at 36 weeks' PMA and severe BPD as O<sup>2</sup> for* ≥ *28 days plus* ≥ *30% FiO<sup>2</sup> and/or positive pressure at 36 weeks' PMA.*

<sup>∧</sup>*Modified bell staging criteria for NEC (Lee and Polin, 2003).*

There was clustering of NNNS exam findings in preterm children with IVH. In terms of motor development, those with abnormally high levels of motor, state, and physiologic reactivity (higher excitability scores) were more likely to have abnormal movement (quality of movement score), less regulation, and higher stress/abstinence and arousal scores. Those with low levels of activity, or lethargy, were more hypotonic, and exhibited lower attention and arousal scores. Interestingly, hypotonicity was correlated with hypertonicity (correlation 0.46, 95% CI: 0.11–0.71, p = 0.014) and both were correlated with nonoptimal reflexes: (hypertonicity—0.71, 95% CI: 0.46– 0.86, p = 0.00) and (hypotonicity—0.45, 95% CI: 0.10–0.71, p = 0.015) (**Figure 2**).

NNNS scores were also associated with medical morbidities. Infants with severe ROP had higher NNNS Excitability [average (SD) 4.6 (1.9) vs. 2.3 (1.9), p = 0.006). Severe BPD was also associated with higher nonoptimal reflexes [5.4 (1.9) for those with BPD, 3.6 (1.6) without, p = 0.027] and lower movement [3.7 (0.8) for those with BPD and 4.7 (0.6) without, p = 0.003].

Of note, BPD does not confound the relationship of nonoptimal reflexes with AHW; the mean AHW for infants without BPD is 14.4 mm and with moderate/severe BPD was 18.2, p = 0.308. No NNNS items were independently associated with Sepsis or NEC.

#### DISCUSSION

This is the first study to analyze the relationship between HUS and MRI imaging with neonatal neurobehavioral exams in posthemorrhagic ventricular dilation and hydrocephalus of prematurity. It is also the first to describe the range of NNNS findings in this group. Our patient population had severe ventriculomegaly, with means for all ventricular size measurements on HUS far above norms for age. As intended, the NNNS exams were performed around term equivalent age (average 37.3 weeks) in the attempt to correlate with published norms for term babies (Tronick et al., 2004). MRIs were also performed at term age equivalent (39.5 weeks), but only fourth ventricle dilation and anterior-posterior diameter of the cerebellum were analyzed due to technical challenges and lack of standardization of MRI reads for posthemorrhagic ventricular dilatation.

For this cohort of 28 preterm infants, the degree of ventriculomegaly—using precise imaging parameters such as V/B ratio and AHW—correlated with surgical intervention, as expected. This suggests that decisions regarding intervention are made either on the basis of ventricular size alone or due to changes in ventricular size in combination with the clinical course. Importantly, different radiologic measurements of the lateral ventricles were all related to each other, suggesting that the choice of ventricular measurement may be less important than choosing a standard measure. Specific ventricular measurements may be superior, however, for different purposes. In this study, intervention had the highest association with V/B ratio, AHW with NNNS motor subscores and the VI with NNNS cognitive subscores. The NNNS cognitive exam, specifically the habituation subscore, also correlated with receipt of surgical intervention. The relationship of intervention and NNNS exam suggests that posthemorrhagic hydrocephalus and/or intervention itself may create a detectable signal of reactivity.

In this participant group, V/B ratio best correlated with receipt of intervention. V/B ratios > 0.35 on ultrasound have been associated with smaller cerebrum and cerebellar volumes on term-corrected MRI (Govaert and de Vries, 1997). Decreased cerebral and cerebellar parenchymal volumes are associated with lower cognitive and language scores, abnormal motor outcome and processing speed (Nosarti et al., 2008; Maunu et al., 2009). Smaller cerebellar size, which has previously been shown to correlate with worse future cognitive outcomes (de Vries et al., 2019), was associated both with larger AHW and more fourth ventricle dilatation in our population.

Higher AHWs were associated with worse motor exams, specifically a higher number of nonoptimal reflexes and more hypotonicity. An increase in AHW has been suggested to be a more sensitive marker for early worsening of hydrocephalus and is seen subjectively as rounding of the frontal horns (Brouwer et al., 2010). Both lower (>6 mm) and higher (>10mm) AHW

*PVHI, Periventricular Hemorrhagic Infarction.*

\**4th ventricle dilatation: 0-none, 1- compresses vermis, 2-compresses dorsal brainstem, 3-compresses ventral brainstem against clivus, 4- to supratentorial compartment.*

size cutoffs for surgical intervention have been used (Leijser et al., 2018; de Vries et al., 2019). The fact that early AHW dilatation correlates to exam findings in the neonatal period provides an opportunity to follow this parameter in a more multifaceted way.

Ventricle sizes and white matter injury were associated with worst neonatal cognitive assessment scores. Children with larger ventricles had lower attention scores; larger VI measurements were negatively correlated with the NNNS attention cognitive subscore. Attention is the ability to pay attention to salient stimuli in the environment. Additionally, PVHI, present in 10 of our children, correlated with decreased habituation to stimuli. Habituation is the ability to stop responding to (i.e., to ignore) repetitive stimuli. An inability to inhibit a response is one of the earliest measures of executive function and is well-developed by 6–7 months of age in typically developing infants (Diamond et al., 1994). In general, the children with larger VI and with PVHI were more irritable and responded more continuously to noxious stimuli, at the expense of paying attention to important stimuli such as a face or voice. It is possible that difficulty with attention and habituation in this early time frame are signs of later difficulty with inhibition and attention and a marker for later executive dysfunction (Diamond et al., 1994). Executive function and attention difficulties are well described in the hydrocephalus population (Holwerda et al., 2016). This early internal disorganization and over-responsiveness may be early markers of such difficulties.

Fourth ventricle dilatation on MRI was not associated with changes in NNNS exam (**Table 5**). While this attempt to evaluate fourth ventricle dilatation did not correlate with neonatal exams, this parameter has not yet been adequately assessed in this population. Further data are needed to understand the impact of fourth ventricular dilatation and on exams and outcomes.

We also saw informative patterns in the NNNS exam. Both ends of the arousal spectrum were seen; abnormally high excitability, stress and arousal score subgroups were present as were lethargy, hypotonia, and lower attention and arousal scores. TABLE 4 | NICU Network Neurobehavioral Scale (NNNS) measurements.

\**, per Maternal Lifestyle Study (Lester et al., 2011).*

*SD, Standard deviation.*

These groups were not mutually exclusive; indeed, many children scored high on both lethargy and excitability, meaning during the exam they were often either too sleepy or too irritable. Both result in a failure to interact and pay attention to informative stimuli. Along the same lines, hypotonicity within our cohort was correlated with hypertonicity (correlation 0.46, 0.11–0.71, p = 0.014) and both were correlated with nonoptimal reflexes: (hypertonicity- 0.71, 0.46–0.86, p = 0.00) and (hypotonicity- 0.45 (0.10–0.71, p = 0.015). These patterns fit what we often see as a

concerning pattern in ventricular dilatation; axial hypotonia and poor head control with high appendicular tone in the extremities. Together, these patterns of extreme reactions reflect a limited ability of the injured brain to regulate responses.

It is important to consider that ventricular dilatation and hydrocephalus are not isolated in preterm infants; the relationship of HUS and MRI measures to NNNS scores may be influenced by comorbidities. We found that severe ROP and moderate/severe BPD were associated with abnormalities in NNNS items regardless of ventricular size. Severe ROP was associated with higher NNNS excitability scores and moderate/severe BPD was associated with more nonoptimal reflexes and lower movement scores. Although both moderate/severe BPD and AHW were both associated with worse non-optimal reflexes, on further analysis BPD did not confound the relationship of non-optimal reflexes with AHW. It will be important in the future to consider both effects from hydrocephalus and comorbid conditions to better describe the contribution of hydrocephalus to neurodevelopmental delay.

Our data suggests that ventricular size is associated with neonatal exam as larger ventricle sizes were related to poorer scores on both motor and cognitive sections of the NNNS exam. The observed modest correlations are not unexpected; likely other factors in the infant's clinical course might also affect exam performance. Given that aspects of the neonatal neurobehavioral exam are associated with ventricular size measurements, our data suggests that exam and imaging may provide complementary and additive information on existing neurodevelopmental status.

There are limitations to the present study. Although a large portion of infants with posthemorrhagic hydrocephalus were captured, not all consecutive admissions to the NICU with IVH TABLE 5 | Means of HUS and MRI parameters, NNNS subscales by infants with and without neurosurgical intervention for hydrocephalus.

\**Significance determined by Student's t-test or by Fisher's exact test for categorical factors.*

and some ventricular dilatation completed a NNNS evaluation; this may introduce selection bias. The average postmenstrual age at NNNS examination was 37.2 weeks, but some infants had exams earlier, as seen in **Table 2**. The nature of our NICUs are such that convalescing preterm infants, even after neurosurgery, are often transferred to lower acuity locations. As such, some infants were examined before term-corrected age due to imminent discharge. The impact of postnatal age on the NNNS is unclear in this study; previous studies have shown that postmenstrual age has an effect on NNNS subscores (Spittle et al., 2016). Additionally, given our small sample size, inclusion and analysis of all potential covariates was not feasible and we could not evaluate the cumulative impact of measured co-morbidities, such as NEC, on NNNS scores. Adequately powered larger samples are needed to address this important issue.

