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BMJ Paediatrics Open is committed to open peer review. As part of this commitment we make the peer review history of every article we publish publicly available. When an article is published we post the peer reviewers’ comments and the authors’ responses online. We also post the versions of the paper that were used during peer review. These are the versions that the peer review comments apply to. The versions of the paper that follow are the versions that were submitted during the peer review process. They are not the versions of record or the final published versions. They should not be cited or distributed as the published version of this manuscript. BMJ Paediatrics Open is an open access journal and the full, final, typeset and author-corrected version of record of the manuscript is available on our site with no access controls, subscription charges or pay-per-view fees (http://bmjpaedsopen.bmj.com). If you have any questions on BMJ Paediatrics Open’s open peer review process please email
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The continuum of neurobehavior and its associations with
brain MRI in infants born preterm
Journal: BMJ Paediatrics Open
Manuscript ID bmjpo-2017-000136
Article Type: Original article
Date Submitted by the Author: 31-May-2017
Complete List of Authors: Eeles, Abbey; Murdoch Childrens Research Institute, Victorian Infant Brain Study Walsh, Jennifer; The Royal Women's Hospital, Newborn Research Olsen, Joy; Murdoch Childrens Research Institute Cuzzilla, Rocco Thompson, Deanne
Anderson, Peter Doyle, Lex; Royal WOmen's Hospital, Cheong, Jeanie Spittle, Alicia; University of Melbourne School of Health Services, Physiotherapy
Keywords: Neurodevelopment
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The continuum of neurobehavior and its associations with brain MRI in infants
born preterm
Abbey L Eeles,b,c
Jennifer M Walsh,b,c,d
Joy E Olsen,b,c,e
Rocco Cuzzilla,b,c,e
Deanne
K Thompson,b,f,h
Peter J Anderson, b,f,g
Lex W Doyle, b,c,e,f
Jeannie L Y Cheong,b,c,f
Alicia J Spittlea,b,c
Author Affiliations: aDepartment of Physiotherapy, University of Melbourne,
Melbourne, Vic; bMurdoch Childrens Research Institute, Melbourne, Vic;
cNewborn
Research, Royal Women’s Hospital, Melbourne, Vic; dPaediatric Infant Perinatal
Emergency Retrieval (PIPER), The Royal Children’s Hospital, Melbourne, Vic; eDepartment of Obstetrics and Gynaecology, University of Melbourne, Melbourne,
Vic; fDepartment of Paediatrics, University of Melbourne, Melbourne, Vic;
gMonash
Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne,
Vic; hFlorey Institute of Neuroscience and Mental Health, Melbourne, Vic
Corresponding Author:
Alicia J Spittle
7th Floor, Alan Gilbert Building, University of Melbourne, Grattan Street, Parkville,
Vic, Australia, 3052.
E-mail: [email protected]
Copyright:
The Corresponding Author has the right to grant on behalf of all authors and does
grant on behalf of all authors, a worldwide licence to the Publishers and its licensees
in perpetuity, in all forms, formats and media (whether known now or created in the
future), to i) publish, reproduce, distribute, display and store the Contribution, ii)
translate the Contribution into other languages, create adaptations, reprints, include
within collections and create summaries, extracts and/or, abstracts of the
Contribution, iii) create any other derivative work(s) based on the Contribution, iv) to
exploit all subsidiary rights in the Contribution, v) the inclusion of electronic links
from the Contribution to third party material where-ever it may be located; and, vi)
licence any third party to do any or all of the above.
What is known about this subject?
Infants born very preterm are at increased risk of long-term neurodevelopmental
deficits. Early interventions can improve developmental outcomes and thus early
identification of infants at greatest need for these services is paramount. Brain MRI
abnormalities has been associated with neurobehaviour functioning in very preterm
infants, however, this relationship is yet to be explored in moderate-to-late preterm
infants.
What this study adds:
This study highlights a clear continuum of neurobehaviour with increased suboptimal
functioning on three neurobehavioral assessments with decreasing gestational age.
The relationships between brain abnormality scores and suboptimal neurobehavior
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provides evidence that neurobehavioral assessments may be useful in earlier
identification of the highest-risk infants.
Word count: 2857
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ABSTRACT
Background
Infants born very preterm (VPT) and moderate-to-late preterm (MLPT) are at
increased risk of long-term neurodevelopmental deficits, but how these deficits relate
to early neurobehavior in MLPT children is unclear. The aims of this study were to
compare the neurobehavioral performance of infants born across 3 different
gestational age groups: VPT (<30 weeks’ gestational age); MLPT (32 to 36 weeks’
gestational age); and term age ≥37 weeks’ gestational age), and explore the
relationships between MRI brain abnormalities and neurobehavior at term-equivalent
age.
Methods
Neurobehaviour was assessed at term equivalent age in 149 VPT, 200 MLPT, and 200
term born infants using the NICU Network Neurobehavioral Scale (NNNS), the
Hammersmith Neonatal Neurological Exam (HNNE), and Prechtl’s Qualitative
Assessment of General Movements (GMA). A subset of 110 VPT and 198 MLPT
infants had concurrent brain MRI.
Results
Proportions with abnormal neurobehavior on the NNNS and the HNNE, and
abnormal GMA all increased with decreasing gestational age. Higher brain MRI
abnormality scores in some regions were associated with suboptimal neurobehavior
on the NNNS and HNNE. The relationships between brain MRI abnormality scores
and suboptimal neurobehavior were similar in both VPT and MLPT infants. The
relationship between brain MRI abnormality scores and abnormal GMA was stronger
in VPT infants.
Conclusions
There was a continuum of neurobehaviour across gestational ages. The relationships
between brain abnormality scores and suboptimal neurobehavior provide evidence
that neurobehavioral assessments offer insight into the integrity of the developing
brain, and may be useful in earlier identification of the highest-risk infants.
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BACKGROUND AND RATIONALE
Infants born preterm are at increased risk of long-term neurodevelopmental deficits in
cognitive, neurosensory, physical, and social-emotional development, as well as
impairments in academic functioning compared with their term-born peers.1-4
Early
interventions to mitigate some of these adverse neurodevelopmental deficits are
promising,5,6
and thus, clinicians working with preterm infants and their families aim
to identify those infants at greatest need for early intervention services. Whilst major
preterm brain injuries, such as high-grade intraventricular hemorrhage (IVH) and
cystic periventricular leukomalacia (cPVL), are highly prognostic for adverse
neurodevelopmental outcomes,6,7
other clinical predictors of long-term development
have only modest prognostic utility. Magnetic resonance imaging (MRI) can detect
more subtle preterm brain injury associated with adverse neurodevelopment, however
its use in routine clinical care is limited by availability and cost.8 Clinicians are
increasingly using neonatal neurobehavioral assessments to identify high-risk infants
and to help guide referrals for early intervention. Neurobehavioral assessments are
valid and reliable tools which offer insights into the neurological integrity and
behavioral functioning of an infant.9,10
Compared with their term-born peers, infants born very preterm (VPT: <32 weeks’
gestation) and moderate-to-late preterm (MLPT: 32 to 36 weeks’ gestation) present
with increased rates of atypical neurobehavior at term-equivalent age.11,12
Importantly, these neurobehavioral difficulties have been associated with later
developmental deficits,13,14
providing support for the neurobehavioral assessment as a
predictive tool for later neurodevelopmental outcome. Whilst there is some evidence
that MRI brain abnormalities are associated with neurobehavior in VPT infants at
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term-equivalent age,15-17
this brain-neurobehavior relationship has yet to be explored
in MLPT infants.
The primary aim of this study was to compare the neurobehavioral performance of
infants born across 3 different gestational age groups: VPT (<30 weeks’ gestational
age); MLPT (32 to 36 weeks’ gestational age); and term age (>37 weeks’ gestational
age) using the NICU Network Neurobehavioral Scale (NNNS), the Hammersmith
Neonatal Neurological Exam (HNNE), and Prechtl’s Qualitative Assessment of
General Movements (GMA).18
Secondary aims were to explore the relationships
between MRI brain abnormalities (development and injury) and neurobehavior at
term-equivalent age between gestational age groups (VPT, MLPT and term-born
infants).
METHODS
Participants
Participants were derived from two longitudinal cohorts of infants recruited from the
Royal Women’s Hospital in Melbourne, Australia, between November 2009 and
December 2013. The first cohort comprised 149 VPT infants born <30 weeks’
gestation19
and the second cohort included 201 MLPT infants born between 32 to 36
weeks’ gestation.20
In addition, a cohort of 201 term controls born >37 weeks’
gestation were recruited across the two cohorts. Infants with congenital abnormalities
and/or infants with non-English speaking parents were excluded due to limited
funding for interpreters. In the term control group, infants requiring admission to the
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special care or intensive care nurseries were excluded. Informed parental consent was
obtained for all participants, and both studies were approved by the Royal Women’s
Hospital’s and Royal Children’s Hospital’s Human Research Ethics Committees.
Perinatal data were recorded by research nurses, including gestation at birth, sex,
birthweight z-score (calculated according to gestational age and sex using the British
Growth Reference norms),21
multiple birth, use of antenatal corticosteroids, and
respiratory support.
Neurobehavioral measures
At term-equivalent age (38-44 weeks’ postmenstrual age), neurobehavior was
assessed by one of five trained and certified assessors using NNNS,22
HNNE,23
and
GMA. All assessors had advanced GMA certification and were masked to the
participants’ clinical history. All assessments were administered according to their
standardized procedures, as previously published in the study protocol.19
Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS)
The NNNS is a neurobehavioral assessment that examines the neurological integrity,
behavioral functioning, and responses to stress in high-risk infants using 45 items
which correspond to 13 summary scales including habituation, attention, arousal,
regulation, handling, quality of movement, excitability, lethargy, non-optimal
reflexes, asymmetrical reflexes, hypertonicity, hypotonicity, and stress.24,25
The
habituation scale was not included in this study as infants were not consistently in an
appropriate state (sleep) to administer the scale at the start of the assessment. The
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scoring and classification of infants’ performance on the NNNS summary scales has
been described previously by our group.
Hammersmith Neonatal Neurological Examination (HNNE)
The HNNE is primarily a neurological exam developed for term and preterm infants.
It consists of 34 individual items with six subtotals including tone, tone patterns,
reflexes, spontaneous movements, abnormal neurological signs, and behavior, which
are added for a total score. Suboptimal performance on the HNNE was categorized as
previously published.26
Prechtl’s qualitative assessment of General Movements (GMA)
The GMA is an observational assessment of the infant’s spontaneous movements
(GMs) with good predictive validity for neurodevelopmental outcomes, including
cerebral palsy, motor impairment and cognitive outcomes.27
GMs were scored from
video recordings according to Prechtl’s method of qualitative assessment.18
GMs were
categorized as normal or abnormal, with abnormal GMs further categorized as poor
repertoire, cramped synchronized, or chaotic. GMs were classified unscorable if the
infant was crying or hypokinetic.
Magnetic Resonance Imaging
Brain MRI was performed using the Siemens 3T Magnetom Trio MRI system
(Siemens, Erlangen, Germany) during natural sleep, on the same day as
neurobehavioral assessments. The details of the imaging protocol have previously
been published, and the T1 and T2-weighted structural brain images were used for the
current study.20
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A validated neonatal brain MRI scoring system was used to assess brain maturation,
injury and size, utilizing two dimensional brain metrics28
and conventional methods
of assessing brain injury.29
Four regional abnormality scores were calculated based on
assessment of injury, growth and maturation: cerebral white matter, cortical grey
matter, deep grey matter (basal ganglia & thalamus), and cerebellar abnormality.30
A
global abnormality score was computed using the sum of the four regional
abnormality scores; higher scores indicate greater abnormality. Brain MRI were
scored independently by one of four experienced neuroradiologists and neonatologists
who had received training in this scoring system with excellent inter- and intra-rater
reliability.20
Statistical analysis
Data were analyzed using Stata Version 14 (StatCorp, Texas, USA). To explore the
relationships between gestational age group and neurobehavior at term-equivalent
age, proportions with suboptimal neurobehavior within gestational age groups were
compared by chi-square test for trend. Relationships between brain MRI
abnormalities and suboptimal neurobehavior in the preterm infants (<37 weeks’
gestational age) were explored using logistic regression, fitted using generalized
estimating equations to allow for multiple births, and adjusted for sex and age at brain
MRI. For any significant relationships between neonatal brain abnormality and
suboptimal neurobehavior, an interaction term was included in the model to explore
whether the relationship differed according to the preterm infants’ gestational age
group (VPT or MLPT). We have previously demonstrated a strong relationship
between brain abnormality scores and abnormal GMA in VPT infants,31
thus, an
interaction term was included in this model, irrespective of the initial findings.
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RESULTS
Neurobehavioral assessments were performed at term-equivalent age in 140 of 149
VPT infants, 200/201 MLPT infants, and 200/201 term controls recruited for this
study. Participant characteristics are summarized in Table 1. A subset of VPT (n=110)
and MLPT (n=198) infants had concurrent brain MRI. Infants ≥45 weeks’
postmenstrual age at the time of neurobehavioral assessment and brain MRI were
excluded, as were infants who were not in an appropriate state to allow their GMA to
be reliably scored (see Figure 1). Differences between infants with and without
neurobehavioral data are shown in Supplementary Table 1a-c. In the VPT group, six
infants died prior to term-equivalent age and were not included in this baseline
analysis.
INSERT Figure 1: Participant Recruitment and Neurobehavior Assessment and
MRI Follow-up
Table 1: Participant Characteristics of Infants Assessed at Term Equivalent Age
(Based on HNNE sample size)
VPT
n=133
MLPT
n=196
Term Control
n=186
Postnatal corticosteroids – n (%)
12 (9) 0 0
Multiple birth – n (%) 59 (44) 73 (37) 2 (1)
Caesarean delivery – n
(%) 75 (72) 134 (68) 72 (39)
Gestational age at birth
(weeks) – mean (SD) 27.9 (1.4) 34.4 (1.3) 39.7 (1.2)
Birth weight z-score –
mean (SD) -0.33 (1) -0.33 (1.2) 0.23 (0.82)
Male – n (%) 54 (52) 93 (47) 99 (53)
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Respiratory distress at birth – n (%)
102 (98) 44 (22) 0
Postmenstrual age at
neurobehaviour
assessment (weeks) – mean (SD)
41.5 (1.9) 41.4 (1.1)
41.9 (1.5)
13 days (10
days)
Postmenstrual age at
MRI (weeks) – mean (SD)
42.4 (1.5) 41.4 (1.1)
Suboptimal Neurobehavior
There were statistically significant linear trends between several suboptimal
neurobehavior subscales and decreasing gestational age group (Error! Reference
source not found.). On the NNNS, the proportion with suboptimal neurobehavior
increased with decreasing gestational age group for the following subscales:
regulation, quality of movement, non-optimal reflexes, hypertonicity and stress.
Excluding abnormal signs and behavior, which were nonlinear, suboptimal
neurobehavior on the HNNE subscales increased with decreasing age. There was a
strong linear trend for decreasing gestational age group and abnormal GMA.
INSERT Figure 1: Sub-optimal Neurobehavior and Abnormal GMs across
Gestational Age Groups
Brain MRI Abnormality and Suboptimal Neurobehavior
Higher brain MRI abnormality scores were associated with suboptimal neurobehavior
on the NNNS and HNNE, with the exception of the deep grey matter abnormality
score (Table 2). A higher global brain abnormality score was associated with greater
odds of suboptimal lethargy and non-optimal reflexes on the NNNS, and greater odds
of suboptimal reflexes, abnormal signs and total score on the HNNE. Similarly, a
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higher cerebral white matter abnormality score was associated with greater odds of
suboptimal lethargy and non-optimal reflexes on the NNNS, and greater odds of
suboptimal reflexes, suboptimal spontaneous movements and total score on the
HNNE. A higher cortical grey matter abnormality score was associated with non-
optimal reflexes on the NNNS. A higher cerebellar abnormality score was associated
with greater odds of suboptimal lethargy on the NNNS and total score on the HNNE.
The cerebellar abnormality score was the only brain MRI abnormality score
associated with suboptimal tone on the HNNE. Brain abnormality scores were not
associated with increased odds of abnormal GMA.