# CONCLUSION

In summary, for this cohort of 28 preterm infants with IVH and ventricular dilatation, surgical intervention was associated with degree of ventriculomegaly, using precise imaging parameters, as well as NNNS exam. Findings on both HUS and MRI imaging correlated with motor and cognitive abnormalities on neonatal neurobehavioral exam. These results suggest that larger neonatal ventricle sizes and white matter injury have detectable correlates on exam in this population. The NNNS exam may be additive with imaging in assessment of a common form of neonatal hydrocephalus. Importantly, the NNNS is identifying deficits at term equivalence, and even earlier, during a preterm infant's NICU stay. This risk identification is much earlier than typical neurodevelopmental exams done in follow-up care. This can allow for better developmental care during the NICU as changes can be made to the environment and rehabilitative interventions begun as soon as neurobehavioral deficits are identified (Mahoney and Cohen, 2005; Symington and Pinelli, 2006; King et al., 2008; Als and McAnulty, 2011; Fucile et al., 2011). It will be important to achieve longer-term follow-up in these infants to determine if these findings correlate with later neurodevelopmental disability.

## REFERENCES

## ETHICS STATEMENT

This study was carried out in accordance with the recommendations and approval of the Institutional Review Board at Johns Hopkins Hospital. Parents of all pediatric subjects gave written informed consent in accordance with the Declaration of Helsinki.

# AUTHOR CONTRIBUTIONS

RD and VB conceived of and designed the study, performed exams, supervised data entry and analysis, analyzed and interpreted data, and wrote and edited manuscript. BS performed neuroimaging analysis. BS, JP, SR, and MA analyzed data and edited manuscript.

## ACKNOWLEDGMENTS

We would like to thank Veena Billioux for her help with statistical work conceptualizing this project. This project was supported by the Thomas Wilson Sanitarium for Children of Baltimore City awarded to RD and MA and the NIH T32 Training Grant (T32HL125239-03) awarded to RD.

intraventricular hemorrhage, post-hemorrhagic hydrocephalus, and associated brain injury. J. Perinatol. 38, 1431–1443. doi: 10.1038/s41372-018-0209-5

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Dorner, Soares, Robinson, Allen, Perin and Burton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Measurement of Neurovascular Coupling in Neonates

Dries Hendrikx1,2, Anne Smits3,4, Mario Lavanga1,2, Ofelie De Wel1,2 , Liesbeth Thewissen3,4, Katrien Jansen3,4,5, Alexander Caicedo<sup>6</sup> , Sabine Van Huffel1,2 and Gunnar Naulaers3,4 \*

<sup>1</sup> Department of Electrical Engineering, KU Leuven, Leuven, Belgium, <sup>2</sup> imec, Leuven, Belgium, <sup>3</sup> Department of Development and Regeneration, KU Leuven, Leuven, Belgium, <sup>4</sup> Neonatal Intensive Care Unit, University Hospitals Leuven, Leuven, Belgium, <sup>5</sup> Child Neurology, University Hospitals Leuven, Leuven, Belgium, <sup>6</sup> Facultad de Ciencias Naturales y Matemáticas, Universidad del Rosario, Bogotá, Colombia

Neurovascular coupling refers to the mechanism that links the transient neural activity to the subsequent change in cerebral blood flow, which is regulated by both chemical signals and mechanical effects. Recent studies suggest that neurovascular coupling in neonates and preterm born infants is different compared to adults. The hemodynamic response after a stimulus is later and less pronounced and the stimulus might even result in a negative (hypoxic) signal. In addition, studies both in animals and neonates confirm the presence of a short hypoxic period after a stimulus in preterm infants. In clinical practice, different methodologies exist to study neurovascular coupling. The combination of functional magnetic resonance imaging or functional near-infrared spectroscopy (brain hemodynamics) with EEG (brain function) is most commonly used in neonates. Especially near-infrared spectroscopy is of interest, since it is a non-invasive method that can be integrated easily in clinical care and is able to provide results concerning longer periods of time. Therefore, near-infrared spectroscopy can be used to develop a continuous non-invasive measurement system, that could be used to study neonates in different clinical settings, or neonates with different pathologies. The main challenge for the development of a continuous marker for neurovascular coupling is how the coupling between the signals can be described. In practice, a wide range of signal interaction measures exist. Moreover, biomedical signals often operate on different time scales. In a more general setting, other variables also have to be taken into account, such as oxygen saturation, carbon dioxide and blood pressure in order to describe neurovascular coupling in a concise manner. Recently, new mathematical techniques were developed to give an answer to these questions. This review discusses these recent developments.

Keywords: neurovascular coupling, neonates, cerebral blood flow, EEG, NIRS, graph theory

# INTRODUCTION

Neurovascular coupling refers to the regulation mechanism that links the transient neural activity to the subsequent change in cerebral blood flow. The first reports on this regulation mechanism date from more than 100 years ago (Donders, 1851). Neurovascular coupling can be studied from different points of view, including a macroscopic and microscopic perspective. At the macroscopic

#### Edited by:

Justin Dean, The University of Auckland, New Zealand

#### Reviewed by:

Rathinaswamy Bhavanandhan Govindan, Children's National Health System, United States Rebecca Maree Dyson, University of Otago, New Zealand

> \*Correspondence: Gunnar Naulaers<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Embryonic and Developmental Physiology, a section of the journal Frontiers in Physiology

Received: 14 October 2018 Accepted: 21 January 2019 Published: 18 February 2019

#### Citation:

Hendrikx D, Smits A, Lavanga M, De Wel O, Thewissen L, Jansen K, Caicedo A, Van Huffel S and Naulaers G (2019) Measurement of Neurovascular Coupling in Neonates. Front. Physiol. 10:65. doi: 10.3389/fphys.2019.00065

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level, the extremely high vascularization and tight regulation of the cerebral blood flow provides the brain with adequate blood flow for a given metabolic demand. There is a close temporal and regional link between neuronal activity and cerebral blood flow, brain regions with high activity receive an increased amount of blood flow. At the microscopic level, the neurovascular unit is comprised of the vascular smooth muscle, the neuron and the astrocyte glial cell. Glutamate is released upon neuronal activation, which causes both neurons and astrocytes to transmit signals in order to regulate cerebral blood flow. Although it was assumed for a long time that these signals were only associated with a vasodilatory effect, recent studies show a more complex balance of vasodilation and vasoconstriction, in which both chemical and mechanical effects play an important role. Known chemical signals include prostaglandins, nitric oxide and adenosine, secreted by both neurons and astrocytes, which cause a vasodilation of the smooth muscle cell (**Figure 1**). In addition, astrocytes also secrete other vasodilators like potassium and epoxyeicosatrienoic acids. Besides these vasodilators, astrocytes are also known to secrete arachidonic acid, which has a vasoconstrictive effect. All these mediators have a direct impact on the smooth muscle of the arterioles and therefore control cerebral blood flow in a direct way. In addition to these mechanisms present in the neurovascular unit, a second mechanism was recently described by which also pericytes around the capillaries cause vasodilation, probably also playing a role in the local flow distribution. For further description of these mechanisms, we refer to recent reviews (Huneau et al., 2015; Phillips et al., 2016). As a concluding remark, it is also important to mention that most of the studies on neurovascular coupling are done in the adult population; currently, we do not know whether these mechanisms are present in preterm neonates, and if they work in the same way as described in adults.

In general, there are two main categories of studies to describe and investigate neurovascular coupling. First of all, there are spatiotemporal studies, which link changes in local blood flow to an artificially applied stimulus. Second, general studies are available that investigate resting-state neurovascular coupling using spontaneous electrical activity of the brain.

# SPATIOTEMPORAL STUDIES

In order to test neurovascular coupling hypotheses in adults, spatiotemporal studies are standard of practice. In such studies, a standardized motor, visual, auditive, or cognitive task is given and simultaneously, the flow and/or activity in the brain is measured. There are different recommendations regarding optimal stimulus and assessment methods for adult research (Huneau et al., 2015). The most widely used stimulus in adults is finger tapping, which is known to cause an increase in blood flow at the contralateral motor cortex (Dettmers et al., 1996). In (preterm) neonates, spatiotemporal studies are also of interest, but they are inherently more difficult to perform. The majority the spatiotemporal studies in neonates makes use of visual stimuli, which includes the projection of checkerboard patterns on LCD displays (Meek

et al., 1998; Liao et al., 2010), or flashing LEDs (Karen et al., 2008). The latter has the advantage that the neonates can be kept asleep throughout the measurements. Other less frequently used stimuli in preterm neonates include auditory (Sakatani et al., 1999; Zaramella et al., 2001) and somatosensory stimuli (Erberich et al., 2006; Arichi et al., 2010, 2012). To the best of our knowledge, guidelines regarding optimal stimulus selection for newborns are not available.

The classical response to the stimulus described in adults is a sudden increase in cerebral blood flow and cerebral oxygenation with a secondary, less pronounced decrease (Heekeren et al., 1997). This type of response is typically called 'functional hyperemia,' and is generally referred to as a positive response. A negative response, on the other hand, occurs when the increase in blood flow to the brain is insufficient to meet the metabolic demand. In this setting, however, the relation of functional hyperemia to neuronal metabolisms is unclear (Hillman, 2014). Since functional hyperemia is observed in hyperoxic, hypoglycemic and hyperglycemic states, it is clear that the blood flow increases are not simply triggered by local sensing of depleted nutrients (Powers et al., 1996; Wolf et al., 1997; Lindauer et al., 2010). The relative delay in the peak of increased blood flow further confirms that neurons do not rely upon functional hyperemia to meet their initial needs for increased oxygen and glucose, since neuronal firing may have ended prior to measurable changes in blood flow (Hillman, 2014).

In practice, different techniques are used to test neurovascular coupling in humans. In spatiotemporal studies, functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) are the most frequently used methods. The fMRI studies investigate the response of the blood oxygen level dependent (BOLD) signal. According to the definition,

fMRI and fNIRS studies only measure blood flow changes and thus not neural activity. However, since the change in blood flow occurs mainly at the part of the brain responsible for the imposed activity (verbal, motor, visual), the measured response can be a good surrogate measurement for neurovascular coupling. In addition to the sign of the underlying mechanism (positive versus negative response), fMRI studies are also used to determine the location of brain activity after a specific stimulus. In healthy humans, cognitive, verbal, and motor tasks will on average lead to a 10–20% increase in cerebral blood flow in the posterior cerebral artery and a 5–8% increase in the middle cerebral artery (Huneau et al., 2015). In fNIRS studies, a positive response is comprised of an increase in oxyhemoglobin and total hemoglobin, together with a slight decrease in deoxygenated hemoglobin, which overall leads to an increase in tissue oxygenation. In adults, impaired neurovascular coupling has been described in different pathological conditions like stroke, hypertension, hypotension, autonomic dysfunction, Alzheimer's disease, diabetes, and traumatic brain injury (Duschek and Schandry, 2004; Girouard and Iadecola, 2006; Azevedo et al., 2011; Vetri et al., 2012; Phillips et al., 2014; Jang et al., 2017). In addition, also smoking and healthy aging are associated with a negative effect on neurovascular coupling (Boms et al., 2010; Tarantini et al., 2017; Nowak-Fluck et al., 2018).

Besides the use of fMRI and fNIRS, other techniques can also be used to carry out spatiotemporal studies. These techniques are however less frequently used – and, to the best of our knowledge – not yet employed in studies on neurovascular coupling in preterm neonates. fMRI BOLD imaging depends on specific coupling relations between cerebral metabolic rate of oxygen, cerebral blood flow and cerebral blood volume, which are not unambiguously described in preterm neonates to date (Muramoto et al., 2002; Kusaka et al., 2004). Furthermore, these relations are altered in case of brain injury (Lin et al., 2016). Perfusion-MRI techniques, such as arterial spin labeling (ASL) MRI, can be used to overcome these limits (Tortora et al., 2017). In addition, oxygen based techniques such as BOLD fMRI and fNIRS only perform indirect measures of functional hyperemia (Hyder, 2009). More precise, direct measurements of cerebral blood flow can be obtained using laser Doppler flowmetry (Dyson et al., 2014) or laser speckle imaging (Farraro et al., 2016). A disadvantage of these techniques is the sensitivity of the probe, which results in signals that are prone to artifacts, and which impedes continuous measurements – typically, laser imaging measurements are taken at discrete points in time. A new promising technique is functional ultrasound imaging, where fast ultrasound sonography is coupled with EEG (Demene et al., 2017).

Recent studies suggest that neurovascular coupling in neonates and preterm born infants can differ compared to adults. This is typically attributed to the fact that many of the components involved in actuating and propagating the hemodynamic response are still in further development, including perivascular cells such as astrocytes and pericytes (Kozberg and Hillman, 2016a). Neural and vascular networks develop, expand and are then selectively pruned over the first year of human life. In addition, the metabolic demands of

the newborn brain are still evolving and are vastly different compared to the adult brain. On the first day after birth, very low values of brain oxygenation are observed, in combination with high values of cerebral oxygen extraction (Brew et al., 2014). Brain oxygenation increases during the first days of life, while cerebral oxygen extraction decreases. In extremely preterm infants, there is no correlation between cerebral blood flow and spontaneous changes in the cerebral metabolic rate of oxygen during the first 2 days after birth (Kissack et al., 2005; Wong et al., 2009). Instead, changes in cerebral oxygen extraction rather than cerebral blood flow meet changes in oxygen requirements arising from variations of the cerebral metabolic rate of oxygen. The vast differences in both neural and vascular network structure, as well as substantially different metabolic needs of the preterm brain are highly likely to affect early postnatal neurovascular coupling.

The differences in neurovascular coupling hypotheses in neonates versus adults are confirmed in fMRI studies, which are discussed in this paragraph, and in fNIRS studies, which are summarized in the next paragraph. Heep et al. (2009) described a negative response in preterm infants after stimulation, which was also confirmed by others (Yamada et al., 1997; Born et al., 1998; Erberich et al., 2006). Arichi et al. (2012) found a positive BOLD response in adults, term infants and preterm infants. Furthermore, they identified a systematic maturational trend in terms of decreasing time-to-peak and increasing positive peak amplitude. These findings suggest that in young infants the increase in cerebral oxygen consumption may be relatively greater than the corresponding increase in cerebral blood flow during functional activation. The age-dependency of the neurovascular coupling was also confirmed in a rodent model by Kozberg et al. (2013). The development of the neurovascular coupling alongside neuronal development in the postnatal brain suggests that in the developmental period the brain may be experiencing differences in energy supply and demand dynamics compared to the adult brain (Kozberg and Hillman, 2016a).

Another approach to study the neurovascular coupling is by means of fNIRS. Again, the results and conclusions of the different studies available in literature are not unambiguous. Liao et al. (2010) described a normal hyperemic signal using NIRS in the visual cortex after visual stimulation in a cohort of term neonates in the first weeks of life. Furthermore, no simultaneous reaction was observed at the motor cortex, indicating that the observed response was a local reaction. On the other hand, however, an increase in brain deoxygenation was observed in awake infants in another study (Meek et al., 1998). Verriotis et al. (2016) described the response on noxious (heel lance prick) and innoxious stimuli (tactile cutaneous stimulation) in 30 term infants. In general, noxious stimulation was found to elicit a more pronounced hemodynamic response than innoxious stimulation. However, the hemodynamic pattern after stimulation was observed to be characterized by pronounced inter-patient differences, which might suggest that increased oxygen consumption does not always lead to regional overperfusion, likely due to the immature vascular regulation or to greater metabolic demands of neurotransmission in unmyelinated white matter.

In addition to the fNIRS studies on neonates, other authors have used this technique on animals. Nakamura et al. (2017) studied the neurovascular coupling in newborn lambs. They found subjects with a positive response, i.e., an increase in oxygenated hemoglobin, but also subjects with a negative response after a motor stimulation. Furthermore, blood pressure at the start of the investigation did not seem to have any influence, but the negative response became positive in hypercapnia (Nakamura et al., 2017). Kozberg et al. (2016) studied the neurovascular coupling in rats of different ages. They described a less positive response in rats of postnatal age P10–P13, and even a negative response in rats of postnatal age P7–P8. In order to compare these results to the human population, it is common to assume that the newborn full-term and 1 year old human brain are developmentally equivalent to the postnatal days 7 and 14 in rats, respectively, based on the codevelopment of factors such as eye opening and myelination (Quinn, 2005).

In addition to the uncertainty regarding the sign of the hemodynamic response (positive versus negative) in neonates, more questions arise when we focus on specific pathologic conditions like bronchopulmonary dysplasia, congenital heart disease and pulmonary hypertension, where a lower oxygen saturation is present (Rosengarten et al., 2006). Furthermore, the effect of anesthetic and sedative drugs, which are frequently used in fMRI studies, needs to be investigated (Franceschini et al., 2010). From a broader perspective, also the effect of other drugs, such as anti-epileptic drugs on neurovascular coupling in neonates is not properly defined to date (Schwartz, 2007). Since an elaborate discussion on these topics is out of the scope of this text, we will not go into further detail.

It is important to mention that studies both in animals and neonates confirm the presence of a short hypoxic period in the brain after a stimulus in preterm infants. These short hypoxic moments are hypothesized to cause an increase in vascular endothelial growth factor and oligodendrocyte-encoded hypoxiainducible factor function, resulting in new angiogenesis and neurogenesis in the brain (Kozberg and Hillman, 2016b). This hypothesis could explain the mechanisms of the positive effect of sensitive stimulation and the negative effect of overstimulation in newborns (Kozberg and Hillman, 2016a).