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Table 2: Relationship between brain abnormality scores and suboptimal neurobehavior
Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
NNNS
Attention
1.03 (0.87 to 1.21), 0.67 1.18 (0.91 to 1.53), 0.20 0.75 (0.50 to 1.13), 0.16 1.59 (0.42 to 5.95), 0.50 1.08 (0.61 to 1.90), 0.80
NNNS Arousal 0.93 (0.82 to 1.04), 0.21 0.89 (0.72 to 1.09), 0.26 1 (0.77 to 1.28), 0.98 0.74 (0.29 to 1.92), 0.54 0.77 (0.48 to 1.25), 0.30
NNNS
Regulation
0.99 (0.89 to 1.11), 0.88 1.01 (0.84 to 1.21), 0.94 0.98 (0.76 to 1.27), 0.90 0.21 (0.03 to 1.30), 0.09 1.01 (0.69 to 1.47), 0.97
NNNS
Handling
0.85 (0.68 to 1.07), 0.16 0.72 (0.42 to 1.23), 0.23 1.08 (0.78 to 1.51), 0.65 Omitted* 0.51 (0.18 to 1.44), 0.20
NNNS Quality
of Movement
1.07 (0.94 to 1.21), 0.31 1.07 (0.87 to 1.32), 0.51 1.02 (0.78 to 1.34), 0.88 0.44 (0.11 to 1.70), 0.23 1.45 (0.97 to 2.16), 0.07
NNNS
Excitability
0.93 (0.83 to 1.05), 0.25 0.87 (0.71 to 1.06), 0.17 1.05 (0.83 to 1.35), 0.67 0.20 (0.03 to 1.50), 0.12 0.85 (0.57 to 1.27), 0.43
NNNS Lethargy
1.20 (1.01 to 1.43), 0.04 1.44 (1.09 to 1.89), 0.01 0.78 (0.47 to 1.27), 0.32 Omitted* 2.22 (1.38 to 3.59), <0.01
NNNS Non-
optimal
Reflexes
1.23 (1.07 to 1.41), <0.01 1.30 (1.06 to 1.59), 0.01 1.33 (1.03 to 1.72), 0.03 1.12 (0.36 to 3.53), 0.85 1.47 (0.97 to 2.23), 0.07
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Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
NNNS Asymmetrical
Reflexes
0.86 (0.69 to 1.08), 0.20 0.89 (0.63 to 1.26), 0.52 0.83 (0.50 to 1.36), 0.45 Omitted* 0.43 (0.18 to 1), 0.05
NNNS
Hypertonicity
1.07 (0.92 to 1.24), 0.40 1.17 (0.91 to 1.52), 0.23 1.07 (0.78 to 1.47), 0.68 Omitted* 0.99 (0.58 to 1.68), 0.97
NNNS Hypotonicity
1.05 (0.93 to 1.19), 0.40 1.21 (1 to 1.46), 0.05 0.92 (0.67 to 1.27), 0.62 Omitted* 1.08 (0.67 to 1.73), 0.75
NNNS Stress 1.10 (0.98 to 1.22), 0.11 1.15 (0.96 to 1.38), 0.12 1.05 (0.84 to 1.32), 0.66 0.90 (0.37 to 2.16), 0.81 1.35 (0.91 to 1.99), 0.13
HNNE Total
Score
1.19 (1.05 to 1.33), <0.01 1.29 (1.06 to 1.57), 0.01 1.16 (0.91 to 1.49), 0.23 0.68 (0.23 to 2.02), 0.49 1.61 (1.09 to 2.38), 0.02
HNNE Tone
Optimality
1.10 (0.97 to 1.23), 0.14 1.14 (0.94 to 1.38), 0.17 1 (0.77 to 1.29), 0.97 1.38 (0.58 to 3.29), 0.46 1.55 (1.02 to 2.34), 0.04
HNNE Tone
Patterns
1.13 (0.98 to 1.31), 0.10 1.09 (0.86 to 1.37), 0.48 1.22 (0.87 to 1.70), 0.25 0.54 (0.09 to 3.06) 0.48 1.50 (0.91 to 2.46), 0.11
HNNE
Reflexes
1.17 (1.03 to 1.33), 0.01 1.37 (1.09 to 1.71), 0.01 1.05 (0.80 to 1.39), 0.72 0.38 (0.05 to 2.76), 0.34 1.41 (0.94 to 2.12), 0.10
HNNE
Spontaneous
Movement
1.11 (0.98 to 1.25), 0.09 1.26 (1.04 to 1.52), 0.02 1.04 (0.81 to 1.35), 0.74 0.42 (0.10 to 1.88), 0.26 1.15 (0.77 to 1.72), 0.50
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Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
HNNE Abnormal
Signs
1.14 (1.01 to 1.28), 0.03 1.12 (0.93 to 1.34), 0.24 1.23 (0.94 to 1.61), 0.13 1.09 (0.44 to 2.69), 0.86 1.34 (0.88 to 2.06), 0.18
HNNE
Behaviour
1.11 (0.90 to 1.36), 0.32 1.20 (0.89 to 1.63), 0.23 1.02 (0.66 to 1.58), 0.94 Omitted* 1.52 (0.84 to 2.74), 0.16
Abnormal GMs 1.12 (0.99 to 1.26) 0.07 1.21 (1.00 to 1.46) 0.05 1.15 (0.91 to 1.45) 0.24 0.92 (0.41 to 2.05) 0.84 1.04 (0.71 to 1.53) 0.83
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Statistically significant relationships between brain MRI abnormality scores and
suboptimal neurobehavior on the NNNS and HNNE were similar in both the VPT and
MLPT groups (all p values for interaction >0.05). The relationships between brain
MRI abnormality scores and abnormal GMA were stronger in the VPT group for
global brain (OR 1.39; 95% CI 1.04, 1.86; p=0.03) and cortical grey matter (OR 1.75;
95% CI 1.05, 2.93; p=0.03) abnormality scores.
DISCUSSION
This study demonstrates the continuum of neurobehavior across gestational age
groups, demonstrating more infants with suboptimal neurobehavior for many items on
the HNNE, NNNS and GMA with decreasing gestational age. This study extends
previous research that has shown differences in neurobehavior between VPT and
term-born infants at term-equivalent age by exploring brain-behavior relationships on
concurrent neurobehavioral assessment and brain MRI.
Regulation and Behavioral Domains of the NNNS and HNNE
The current study demonstrated a linear relationship between suboptimal regulation
on the NNNS (which assesses the infant’s capacity to organize motor activity,
physiology and state during the examination)24
and decreasing gestational age, and is
consistent with other reports describing VPT infants as more irritable with poorer
self-regulation at term-equivalent age than their term-born peers.32
Compromises to
the development of preterm infants’ self-regulation may influenced by the ‘chaotic
context’ of the neonatal intensive care unit environment,33
which includes perinatal
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care experiences associated with pain and stress to a system undergoing rapid growth
and organization.34
Previous studies have demonstrated a relationship between lower
gestational age and greater exposure to stress in the neonatal period,35
which may
explain the current study’s finding of increasing rates of suboptimal regulation at
term-equivalent age in infants born at lower gestations.
Poor regulation is often exhibited through infant behaviors known as stress cues.36
Accordingly, it is not surprising that the current study also demonstrated a linear
relationship between greater suboptimal levels of stress-related behaviors on the
NNNS and decreasing gestational age. The NNNS stress scale provides information
about the infant’s capacity to regulate and organize multiple systems (e.g. visual,
state, physiological, motor) in response to handling and interaction demands of their
environment. Interestingly, a significant proportion of the MLPT infants showed
suboptimal stress responses on the NNNS (36%), suggesting that despite their greater
gestational age, MLPT infants, like their VPT infant peers, are vulnerable to
environmental stressors and may require external support to regulate their developing
regulatory systems.37
Influenced in part by an infant’s regulatory capacity, it is not surprising that compared
with their term-born peers, both VPT and MLPT infants had higher rates of
suboptimal arousal and excitability on the NNNS. This relationship was not, however,
linear, which may be explained by the bidirectional nature of these scales, with higher
scores representing over-arousal or over-excitability and lower scores reflecting
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under-arousal or under-excitability. The suboptimal score does not differentiate the
direction of the pattern.
Other non-linear relationships in the current study may be explained by the age of
term infants at assessment. Compared with the MLPT infants, term infants had higher
proportions of suboptimal lethargy and hypotonicity on the NNNS. These infants
were often assessed before hospital discharge, when the physiological changes that
occur in the first days after birth may have influenced the presentation of lethargy and
apparent hypotonicity.38
The NNNS handling scale includes the strategies used to
obtain focused attention and the non-linear relationship is somewhat surprising. Term
infants did not require as much supportive handling, as expected, but MLPT infants
required more handling than the VPT infants, which may reflect their different
responses to stress. On the HNNE, the proportions of suboptimality in the abnormal
signs domain were similar between VPT and MLPT infants and the behavior domain
had low levels of suboptimality in all three groups.
The orientation and attention performance of infants across the three gestational age
groups, whilst linear, was similar between groups. Despite the effects of gestational
age at birth on regulation at term-equivalent age, all three groups demonstrated
similar orientation and attentional responses to visual and auditory stimuli, although
perhaps at the cost of the VPT and MLP infants’ stress and regulation performance.
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Neurological Domains of the NNNS and HNNE, and the General Movements
Assessment
Having spent longer in physiological flexion in-utero, term-born infants often present
with greater flexion and smoother movement patterns compared with preterm
peers.16,39,40
Higher rates of tone abnormalities, both hypertonicity and hypotonicity,
are reported in preterm infants at term-equivalent age.16
Consistent with this pattern,
the current study demonstrated a linear trend with suboptimal hypertonicity on the
NNNS, suboptimal tone optimality and suboptimal tone patterns on the HNNE, and
decreasing gestational age. Similarly, there was a linear trend for poorer quality of
movement and decreasing gestational age on all three neurobehavioral assessments.
On the HNNE, VPT infants demonstrated significantly higher rates of suboptimal
spontaneous movement (41%) compared with MLPT and term infants (14% and 4%,
respectively) and higher rates of abnormal GMA with decreased gestational age.
Reduced quality of movement at lower gestational ages may reflect differences in
central nervous system integrity and the extra-uterine environment, including limited
opportunities for movement.
Similarly, the reflex scales of the NNNS and HNNE also reflect maturation of the
infants’ central nervous system with a linear trend observed between decreasing
gestational age and increasing suboptimal reflexes on the NNNS and HNNE. Infants
born VPT had a significantly higher rate of suboptimal reflexes (32%) compared with
MLPT and term-born infants (9% and 7%, respectively). Furthermore, the rates of
suboptimal total scores on the HNNE, a reflection of an infant’s overall performance
on the more neurologically-based assessment, increased significantly with decreasing
gestational age, highlighting the impact of VPT birth on the maturation and integrity
of the central nervous system. This finding contrasts with other studies having
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reported that motor reflexes do not seem to be differentially affected by a short
gestation, severity of illness, or brain injury.41
Brain behavior relationships
A number of relationships between brain MRI abnormality scores and suboptimal
neurobehavior were identified in the current study. These brain behavior
relationships were more common on the HNNE than the NNNS, which may reflect its
greater neurological focus (ref Alicia paper).
On the NNNS, various brain abnormality scores were associated with greater odds of
non-optimal reflexes and suboptimal lethargy. In particular, the global, cerebral white
matter, and cortical grey matter abnormality scores were associated with suboptimal
reflexes and the global, cerebral white matter and cerebellar with suboptimal lethargy
on the NNNS. This is consistent with the study by Brown,17
who also reported both
white matter signal abnormality and delayed gyral maturation to be associated with
worse performance on the NNNS non-optimal reflex scale.
Previous studies have linked poor quality of movement with cerebral injury, in
particular, white matter abnormality and poor outcome.10
Similarly, the current study
demonstrated a significant relationship between white matter abnormality and
suboptimal performance on the HNNE spontaneous movement scale. This is
consistent with the study by Brown who reported a linear relationship between grade
of white matter abnormality and HNNE spontaneous movement scores. In keeping
with the current study’s findings, previous studies found no relationship between
brain abnormality scores and the NNNS quality of movement scale.16,17
No other
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regional abnormality scores were associated with suboptimal movement in contrast
with two earlier studies that reported delayed gyral maturation and higher grey matter
abnormality were associated with lower HNNE spontaneous movement scores.17,42
On the GMA, however, global brain abnormality and cortical grey abnormality, a
composite of signal abnormality, delayed gyration and dilated extra-cerebral space,
was associated with abnormal GMA.
Limitations of the current study are important to consider in the interpretation of the
findings. In particular, the low rates of brain abnormality scores may have influenced
the power to detect relationships between brain abnormality and suboptimal
neurobehavioral outcomes.
CONCLUSION
The current study demonstrated a clear continuum of neurobehavior across three
gestational age groups, with poorer behavior regulation, increased stress response,
greater tone abnormality, abnormal reflexes, and poorer quality of movement with
decreasing gestational age. A number of relationships between brain abnormality
scores and suboptimal neurobehavior were found, particularly the global, white
matter, and cerebellar abnormality scores with the more neurologically focused
domains of the NNNS and HNNE, evidence that the neurobehavioral assessment can
provide insight into the integrity of the developing brain and can both supplement
imaging findings when available, or, in the absence of MRI, provide guidance to
clinicians who are identifying high risk infants for early intervention services.
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Competing Interests: All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for
the submitted work; no financial relationships with any organisations that might have
an interest in the submitted work in the previous three years; no other relationships or
activities that could appear to have influenced the submitted work.
Funding source:
This work was supported in part by the Australian National Health and Medical
Research Council (NHMRC) (Project Grant ID 1028822); Centre of Clinical
Research Excellence Grant ID 546519; Centre of Research Excellence Grant ID
1060733; Senior Research Fellowship ID 1081288 to PJA.; Early Career Fellowship
ID 1053787 to JLYC, ID 1053767 to AJS; Career Development Fellowship ID
1108714 to AJS; Australian Postgraduate Scholarship to JEO, Murdoch Children’s
Research Institute, Clinical Sciences Theme Grant, the Victorian Government
Operational Infrastructure Support Program.
Financial Disclosure: The authors have no financial relationships relevant to this
article to disclose.
We attest that we have obtained appropriate permissions and paid any required fees
for use of copyright protected materials.