Despite the fact that the fMRI and fNIRS studies listed above aim to investigate neurovascular coupling, they actually only describe the hemodynamic changes observed in the brain after applying a stimulus. These studies can be used to describe these hemodynamic changes after a particular stimulus and how these hemodynamic patterns change with age, from preterm neonates to adults. Precise studies on neurovascular coupling are based on concomitant measurements of brain hemodynamics and brain function (electrical activity of the brain). This can be done by combining EEG measurements with fMRI measurements (Mullinger and Bowtell, 2011), with fNIRS measurements (Shin et al., 2018) or with positron emission tomography measurements (Juhász et al., 2000), although such studies are challenging from a technical point of view due to for example electrode heating (Vanhatalo et al., 2014) and the presence of numerous artifacts (Galderisi et al., 2016; Abrue et al., 2018). Therefore, such studies are very scarce, especially in (preterm) neonates. Singh et al. (2014) explored the use of concomitant multichannel EEG-fNIRS measurements during epileptic seizures in neonates and found a hemodynamic response to seizure activity which consists of an initial increase in cortical blood volume prior to a large and extended decrease typically lasting several minutes.

#### GENERAL STUDIES

Another approach to investigate the neurovascular coupling is to study the relation between the general metabolism of the brain and cerebral blood flow. Obviously, EEG is the most commonly used non-invasive method to assess the electrical activity of the brain, although it remains limited to the cortical activity. Cerebral blood flow can be measured at the bedside in a non-invasive way using transcranial Doppler ultrasound or nearinfrared spectroscopy (NIRS). Therefore, these two modalities are generally used in general studies on neurovascular coupling. Both technologies are safe, relatively cheap and easy to use, however, ultrasound imaging has the disadvantage of being investigator dependent.

Roche-Labarbe et al. (2007) used NIRS in combination with EEG and found a decrease in oxygenation during bursts (high EEG activity in very preterm infants), followed by an important overflow (increase in cerebral oxygenation), suggesting a neurovascular signal that differs from the pattern commonly observed in adults. Tataranno et al. (2015) investigated functional brain activity and found an increase in oxygen extraction in preterm infants with increased electro-cerebral activity. In another study, Mahmoudzadeh et al. (2018) compared the neurovascular coupling in preterm neonates with versus without intraventricular hemorrhage. They reported that in neonates with brain injury the cerebral vascular network was unable to compensate for the increased metabolism resulting from neuronal activation in response to external stimulation. Pfurtscheller et al. (2008) investigated the behavior of the heart rate (HR) variability during the preterm EEG transients and found that EEG burst are associated with increases in HR. In addition, this positive variation of HR was found to disappear over age with the emergence of a continuous EEG trace.

The most recent advancements in signal processing allow an investigation of the neurovascular coupling in a non-invasive way focusing on "resting-state" conditions. In such a setting, the clinical goal is to develop a non-invasive and continuous measurement of the neurovascular coupling, which can be used as bedside monitoring without evoking a potential, i.e., inducing an artificial response via a stimulus. In the past decades, one of the most commonly studied neuronal couplings has been the scalp EEG connectivity, for which a large variety of methodologies has been developed (Pereda et al., 2005). In general, the methods can be classified in functional methods on the one hand, if only statistical correlations among the time series are taken into account, versus effective methods on the other hand, if the directionality of the coupling is considered as well (Friston, 2011). An overview of these methodologies is presented in **Table 1**.

Based on the EEG connectivity literature, further coupling methodologies have been developed to assess the interaction

among signals with different origins or sources. These methodologies can consequently be used to assess regulation mechanisms like neurovascular coupling. Clinical examples on neurovascular coupling are the studies of Pfurtscheller et al. (2008) and Roche-Labarbe et al. (2008). Although these studies investigate the interaction of signals of different nature, these studies do not assess the concise nature of their interaction. The main difficulty to tackle in studies on multimodal integration is the fact that the different time series operate on different time scale, while simultaneously interacting with each other with a certain linear or non-linear degree.

In order to overcome this limitation, the new field of Network Physiology has the scope to describe how the time series of multiple origins interact, either linearly or non-linearly at a specific scale, in a graph or lattice structure (Bashan et al., 2012; Bartsch et al., 2015). Based on a Network Physiology framework, this review will explain new mathematical models to assess the neurovascular coupling, with a specific focus on:

#### Preprocessing Steps

In order to measure the neurovascular coupling in a correct and robust way, it is very important that the dynamics of the various signals are represented and matched in a careful way. In general, biomedical signals arise from a variety of sources, including movements, breathing, electrical activity of neurons (EEG) and optical absorption of light (NIRS), among others (Clemenson and Lancaster, 2016). Due to the different sources, biomedical time series are typically associated with different time scales of operation. An EEG signal, for example, changes rapidly due to the (de)synchronization of numerous neurons, while a NIRS signal only changes very slowly, due to the fact that the hemodynamic effects that it captures work on a slow time scale. When one wants to compute the interaction between two signals, it is important to define on which time scale the coupling is ought to be computed (the signals are interacting), and the signals have to be matched correspondingly. Although biological systems are extremely complex, it is possible to decompose their macroscopic behavior using mathematical techniques (**Table 1**).

One of the concepts that is frequently used is the notion of frequency, which gives an indication about the dynamics of a signal. By looking at the frequency content of a signal, we can make conclusions on how fast (slow) the signal changes over time. Information about the frequency content of a signal can be deduced from its frequency spectrum, which can be computed using a mathematical transformation (Challis and Kitney, 1991). Some signals change very rapidly, and are therefore mainly associated with high-frequency components. Other signals change very slowly; the frequency spectrum of such signal is mainly comprised of a very narrow band of low frequency components. Examples of the former and the latter include EEG and cerebral hemodynamics, measured by NIRS, respectively. In addition, one can also describe the biological oscillations at different time scales via a time-frequency representation. The extraction of the oscillatory components is not only useful to reduce the complexity of the system, but can also be used to describe the underlying physiology. In the world of neurovascular coupling, the brain heart interaction takes place at long-term scale (low frequency activity), since the NIRS reflects the cardiovascular oscillations which develop in a timeframe of seconds. The speed of fluctuations of NIRS is then much lower compared to EEG oscillations. In addition, however, a NIRS signal can also contain frequency components which are faster, which are caused interference from cardiac pulsations. These frequency components are typically not considered in EEG-NIRS neurovascular coupling studies.

In addition, the recent literature on EEG-NIRS integration also focuses on the low-frequency activity of the EEG, i.e., the delta oscillations. According to Knyazev, the delta oscillation belongs to the "old brain," which phylogenetically traces back to lizards (Knyazev, 2012). This explains why delta brushes dominate the preterm and the neonatal EEG activity. During the development, the scalp electrical activity tends to be discontinuous and is centered around a frequency between 1 and 2 Hz, which defines the periodicity of the bursts in the cortical trace (Andre et al., 2010). Consequently, delta oscillations are of primary importance in the preterm brain. An additional reason is the implication that delta oscillations regulate basic homeostatic needs, such as the blood flow circulation and normotension enforcement. The slow-wave EEG is considered as the expression of the regulation of the brainstem (which is in charge of cerebral hemodynamics) or, at least, as a projection of the subcortical activity to the cortical areas.

Finally, when comparing biomedical signals of different origin, it is very important that the time scales of the various signals are synchronized. In the setting of neurovascular coupling, this means that a rapidly changing neuronal signal (EEG) has to be matched with a very slowly varying cerebral hemodynamics signal (NIRS). In general, there are multiple approaches to overcome

this problem. However, it is common practice to use a running estimate of the power of the slow wave activity, i.e., the delta oscillations. There are mainly three methods to extract such a running measure from the EEG. The most commonly used methodology is the use of the wavelet transform and average the power in the frequency band of interest, as reported byClemenson and Lancaster (2016). A second option is to extract the delta band from an estimate of the power spectral density that is computed in running windows (Faes et al., 2015). The last option, which is very simple, but also very effective, is the use of the running root mean square (RMS) value, where the window length is chosen large enough to cut off the high frequency components, as reported by Caicedo et al. (2016).

#### Methodologies to Assess Coupling

Despite the fact that research on bedside markers of neurovascular coupling is a very new domain, there are some studies available that have investigated how different signal processing techniques can be used to quantify the neurovascular coupling based on spontaneous cerebral and vascular activities. As mentioned before, these studies mainly focus on the quantification of interaction between EEG and NIRS as surrogates for brain function and brain hemodynamics, respectively.

In signal processing terms, this interaction can be regarded as a pairwise similarity, which denotes the degree to which a given time series resembles another one. Therefore, pairwise similarity measures can be used to evaluate a regulation mechanism such as neurovascular coupling. As mentioned before, the pairwise similarity belongs to the functional connectivity field, which focuses (only) on the statistical correlation among the investigated signals (**Table 1**).

The most commonly used linear similarity methods are the cross-correlation function (CCF) and magnitude squared coherence (MSC), which quantify the linear correlation between two signals at different lags or frequencies, respectively. In the world of neurovascular coupling, the former has been applied by Bari et al. (2011) to study the regulation mechanism in adults during a divided attention cognitive task. In summary, the CCF was able to reveal the presence of a cascade of responses, which was observed to be strongly influenced by the task performed by the patient. Govindan et al. (2016) used the MSC to obtain a measure for the degree of coupling between EEG and NIRS data in premature neonates and identified more signal interaction (higher MSC values) in infants without brain injury compared to other patients that died of brain injury during the course of the study.

A well-known extension of these linear methods is the wavelet coherence, which is a time-variant representation of the neurovascular coupling dynamics, as performed by Musizza et al. (2007) and Chalak et al. (2017). The main advantage of using a wavelet coherence approach is the fact that this technique is able to deal with the non-stationary nature of NIRS and EEG signals. Chalak et al. (2017) performed the time-frequency coherence analysis on the raw data of amplitude-integrated EEG and brain oxygenation signals, while Musizza applied the continuous wavelet transform to extract the oscillation of interest (with frequency below 0.05 Hz). In both studies, the authors emphasize the importance of phase coupling, which defines at which time delay the oscillations lock in.

A further development consists in the usage of the effective connectivity, which takes into account the directionality of the neurovascular coupling (**Table 1**). Caicedo et al. (2016) have applied the linear transfer entropy in neonates to quantify the directionality between NIRS signals and the RMS of the EEG (where the window length was chosen large enough to capture only the delta oscillations). In general, transfer entropy assesses whether the current transient of a signal can be explained by the past trajectory of another signal (Lee et al., 2012). More specifically, Caicedo et al. (2016) found that the cortical activity is caused by the hemodynamic and metabolic course of the brain. Remarkably, however, the directionality was found to be reversed when, in addition to the linear interactions, also the non-linear interactions were taken into account (Hendrikx et al., 2018a).

Another mathematical technique to study directionality is the use of parametric transfer function models, as applied by Talukdar et al. (2015). In this study, multiple sets of gamma transfer functions were used. Using such sets, the authors were able to predict NIRS hemodynamics from EEG spectral envelopes, indicating that the gamma transfer function approach can be used to study neurovascular coupling. In addition, the resulting parameters of the model could provide additional insights into the neurovascular coupling mechanism.

To the best of our knowledge, there are currently no studies available that have investigated non-linear signal processing techniques, except for the preliminary results reported by Hendrikx et al. (2018a). In addition to the methods discussed in this section, other methodologies can be considered for the computation of neurovascular coupling markers, based on studies of the brain heart connectivity literature:

# Graph Theory

#### Including More Signal Modalities in the Assessment of Neurovascular Coupling

In the previous section, several techniques were introduced to compute parameters for neurovascular coupling, based on multimodal processing of NIRS and EEG signals. From a broader perspective, however, there are in fact multiple regulation mechanisms that are working simultaneously in order to provide and maintain an adequate brain perfusion (Banaji et al., 2005). On a very general level, two major regulation mechanisms can be distinguished in addition to the neurovascular coupling: cerebral autoregulation and cerebral oxygen balance (Smits et al., 2017).

The former ensures that cerebral blood flow is kept more or less constant, despite variations in cerebral perfusion pressure, while the latter determines the relation between oxygen delivery and metabolic demand of the brain.

A concise study on brain perfusion thus has to take into account that multiple regulation mechanisms are working at the same time. Therefore, such an analysis requires the incorporation of other signal modalities besides EEG and NIRS, such as arterial blood pressure (ABP) and fractional tissue oxygen extraction (FTOE). The former is needed to compute a marker for cerebral autoregulation, while the latter can be used to get insight into the cerebral oxygen balance (Naulaers et al., 2007). A simultaneous analysis of EEG, NIRS, ABP, and FTOE measurements leads to the definition of three markers, each indicating and assessing the status of one of the regulation mechanisms that are essential in providing the brain with a proper amount of energy in order to support its function.

Once all of these signals are available, recent studies indicate that it is also useful to compare the interaction between the remaining pairs of signals. Indeed, Semenova et al. (2017) compared the dynamics of EEG and ABP signals in preterm neonates and found that a higher degree of EEG-ABP interaction is associated with a worse clinical outcome, assessed using clinical risk index for babies II scores. Furthermore, if we add other signals like HR, which is known to have a key influence on cerebral hemodynamics in preterm infants, simultaneous markers on HR passivity can be obtained (Mitra et al., 2014). Such markers were found to be of diagnostic value in identifying impaired cerebrovascular reactivity, leading to adverse clinical outcome. In summary, it is important to incorporate a wide variety of signal modalities in order to enable an adequate study on brain perfusion, since the pairwise interactions between the different modalities can be used as physiological markers.

One of the first studies that investigated the simultaneous interactions in a multimodal dataset, is a study by Pfurtscheller et al. (2008). More specifically, in this study, NIRS, EEG, ABP, respiration and HR were combined with cross spectral and sliding cross correlation calculations, which are both linear, functional methods (**Table 1**). Results indicate that slow precentral (de)oxyhemoglobin concentration oscillations during awake rest can be temporarily coupled with EEG fluctuations in sensorimotor areas and modulate the excitability level in the brains' motor areas, respectively. Therefore, this study provides support for the idea that resting state networks fluctuate with frequencies between 0.01 and 0.1 Hz (Mantini et al., 2007).

#### Graph Theory for Functional Connectivity

The main limitation of the methodology outlined above is the combinatorial number of couplings based on the variables involved. If one starts with five variables (e.g., HR, ABP, SpO2, NIRS, and EEG), one gets 10 couples of interaction. The complexity of the interaction problem thus increases exponentially, which is difficult to manage statistically (Pavlopoulos et al., 2011). Furthermore, it is often of interest to study the interaction among systems, rather than the interaction between single signals (e.g., the interaction between the cerebral system and the cardiovascular system). A possible solution is the description of the couplings among variables as a graph of a network, whose activity can be compactly represented using graph theory.

Formally, a graph is defined by a non-zero number of nodes (vertices) and a number of links (edges) between these nodes. In general, graphs can be used to describe a great variety of real-world situations, which explains the extensive use of these mathematical objects in many different fields. A good introduction on graph theory mathematics and an overview of the numerous applications of graph theory is presented in Pavlopoulos et al. (2011). Using graph structures has some advantages:

Despite the fact that graph theory was only recently introduced in the field of Network Physiology, it has been used extensively in studies on functional connectivity of the brain, as outlined by Fallani et al. (2010). Naturally, the brain is modeled by a graph in these studies, which allows to study the interactions between different brain regions. Depending on the number of nodes, it can even be possible to study the interactions in one particular brain region. However, in such case, the number of nodes has to be large enough, which is mainly limited by the data at hand, i.e., the number of channels in the multichannel EEG recording. In general, studies on functional connectivity of the brain build graph models using one signal or imaging modality, mostly EEG (Hata et al., 2016; Tokariev et al., 2016) or fMRI (Wang et al., 2010; Smitha et al., 2017).

Gao et al. (2011) constructed graph models from fMRI BOLD data recorded in a large cohort of normal pediatric subjects. Using sequential measurements, they observed a remarkable improvement in whole brain wiring from 3 weeks up to 2 years after birth, which is a critical time period for brain development. Lavanga et al. (2018) investigated the use of graph models constructed from multichannel EEG measurements in order to construct a brain-age model for preterm neonates (**Figure 3**). Using 8-channel EEG measurements, they observed a decrease in functional connectivity with post-menstrual age (PMA). In addition, the functional connectivity was found to be an accurate predictor for the age of neonates.

#### Graph Theory in Network Physiology

In the field of Network Physiology, graph models are constructed from a multimodal dataset (Bashan et al., 2012; Bartsch et al., 2015). Before constructing such graphs, one has to make sure that the time scales of the signals are synchronized, as

explained in subsection G1. Afterward, a signal interaction graph is constructed as follows (**Figure 2**).

#### **Graph topology**

The structure of the graph is defined by the nodes. In a signal interaction graph, a node corresponds to one particular signal modality of the analysis, or a feature extracted from a signal (e.g., the power in the delta frequency band of the EEG).

#### **Graph connectivity**

Regarding the graph topology, there is an endless amount of possibilities to define the structure of signal interaction graphs. Some networks to model physiological function can be quite simple (Bashan et al., 2012), while other networks are more detailed, and thus, more complex (Bartsch et al., 2015). To illustrate the variety of applications in the field of biomedical signal processing, some examples are presented in **Figure 3**, based on studies by Hendrikx et al. (2018b) and Lavanga et al. (2018). For each application, a graph with a specific topology can be constructed, based on the research question(s) of the analysis. The structure of the graph is limited by the signal modalities available in the dataset and/or the features derived from these signals.

Regarding the graph connectivity, there is again a wide variety of design choices on how to connect the nodes and how to define the weights associated with the links. First of all, one has to define which nodes are connected and which nodes lack a connection. Second, one has to specify how the weights associated with the links are computed. This is equivalent to the choice of one particular signal processing technique, since the weights denote signal interaction (coupling). Which signal processing technique to use has to be defined by the researcher, based on the characteristics of the research. In particular, one has to think about the nature of the interactions that needs to be captured: linear versus non-linear interactions and similarity (functional

physiology, which includes five different signal modalities.

methods) versus directionality (effective methods). The most commonly used signal processing techniques are summarized in **Table 1**. In the setting of graph connectivity, two additional remarks need to be taken into account.