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Supplementary Table 1a: Differences in infant characteristics for those infants with and without HNNE data across three gestational age groups
VPT
with
HNNE
data
VPT
no
HNNE
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
HNNE
data
MLPT
no
HNNE
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
HNNE
data
TERM
no
HNNE
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=133 n=10 n=196 n=5 n=186 n=15
Postnatal
corticosteroids
n (%)
12 (9) 4 (40) 8.98
(0.003) 0 0 0 0
Multiple birth
n (%) 59 (44) 4 (40) 0.07 (0.79) 71 (36) 3 (60) 1.18 (0.28) 2 (1) 0 (0) 0.16 (0.69)
Caesarean
delivery
n (%)
97 (73) 8 (80) 0.24 (0.63) 133 (68) 4 (80) 0.33 (0.57) 72 (39) 5 (33) 0.17 (0.68)
Gestational age
at birth (weeks)
mean (SD)
27.8 (1.5) 27.2 (1.5)
-0.59 (-1.54
to 0.37),
0.23
34.4 (1.3) 34.1
(0.4)
-0.35 (-1.46 to
0.76), 0.53 39.7 (1.2)
40.2
(1.3)
0.52 (-0.12
to 1.16),
0.11
Birth weight z-
score
mean (SD)
-0.41
(1.02)
-0.97
(1.11)
-0.57 (-1.23
to 0.10),
0.09
-0.35
(1.2)
-0.43
(0.6)
-0.08 (-1.12 to
0.96), 0.88
0.23
(0.82)
0.04
(0.84)
-0.19 (-0.62
to 0.25),
0.39
Male
n (%) 66 (50) 5 (50)
0.0005
(0.98) 94 (48) 2 (40) 0.12 (0.73) 99 (53) 8 (53)
0.0001
(0.99)
Respiratory
distress at birth
n (%)
130 (98) 10 (100) 0.23 (0.63) 44 (22) 1 (20) 0.02 (0.90) 0 0
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Supplementary Table 1b: Differences in infant characteristics for those infants with and without NNNS data across three gestational age groups
VPT
with
NNNS
data
VPT
no
NNNS
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
NNNS
data
MLPT
no
NNNS
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
NNNS
data
TERM
no
NNNS
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=130 n=13 n=187 n=14 n=183 n=18
Postnatal
corticosteroids
n (%)
11 (8) 5 (38) 10.70
(0.001) 0 0 0 0
Multiple birth
n (%) 57 (44) 6 (46) 0.03 (0.87) 65 (35) 9 (64) 4.88 (0.02) 2 (1) 0 (0) 0.20 (0.66)
Caesarean
delivery
n (%)
96 (74) 9 (69) 0.13 (0.72) 128 (68) 9 (64) 0.10 (0.75) 71 (39) 6 (33) 0.21 (0.65)
Gestational age
at birth (weeks)
mean (SD)
27.9 (1.4) 26.8 (1.6)
-1.07 (-1.90
to -0.24),
0.01
34.4 (1.3) 34.3
(0.8)
-0.14 (-0.83 to
0.54), 0.67 39.7 (1.2)
40.3
(1.2)
0.56 (-0.03
to 1.14),
0.06
Birth weight z-
score
mean (SD)
-0.39
(1.01)
-0.98
(1.10)
-0.59 (-1.18
to -0.00),
0.05
-0.32
(1.2)
-0.74
(1)
-0.41 (-1.04 to
0.22), 0.20
0.22
(0.82)
0.16
(0.85)
-0.09 (-0.46
to 0.34),
0.77
Male
n (%) 64 (49) 7 (54) 0.10 (0.75) 88 (47) 8 (57) 0.53 (0.47) 97 (53) 10 (56) 0.04 (0.84)
Respiratory
distress at birth
n (%)
127 (98) 13 (100) 0.31 (0.58) 43 (23) 2 (14) 0.57 (0.45) 0 0
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Supplementary Table 1c: Differences in infant characteristics for those infants with and without GMA data across three gestational age groups
VPT
with
GMA
data
VPT
no GMA
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
GMA
data
MLPT
no
GMA
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
GMA
data
TERM
no
GMA
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=113 n=30 n=154 n=47 n=142 n=59
Postnatal
corticosteroids
n (%)
14 (12) 2 (7) 0.78 (0.38) 0 0 0 0
Multiple birth
n (%) 47 (42) 16 (53) 1.33 (0.25) 56 (36) 18 (38) 0.06 (0.81) 0 (0) 2 (3) 4.86 (0.03)
Caesarean
delivery
n (%)
79 (70) 26 (87) 3.41 (0.07) 106 (69) 31 (66) 0.14 (0.71) 52 (37) 25 (42) 0.58 (0.45)
Gestational age
at birth (weeks)
mean (SD)
27.7 (1.6) 28.0 (1.1)
0.34 (-0.25
to 0.94),
0.26
34.5 (1.3) 34.1
(1.0)
-0.37 (-0.78 to
0.03), 0.07 39.8 (1.2)
39.6
(1.3)
-0.28 (-0.65
to 0.09),
0.14
Birth weight z-
score
mean (SD)
-0.43
(1.02)
-0.53
(1.10)
-0.10 (-0.52
to 0.32).
0.64
-0.35
(1.2)
-0.36
(1.1)
-0.01 (-0.39 to
0.37), 0.97
0.23
(0.82)
0.17
(0.82)
-0.06 (-0.31
to 0.19),
0.63
Male
n (%) 55 (49) 16 (53) 0.21 (0.65) 73 (47) 23 (49) 0.03 (0.85) 79 (56) 28 (47) 1.12 (0.29)
Respiratory
distress at birth
n (%)
112 (99) 28 (93) 3.86 (0.05) 34 (22) 11 (23) 0.04 (0.85) 0 0
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The continuum of neurobehavior and its associations with
brain MRI in infants born preterm
Journal: BMJ Paediatrics Open
Manuscript ID bmjpo-2017-000136.R1
Article Type: Original article
Date Submitted by the Author: 05-Jul-2017
Complete List of Authors: Eeles, Abbey; Murdoch Childrens Research Institute, Victorian Infant Brain Study Walsh, Jennifer; The Royal Women's Hospital, Newborn Research Olsen, Joy; Murdoch Childrens Research Institute Cuzzilla, Rocco Thompson, Deanne
Anderson, Peter Doyle, Lex; Royal WOmen's Hospital, Cheong, Jeanie Spittle, Alicia; Murdoch Childrens Research Institute, Victorian Infant Brian Study
Keywords: Neurodevelopment
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The continuum of neurobehavior and its associations with brain MRI in infants
born preterm
Abbey L Eeles,b,c
Jennifer M Walsh,b,c,d
Joy E Olsen,b,c,e
Rocco Cuzzilla,b,c,e
Deanne
K Thompson,b,f,h
Peter J Anderson, b,f,g
Lex W Doyle, b,c,e,f
Jeannie L Y Cheong,b,c,f
Alicia J Spittlea,b,c
Author Affiliations: aDepartment of Physiotherapy, University of Melbourne,
Melbourne, Vic; bMurdoch Childrens Research Institute, Melbourne, Vic;
cNewborn
Research, Royal Women’s Hospital, Melbourne, Vic; dPaediatric Infant Perinatal
Emergency Retrieval (PIPER), The Royal Children’s Hospital, Melbourne, Vic; eDepartment of Obstetrics and Gynaecology, University of Melbourne, Melbourne,
Vic; fDepartment of Paediatrics, University of Melbourne, Melbourne, Vic;
gMonash
Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne,
Vic; hFlorey Institute of Neuroscience and Mental Health, Melbourne, Vic
Corresponding Author:
Alicia J Spittle
7th Floor, Alan Gilbert Building, University of Melbourne, Grattan Street, Parkville,
Vic, Australia, 3052.
E-mail: [email protected]
Copyright:
The Corresponding Author has the right to grant on behalf of all authors and does
grant on behalf of all authors, a worldwide licence to the Publishers and its licensees
in perpetuity, in all forms, formats and media (whether known now or created in the
future), to i) publish, reproduce, distribute, display and store the Contribution, ii)
translate the Contribution into other languages, create adaptations, reprints, include
within collections and create summaries, extracts and/or, abstracts of the
Contribution, iii) create any other derivative work(s) based on the Contribution, iv) to
exploit all subsidiary rights in the Contribution, v) the inclusion of electronic links
from the Contribution to third party material where-ever it may be located; and, vi)
licence any third party to do any or all of the above.
What is known about this subject?
Infants born very preterm are at increased risk of long-term neurodevelopmental
deficits. Early interventions can improve developmental outcomes and thus early
identification of infants at greatest need for these services is paramount. Brain MRI
abnormalities has been associated with neurobehaviour functioning in very preterm
infants, however, this relationship is yet to be explored in moderate-to-late preterm
infants.
What this study adds:
This study highlights a clear continuum of neurobehaviour with increased suboptimal
functioning on three neurobehavioral assessments with decreasing gestational age.
The relationships between brain abnormality scores and suboptimal neurobehavior
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provides evidence that neurobehavioral assessments may be useful in earlier
identification of the highest-risk infants.
Word count: 2891
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ABSTRACT
Background
Infants born very preterm (VPT) and moderate-to-late preterm (MLPT) are at
increased risk of long-term neurodevelopmental deficits, but how these deficits relate
to early neurobehavior in MLPT children is unclear. The aims of this study were to
compare the neurobehavioral performance of infants born across 3 different
gestational age groups: Preterm <30 weeks’ gestational age (PT<30); MLPT (32 to 36
weeks’ gestational age); and term age (≥37 weeks’ gestational age), and explore the
relationships between MRI brain abnormalities and neurobehavior at term-equivalent
age.
Methods
Neurobehaviour was assessed at term equivalent age in 149 PT<30, 200 MLPT, and
200 term born infants using the NICU Network Neurobehavioral Scale (NNNS), the
Hammersmith Neonatal Neurological Exam (HNNE), and Prechtl’s Qualitative
Assessment of General Movements (GMA). A subset of 110 PT<30 and 198 MLPT
infants had concurrent brain MRI.
Results
Proportions with abnormal neurobehavior on the NNNS and the HNNE, and
abnormal GMA all increased with decreasing gestational age. Higher brain MRI
abnormality scores in some regions were associated with suboptimal neurobehavior
on the NNNS and HNNE. The relationships between brain MRI abnormality scores
and suboptimal neurobehavior were similar in both PT<30 and MLPT infants. The
relationship between brain MRI abnormality scores and abnormal GMA was stronger
in PT<30 infants.
Conclusions
There was a continuum of neurobehaviour across gestational ages. The relationships
between brain abnormality scores and suboptimal neurobehavior provide evidence
that neurobehavioral assessments offer insight into the integrity of the developing
brain, and may be useful in earlier identification of the highest-risk infants.
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BACKGROUND AND RATIONALE
Infants born preterm are at increased risk of long-term neurodevelopmental deficits in
cognitive, neurosensory, physical, and social-emotional development, as well as
impairments in academic functioning compared with their term-born peers.1-4
Early
interventions to mitigate some of these adverse neurodevelopmental deficits are
promising,5,6
and thus, clinicians working with preterm infants and their families aim
to identify those infants at greatest need for early intervention services. Whilst major
preterm brain injuries, such as high-grade intraventricular hemorrhage (IVH) and
cystic periventricular leukomalacia (cPVL), are highly prognostic for adverse
neurodevelopmental outcomes,6,7
other clinical predictors of long-term development
have only modest prognostic utility. Magnetic resonance imaging (MRI) can detect
more subtle preterm brain injury associated with adverse neurodevelopment, however
its use in routine clinical care is limited by availability and cost.8 Clinicians are
increasingly using neonatal neurobehavioral assessments to identify high-risk infants
and to help guide referrals for early intervention. Neurobehavioral assessments are
valid and reliable tools which offer insights into the neurological integrity and
behavioral functioning of an infant.9,10
Compared with their term-born peers, infants born very preterm (VPT: <32 weeks’
gestation) and moderate-to-late preterm (MLPT: 32 to 36 weeks’ gestation) present
with increased rates of atypical neurobehavior at term-equivalent age.11,12
Importantly, these neurobehavioral difficulties have been associated with later
developmental deficits,13,14
providing support for the neurobehavioral assessment as a
predictive tool for later neurodevelopmental outcome. Whilst there is some evidence
that MRI brain abnormalities are associated with neurobehavior in VPT infants at
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term-equivalent age,15-17
this brain-neurobehavior relationship has yet to be explored
in MLPT infants.
The primary aim of this study was to compare the neurobehavioral performance of
infants born across 3 different gestational age groups: Preterm <30 weeks’
gestational age (PT<30); MLPT (32 to 36 weeks’ gestational age); and term age (≥37
weeks’ gestational age) using the NICU Network Neurobehavioral Scale (NNNS), the
Hammersmith Neonatal Neurological Exam (HNNE), and Prechtl’s Qualitative
Assessment of General Movements (GMA).18
Secondary aims were to explore the
relationships between MRI brain abnormalities (development and injury) and
neurobehavior at term-equivalent age in infants born preterm (PT<30 and MLPT).
METHODS
Participants
Participants were derived from two longitudinal cohorts of infants recruited from the
Royal Women’s Hospital in Melbourne, Australia, between November 2009 and
December 2013. The first cohort comprised 149 preterm infants born <30 weeks’
gestation (PT<30)19
and the second cohort included 201 MLPT infants born between
32 to 36 weeks’ gestation.20
In addition, a cohort of 201 term controls born ≥37
weeks’ gestation were recruited across the two cohorts. Infants with congenital
abnormalities and/or infants with non-English speaking parents were excluded due to
limited funding for interpreters. In the term control group, infants requiring admission
to the special care or intensive care nurseries were excluded. Informed parental
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consent was obtained for all participants, and both studies were approved by the
Royal Women’s Hospital’s and Royal Children’s Hospital’s Human Research Ethics
Committees.
Perinatal data were recorded by research nurses, including gestation at birth, sex,
birthweight z-score (calculated according to gestational age and sex using the British
Growth Reference norms),21
multiple birth, use of antenatal corticosteroids, and
respiratory support.
Neurobehavioral measures
At term-equivalent age (38-44 weeks’ postmenstrual age), neurobehavior was
assessed by one of five trained and certified assessors using NNNS,22
HNNE,23
and
GMA. All assessors had advanced GMA certification and were masked to the
participants’ clinical history. All assessments were administered according to their
standardized procedures, as previously published in the study protocol.19
Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS)
The NNNS is a neurobehavioral assessment that examines the neurological integrity,
behavioral functioning, and responses to stress in high-risk infants using 45 items
which correspond to 13 summary scales including habituation, attention, arousal,
regulation, handling, quality of movement, excitability, lethargy, non-optimal
reflexes, asymmetrical reflexes, hypertonicity, hypotonicity, and stress.24,25
The
habituation scale was not included in this study as infants were not consistently in an
appropriate state (sleep) to administer the scale at the start of the assessment. The
scoring and classification of infants’ performance on the NNNS summary scales has
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been described previously by our group.26
Hammersmith Neonatal Neurological Examination (HNNE)
The HNNE is primarily a neurological exam developed for term and preterm infants.
It consists of 34 individual items with six subtotals including tone, tone patterns,
reflexes, spontaneous movements, abnormal neurological signs, and behavior, which
are added for a total score. Suboptimal performance on the HNNE was categorized as
previously published.27
Prechtl’s qualitative assessment of General Movements (GMA)
The GMA is an observational assessment of the infant’s spontaneous movements
(GMs) with good predictive validity for neurodevelopmental outcomes, including
cerebral palsy, motor impairment and cognitive outcomes.28
GMs were scored from
video recordings according to Prechtl’s method of qualitative assessment.18
GMs were
categorized as normal or abnormal, with abnormal GMs further categorized as poor
repertoire, cramped synchronized, or chaotic. GMs were classified unscorable if the
infant was crying or hypokinetic.
Magnetic Resonance Imaging
Brain MRI was performed using the Siemens 3T Magnetom Trio MRI system
(Siemens, Erlangen, Germany) during natural sleep, on the same day as
neurobehavioral assessments. The details of the imaging protocol have previously
been published, and the T1 and T2-weighted structural brain images were used for the
current study.20
A validated neonatal brain MRI scoring system was used to assess brain maturation,
injury and size, utilizing two dimensional brain metrics29
and conventional methods
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of assessing brain injury.30
Four regional abnormality scores were calculated based on
assessment of injury, growth and maturation: cerebral white matter, cortical grey
matter, deep grey matter (basal ganglia & thalamus), and cerebellar abnormality.31
A
global abnormality score was computed using the sum of the four regional
abnormality scores; higher scores indicate greater abnormality. Brain MRI were
scored independently by one of four experienced neuroradiologists and neonatologists
who had received training in this scoring system with excellent inter- and intra-rater
reliability.20
Statistical analysis
Data were analyzed using Stata Version 14 (StatCorp, Texas, USA). To explore the
relationships between gestational age group and neurobehavior at term-equivalent
age, proportions with suboptimal neurobehavior within gestational age groups were
compared by chi-square test for trend. Relationships between brain MRI
abnormalities and suboptimal neurobehavior in the preterm infants (<37 weeks’
gestational age) were explored using logistic regression, fitted using generalized
estimating equations to allow for multiple births, and adjusted for sex and age at brain
MRI. For any significant relationships between neonatal brain abnormality and
suboptimal neurobehavior, an interaction term was included in the model to explore
whether the relationship differed according to the preterm infants’ gestational age
group (PT<30 or MLPT). We have previously demonstrated a strong relationship
between brain abnormality scores and abnormal GMA in infants born <30 weeks’
gestational age,32
thus, an interaction term was included in this model, irrespective of
the initial findings.
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RESULTS
Neurobehavioral assessments were performed at term-equivalent age in 140 of 149
PT<30 infants, 200/201 MLPT infants, and 200/201 term controls recruited for this
study. Participant characteristics are summarized in Table 1. A subset of PT<30
(n=110) and MLPT (n=198) infants had concurrent brain MRI. Infants ≥45 weeks’
postmenstrual age at the time of neurobehavioral assessment and brain MRI were
excluded, as were infants who were not in an appropriate state to allow their GMA to
be reliably scored (see Figure 1). Differences between infants with and without
neurobehavioral data are shown in Supplementary Table 1a-c. In the PT<30 group,
six infants died prior to term-equivalent age and were not included in this baseline
analysis.
INSERT Figure 1: Participant Recruitment and Neurobehavior Assessment and
MRI Follow-up
Table 1: Participant Characteristics of Infants Assessed at Term Equivalent Age
(Based on HNNE sample size)
PT<30
n=133
MLPT
n=196
Term Control
n=186
Postnatal corticosteroids – n (%)
12 (9) 0 0
Multiple birth – n (%) 59 (44) 73 (37) 2 (1)
Caesarean delivery – n
(%) 75 (72) 134 (68) 72 (39)
Gestational age at birth
(weeks) – mean (SD) 27.9 (1.4) 34.4 (1.3) 39.7 (1.2)
Birth weight z-score –
mean (SD) -0.33 (1) -0.33 (1.2) 0.23 (0.82)
Male – n (%) 54 (52) 93 (47) 99 (53)
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Respiratory distress at birth – n (%)
102 (98) 44 (22) 0
Postmenstrual age at
neurobehaviour
assessment (weeks) – mean (SD)
41.5 (1.9) 41.4 (1.1) 41.9 (1.5)
Postmenstrual age at
MRI (weeks) – mean (SD)
42.4 (1.5) 41.4 (1.1) N/A
Global brain abnormality score
n, mean (SD)
101, 4.30 (2.02) 187, 1.82 (1.87) N/A
Cerebral WM brain
abnormality score
n, mean (SD)
101, 2.29 (1.35) 187, 0.98 (1.17) N/A
Cortical GM abnormality
score
n, mean (SD)
102, 1.39 (1.28) 193, 0.62 (0.78) N/A
Deep Nuclear GM
abnormality score
n, mean (SD)
102, 0.11 (0.34) 194, 0.04 (0.19) N/A
Cerebellar abnormality score
n, mean (SD)
102, 0.51 (0.73) 192, 0.19 (0.58) N/A
SD: standard deviation; MRI: Magnetic resonance imaging; N/A: Not applicable (term infants
did not have an MRI for this study)
Suboptimal Neurobehavior
There were statistically significant decreasing trends across the gestational age groups
for several suboptimal neurobehavior subscales (Figure 2). On the NNNS, the
proportions with suboptimal neurobehavior decreased with increasing gestational age
group for the following subscales: regulation, quality of movement, non-optimal
reflexes, hypertonicity and stress. Excluding abnormal signs and behavior,
suboptimal neurobehavior on the remaining HNNE subscales decreased with
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increasing age. There was a strong decreasing trend across the gestational age groups
for abnormal GMA.
INSERT Figure 2: Sub-optimal Neurobehavior and Abnormal GMs across
Gestational Age Groups
Brain MRI Abnormality and Suboptimal Neurobehavior
Higher brain MRI abnormality scores were associated with suboptimal neurobehavior
on the NNNS and HNNE, with the exception of the deep grey matter abnormality
score (Table 2). A higher global brain abnormality score was associated with greater
odds of being suboptimal on the lethargy domain and non-optimal reflexes on the
NNNS, and greater odds of suboptimal reflexes, abnormal signs and total score on the
HNNE. Similarly, a higher cerebral white matter abnormality score was associated
with greater odds of being suboptimal on the lethargy domain and non-optimal
reflexes on the NNNS, and greater odds of suboptimal reflexes, suboptimal
spontaneous movements and total score on the HNNE. A higher cortical grey matter
abnormality score was associated with non-optimal reflexes on the NNNS. A higher
cerebellar abnormality score was associated with greater odds of being suboptimal on
the lethargy domain on the NNNS and total score on the HNNE. The cerebellar
abnormality score was the only brain MRI abnormality score associated with
suboptimal tone on the HNNE. Brain abnormality scores were not associated with
increased odds of abnormal GMA.
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Table 2: Relationship between brain abnormality scores and suboptimal neurobehavior
Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
NNNS
Attention
1.03 (0.87 to 1.21), 0.67 1.18 (0.91 to 1.53), 0.20 0.75 (0.50 to 1.13), 0.16 1.59 (0.42 to 5.95), 0.50 1.08 (0.61 to 1.90), 0.80
NNNS Arousal 0.93 (0.82 to 1.04), 0.21 0.89 (0.72 to 1.09), 0.26 1 (0.77 to 1.28), 0.98 0.74 (0.29 to 1.92), 0.54 0.77 (0.48 to 1.25), 0.30
NNNS
Regulation
0.99 (0.89 to 1.11), 0.88 1.01 (0.84 to 1.21), 0.94 0.98 (0.76 to 1.27), 0.90 0.21 (0.03 to 1.30), 0.09 1.01 (0.69 to 1.47), 0.97
NNNS
Handling
0.85 (0.68 to 1.07), 0.16 0.72 (0.42 to 1.23), 0.23 1.08 (0.78 to 1.51), 0.65 Omitted* 0.51 (0.18 to 1.44), 0.20
NNNS Quality
of Movement
1.07 (0.94 to 1.21), 0.31 1.07 (0.87 to 1.32), 0.51 1.02 (0.78 to 1.34), 0.88 0.44 (0.11 to 1.70), 0.23 1.45 (0.97 to 2.16), 0.07
NNNS
Excitability
0.93 (0.83 to 1.05), 0.25 0.87 (0.71 to 1.06), 0.17 1.05 (0.83 to 1.35), 0.67 0.20 (0.03 to 1.50), 0.12 0.85 (0.57 to 1.27), 0.43
NNNS Lethargy
1.20 (1.01 to 1.43), 0.04 1.44 (1.09 to 1.89), 0.01 0.78 (0.47 to 1.27), 0.32 Omitted* 2.22 (1.38 to 3.59), <0.01
NNNS Non-
optimal
Reflexes
1.23 (1.07 to 1.41), <0.01 1.30 (1.06 to 1.59), 0.01 1.33 (1.03 to 1.72), 0.03 1.12 (0.36 to 3.53), 0.85 1.47 (0.97 to 2.23), 0.07
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Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
NNNS Asymmetrical
Reflexes
0.86 (0.69 to 1.08), 0.20 0.89 (0.63 to 1.26), 0.52 0.83 (0.50 to 1.36), 0.45 Omitted* 0.43 (0.18 to 1), 0.05
NNNS
Hypertonicity
1.07 (0.92 to 1.24), 0.40 1.17 (0.91 to 1.52), 0.23 1.07 (0.78 to 1.47), 0.68 Omitted* 0.99 (0.58 to 1.68), 0.97
NNNS Hypotonicity
1.05 (0.93 to 1.19), 0.40 1.21 (1 to 1.46), 0.05 0.92 (0.67 to 1.27), 0.62 Omitted* 1.08 (0.67 to 1.73), 0.75
NNNS Stress 1.10 (0.98 to 1.22), 0.11 1.15 (0.96 to 1.38), 0.12 1.05 (0.84 to 1.32), 0.66 0.90 (0.37 to 2.16), 0.81 1.35 (0.91 to 1.99), 0.13
HNNE Total
Score
1.19 (1.05 to 1.33), <0.01 1.29 (1.06 to 1.57), 0.01 1.16 (0.91 to 1.49), 0.23 0.68 (0.23 to 2.02), 0.49 1.61 (1.09 to 2.38), 0.02
HNNE Tone
Optimality
1.10 (0.97 to 1.23), 0.14 1.14 (0.94 to 1.38), 0.17 1 (0.77 to 1.29), 0.97 1.38 (0.58 to 3.29), 0.46 1.55 (1.02 to 2.34), 0.04
HNNE Tone
Patterns
1.13 (0.98 to 1.31), 0.10 1.09 (0.86 to 1.37), 0.48 1.22 (0.87 to 1.70), 0.25 0.54 (0.09 to 3.06) 0.48 1.50 (0.91 to 2.46), 0.11
HNNE
Reflexes
1.17 (1.03 to 1.33), 0.01 1.37 (1.09 to 1.71), 0.01 1.05 (0.80 to 1.39), 0.72 0.38 (0.05 to 2.76), 0.34 1.41 (0.94 to 2.12), 0.10
HNNE
Spontaneous
Movement
1.11 (0.98 to 1.25), 0.09 1.26 (1.04 to 1.52), 0.02 1.04 (0.81 to 1.35), 0.74 0.42 (0.10 to 1.88), 0.26 1.15 (0.77 to 1.72), 0.50
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Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
HNNE Abnormal
Signs
1.14 (1.01 to 1.28), 0.03 1.12 (0.93 to 1.34), 0.24 1.23 (0.94 to 1.61), 0.13 1.09 (0.44 to 2.69), 0.86 1.34 (0.88 to 2.06), 0.18
HNNE
Behaviour
1.11 (0.90 to 1.36), 0.32 1.20 (0.89 to 1.63), 0.23 1.02 (0.66 to 1.58), 0.94 Omitted* 1.52 (0.84 to 2.74), 0.16
Abnormal GMs 1.12 (0.99 to 1.26) 0.07 1.21 (1.00 to 1.46) 0.05 1.15 (0.91 to 1.45) 0.24 0.92 (0.41 to 2.05) 0.84 1.04 (0.71 to 1.53) 0.83
NNNS: Neonatal Intensive Care Unit Network Neurobehavioral Scale; HNNE: Hammersmith Neonatal Neurological Examination; WM: white matter; OR:
odds ratio; GM: grey matter; CI: confidence interval; GMs: General Movements; *Result omitted as too few numbers with suboptimal neurobehavior and
deep nuclear GM abnormality to analyze.
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When an interaction term was included in the model to explore whether the
relationship differed according to the preterm infants’ gestational age group,
statistically significant relationships between brain MRI abnormality scores and
suboptimal neurobehavior on the NNNS and HNNE were similar in both the PT<30
and MLPT groups (all p values for interaction >0.05). The relationships between
brain MRI abnormality scores and abnormal GMA were stronger in the PT<30 group
for global brain (OR 1.39; 95% CI 1.04, 1.86; p=0.03) and cortical grey matter (OR
1.75; 95% CI 1.05, 2.93; p=0.03) abnormality scores.
DISCUSSION
This study demonstrates the continuum of neurobehavior across gestational age
groups, demonstrating more infants with suboptimal neurobehavior for many items on
the HNNE, NNNS and GMA with decreasing gestational age. This study extends
previous research that has shown differences in neurobehavior between VPT and
term-born infants at term-equivalent age by exploring brain-behavior relationships on
concurrent neurobehavioral assessment and brain MRI.
Regulation and Behavioral Domains of the NNNS and HNNE
The current study demonstrated that suboptimal regulation on the NNNS (which
assesses the infant’s capacity to organize motor activity, physiology and state during
the examination)24
was more common with decreasing gestational age, and is
consistent with other reports describing VPT infants as more irritable with poorer
self-regulation at term-equivalent age than their term-born peers.33
Compromises to
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the development of preterm infants’ self-regulation may influenced by the ‘chaotic
context’ of the neonatal intensive care unit environment,34
which includes perinatal
care experiences associated with pain and stress to a system undergoing rapid growth
and organization.35
Previous studies have demonstrated a relationship between lower
gestational age and greater exposure to stress in the neonatal period,36
which may
explain the current study’s finding of increasing rates of suboptimal regulation at
term-equivalent age in infants born at lower gestations.
Poor regulation is often exhibited through infant behaviors known as stress cues.37
Accordingly, it is not surprising that the current study also demonstrated greater
suboptimal levels of stress-related behaviors on the NNNS with decreasing
gestational age. The NNNS stress scale provides information about the infant’s
capacity to regulate and organize multiple systems (e.g. visual, state, physiological,
motor) in response to handling and interaction demands of their environment.
Interestingly, a significant proportion of the MLPT infants showed suboptimal stress
responses on the NNNS (36%), suggesting that despite their greater gestational age,
MLPT infants, like their PT<30 infant peers, are vulnerable to environmental stressors
and may require external support to regulate their developing regulatory systems.38
Influenced in part by an infant’s regulatory capacity, it is not surprising that compared
with their term-born peers, both PT<30 and MLPT infants had higher rates of
suboptimal arousal and excitability on the NNNS, however the trend across groups
was not significant, which may be explained by the bidirectional nature of these
scales, with higher scores representing over-arousal or over-excitability and lower
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scores reflecting under-arousal or under-excitability. The suboptimal score does not
differentiate the direction of the pattern.
The lack of significant trends across groups for some variables in the current study
may be explained by the age of term infants at assessment. Compared with the MLPT
infants, term infants had higher proportions of suboptimal lethargy and hypotonicity
on the NNNS. These infants were often assessed before hospital discharge, when the
physiological changes that occur in the first days after birth may have influenced the
presentation of lethargy and apparent hypotonicity.39
The NNNS handling scale
includes the strategies used to obtain focused attention and the non-significant
relationship across groups is somewhat surprising. Term infants did not require as
much supportive handling, as expected, but MLPT infants required more handling
than the PT<30 infants, which may reflect their different responses to stress. On the
HNNE, the proportions of suboptimality in the abnormal signs domain were similar
between PT<30 and MLPT infants and the behavior domain had low levels of
suboptimality in all three groups.
Despite the effects of gestational age at birth on regulation at term-equivalent age, all
three groups demonstrated similar orientation and attentional responses to visual and
auditory stimuli, although perhaps at the cost of the PT<30 and MLPT infants’ stress
and regulation performance.
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Neurological Domains of the NNNS and HNNE, and the General Movements
Assessment
Having spent longer in physiological flexion in-utero, term-born infants often present
with greater flexion and smoother movement patterns compared with preterm
peers.16,40,41
Higher rates of tone abnormalities, both hypertonicity and hypotonicity,
are reported in preterm infants at term-equivalent age.16
Consistent with this pattern,
the current study demonstrated higher rates of suboptimal hypertonicity on the
NNNS, suboptimal tone optimality and suboptimal tone patterns on the HNNE with
decreasing gestational age. Similarly, there was poorer quality of movement with
decreasing gestational age on all three neurobehavioral assessments. On the HNNE,
PT<30 infants demonstrated substantially higher rates of suboptimal spontaneous
movement (41%) compared with MLPT and term infants (14% and 4%, respectively),
and there were higher rates of abnormal GMA with decreasing gestational age.
Reduced quality of movement at lower gestational ages may reflect differences in
central nervous system integrity and the extra-uterine environment, including limited
opportunities for movement.
Similarly, the reflex scales of the NNNS and HNNE also reflect maturation of the
infants’ central nervous system with increasing suboptimal reflexes on the NNNS and
HNNE with decreasing gestational age. Infants born PT<30 had a substantially
higher rate of suboptimal reflexes (32%) compared with MLPT and term-born infants
(9% and 7%, respectively). Furthermore, the rates of suboptimal total scores on the
HNNE, a reflection of an infant’s overall performance on the more neurologically-
based assessment, increased significantly with decreasing gestational age,
highlighting the impact of preterm birth on the maturation and integrity of the central
nervous system. This finding contrasts with other studies having reported that motor
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reflexes do not seem to be differentially affected by a short gestation, severity of
illness, or brain injury.42
Brain behavior relationships
A number of relationships between brain MRI abnormality scores and suboptimal
neurobehavior were identified in the current study. These brain behavior
relationships were more common on the HNNE than the NNNS, which may reflect its
greater neurological focus.9
On the NNNS, various brain abnormality scores were associated with greater odds of
non-optimal reflexes and suboptimal lethargy. In particular, the global, cerebral white
matter, and cortical grey matter abnormality scores were associated with suboptimal
reflexes and the global, cerebral white matter and cerebellar with suboptimal lethargy
on the NNNS. This is consistent with the study by Brown,17
who also reported both
white matter signal abnormality and delayed gyral maturation to be associated with
worse performance on the NNNS non-optimal reflex scale.