As a first remark, it is important to note that the graphs depicted in **Figure 3** are characterized by the presence of a link between every pair of nodes. Formally, this type of graph is referred to as a complete graph. Such graphs are the most general type of graphs, since they allow to investigate the interaction between every possible pair of signals. It is important to note that in general it is always a good strategy to consider a link between every pair of signals, even when the signals are not expected to interact. Indeed, the weights of such links could be used as a validation for signal processing technique used to define the signal interaction: can the method detect that these signals are not interacting?

As a second remark, it is also possible to determine the presence (or absence) of a link automatically. In such an analysis, the weights are generally chosen binary. Graphs which only include the presence and absence of links (and no specific weight value), are formally referred to as simple graphs. An example: one could assign a '1' (presence of a link) if there is a significant correlation between two signals, while a '0' (absence of a link) denotes the lack of a significant correlation. In this setting, it is thus not necessary to manually define the presence of the links, since this is automatically detected by the signal processing technique.

The first study on the use of graph models in the field of Network Physiology is a study by Bashan et al. (2012). This study demonstrated that each physiological state is characterized by a specific network structure, demonstrating a robust interplay between network topology and function. Furthermore, high network flexibility in response to perturbations was observed, since the network was observed to be able to reorganize on time scales of a few minutes. A follow-up study on graph models in Network Physiology, is a study by Bartsch et al. (2015), who used the TDS framework in order to define the strength of the signal interactions. In short, TDS is based on linear correlations and is therefore only able of capturing linear signal interactions. The results of this study again demonstrated a direct association between network topology and physiologic function. In addition, the graph models were found to be useful in understanding how health and distinct physiologic states emerge from networked interactions among non-linear multicomponent complex systems.

A second study on graph models in the field of Network Physiology is a study by our own group (Hendrikx et al., 2018b). In this study, graph models were used to investigate the interaction between cerebral hemodynamics, brain function and systemic variables. The graph models were constructed using the RBF kernel function, which is a non-linear similarity measure. Therefore, the methodological framework is able to capture nonlinear signal interactions. The study showed that graph theory can be used to capture changes in signal interaction accurately and that the resulting graph models can be used to study the difference between distinct physiological states (Hendrikx et al., 2018b).

Graph models do not only provide a compact, efficient, and statistically well-defined framework to study simultaneous signal interaction, but they are in fact an entirely new way of thinking, which has been shown to produce an added value in multiple studies already (Bashan et al., 2012; Bartsch et al., 2015; Hendrikx et al., 2018b). The usage of interaction and coupling techniques provides an in-depth view of the interaction among physiological systems, moving from sectorial or univariate medicine to a more holistic approach. The latter is well-suited in case of continuous monitoring of critical states (e.g., in cases where it's likely to have multiple organ failures), and the well-being and mental status of a person, which is known to rely on the interaction between multiple systems (Chaparro-Vargas et al., 2016). Moreover, the necessity to improve the sleep quality and the developmental and cognitive abilities of neonates in the near future (which interact differently in neonates compared to adults) will require the

summarized view of a graph approach instead of long recordings of multiple apparently unrelated variables. In this perspective, Network Physiology gives a broader view on the body activity (Bashan et al., 2012), comparable to the broader view on the cortical status given by the brain connectivity at its discovery (Friston, 1994).

In general, the overall purpose of the signal processing techniques (such as computational graph models) is to obtain physiological markers starting from raw measurements that are routinely obtained in neonatal intensive care units. These physiological markers could in turn be used in clinical practice to make neonatal intensive care more patient-specific. In addition, signal processing advancements are also of high importance in neonatal pharmacological studies, since the effect of medication on the neurovascular coupling in neonates is yet unclear (Smits et al., 2017). Neonates are exposed to a large number of medications, most of which are used off-label in infants because clinical trials for safety, dosing, and efficacy of drugs are lacking in this population (Hsieh et al., 2014; Ku and Smith, 2015). In this setting, signal processing techniques could thus be used to identify neonates at risk for neurodevelopmental disabilities and could assist clinicians in making a timely diagnosis, which enables to start appropriate personalized therapies early. Therefore, this patient-centered neonatal intensive care can help to reduce the occurrence of neurodevelopmental disabilities in the preterm population (Molteno et al., 1999; Cioni et al., 2016).

# CONCLUSION

We suggest that further research in the field of physiology is required in order to gain more insight into the exact working principle of the neurovascular coupling in neonates (spatiotemporal studies). In addition, further studies on the use of signal processing in EEG-NIRS integration are also required (general studies). Such studies enable to construct new software, based on algorithms that describe the relation between cerebral metabolism and oxygen delivery. Graph models in particular can be of special interest in future studies, since these models allow to study the simultaneous action of different regulation mechanisms, which is essential in studies on brain perfusion. These new measurements will provide a better understanding of the coupling mechanisms in the neonatal brain, and will eventually lead to an improvement of the neonatal intensive care in general.

# REFERENCES

# AUTHOR CONTRIBUTIONS

DH, ML, and GN wrote the review together. AS, ODW, LT, KJ, AC, and SVH reviewed and corrected the text.

#### FUNDING

The authors acknowledge the financial support of Bijzonder Onderzoeksfonds KU Leuven (BOF): SPARKLE – Sensorbased Platform for the Accurate and Remote monitoring of Kinematics Linked to E-health #: IDO-13-0358, The effect of perinatal stress on the later outcome in preterm babies #: C24/15/036, and TARGID – Development of a novel diagnostic medical device to assess gastric motility #: C32- 16-00364; Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO): Hercules Foundation (AKUL 043) 'Flanders BCI Lab - High-End, Modular EEG Equipment for Brain Computer Interfacing'; Agentschap Innoveren en Ondernemen (VLAIO): 150466: OSA+, and O&O HBC 2016 0184 eWatch; imec funds 2017; imec ICON projects: ICON HBC.2016.0167, 'SeizeIT'; Belgian Foreign Affairs-Development Cooperation: VLIR UOS programs (2013–2019); EU: European Union's Seventh Framework Program (FP7/2007–2013) The HIP Trial: #260777, EU H2020 FETOPEN 'AMPHORA' #766456, EU EFRO Interreg: Nano4Sports, EU H2020 MSCA-ITN-2018: 'INtegrating Magnetic Resonance SPectroscopy and Multimodal Imaging for Research and Education in MEDicine (INSPiRE-MED)', funded by the European Commission under Grant Agreement #813120, and EU H2020 MSCA-ITN-2018: 'INtegrating Functional Assessment measures for Neonatal Safeguard (INFANS)', funded by the European Commission under Grant Agreement #813483. ERASMUS+: INGDIVS 2016-1-SE01-KA203-022114; European Research Council: The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007–2013)/ERC Advanced Grant: BIOTENSORS (n◦ 339804). This paper reflects only the authors' views and the Union is not liable for any use that may be made of the contained information. Dries Hendrikx and Mario Lavanga are SB Ph.D. fellows at Fonds voor Wetenschappelijk Onderzoek (FWO), Vlaanderen, supported by the Flemish government. The research activities of AS are supported by the Clinical Research and Education Council of the University Hospitals Leuven.

means of EEG, ECG and NIRS signals. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 1403–1406. doi: 10.1109/IEMBS.2011.6090330

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Hendrikx, Smits, Lavanga, De Wel, Thewissen, Jansen, Caicedo, Van Huffel and Naulaers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Role of Connexin and Pannexin Channels in Perinatal Brain Injury and Inflammation

Kelly Q. Zhou<sup>1</sup> , Colin R. Green<sup>2</sup> , Laura Bennet <sup>1</sup> , Alistair J. Gunn<sup>1</sup> and Joanne O. Davidson\*

*<sup>1</sup> Department of Physiology, The University of Auckland, Auckland, New Zealand, <sup>2</sup> Department of Ophthalmology, The University of Auckland, Auckland, New Zealand*

Perinatal brain injury remains a major cause of death and life-long disability. Perinatal brain injury is typically associated with hypoxia-ischemia and/or infection/inflammation. Both hypoxia-ischemia and infection trigger an inflammatory response in the brain. The inflammatory response can contribute to brain cell loss and chronic neuroinflammation leading to neurological impairments. It is now well-established that brain injury evolves over time, and shows a striking spread from injured to previously uninjured regions of the brain. There is increasing evidence that this spread is related to opening of connexin hemichannels and pannexin channels, both of which are large conductance membrane channels found in almost all cell types in the brain. Blocking connexin hemichannels within the first 3 h after hypoxia-ischemia has been shown to improve outcomes in term equivalent fetal sheep but it is important to also understand the downstream pathways linking membrane channel opening with the development of injury in order to identify new therapeutic targets. Open membrane channels release adenosine triphosphate (ATP), and other neuroactive molecules, into the extracellular space. ATP has an important physiological role, but has also been reported to act as a damage-associated molecular pattern (DAMP) signal mediated through specific purinergic receptors and so act as a primary signal 1 in the innate immune system inflammasome pathway. More crucially, extracellular ATP is a key inflammasome signal 2 activator, with purinergic receptor binding triggering the assembly of the multi-protein inflammasome complex. The inflammasome pathway and complex formation contribute to activation of inflammatory caspases, and the release of inflammatory cytokines, including interleukin (IL)-1β, tumor necrosis factor (TNF)-α, IL-18, and vascular endothelial growth factor (VEGF). We propose that the NOD-like receptor protein-3 (NLRP3) inflammasome, which has been linked to inflammatory responses in models of ischemic stroke and various inflammatory diseases, may be one mechanism by which connexin hemichannel opening especially mediates perinatal brain injury.