Previous studies have linked poor quality of movement with cerebral injury, in
particular, white matter abnormality and poor outcome.10
Similarly, the current study
demonstrated a significant relationship between white matter abnormality and
suboptimal performance on the HNNE spontaneous movement scale. This is
consistent with the study by Brown et al who reported an association between a worse
grade of white matter abnormality and suboptimal HNNE spontaneous movement
scores. In keeping with the current study’s findings, previous studies found no
relationship between brain abnormality scores and the NNNS quality of movement
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scale.16,17
No other regional abnormality scores were associated with suboptimal
movement in contrast with two earlier studies that reported delayed gyral maturation
and higher grey matter abnormality were associated with lower HNNE spontaneous
movement scores.17,43
On the GMA, however, global brain abnormality and cortical
grey abnormality, a composite of signal abnormality, delayed gyration and dilated
extra-cerebral space, was associated with abnormal GMA.
Limitations of the current study are important to consider in the interpretation of the
findings. In particular, the low rates of brain abnormality scores may have influenced
the power to detect relationships between brain abnormality and suboptimal
neurobehavioral outcomes. Also, not all infants were able to have all assessments.
CONCLUSION
The current study demonstrated a clear continuum of neurobehavior across three
gestational age groups, with poorer behavior regulation, increased stress response,
greater tone abnormality, abnormal reflexes, and poorer quality of movement with
decreasing gestational age. A number of relationships between brain abnormality
scores and suboptimal neurobehavior were found, particularly the global, white
matter, and cerebellar abnormality scores with the more neurologically focused
domains of the NNNS and HNNE, evidence that the neurobehavioral assessment can
provide insight into the integrity of the developing brain and can both supplement
imaging findings when available, or, in the absence of MRI, provide guidance to
clinicians who are identifying high risk infants for early intervention services.
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Competing Interests: All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for
the submitted work; no financial relationships with any organisations that might have
an interest in the submitted work in the previous three years; no other relationships or
activities that could appear to have influenced the submitted work.
Funding source:
This work was supported in part by the Australian National Health and Medical
Research Council (NHMRC) (Project Grant ID 1028822); Centre of Clinical
Research Excellence Grant ID 546519; Centre of Research Excellence Grant ID
1060733; Senior Research Fellowship ID 1081288 to PJA.; Early Career Fellowship
ID 1053787 to JLYC, ID 1053767 to AJS; Career Development Fellowship ID
1108714 to AJS; Australian Postgraduate Scholarship to JEO, Murdoch Children’s
Research Institute, Clinical Sciences Theme Grant, the Victorian Government
Operational Infrastructure Support Program.
Financial Disclosure: The authors have no financial relationships relevant to this
article to disclose.
We attest that we have obtained appropriate permissions and paid any required fees
for use of copyright protected materials.
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30. Kidokoro H, Neil JJ, Inder TE. New MR imaging assessment tool to define
brain abnormalities in very preterm infants at term. AJNR Am J Neuroradiol.
Nov-Dec 2013;34(11):2208-2214.
31. Kidokoro H, Anderson PJ, Doyle LW, Woodward LJ, Neil JJ, Inder TE. Brain
injury and altered brain growth in preterm infants: predictors and prognosis.
Pediatrics. 2014(2):444.
32. Olsen JE. Neurobehavioural trajectories of infants born <30 weeks' gestation
from birth to term equivalent age: Are preterm general movements related to
neurobehavioural outcome at term equivalent age? 2014.
33. Brown NCDLWBMJITE. Alterations in Neurobehavior at Term Reflect
Differing Perinatal Exposures in Very Preterm Infants. Pediatrics. 12//
2006;118(6):2461-2471.
34. Gorzilio DM, Garrido E, Gaspardo CM, Martinez FE, Linhares MBM.
Neurobehavioral development prior to term-age of preterm infants and acute
stressful events during neonatal hospitalization. Early Human Development.
12/1/December 2015 2015;91:769-775.
35. Anand KJS, Scalzo FM. Can Adverse Neonatal Experiences Alter Brain
Development and Subsequent Behavior. Biology of the Neonate
2000;77(2):69-82.
36. Smith GC, Gutovich J, Smyser C, et al. Neonatal intensive care unit stress is
associated with brain development in preterm infants. Annals of Neurology.
2011;70(4):541-549.
37. Als H. The Newborn Communicates. Journal of Communication.
1977;27(2):66-73.
38. Als H, Duffy FH, McAnulty GB, et al. Early experience alters brain function
and structure.[see comment]. Pediatrics. Apr 2004;113(4):846-857.
39. Xu Y, Yolton K, Khoury J. Earliest appropriate time for administering
neurobehavioral assessment in newborn infants. Pediatrics. 2011;127:e69-75.
40. Dubowitz L, Dubowitz V, Mercuri E. The neurological assessment of the
preterm and full-term newborn infant. 2nd ed. London: Mac Keith Press;
1999.
41. Allen MC, Capute AJ. Tone and reflex development before term. Pediatrics.
1990(3):393.
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42. Constantinou JC, Adamson-Macedo EN, Mirmiran M, Ariagno RL, Fleisher
BE. Neurobehavioral assessment predicts differential outcome between
VLBW and ELBW preterm infants. Journal of Perinatology.
2005;25(12):788-793 786p.
43. Woodward LJ, Mogridge N, Wells SW, Inder TE. Can Neurobehavioral
Examination Predict the Presence of Cerebral Injury in the Very Low Birth
Weight Infant? J Dev Behav Pediatr. 2004;25(5):326-334.
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Supplementary Table 1a: Differences in infant characteristics for those infants with and without HNNE data across three gestational age groups
VPT
with
HNNE
data
VPT
no
HNNE
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
HNNE
data
MLPT
no
HNNE
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
HNNE
data
TERM
no
HNNE
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=133 n=10 n=196 n=5 n=186 n=15
Postnatal
corticosteroids
n (%)
12 (9) 4 (40) 8.98
(0.003) 0 0 0 0
Multiple birth
n (%) 59 (44) 4 (40) 0.07 (0.79) 71 (36) 3 (60) 1.18 (0.28) 2 (1) 0 (0) 0.16 (0.69)
Caesarean
delivery
n (%)
97 (73) 8 (80) 0.24 (0.63) 133 (68) 4 (80) 0.33 (0.57) 72 (39) 5 (33) 0.17 (0.68)
Gestational age
at birth (weeks)
mean (SD)
27.8 (1.5) 27.2 (1.5)
-0.59 (-1.54
to 0.37),
0.23
34.4 (1.3) 34.1
(0.4)
-0.35 (-1.46 to
0.76), 0.53 39.7 (1.2)
40.2
(1.3)
0.52 (-0.12
to 1.16),
0.11
Birth weight z-
score
mean (SD)
-0.41
(1.02)
-0.97
(1.11)
-0.57 (-1.23
to 0.10),
0.09
-0.35
(1.2)
-0.43
(0.6)
-0.08 (-1.12 to
0.96), 0.88
0.23
(0.82)
0.04
(0.84)
-0.19 (-0.62
to 0.25),
0.39
Male
n (%) 66 (50) 5 (50)
0.0005
(0.98) 94 (48) 2 (40) 0.12 (0.73) 99 (53) 8 (53)
0.0001
(0.99)
Respiratory
distress at birth
n (%)
130 (98) 10 (100) 0.23 (0.63) 44 (22) 1 (20) 0.02 (0.90) 0 0
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Supplementary Table 1b: Differences in infant characteristics for those infants with and without NNNS data across three gestational age groups
VPT
with
NNNS
data
VPT
no
NNNS
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
NNNS
data
MLPT
no
NNNS
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
NNNS
data
TERM
no
NNNS
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=130 n=13 n=187 n=14 n=183 n=18
Postnatal
corticosteroids
n (%)
11 (8) 5 (38) 10.70
(0.001) 0 0 0 0
Multiple birth
n (%) 57 (44) 6 (46) 0.03 (0.87) 65 (35) 9 (64) 4.88 (0.02) 2 (1) 0 (0) 0.20 (0.66)
Caesarean
delivery
n (%)
96 (74) 9 (69) 0.13 (0.72) 128 (68) 9 (64) 0.10 (0.75) 71 (39) 6 (33) 0.21 (0.65)
Gestational age
at birth (weeks)
mean (SD)
27.9 (1.4) 26.8 (1.6)
-1.07 (-1.90
to -0.24),
0.01
34.4 (1.3) 34.3
(0.8)
-0.14 (-0.83 to
0.54), 0.67 39.7 (1.2)
40.3
(1.2)
0.56 (-0.03
to 1.14),
0.06
Birth weight z-
score
mean (SD)
-0.39
(1.01)
-0.98
(1.10)
-0.59 (-1.18
to -0.00),
0.05
-0.32
(1.2)
-0.74
(1)
-0.41 (-1.04 to
0.22), 0.20
0.22
(0.82)
0.16
(0.85)
-0.09 (-0.46
to 0.34),
0.77
Male
n (%) 64 (49) 7 (54) 0.10 (0.75) 88 (47) 8 (57) 0.53 (0.47) 97 (53) 10 (56) 0.04 (0.84)
Respiratory
distress at birth
n (%)
127 (98) 13 (100) 0.31 (0.58) 43 (23) 2 (14) 0.57 (0.45) 0 0
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Supplementary Table 1c: Differences in infant characteristics for those infants with and without GMA data across three gestational age groups
VPT
with
GMA
data
VPT
no GMA
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
GMA
data
MLPT
no
GMA
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
GMA
data
TERM
no
GMA
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=113 n=30 n=154 n=47 n=142 n=59
Postnatal
corticosteroids
n (%)
14 (12) 2 (7) 0.78 (0.38) 0 0 0 0
Multiple birth
n (%) 47 (42) 16 (53) 1.33 (0.25) 56 (36) 18 (38) 0.06 (0.81) 0 (0) 2 (3) 4.86 (0.03)
Caesarean
delivery
n (%)
79 (70) 26 (87) 3.41 (0.07) 106 (69) 31 (66) 0.14 (0.71) 52 (37) 25 (42) 0.58 (0.45)
Gestational age
at birth (weeks)
mean (SD)
27.7 (1.6) 28.0 (1.1)
0.34 (-0.25
to 0.94),
0.26
34.5 (1.3) 34.1
(1.0)
-0.37 (-0.78 to
0.03), 0.07 39.8 (1.2)
39.6
(1.3)
-0.28 (-0.65
to 0.09),
0.14
Birth weight z-
score
mean (SD)
-0.43
(1.02)
-0.53
(1.10)
-0.10 (-0.52
to 0.32).
0.64
-0.35
(1.2)
-0.36
(1.1)
-0.01 (-0.39 to
0.37), 0.97
0.23
(0.82)
0.17
(0.82)
-0.06 (-0.31
to 0.19),
0.63
Male
n (%) 55 (49) 16 (53) 0.21 (0.65) 73 (47) 23 (49) 0.03 (0.85) 79 (56) 28 (47) 1.12 (0.29)
Respiratory
distress at birth
n (%)
112 (99) 28 (93) 3.86 (0.05) 34 (22) 11 (23) 0.04 (0.85) 0 0
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The continuum of neurobehavior and its associations with
brain MRI in infants born preterm
Journal: BMJ Paediatrics Open
Manuscript ID bmjpo-2017-000136.R2
Article Type: Original article
Date Submitted by the Author: 30-Jul-2017
Complete List of Authors: Eeles, Abbey; Murdoch Childrens Research Institute, Victorian Infant Brain Study Walsh, Jennifer; The Royal Women's Hospital, Newborn Research Olsen, Joy; Murdoch Childrens Research Institute Cuzzilla, Rocco Thompson, Deanne
Anderson, Peter Doyle, Lex; Royal WOmen's Hospital, Cheong, Jeanie Spittle, Alicia; Murdoch Childrens Research Institute, Victorian Infant Brian Study
Keywords: Neurodevelopment
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The continuum of neurobehavior and its associations with brain MRI in infants
born preterm
Abbey L Eeles,b,c
Jennifer M Walsh,b,c,d
Joy E Olsen,b,c,e
Rocco Cuzzilla,b,c,e
Deanne
K Thompson,b,f,h
Peter J Anderson, b,f,g
Lex W Doyle, b,c,e,f
Jeannie L Y Cheong,b,c,f
Alicia J Spittlea,b,c
Author Affiliations: aDepartment of Physiotherapy, University of Melbourne,
Melbourne, Vic; bMurdoch Childrens Research Institute, Melbourne, Vic;
cNewborn
Research, Royal Women’s Hospital, Melbourne, Vic; dPaediatric Infant Perinatal
Emergency Retrieval (PIPER), The Royal Children’s Hospital, Melbourne, Vic; eDepartment of Obstetrics and Gynaecology, University of Melbourne, Melbourne,
Vic; fDepartment of Paediatrics, University of Melbourne, Melbourne, Vic;
gMonash
Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne,
Vic; hFlorey Institute of Neuroscience and Mental Health, Melbourne, Vic
Corresponding Author:
Alicia J Spittle
7th Floor, Alan Gilbert Building, University of Melbourne, Grattan Street, Parkville,
Vic, Australia, 3052.
E-mail: [email protected]
Copyright:
The Corresponding Author has the right to grant on behalf of all authors and does
grant on behalf of all authors, a worldwide licence to the Publishers and its licensees
in perpetuity, in all forms, formats and media (whether known now or created in the
future), to i) publish, reproduce, distribute, display and store the Contribution, ii)
translate the Contribution into other languages, create adaptations, reprints, include
within collections and create summaries, extracts and/or, abstracts of the
Contribution, iii) create any other derivative work(s) based on the Contribution, iv) to
exploit all subsidiary rights in the Contribution, v) the inclusion of electronic links
from the Contribution to third party material where-ever it may be located; and, vi)
licence any third party to do any or all of the above.
What is known about this subject?
Infants born very preterm are at increased risk of long-term neurodevelopmental
deficits. Early interventions can improve developmental outcomes and thus early
identification of infants at greatest need for these services is paramount. Brain MRI
abnormalities has been associated with neurobehaviour functioning in very preterm
infants, however, this relationship is yet to be explored in moderate-to-late preterm
infants.
What this study adds:
This study highlights a clear continuum of neurobehaviour with increased suboptimal
functioning on three neurobehavioral assessments with decreasing gestational age.
The relationships between brain abnormality scores and suboptimal neurobehavior
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provides evidence that neurobehavioral assessments may be useful in earlier
identification of the highest-risk infants.
Word count: 2891
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ABSTRACT
Background
Infants born very preterm (VPT) and moderate-to-late preterm (MLPT) are at
increased risk of long-term neurodevelopmental deficits, but how these deficits relate
to early neurobehavior in MLPT children is unclear. The aims of this study were to
compare the neurobehavioral performance of infants born across 3 different
gestational age groups: Preterm <30 weeks’ gestational age (PT<30); MLPT (32 to 36
weeks’ gestational age); and term age (≥37 weeks’ gestational age), and explore the
relationships between MRI brain abnormalities and neurobehavior at term-equivalent
age.
Methods
Neurobehaviour was assessed at term equivalent age in 149 PT<30, 200 MLPT, and
200 term born infants using the NICU Network Neurobehavioral Scale (NNNS), the
Hammersmith Neonatal Neurological Exam (HNNE), and Prechtl’s Qualitative
Assessment of General Movements (GMA). A subset of 110 PT<30 and 198 MLPT
infants had concurrent brain MRI.
Results
Proportions with abnormal neurobehavior on the NNNS and the HNNE, and
abnormal GMA all increased with decreasing gestational age. Higher brain MRI
abnormality scores in some regions were associated with suboptimal neurobehavior
on the NNNS and HNNE. The relationships between brain MRI abnormality scores
and suboptimal neurobehavior were similar in both PT<30 and MLPT infants. The
relationship between brain MRI abnormality scores and abnormal GMA was stronger
in PT<30 infants.
Conclusions
There was a continuum of neurobehaviour across gestational ages. The relationships
between brain abnormality scores and suboptimal neurobehavior provide evidence
that neurobehavioral assessments offer insight into the integrity of the developing
brain, and may be useful in earlier identification of the highest-risk infants.