Keywords: connexin, pannexin, hemichannel, ischemia, inflammation, inflammasome, ATP

#### INTRODUCTION

Perinatal brain injury is associated with death or significant long-term neurodevelopmental impairment, affecting 1.15 million infants in the world in 2010 (Lee et al., 2013). It affects both term and preterm infants (born <37 weeks of completed gestation), with the greatest incidence in the most preterm infants (Larroque et al., 2008). In term born infants, perinatal brain injury

Edited by:

*Mary Tolcos, RMIT University, Australia*

#### Reviewed by:

*Julie Wixey, University of Queensland, Australia Meredith Anne Kelleher, Oregon Health & Science University, United States*

\*Correspondence:

*Joanne O. Davidson<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Embryonic and Developmental Physiology, a section of the journal Frontiers in Physiology*

Received: *12 November 2018* Accepted: *07 February 2019* Published: *27 February 2019*

#### Citation:

*Zhou KQ, Green CR, Bennet L, Gunn AJ and Davidson JO (2019) The Role of Connexin and Pannexin Channels in Perinatal Brain Injury and Inflammation. Front. Physiol. 10:141. doi: 10.3389/fphys.2019.00141*

**121**

is most often linked to hypoxia-ischemia (HI) (Vannucci, 2000; Shankaran et al., 2005), where the incidence of moderate to severe hypoxic-ischemic encephalopathy (HIE) is ∼1–3 in 1,000 live births (Edwards et al., 2010). By contrast, in preterm infants, brain injury arises from the complex interaction of HI, infection/inflammation and preterm birth itself (Galinsky et al., 2018b). Strikingly, ∼50% of extremely preterm infants (born <26 weeks of gestation) develop moderate to severe disability (Marlow et al., 2005).

Currently, there is only one established neuroprotective treatment for term babies born with HIE, which is therapeutic hypothermia. Clinically, this treatment is only partially effective, with a number needed to treat of ∼8, meaning of 8 treated infants with HIE, one additional infant will survive without moderate to severe disability (Gunn et al., 2017; Natarajan et al., 2018). However, there are currently no proven treatments to reduce brain damage for preterm infants with HIE or perinatal infection/inflammation. Therefore, there is significant interest in developing complementary or novel treatments for perinatal brain injury after HI or infection/inflammation. For term infants, it is important to develop interventions with additive benefit to therapeutic hypothermia, as it is now standard therapy. Novel treatments are needed for term and preterm infection/inflammation.

After HI, there is a striking evolution of injury which progresses over time from injured to previously uninjured regions of the brain (Azzopardi et al., 1989; Thornton et al., 1998). The mechanisms behind this spread are not well-understood. Increasing evidence supports the opening of cell membrane channels after HI as one of the mediators of this propagation of injury, particularly connexin hemichannels, which are the unopposed half of a gap junction, forming large conductance channels in the cell membrane (Davidson et al., 2012, 2014; Wang et al., 2014; Li et al., 2015). Pannexin channels may also open under pathological conditions, although they appear to be tightly regulated (Qiu and Dahl, 2009) and their role in the developing brain remains unclear. In this review, we examine the hypothesis that prolonged opening of connexin hemichannels leads to a cascade of injurious mechanism, potentially through inflammation by activation, amplification and perpetuation of the inflammasome pathway.

An inflammatory response is seen in perinatal brain injury after HI as well as infection/inflammation, and if chronic inflammation is established can have deleterious effects (Hagberg et al., 2015; Bennet et al., 2018). Hemichannels release adenosine triphosphate (ATP) (Kang et al., 2008; Chekeni et al., 2010; Orellana et al., 2011; Bennett et al., 2012), which may act as a damage associated molecular pattern (DAMP), but is also a key inflammasome activator signal. The release of ATP through connexin hemichannels can perpetuate inflammation through purinergic signaling (Pedata et al., 2016). Purinergic signaling has been associated with the activation of microglia (Li et al., 2011; Kaiser et al., 2016) and the inflammasome complex—a multi-protein complex involved in initiating an innate immune system inflammatory response (Feng et al., 2015). This review will dissect the evidence for these pathways downstream of connexin hemichannel and pannexin channel opening and how they contribute to inflammation. One other membrane pore forming protein, Gasdermin D, has been implicated in the inflammasome pathway (Groslambert and Py, 2018; Kerur et al., 2018), but that channel is very small in comparison with the pannexin and connexin channels and appears to be primarily associated with apoptosis. As will become evident in this review, both in vitro and animal studies using connexin hemichannel or pannexin channel blockers, would suggest that other channels are likely to play minor roles in relation to ATP release, and inflammasome activation in particular.

# EVOLUTION OF INJURY

Perinatal brain injury after HI is an evolving process that can be characterized into four phases (Bennet et al., 2010; Davidson et al., 2015b). The primary phase of injury occurs during the HI insult itself, when the failure of oxidative metabolism results in anoxic depolarization, edema and necrosis (Wassink et al., 2018). After restoration of blood flow and oxygen supply, there is a period of apparent transient recovery when oxidative metabolism is at least partially restored, known as the latent phase (Davidson et al., 2018a). However, the latent phase is the key time when the deleterious mechanisms leading to the spread of brain injury may be initiated; for example, the opening of connexin hemichannels, which will be discussed further below (Davidson et al., 2012).

 This phase is characterized by delayed cerebral energy failure followed by seizures and secondary cell swelling (Bennet et al., 2006; Davidson et al., 2015c). There is marked neuronal injury after HI at term, with the majority of neuronal loss occurring during the secondary phase, through a continuum of necrosis-apoptosis and autophagy (Northington et al., 2007, 2011).

The tertiary phase is a period of repair and reorganization, persisting for weeks to years after the initial insult (Fleiss and Gressens, 2012). During this period, surviving cells in the brain can rewire, but there may be a low level of ongoing cell death due to the loss of trophic support and problems with connectivity (Ness et al., 2001; Romanko et al., 2004). Long-term impairment in perinatal brain injury may also be associated with epigenetic changes (Fleiss and Gressens, 2012), but also persistent inflammation (Bennet et al., 2018) as reported for other types of brain injury and degenerative diseases (Patterson and Holahan, 2012; Freeman and Ting, 2016).

# INFLAMMATION IN PERINATAL BRAIN INJURY

Perinatal brain injury associated with HI or infection/inflammation can trigger an inflammatory response. The innate immune response is the body's first line of defense against pathogens, reacting rapidly following exposure to invading organisms (Medzhitov, 2007). As part of the innate immune response, pattern recognition receptors (PRRs) expressed on immune cells recognize both the conserved molecular structures found on the pathogen known as pathogen-associated molecular patterns (PAMPs), and the endogenous signals released by damaged tissues known as danger-associated molecular patterns (DAMPs) (Medzhitov, 2007; Takeuchi and Akira, 2010).

The activation of PRRs initiates the inflammatory response leading to release of inflammatory cytokines, such as interleukin (IL)-1β (Turner et al., 2014). This early phase of inflammation targets the invading pathogens and/or clears injured tissue, which is beneficial to the host. However, this inflammatory response also leads to the death of uninjured neural cells in a process known as "bystander cell loss" (Hagberg et al., 2015). It is believed that the initial pro-inflammatory response is followed by anti-inflammatory and reparative processes, and either eventual resolution of inflammation or chronic inflammation (Gilroy and De Maeyer, 2015; Hagberg et al., 2015).

Elevated levels of inflammatory markers after birth are associated with adverse neurodevelopmental outcomes. A prospective cohort study of 73 term infants exposed to perinatal asphyxia showed that those who died or were diagnosed with cerebral palsy at a 1 year follow up, were associated with higher levels of IL-1, IL-6 and tumor necrosis factor (TNF)-α in heelstick blood samples collected on the first or second day of birth (Foster-Barber et al., 2001). In the same cohort, followed up at 30 months of age, those with higher serum cytokine levels of IL-1β, IL-6, and IL-8 at birth were associated with abnormal cognitive and motor outcomes (Bartha et al., 2004). The extremely low gestational age newborns (born before 28 weeks of gestation) (ELGANs) study is a large multi-center observational study. In this cohort, elevated blood concentrations of inflammatory proteins, measured in the first few weeks of life was associated with mental and motor impairments at 2 years old (O'Shea et al., 2012) and adverse cognitive outcome at 10 years of age (Kuban et al., 2017), as well as increased risk of behavioral problems, including autism (Korzeniewski et al., 2018). Cytokine levels were not measured at later time points to determine if inflammation was sustained. However, a study by Lin and colleagues has suggested that perinatal brain injury is associated with long-lasting alterations to the inflammatory response (Lin et al., 2010). Peripheral blood mononuclear cells from a small cohort of preterm born children with periventricular leukomalacia induced cerebral palsy, were shown to have higher mRNA levels of inflammatory molecules, both before and after lipopolysaccharide (LPS) stimulation (Lin et al., 2010). Non-resolving inflammation could be the result of a prolonged or excessive inflammatory response, which may lead to the disruption of pathways that normally induce inflammatory resolution (Nathan and Ding, 2010; Bennet et al., 2018).