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BACKGROUND AND RATIONALE
Infants born preterm are at increased risk of long-term neurodevelopmental deficits in
cognitive, neurosensory, physical, and social-emotional development, as well as
impairments in academic functioning compared with their term-born peers.1-4
Early
interventions to mitigate some of these adverse neurodevelopmental deficits are
promising,5,6
and thus, clinicians working with preterm infants and their families aim
to identify those infants at greatest need for early intervention services. Whilst major
preterm brain injuries, such as high-grade intraventricular hemorrhage (IVH) and
cystic periventricular leukomalacia (cPVL), are highly prognostic for adverse
neurodevelopmental outcomes,6,7
other clinical predictors of long-term development
have only modest prognostic utility. Magnetic resonance imaging (MRI) can detect
more subtle preterm brain injury associated with adverse neurodevelopment, however
its use in routine clinical care is limited by availability and cost.8 Clinicians are
increasingly using neonatal neurobehavioral assessments to identify high-risk infants
and to help guide referrals for early intervention. Neurobehavioral assessments are
valid and reliable tools which offer insights into the neurological integrity and
behavioral functioning of an infant.9,10
Compared with their term-born peers, infants born very preterm (VPT: <32 weeks’
gestation) and moderate-to-late preterm (MLPT: 32 to 36 weeks’ gestation) present
with increased rates of atypical neurobehavior at term-equivalent age.11,12
Importantly, these neurobehavioral difficulties have been associated with later
developmental deficits,13,14
providing support for the neurobehavioral assessment as a
predictive tool for later neurodevelopmental outcome. Whilst there is some evidence
that MRI brain abnormalities are associated with neurobehavior in VPT infants at
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term-equivalent age,15-17
this brain-neurobehavior relationship has yet to be explored
in MLPT infants.
The primary aim of this study was to compare the neurobehavioral performance of
infants born across 3 different gestational age groups: Preterm <30 weeks’
gestational age (PT<30); MLPT (32 to 36 weeks’ gestational age); and term age (≥37
weeks’ gestational age) using the NICU Network Neurobehavioral Scale (NNNS), the
Hammersmith Neonatal Neurological Exam (HNNE), and Prechtl’s Qualitative
Assessment of General Movements (GMA).18
Secondary aims were to explore the
relationships between MRI brain abnormalities (development and injury) and
neurobehavior at term-equivalent age in infants born preterm (PT<30 and MLPT).
METHODS
Participants
Participants were derived from two longitudinal cohorts of infants recruited from the
Royal Women’s Hospital in Melbourne, Australia, between November 2009 and
December 2013. The first cohort comprised 149 preterm infants born <30 weeks’
gestation (PT<30)19
and the second cohort included 201 MLPT infants born between
32 to 36 weeks’ gestation.20
In addition, a cohort of 201 term controls born ≥37
weeks’ gestation were recruited across the two cohorts. Infants with congenital
abnormalities and/or infants with non-English speaking parents were excluded due to
limited funding for interpreters. In the term control group, infants requiring admission
to the special care or intensive care nurseries were excluded. Informed parental
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consent was obtained for all participants, and both studies were approved by the
Royal Women’s Hospital’s and Royal Children’s Hospital’s Human Research Ethics
Committees.
Perinatal data were recorded by research nurses, including gestation at birth, sex,
birthweight z-score (calculated according to gestational age and sex using the British
Growth Reference norms),21
multiple birth, use of antenatal corticosteroids, and
respiratory support.
Neurobehavioral measures
At term-equivalent age (38-44 weeks’ postmenstrual age), neurobehavior was
assessed by one of five trained and certified assessors using NNNS,22
HNNE,23
and
GMA. All assessors had advanced GMA certification and were masked to the
participants’ clinical history. All assessments were administered according to their
standardized procedures, as previously published in the study protocol.19
Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS)
The NNNS is a neurobehavioral assessment that examines the neurological integrity,
behavioral functioning, and responses to stress in high-risk infants using 45 items
which correspond to 13 summary scales including habituation, attention, arousal,
regulation, handling, quality of movement, excitability, lethargy, non-optimal
reflexes, asymmetrical reflexes, hypertonicity, hypotonicity, and stress.24,25
The
habituation scale was not included in this study as infants were not consistently in an
appropriate state (sleep) to administer the scale at the start of the assessment. The
scoring and classification of infants’ performance on the NNNS summary scales has
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been described previously by our group.26
Hammersmith Neonatal Neurological Examination (HNNE)
The HNNE is primarily a neurological exam developed for term and preterm infants.
It consists of 34 individual items with six subtotals including tone, tone patterns,
reflexes, spontaneous movements, abnormal neurological signs, and behavior, which
are added for a total score. Suboptimal performance on the HNNE was categorized as
previously published.27
Prechtl’s qualitative assessment of General Movements (GMA)
The GMA is an observational assessment of the infant’s spontaneous movements
(GMs) with good predictive validity for neurodevelopmental outcomes, including
cerebral palsy, motor impairment and cognitive outcomes.28
GMs were scored from
video recordings according to Prechtl’s method of qualitative assessment.18
GMs were
categorized as normal or abnormal, with abnormal GMs further categorized as poor
repertoire, cramped synchronized, or chaotic. GMs were classified unscorable if the
infant was crying or hypokinetic.
Magnetic Resonance Imaging
Brain MRI was performed using the Siemens 3T Magnetom Trio MRI system
(Siemens, Erlangen, Germany) during natural sleep, on the same day as
neurobehavioral assessments. The details of the imaging protocol have previously
been published, and the T1 and T2-weighted structural brain images were used for the
current study.20
A validated neonatal brain MRI scoring system was used to assess brain maturation,
injury and size, utilizing two dimensional brain metrics29
and conventional methods
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of assessing brain injury.30
Four regional abnormality scores were calculated based on
assessment of injury, growth and maturation: cerebral white matter, cortical grey
matter, deep grey matter (basal ganglia & thalamus), and cerebellar abnormality.31
A
global abnormality score was computed using the sum of the four regional
abnormality scores; higher scores indicate greater abnormality. Brain MRI were
scored independently by one of four experienced neuroradiologists and neonatologists
who had received training in this scoring system with excellent inter- and intra-rater
reliability.20
Statistical analysis
Data were analyzed using Stata Version 14 (StatCorp, Texas, USA). To explore the
relationships between gestational age group and neurobehavior at term-equivalent
age, proportions with suboptimal neurobehavior within gestational age groups were
compared by chi-square test for trend. Relationships between brain MRI
abnormalities and suboptimal neurobehavior in the preterm infants (<37 weeks’
gestational age) were explored using logistic regression, fitted using generalized
estimating equations to allow for multiple births, and adjusted for sex and age at brain
MRI. For any significant relationships between neonatal brain abnormality and
suboptimal neurobehavior, an interaction term was included in the model to explore
whether the relationship differed according to the preterm infants’ gestational age
group (PT<30 or MLPT). We have previously demonstrated a strong relationship
between brain abnormality scores and abnormal GMA in infants born <30 weeks’
gestational age,32
thus, an interaction term was included in this model, irrespective of
the initial findings.
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RESULTS
Neurobehavioral assessments were performed at term-equivalent age in 140 of 149
PT<30 infants, 200/201 MLPT infants, and 200/201 term controls recruited for this
study. Participant characteristics are summarized in Table 1. A subset of PT<30
(n=110) and MLPT (n=198) infants had concurrent brain MRI. Infants ≥45 weeks’
postmenstrual age at the time of neurobehavioral assessment and brain MRI were
excluded, as were infants who were not in an appropriate state to allow their GMA to
be reliably scored (see Figure 1). Differences between infants with and without
neurobehavioral data are shown in Supplementary Table 1a-c. In the PT<30 group,
six infants died prior to term-equivalent age and were not included in this baseline
analysis.
INSERT Figure 1: Participant Recruitment and Neurobehavior Assessment and
MRI Follow-up
Table 1: Participant Characteristics of Infants Assessed at Term Equivalent Age
(Based on HNNE sample size)
PT<30
n=133
MLPT
n=196
Term Control
n=186
Postnatal corticosteroids – n (%)
12 (9) 0 0
Multiple birth – n (%) 59 (44) 73 (37) 2 (1)
Caesarean delivery – n
(%) 75 (72) 134 (68) 72 (39)
Gestational age at birth
(weeks) – mean (SD) 27.9 (1.4) 34.4 (1.3) 39.7 (1.2)
Birth weight z-score –
mean (SD) -0.33 (1) -0.33 (1.2) 0.23 (0.82)
Male – n (%) 54 (52) 93 (47) 99 (53)
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Respiratory distress at birth – n (%)
102 (98) 44 (22) 0
Postmenstrual age at
neurobehaviour
assessment (weeks) – mean (SD)
41.5 (1.9) 41.4 (1.1) 41.9 (1.5)
Postmenstrual age at
MRI (weeks) – mean (SD)
42.4 (1.5) 41.4 (1.1) N/A
Global brain abnormality score
n participants, mean
score (SD)
101, 4.30 (2.02) 187, 1.82 (1.87) N/A
Cerebral WM brain
abnormality score
n participants, mean
score (SD)
101, 2.29 (1.35) 187, 0.98 (1.17) N/A
Cortical GM abnormality
score
n participants, mean
score (SD)
102, 1.39 (1.28) 193, 0.62 (0.78) N/A
Deep Nuclear GM
abnormality score n participants, mean
score (SD)
102, 0.11 (0.34) 194, 0.04 (0.19) N/A
Cerebellar abnormality
score
n participants, mean
score (SD)
102, 0.51 (0.73) 192, 0.19 (0.58) N/A
SD: standard deviation; MRI: Magnetic resonance imaging; N/A: Not applicable (term infants
did not have an MRI for this study)
Suboptimal Neurobehavior
There were statistically significant trends across the gestational age groups for several
suboptimal neurobehavior subscales (Figure 2). On the NNNS, the proportions with
suboptimal neurobehavior decreased with increasing gestational age group for the
following subscales: regulation, quality of movement, non-optimal reflexes,
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hypertonicity and stress. Excluding abnormal signs and behavior, suboptimal
neurobehavior on the remaining HNNE subscales decreased with increasing age.
There was a strong decreasing trend across the gestational age groups for abnormal
GMA.
INSERT Figure 2: Sub-optimal Neurobehavior and Abnormal GMs across
Gestational Age Groups
Brain MRI Abnormality and Suboptimal Neurobehavior
Higher brain MRI abnormality scores were associated with suboptimal neurobehavior
on the NNNS and HNNE, with the exception of the deep grey matter abnormality
score (Table 2). A higher global brain abnormality score was associated with greater
odds of being suboptimal on the lethargy domain and non-optimal reflexes on the
NNNS, and greater odds of suboptimal reflexes, abnormal signs and total score on the
HNNE. Similarly, a higher cerebral white matter abnormality score was associated
with greater odds of being suboptimal on the lethargy domain and non-optimal
reflexes on the NNNS, and greater odds of suboptimal reflexes, suboptimal
spontaneous movements and total score on the HNNE. A higher cortical grey matter
abnormality score was associated with non-optimal reflexes on the NNNS. A higher
cerebellar abnormality score was associated with greater odds of being suboptimal on
the lethargy domain on the NNNS and total score on the HNNE. The cerebellar
abnormality score was the only brain MRI abnormality score associated with
suboptimal tone on the HNNE. Brain abnormality scores were not associated with
increased odds of abnormal GMA.
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Table 2: Relationship between brain abnormality scores and suboptimal neurobehavior
Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
NNNS
Attention
1.03 (0.87 to 1.21), 0.67 1.18 (0.91 to 1.53), 0.20 0.75 (0.50 to 1.13), 0.16 1.59 (0.42 to 5.95), 0.50 1.08 (0.61 to 1.90), 0.80
NNNS Arousal 0.93 (0.82 to 1.04), 0.21 0.89 (0.72 to 1.09), 0.26 1 (0.77 to 1.28), 0.98 0.74 (0.29 to 1.92), 0.54 0.77 (0.48 to 1.25), 0.30
NNNS
Regulation
0.99 (0.89 to 1.11), 0.88 1.01 (0.84 to 1.21), 0.94 0.98 (0.76 to 1.27), 0.90 0.21 (0.03 to 1.30), 0.09 1.01 (0.69 to 1.47), 0.97
NNNS
Handling
0.85 (0.68 to 1.07), 0.16 0.72 (0.42 to 1.23), 0.23 1.08 (0.78 to 1.51), 0.65 Omitted* 0.51 (0.18 to 1.44), 0.20
NNNS Quality
of Movement
1.07 (0.94 to 1.21), 0.31 1.07 (0.87 to 1.32), 0.51 1.02 (0.78 to 1.34), 0.88 0.44 (0.11 to 1.70), 0.23 1.45 (0.97 to 2.16), 0.07
NNNS
Excitability
0.93 (0.83 to 1.05), 0.25 0.87 (0.71 to 1.06), 0.17 1.05 (0.83 to 1.35), 0.67 0.20 (0.03 to 1.50), 0.12 0.85 (0.57 to 1.27), 0.43
NNNS Lethargy
1.20 (1.01 to 1.43), 0.04 1.44 (1.09 to 1.89), 0.01 0.78 (0.47 to 1.27), 0.32 Omitted* 2.22 (1.38 to 3.59), <0.01
NNNS Non-
optimal
Reflexes
1.23 (1.07 to 1.41), <0.01 1.30 (1.06 to 1.59), 0.01 1.33 (1.03 to 1.72), 0.03 1.12 (0.36 to 3.53), 0.85 1.47 (0.97 to 2.23), 0.07
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Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
NNNS Asymmetrical
Reflexes
0.86 (0.69 to 1.08), 0.20 0.89 (0.63 to 1.26), 0.52 0.83 (0.50 to 1.36), 0.45 Omitted* 0.43 (0.18 to 1), 0.05
NNNS
Hypertonicity
1.07 (0.92 to 1.24), 0.40 1.17 (0.91 to 1.52), 0.23 1.07 (0.78 to 1.47), 0.68 Omitted* 0.99 (0.58 to 1.68), 0.97
NNNS Hypotonicity
1.05 (0.93 to 1.19), 0.40 1.21 (1 to 1.46), 0.05 0.92 (0.67 to 1.27), 0.62 Omitted* 1.08 (0.67 to 1.73), 0.75
NNNS Stress 1.10 (0.98 to 1.22), 0.11 1.15 (0.96 to 1.38), 0.12 1.05 (0.84 to 1.32), 0.66 0.90 (0.37 to 2.16), 0.81 1.35 (0.91 to 1.99), 0.13
HNNE Total
Score
1.19 (1.05 to 1.33), <0.01 1.29 (1.06 to 1.57), 0.01 1.16 (0.91 to 1.49), 0.23 0.68 (0.23 to 2.02), 0.49 1.61 (1.09 to 2.38), 0.02
HNNE Tone
Optimality
1.10 (0.97 to 1.23), 0.14 1.14 (0.94 to 1.38), 0.17 1 (0.77 to 1.29), 0.97 1.38 (0.58 to 3.29), 0.46 1.55 (1.02 to 2.34), 0.04
HNNE Tone
Patterns
1.13 (0.98 to 1.31), 0.10 1.09 (0.86 to 1.37), 0.48 1.22 (0.87 to 1.70), 0.25 0.54 (0.09 to 3.06) 0.48 1.50 (0.91 to 2.46), 0.11
HNNE
Reflexes
1.17 (1.03 to 1.33), 0.01 1.37 (1.09 to 1.71), 0.01 1.05 (0.80 to 1.39), 0.72 0.38 (0.05 to 2.76), 0.34 1.41 (0.94 to 2.12), 0.10
HNNE
Spontaneous
Movement
1.11 (0.98 to 1.25), 0.09 1.26 (1.04 to 1.52), 0.02 1.04 (0.81 to 1.35), 0.74 0.42 (0.10 to 1.88), 0.26 1.15 (0.77 to 1.72), 0.50
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Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
HNNE Abnormal
Signs
1.14 (1.01 to 1.28), 0.03 1.12 (0.93 to 1.34), 0.24 1.23 (0.94 to 1.61), 0.13 1.09 (0.44 to 2.69), 0.86 1.34 (0.88 to 2.06), 0.18
HNNE
Behaviour
1.11 (0.90 to 1.36), 0.32 1.20 (0.89 to 1.63), 0.23 1.02 (0.66 to 1.58), 0.94 Omitted* 1.52 (0.84 to 2.74), 0.16
Abnormal GMs 1.12 (0.99 to 1.26) 0.07 1.21 (1.00 to 1.46) 0.05 1.15 (0.91 to 1.45) 0.24 0.92 (0.41 to 2.05) 0.84 1.04 (0.71 to 1.53) 0.83
NNNS: Neonatal Intensive Care Unit Network Neurobehavioral Scale; HNNE: Hammersmith Neonatal Neurological Examination; WM: white matter; OR:
odds ratio; GM: grey matter; CI: confidence interval; GMs: General Movements; *Result omitted as too few numbers with suboptimal neurobehavior and
deep nuclear GM abnormality to analyze.
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When an interaction term was included in the model to explore whether the
relationship differed according to the preterm infants’ gestational age group,
statistically significant relationships between brain MRI abnormality scores and
suboptimal neurobehavior on the NNNS and HNNE were similar in both the PT<30
and MLPT groups (all p values for interaction >0.05). The relationships between
brain MRI abnormality scores and abnormal GMA were stronger in the PT<30 group
for global brain (OR 1.39; 95% CI 1.04, 1.86; p=0.03) and cortical grey matter (OR
1.75; 95% CI 1.05, 2.93; p=0.03) abnormality scores.
DISCUSSION
This study demonstrates the continuum of neurobehavior across gestational age
groups, demonstrating more infants with suboptimal neurobehavior for many items on
the HNNE, NNNS and GMA with decreasing gestational age. This study extends
previous research that has shown differences in neurobehavior between VPT and
term-born infants at term-equivalent age by exploring brain-behavior relationships on
concurrent neurobehavioral assessment and brain MRI.
Regulation and Behavioral Domains of the NNNS and HNNE
The current study demonstrated that suboptimal regulation on the NNNS (which
assesses the infant’s capacity to organize motor activity, physiology and state during
the examination)24
was more common with decreasing gestational age, and is
consistent with other reports describing VPT infants as more irritable with poorer
self-regulation at term-equivalent age than their term-born peers.33
Compromises to
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the development of preterm infants’ self-regulation may influenced by the ‘chaotic
context’ of the neonatal intensive care unit environment,34
which includes perinatal
care experiences associated with pain and stress to a system undergoing rapid growth
and organization.35
Previous studies have demonstrated a relationship between lower
gestational age and greater exposure to stress in the neonatal period,36
which may
explain the current study’s finding of increasing rates of suboptimal regulation at
term-equivalent age in infants born at lower gestations.
Poor regulation is often exhibited through infant behaviors known as stress cues.37
Accordingly, it is not surprising that the current study also demonstrated greater
suboptimal levels of stress-related behaviors on the NNNS with decreasing
gestational age. The NNNS stress scale provides information about the infant’s
capacity to regulate and organize multiple systems (e.g. visual, state, physiological,
motor) in response to handling and interaction demands of their environment.
Interestingly, a significant proportion of the MLPT infants showed suboptimal stress
responses on the NNNS (36%), suggesting that despite their greater gestational age,
MLPT infants, like their PT<30 infant peers, are vulnerable to environmental stressors
and may require external support to regulate their developing regulatory systems.38
Influenced in part by an infant’s regulatory capacity, it is not surprising that compared
with their term-born peers, both PT<30 and MLPT infants had higher rates of
suboptimal arousal and excitability on the NNNS, however the trend across groups
was not significant, which may be explained by the bidirectional nature of these
scales, with higher scores representing over-arousal or over-excitability and lower
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scores reflecting under-arousal or under-excitability. The suboptimal score does not
differentiate the direction of the pattern.
The lack of significant trends across groups for some variables in the current study
may be explained by the age of term infants at assessment. Compared with the MLPT
infants, term infants had higher proportions of suboptimal lethargy and hypotonicity
on the NNNS. These infants were often assessed before hospital discharge, when the
physiological changes that occur in the first days after birth may have influenced the
presentation of lethargy and apparent hypotonicity.39
The NNNS handling scale
includes the strategies used to obtain focused attention and the non-significant
relationship across groups is somewhat surprising. Term infants did not require as
much supportive handling, as expected, but MLPT infants required more handling
than the PT<30 infants, which may reflect their different responses to stress. On the
HNNE, the proportions of suboptimality in the abnormal signs domain were similar
between PT<30 and MLPT infants and the behavior domain had low levels of
suboptimality in all three groups.
Despite the effects of gestational age at birth on regulation at term-equivalent age, all
three groups demonstrated similar orientation and attentional responses to visual and
auditory stimuli, although perhaps at the cost of the PT<30 and MLPT infants’ stress
and regulation performance.
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Neurological Domains of the NNNS and HNNE, and the General Movements
Assessment
Having spent longer in physiological flexion in-utero, term-born infants often present
with greater flexion and smoother movement patterns compared with preterm
peers.16,40,41
Higher rates of tone abnormalities, both hypertonicity and hypotonicity,
are reported in preterm infants at term-equivalent age.16
Consistent with this pattern,
the current study demonstrated higher rates of suboptimal hypertonicity on the
NNNS, suboptimal tone optimality and suboptimal tone patterns on the HNNE with
decreasing gestational age. Similarly, there was poorer quality of movement with
decreasing gestational age on all three neurobehavioral assessments. On the HNNE,
PT<30 infants demonstrated substantially higher rates of suboptimal spontaneous
movement (41%) compared with MLPT and term infants (14% and 4%, respectively),
and there were higher rates of abnormal GMA with decreasing gestational age.
Reduced quality of movement at lower gestational ages may reflect differences in
central nervous system integrity and the extra-uterine environment, including limited
opportunities for movement.
Similarly, the reflex scales of the NNNS and HNNE also reflect maturation of the
infants’ central nervous system with increasing suboptimal reflexes on the NNNS and
HNNE with decreasing gestational age. Infants born PT<30 had a substantially
higher rate of suboptimal reflexes (32%) compared with MLPT and term-born infants
(9% and 7%, respectively). Furthermore, the rates of suboptimal total scores on the
HNNE, a reflection of an infant’s overall performance on the more neurologically-
based assessment, increased significantly with decreasing gestational age,
highlighting the impact of preterm birth on the maturation and integrity of the central
nervous system. This finding contrasts with other studies having reported that motor
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reflexes do not seem to be differentially affected by a short gestation, severity of
illness, or brain injury.42
Brain behavior relationships
A number of relationships between brain MRI abnormality scores and suboptimal
neurobehavior were identified in the current study. These brain behavior
relationships were more common on the HNNE than the NNNS, which may reflect its
greater neurological focus.9
On the NNNS, various brain abnormality scores were associated with greater odds of
non-optimal reflexes and suboptimal lethargy. In particular, the global, cerebral white
matter, and cortical grey matter abnormality scores were associated with suboptimal
reflexes and the global, cerebral white matter and cerebellar with suboptimal lethargy
on the NNNS. This is consistent with the study by Brown,17
who also reported both
white matter signal abnormality and delayed gyral maturation to be associated with
worse performance on the NNNS non-optimal reflex scale.
Previous studies have linked poor quality of movement with cerebral injury, in
particular, white matter abnormality and poor outcome.10
Similarly, the current study
demonstrated a significant relationship between white matter abnormality and
suboptimal performance on the HNNE spontaneous movement scale. This is
consistent with the study by Brown et al who reported an association between a worse
grade of white matter abnormality and suboptimal HNNE spontaneous movement
scores. In keeping with the current study’s findings, previous studies found no
relationship between brain abnormality scores and the NNNS quality of movement
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scale.16,17
No other regional abnormality scores were associated with suboptimal
movement in contrast with two earlier studies that reported delayed gyral maturation
and higher grey matter abnormality were associated with lower HNNE spontaneous
movement scores.17,43
On the GMA, however, global brain abnormality and cortical
grey abnormality, a composite of signal abnormality, delayed gyration and dilated
extra-cerebral space, was associated with abnormal GMA.
Limitations of the current study are important to consider in the interpretation of the
findings. In particular, the low rates of brain abnormality scores may have influenced
the power to detect relationships between brain abnormality and suboptimal
neurobehavioral outcomes. Also, not all infants were able to have all assessments.
CONCLUSION
The current study demonstrated a clear continuum of neurobehavior across three
gestational age groups, with poorer behavior regulation, increased stress response,
greater tone abnormality, abnormal reflexes, and poorer quality of movement with
decreasing gestational age. A number of relationships between brain abnormality
scores and suboptimal neurobehavior were found, particularly the global, white
matter, and cerebellar abnormality scores with the more neurologically focused
domains of the NNNS and HNNE, evidence that the neurobehavioral assessment can
provide insight into the integrity of the developing brain and can both supplement
imaging findings when available, or, in the absence of MRI, provide guidance to
clinicians who are identifying high risk infants for early intervention services.
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Competing Interests: All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for
the submitted work; no financial relationships with any organisations that might have
an interest in the submitted work in the previous three years; no other relationships or
activities that could appear to have influenced the submitted work.
Funding source:
This work was supported in part by the Australian National Health and Medical
Research Council (NHMRC) (Project Grant ID 1028822); Centre of Clinical
Research Excellence Grant ID 546519; Centre of Research Excellence Grant ID
1060733; Senior Research Fellowship ID 1081288 to PJA.; Early Career Fellowship
ID 1053787 to JLYC, ID 1053767 to AJS; Career Development Fellowship ID
1108714 to AJS; Australian Postgraduate Scholarship to JEO, Murdoch Children’s
Research Institute, Clinical Sciences Theme Grant, the Victorian Government
Operational Infrastructure Support Program.
Financial Disclosure: The authors have no financial relationships relevant to this
article to disclose.
We attest that we have obtained appropriate permissions and paid any required fees
for use of copyright protected materials.
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254x190mm (96 x 96 DPI)
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Supplementary Table 1a: Differences in infant characteristics for those infants with and without HNNE data across three gestational age groups
VPT
with
HNNE
data
VPT
no
HNNE
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
HNNE
data
MLPT
no
HNNE
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
HNNE
data
TERM
no
HNNE
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=133 n=10 n=196 n=5 n=186 n=15
Postnatal
corticosteroids
n (%)
12 (9) 4 (40) 8.98
(0.003) 0 0 0 0
Multiple birth
n (%) 59 (44) 4 (40) 0.07 (0.79) 71 (36) 3 (60) 1.18 (0.28) 2 (1) 0 (0) 0.16 (0.69)
Caesarean
delivery
n (%)
97 (73) 8 (80) 0.24 (0.63) 133 (68) 4 (80) 0.33 (0.57) 72 (39) 5 (33) 0.17 (0.68)
Gestational age
at birth (weeks)
mean (SD)
27.8 (1.5) 27.2 (1.5)
-0.59 (-1.54
to 0.37),
0.23
34.4 (1.3) 34.1
(0.4)
-0.35 (-1.46 to
0.76), 0.53 39.7 (1.2)
40.2
(1.3)
0.52 (-0.12
to 1.16),
0.11
Birth weight z-
score
mean (SD)
-0.41
(1.02)
-0.97
(1.11)
-0.57 (-1.23
to 0.10),
0.09
-0.35
(1.2)
-0.43
(0.6)
-0.08 (-1.12 to
0.96), 0.88
0.23
(0.82)
0.04
(0.84)
-0.19 (-0.62
to 0.25),
0.39
Male
n (%) 66 (50) 5 (50)
0.0005
(0.98) 94 (48) 2 (40) 0.12 (0.73) 99 (53) 8 (53)
0.0001
(0.99)
Respiratory
distress at birth
n (%)
130 (98) 10 (100) 0.23 (0.63) 44 (22) 1 (20) 0.02 (0.90) 0 0
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Supplementary Table 1b: Differences in infant characteristics for those infants with and without NNNS data across three gestational age groups
VPT
with
NNNS
data
VPT
no
NNNS
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
NNNS
data
MLPT
no
NNNS
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
NNNS
data
TERM
no
NNNS
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=130 n=13 n=187 n=14 n=183 n=18
Postnatal
corticosteroids
n (%)
11 (8) 5 (38) 10.70
(0.001) 0 0 0 0
Multiple birth
n (%) 57 (44) 6 (46) 0.03 (0.87) 65 (35) 9 (64) 4.88 (0.02) 2 (1) 0 (0) 0.20 (0.66)
Caesarean
delivery
n (%)
96 (74) 9 (69) 0.13 (0.72) 128 (68) 9 (64) 0.10 (0.75) 71 (39) 6 (33) 0.21 (0.65)
Gestational age
at birth (weeks)
mean (SD)
27.9 (1.4) 26.8 (1.6)
-1.07 (-1.90
to -0.24),
0.01
34.4 (1.3) 34.3
(0.8)
-0.14 (-0.83 to
0.54), 0.67 39.7 (1.2)
40.3
(1.2)
0.56 (-0.03
to 1.14),
0.06
Birth weight z-
score
mean (SD)
-0.39
(1.01)
-0.98
(1.10)
-0.59 (-1.18
to -0.00),
0.05
-0.32
(1.2)
-0.74
(1)
-0.41 (-1.04 to
0.22), 0.20
0.22
(0.82)
0.16
(0.85)
-0.09 (-0.46
to 0.34),
0.77
Male
n (%) 64 (49) 7 (54) 0.10 (0.75) 88 (47) 8 (57) 0.53 (0.47) 97 (53) 10 (56) 0.04 (0.84)
Respiratory
distress at birth
n (%)
127 (98) 13 (100) 0.31 (0.58) 43 (23) 2 (14) 0.57 (0.45) 0 0
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Supplementary Table 1c: Differences in infant characteristics for those infants with and without GMA data across three gestational age groups
VPT
with
GMA
data
VPT
no GMA
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
GMA
data
MLPT
no
GMA
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
GMA
data
TERM
no
GMA
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=113 n=30 n=154 n=47 n=142 n=59
Postnatal
corticosteroids
n (%)
14 (12) 2 (7) 0.78 (0.38) 0 0 0 0
Multiple birth
n (%) 47 (42) 16 (53) 1.33 (0.25) 56 (36) 18 (38) 0.06 (0.81) 0 (0) 2 (3) 4.86 (0.03)
Caesarean
delivery
n (%)
79 (70) 26 (87) 3.41 (0.07) 106 (69) 31 (66) 0.14 (0.71) 52 (37) 25 (42) 0.58 (0.45)
Gestational age
at birth (weeks)
mean (SD)
27.7 (1.6) 28.0 (1.1)
0.34 (-0.25
to 0.94),
0.26
34.5 (1.3) 34.1
(1.0)
-0.37 (-0.78 to
0.03), 0.07 39.8 (1.2)
39.6
(1.3)
-0.28 (-0.65
to 0.09),
0.14
Birth weight z-
score
mean (SD)
-0.43
(1.02)
-0.53
(1.10)
-0.10 (-0.52
to 0.32).
0.64
-0.35
(1.2)
-0.36
(1.1)
-0.01 (-0.39 to
0.37), 0.97
0.23
(0.82)
0.17
(0.82)
-0.06 (-0.31
to 0.19),
0.63
Male
n (%) 55 (49) 16 (53) 0.21 (0.65) 73 (47) 23 (49) 0.03 (0.85) 79 (56) 28 (47) 1.12 (0.29)
Respiratory
distress at birth
n (%)
112 (99) 28 (93) 3.86 (0.05) 34 (22) 11 (23) 0.04 (0.85) 0 0
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The continuum of neurobehavior and its associations with
brain MRI in infants born preterm
Journal: BMJ Paediatrics Open
Manuscript ID bmjpo-2017-000136.R3
Article Type: Original article
Date Submitted by the Author: 06-Sep-2017
Complete List of Authors: Eeles, Abbey; Murdoch Childrens Research Institute, Victorian Infant Brain Study Walsh, Jennifer; The Royal Women's Hospital, Newborn Research Olsen, Joy; Murdoch Childrens Research Institute Cuzzilla, Rocco Thompson, Deanne
Anderson, Peter Doyle, Lex; Royal WOmen's Hospital, Cheong, Jeanie Spittle, Alicia; Murdoch Childrens Research Institute, Victorian Infant Brian Study
Keywords: Neurodevelopment
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1
The continuum of neurobehavior and its associations with brain MRI in infants
born preterm
Abbey L Eeles,b,c,h
Jennifer M Walsh,b,c,d
Joy E Olsen,b,c,e
Rocco Cuzzilla,b,c,e
Deanne
K Thompson,b,f,i
Peter J Anderson, b,f,g
Lex W Doyle, b,c,e,f
Jeannie L Y Cheong,b,c,f
Alicia J Spittlea,b,c
Author Affiliations: aDepartment of Physiotherapy, University of Melbourne,
Melbourne, Vic; bMurdoch Childrens Research Institute, Melbourne, Vic;
cNewborn
Research, Royal Women’s Hospital, Melbourne, Vic; dPaediatric Infant Perinatal
Emergency Retrieval (PIPER), The Royal Children’s Hospital, Melbourne, Vic; eDepartment of Obstetrics and Gynaecology, University of Melbourne, Melbourne,
Vic; fDepartment of Paediatrics, University of Melbourne, Melbourne, Vic;
gMonash
Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne,
Vic; hDepartment of Paediatrics, Monash University, Melbourne, Vic;
iFlorey
Institute of Neuroscience and Mental Health, Melbourne, Vic
Corresponding Author:
Alicia J Spittle
7th Floor, Alan Gilbert Building, University of Melbourne, Grattan Street, Parkville,
Vic, Australia, 3052.