#### CONNEXIN HEMICHANNELS

Connexin gap junctions connect the intracellular space of two adjacent cells allowing for the exchange of ions and molecules (Kumar and Gilula, 1996; Alexander and Goldberg, 2003; Davidson et al., 2013a). A connexin hemichannel is comprised of six connexin subunits. Connexin hemichannels from adjacent cells dock together to form a gap junction (Unger et al., 1999; Davidson et al., 2013a; Leybaert et al., 2017) (**Figure 1**). Humans have 21 connexin genes, and 11 of these are expressed in the brain (Theis et al., 2005). Connexin 43 (Cx43) has been of particular interest in the brain as it is abundantly expressed, especially in astrocytes, microglia and microvascular endothelium (Dermietzel et al., 2000; Nagy et al., 2003; Davidson et al., 2013a). Under physiological conditions, prior to forming a gap junction, hemichannels at the cell surface have a low probability of opening, shown by low membrane permeability during resting conditions in cultured cells (Decrock et al., 2009). It is not known whether this is the case in vivo, as cells may be exposed to a wide range of stimuli influencing hemichannel opening (Sáez et al., 2005). When hemichannels do open though, they form a large non-selective membrane channel capable of passing molecules up to about 1 kDa in size (Evans et al., 2006). There is increasing evidence that dysregulated hemichannel opening can be detrimental and they are often referred to as "pathological pores" (Paul et al., 1991; Decrock et al., 2015; Willebrords et al., 2016).

Connexin hemichannels are involved in the spread of brain injury after HI or infection in the adult and immature brain (Davidson et al., 2012, 2013a) (studies are summarized in **Table 1**). They open in response to stimuli mimicking exposure to ischemia and inflammation in vitro. For example, in cultured cells, oxygen-glucose deprivation (OGD), metabolic inhibition, low extracellular Ca2+, and strong depolarization have all been proven to stimulate hemichannel opening (Contreras et al., 2002; Decrock et al., 2009; Orellana et al., 2010). Conditioned medium from LPS-activated microglia or TNF-α and IL-1β increased hemichannel activity in cultured astrocytes (Retamal et al., 2007). In addition, neuroinflammation induced by S. aureus intracerebral inoculation increased Cx43 hemichannel activity in astrocytes surrounding the abscess (Karpuk et al., 2011). The opening of connexin hemichannels allows for the influx of Na+, Cl−, and Ca2<sup>+</sup> and the efflux of K<sup>+</sup> ions. This increases cell permeability, leading to the depolarization of the cell, and cell lysis (Paul et al., 1991; Gómez-Hernández et al., 2003). Molecules, such as ATP, glutamate and aspartate can be released; all of these molecules are associated with injury (Ye et al., 2003; Gomes et al., 2005; Zhao et al., 2005; Kang et al., 2008). Additionally, Cx43 hemichannels are likely involved in the propagation of seizures following severe HI in near-term fetal sheep (Davidson et al., 2012).

An upregulation of Cx43 has been shown to occur in adult and perinatal animals after HI and in adult human postmortem studies of cerebral ischemia (Nakase et al., 2006, 2009; Davidson et al., 2012; Wang et al., 2014; Li et al., 2015). The timing of this Cx43 increase varies between studies, with Cx43 mRNA levels significantly increased at 6 h after carotid artery occlusion in near-term fetal sheep (Davidson et al., 2012). Protein levels progressively increased in the ischemic region from 8 h to 7 days after common carotid artery ligation and hypoxia (commonly referred to as Rice-Vannucci model of HI) in postnatal day (P)7 rats (equivalent to 33–34 weeks of human brain maturation) (Li et al., 2015). In the subventricular zone, however,

Wang and colleagues reported Cx43 protein expression was only significantly increased from 24 h after the same insult in P7 rats (Wang et al., 2014). However, there was no change in hippocampal Cx43 protein expression at all time-points between 10 min and 35 days after acute hypoxia in P10 rats (equivalent to human term brain maturation) (Zeinieh et al., 2010). Thus, the changes in Cx43 expression may be dependent on species, brain region, type and/or severity of insult. It should be noted that, the Rice-Vannucci model of HI in rodents produces a unilateral infarction in the brain but only moderate systemic hypoxia (Rice et al., 1981), whereas bilateral carotid artery occlusion in nearterm fetal sheep produces a global cerebral insult resulting in a watershed pattern of brain injury, without systemic hypoxia (Williams et al., 1992). The reader should note that protein assays and immunohistochemical labeling do not allow ready discrimination between gap junctions and hemichannels, or channel function.

The blockade of Cx43 hemichannels, however, has been neuroprotective in a variety of different animal models of perinatal HI (Davidson et al., 2012, 2014; Li et al., 2015). Intracerebroventricular (i.c.v.) infusion (50µmol/kg over 1 h, then 50µmol/kg over 24 h) of the Cx43 mimetic peptide—Peptide5, started at 90 min after the end of carotid artery occlusion in near-term fetal sheep, was associated with improved neuronal and oligodendrocyte survival and, electroencephalogram (EEG) recovery and reduced seizure burden (Davidson et al., 2012). Similarly, Peptide5 infusion following the same protocol as above in preterm fetal sheep after umbilical cord occlusion improved EEG recovery and reduced neuronal and oligodendrocyte loss (Davidson et al., 2014). Reduced infarct size and improved motor and memory scores were observed in Gap 26 treated P7 rats [a connexin mimetic peptide which blocks hemichannels and gap junctions (Wang et al., 2013)], given i.p., 50 µg/kg, 1 h before HI (Li et al., 2015).

Cx43 hemichannel opening in vivo has been shown to occur after ischemia (in the latent phase), but not during ischemia. Pre-administration of Peptide5 was not neuroprotective when given 1 h before and during ischemia, but was neuroprotective when given after ischemia (Davidson et al., 2012, 2013b). Supporting this, hemichannels in astrocyte cultures subjected to OGD have been shown to open 1 h after OGD (Orellana et al., 2010). In addition, carbenoxolone (100 µmol/L) inhibition of connexin hemichannels did not attenuate ATP release during OGD in hippocampal slices (Frenguelli et al., 2007). Both Cx43 hemichannel and pannexin channel opening was reported to occur during hypoxia in endothelial cells with two thirds of ATP release connexin hemichannel mediated, but upon reperfusion, only Cx43 hemichannel opening occurred (Kim and Green, 2016). These studies indicate that Cx43 hemichannels likely do open after ischemia and may have an ongoing role in the evolution of injury after the insult. This is supported by the demonstration that prolonged infusion of Peptide5 (i.c.v., starting at 90 min) at a dose of 50µmol/kg over 1 h, then 50µmol/kg over 24 h of Peptide5 provided greater neuroprotection compared to a 1 h infusion only (50µmol/kg) (Davidson et al., 2012). Furthermore, delayed administration of another known hemichannel blocker, Gap 26 (i.p., 50 µmol/kg) at 24 h after HI still had neuroprotective effects (Li et al., 2015). Interestingly, when Peptide5 administration (i.c.v., 50 µmol/kg for 1 h followed by 50µmol/kg for 24 h) was delayed until 3 h after the end of ischemia in near-term fetal sheep, the neuroprotective effects were reduced when compared to earlier administration (Davidson et al., 2012, 2015a). The delayed administration reduced the seizure burden, but there was no clear effect on cell survival or EEG recovery (Davidson et al., 2012, 2015a).

The mechanisms behind how Cx43 hemichannels lead to neuronal and oligodendrocyte loss in HI is not well-understood. During and/or after HI there is impaired intracellular Ca2<sup>+</sup> handling. This has been shown to contribute to mitochondrial dysfunction and necrotic and apoptotic cell death (Puka-Sundvall et al., 2000; Mallard et al., 2014). Potentially, the opening of hemichannels could lead to an influx of excessive intracellular Ca2<sup>+</sup> accumulation leading directly to the neuronal and/or oligodendrocyte death (Galinsky et al.,

 and P2X7.

TABLE

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\**Studies that report interactions*

 *between connexin hemichannels,*

 *pannexin channels, and/or purinergic receptors.*

2018a). In support, GABAergic striatal neurons expressing intracellular calcium binding proteins are shown to be highly susceptible to HI injury in near-term fetal sheep (Galinsky et al., 2017).