E-mail: [email protected]
Copyright:
The Corresponding Author has the right to grant on behalf of all authors and does
grant on behalf of all authors, a worldwide licence to the Publishers and its licensees
in perpetuity, in all forms, formats and media (whether known now or created in the
future), to i) publish, reproduce, distribute, display and store the Contribution, ii)
translate the Contribution into other languages, create adaptations, reprints, include
within collections and create summaries, extracts and/or, abstracts of the
Contribution, iii) create any other derivative work(s) based on the Contribution, iv) to
exploit all subsidiary rights in the Contribution, v) the inclusion of electronic links
from the Contribution to third party material where-ever it may be located; and, vi)
licence any third party to do any or all of the above.
What is known about this subject?
Infants born very preterm are at increased risk of long-term neurodevelopmental
deficits. Early interventions can improve developmental outcomes and thus early
identification of infants at greatest need for these services is paramount. Brain MRI
abnormalities has been associated with neurobehaviour functioning in very preterm
infants, however, this relationship is yet to be explored in moderate-to-late preterm
infants.
What this study adds:
This study highlights a clear continuum of neurobehaviour with increased suboptimal
functioning on three neurobehavioral assessments with decreasing gestational age.
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The relationships between brain abnormality scores and suboptimal neurobehavior
provides evidence that neurobehavioral assessments may be useful in earlier
identification of the highest-risk infants.
Word count: 2891
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ABSTRACT
Background
Infants born very preterm (VPT) and moderate-to-late preterm (MLPT) are at
increased risk of long-term neurodevelopmental deficits, but how these deficits relate
to early neurobehavior in MLPT children is unclear. The aims of this study were to
compare the neurobehavioral performance of infants born across 3 different
gestational age groups: Preterm <30 weeks’ gestational age (PT<30); MLPT (32 to 36
weeks’ gestational age); and term age (≥37 weeks’ gestational age), and explore the
relationships between MRI brain abnormalities and neurobehavior at term-equivalent
age.
Methods
Neurobehaviour was assessed at term equivalent age in 149 PT<30, 200 MLPT, and
200 term born infants using the NICU Network Neurobehavioral Scale (NNNS), the
Hammersmith Neonatal Neurological Exam (HNNE), and Prechtl’s Qualitative
Assessment of General Movements (GMA). A subset of 110 PT<30 and 198 MLPT
infants had concurrent brain MRI.
Results
Proportions with abnormal neurobehavior on the NNNS and the HNNE, and
abnormal GMA all increased with decreasing gestational age. Higher brain MRI
abnormality scores in some regions were associated with suboptimal neurobehavior
on the NNNS and HNNE. The relationships between brain MRI abnormality scores
and suboptimal neurobehavior were similar in both PT<30 and MLPT infants. The
relationship between brain MRI abnormality scores and abnormal GMA was stronger
in PT<30 infants.
Conclusions
There was a continuum of neurobehaviour across gestational ages. The relationships
between brain abnormality scores and suboptimal neurobehavior provide evidence
that neurobehavioral assessments offer insight into the integrity of the developing
brain, and may be useful in earlier identification of the highest-risk infants.
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BACKGROUND AND RATIONALE
Infants born preterm are at increased risk of long-term neurodevelopmental deficits in
cognitive, neurosensory, physical, and social-emotional development, as well as
impairments in academic functioning compared with their term-born peers.1-4
Early
interventions to mitigate some of these adverse neurodevelopmental deficits are
promising,5,6
and thus, clinicians working with preterm infants and their families aim
to identify those infants at greatest need for early intervention services. Whilst major
preterm brain injuries, such as high-grade intraventricular hemorrhage (IVH) and
cystic periventricular leukomalacia (cPVL), are highly prognostic for adverse
neurodevelopmental outcomes,6,7
other clinical predictors of long-term development
have only modest prognostic utility. Magnetic resonance imaging (MRI) can detect
more subtle preterm brain injury associated with adverse neurodevelopment, however
its use in routine clinical care is limited by availability and cost.8 Clinicians are
increasingly using neonatal neurobehavioral assessments to identify high-risk infants
and to help guide referrals for early intervention. Neurobehavioral assessments are
valid and reliable tools which offer insights into the neurological integrity and
behavioral functioning of an infant.9,10
Compared with their term-born peers, infants born very preterm (VPT: <32 weeks’
gestation) and moderate-to-late preterm (MLPT: 32 to 36 weeks’ gestation) present
with increased rates of atypical neurobehavior at term-equivalent age.11,12
Importantly, these neurobehavioral difficulties have been associated with later
developmental deficits,13,14
providing support for the neurobehavioral assessment as a
predictive tool for later neurodevelopmental outcome. Whilst there is some evidence
that MRI brain abnormalities are associated with neurobehavior in VPT infants at
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term-equivalent age,15-17
this brain-neurobehavior relationship has yet to be explored
in MLPT infants.
The primary aim of this study was to compare the neurobehavioral performance of
infants born across 3 different gestational age groups: Preterm <30 weeks’
gestational age (PT<30); MLPT (32 to 36 weeks’ gestational age); and term age (≥37
weeks’ gestational age) using the NICU Network Neurobehavioral Scale (NNNS), the
Hammersmith Neonatal Neurological Exam (HNNE), and Prechtl’s Qualitative
Assessment of General Movements (GMA).18
Secondary aims were to explore the
relationships between MRI brain abnormalities (development and injury) and
neurobehavior at term-equivalent age in infants born preterm (PT<30 and MLPT).
METHODS
Participants
Participants were derived from two longitudinal cohorts of infants recruited from the
Royal Women’s Hospital in Melbourne, Australia, between November 2009 and
December 2013. The first cohort comprised 149 preterm infants born <30 weeks’
gestation (PT<30)19
and the second cohort included 201 MLPT infants born between
32 to 36 weeks’ gestation.20
In addition, a cohort of 201 term controls born ≥37
weeks’ gestation were recruited across the two cohorts. Infants with congenital
abnormalities and/or infants with non-English speaking parents were excluded due to
limited funding for interpreters. In the term control group, infants requiring admission
to the special care or intensive care nurseries were excluded. Informed parental
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consent was obtained for all participants, and both studies were approved by the
Royal Women’s Hospital’s and Royal Children’s Hospital’s Human Research Ethics
Committees.
Perinatal data were recorded by research nurses, including gestation at birth, sex,
birthweight z-score (calculated according to gestational age and sex using the British
Growth Reference norms),21
multiple birth, use of antenatal corticosteroids, and
respiratory support.
Neurobehavioral measures
At term-equivalent age (38-44 weeks’ postmenstrual age), neurobehavior was
assessed by one of five trained and certified assessors using NNNS,22
HNNE,23
and
GMA. All assessors had advanced GMA certification and were masked to the
participants’ clinical history. All assessments were administered according to their
standardized procedures, as previously published in the study protocol.19
Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS)
The NNNS is a neurobehavioral assessment that examines the neurological integrity,
behavioral functioning, and responses to stress in high-risk infants using 45 items
which correspond to 13 summary scales including habituation, attention, arousal,
regulation, handling, quality of movement, excitability, lethargy, non-optimal
reflexes, asymmetrical reflexes, hypertonicity, hypotonicity, and stress.24,25
The
habituation scale was not included in this study as infants were not consistently in an
appropriate state (sleep) to administer the scale at the start of the assessment. The
scoring and classification of infants’ performance on the NNNS summary scales has
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been described previously by our group.26
Hammersmith Neonatal Neurological Examination (HNNE)
The HNNE is primarily a neurological exam developed for term and preterm infants.
It consists of 34 individual items with six subtotals including tone, tone patterns,
reflexes, spontaneous movements, abnormal neurological signs, and behavior, which
are added for a total score. Suboptimal performance on the HNNE was categorized as
previously published.27
Prechtl’s qualitative assessment of General Movements (GMA)
The GMA is an observational assessment of the infant’s spontaneous movements
(GMs) with good predictive validity for neurodevelopmental outcomes, including
cerebral palsy, motor impairment and cognitive outcomes.28
GMs were scored from
video recordings according to Prechtl’s method of qualitative assessment.18
GMs were
categorized as normal or abnormal, with abnormal GMs further categorized as poor
repertoire, cramped synchronized, or chaotic. GMs were classified unscorable if the
infant was crying or hypokinetic.
Magnetic Resonance Imaging
Brain MRI was performed using the Siemens 3T Magnetom Trio MRI system
(Siemens, Erlangen, Germany) during natural sleep, on the same day as
neurobehavioral assessments. The details of the imaging protocol have previously
been published, and the T1 and T2-weighted structural brain images were used for the
current study.20
A validated neonatal brain MRI scoring system was used to assess brain maturation,
injury and size, utilizing two dimensional brain metrics29
and conventional methods
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of assessing brain injury.30
Four regional abnormality scores were calculated based on
assessment of injury, growth and maturation: cerebral white matter, cortical grey
matter, deep grey matter (basal ganglia & thalamus), and cerebellar abnormality.31
A
global abnormality score was computed using the sum of the four regional
abnormality scores; higher scores indicate greater abnormality. Brain MRI were
scored independently by one of four experienced neuroradiologists and neonatologists
who had received training in this scoring system with excellent inter- and intra-rater
reliability.20
Statistical analysis
Data were analyzed using Stata Version 14 (StatCorp, Texas, USA). To explore the
relationships between gestational age group and neurobehavior at term-equivalent
age, proportions with suboptimal neurobehavior within gestational age groups were
compared by chi-square test for trend. Relationships between brain MRI
abnormalities and suboptimal neurobehavior in the preterm infants (<37 weeks’
gestational age) were explored using logistic regression, fitted using generalized
estimating equations to allow for multiple births, and adjusted for sex and age at brain
MRI. For any significant relationships between neonatal brain abnormality and
suboptimal neurobehavior, an interaction term was included in the model to explore
whether the relationship differed according to the preterm infants’ gestational age
group (PT<30 or MLPT). We have previously demonstrated a strong relationship
between brain abnormality scores and abnormal GMA in infants born <30 weeks’
gestational age,32
thus, an interaction term was included in this model, irrespective of
the initial findings.
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RESULTS
Neurobehavioral assessments were performed at term-equivalent age in 140 of 149
PT<30 infants, 200/201 MLPT infants, and 200/201 term controls recruited for this
study. Participant characteristics are summarized in Table 1. A subset of PT<30
(n=110) and MLPT (n=198) infants had concurrent brain MRI. Infants ≥45 weeks’
postmenstrual age at the time of neurobehavioral assessment and brain MRI were
excluded, as were infants who were not in an appropriate state to allow their GMA to
be reliably scored (see Figure 1). Differences between infants with and without
neurobehavioral data are shown in Supplementary Table 1a-c. In the PT<30 group,
six infants died prior to term-equivalent age and were not included in this baseline
analysis.
INSERT Figure 1: Participant Recruitment and Neurobehavior Assessment and
MRI Follow-up
Table 1: Participant Characteristics of Infants Assessed at Term Equivalent Age
(Based on HNNE sample size)
PT<30
n=133
MLPT
n=196
Term Control
n=186
Postnatal corticosteroids – n (%)
12 (9) 0 0
Multiple birth – n (%) 59 (44) 73 (37) 2 (1)
Caesarean delivery – n
(%) 75 (72) 134 (68) 72 (39)
Gestational age at birth
(weeks) – mean (SD) 27.9 (1.4) 34.4 (1.3) 39.7 (1.2)
Birth weight z-score –
mean (SD) -0.33 (1) -0.33 (1.2) 0.23 (0.82)
Male – n (%) 54 (52) 93 (47) 99 (53)
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Respiratory distress at birth – n (%)
102 (98) 44 (22) 0
Postmenstrual age at
neurobehaviour
assessment (weeks) – mean (SD)
41.5 (1.9) 41.4 (1.1) 41.9 (1.5)
Postmenstrual age at
MRI (weeks) – mean (SD)
42.4 (1.5) 41.4 (1.1) N/A
Global brain abnormality score
n participants, mean
score (SD)
101, 4.30 (2.02) 187, 1.82 (1.87) N/A
Cerebral WM brain
abnormality score
n participants, mean
score (SD)
101, 2.29 (1.35) 187, 0.98 (1.17) N/A
Cortical GM abnormality
score
n participants, mean
score (SD)
102, 1.39 (1.28) 193, 0.62 (0.78) N/A
Deep Nuclear GM
abnormality score n participants, mean
score (SD)
102, 0.11 (0.34) 194, 0.04 (0.19) N/A
Cerebellar abnormality
score
n participants, mean
score (SD)
102, 0.51 (0.73) 192, 0.19 (0.58) N/A
SD: standard deviation; MRI: Magnetic resonance imaging; N/A: Not applicable (term infants
did not have an MRI for this study)
Suboptimal Neurobehavior
Scores on several neurobehavior subscales were not independent of gestational age
(P<0.05) (Figure 2). On the NNNS, the proportions with suboptimal neurobehavior
decreased with increasing gestational age group for the following subscales:
regulation, quality of movement, non-optimal reflexes, hypertonicity and stress.
Excluding abnormal signs and behavior, suboptimal neurobehavior on the remaining
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HNNE subscales decreased with increasing age. Rates of abnormal GMA were higher
with decreasing gestational age.
INSERT Figure 2: Sub-optimal Neurobehavior and Abnormal GMs across
Gestational Age Groups
Brain MRI Abnormality and Suboptimal Neurobehavior
Higher brain MRI abnormality scores were associated with suboptimal neurobehavior
on the NNNS and HNNE, with the exception of the deep grey matter abnormality
score (Table 2). A higher global brain abnormality score was associated with greater
odds of being suboptimal on the lethargy domain and non-optimal reflexes on the
NNNS, and greater odds of suboptimal reflexes, abnormal signs and total score on the
HNNE. Similarly, a higher cerebral white matter abnormality score was associated
with greater odds of being suboptimal on the lethargy domain and non-optimal
reflexes on the NNNS, and greater odds of suboptimal reflexes, suboptimal
spontaneous movements and total score on the HNNE. A higher cortical grey matter
abnormality score was associated with non-optimal reflexes on the NNNS. A higher
cerebellar abnormality score was associated with greater odds of being suboptimal on
the lethargy domain on the NNNS and total score on the HNNE. The cerebellar
abnormality score was the only brain MRI abnormality score associated with
suboptimal tone on the HNNE. Brain abnormality scores were not associated with
increased odds of abnormal GMA.
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Table 2: Relationship between brain abnormality scores and suboptimal neurobehavior
Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
NNNS
Attention
1.03 (0.87 to 1.21), 0.67 1.18 (0.91 to 1.53), 0.20 0.75 (0.50 to 1.13), 0.16 1.59 (0.42 to 5.95), 0.50 1.08 (0.61 to 1.90), 0.80
NNNS Arousal 0.93 (0.82 to 1.04), 0.21 0.89 (0.72 to 1.09), 0.26 1 (0.77 to 1.28), 0.98 0.74 (0.29 to 1.92), 0.54 0.77 (0.48 to 1.25), 0.30
NNNS
Regulation
0.99 (0.89 to 1.11), 0.88 1.01 (0.84 to 1.21), 0.94 0.98 (0.76 to 1.27), 0.90 0.21 (0.03 to 1.30), 0.09 1.01 (0.69 to 1.47), 0.97
NNNS
Handling
0.85 (0.68 to 1.07), 0.16 0.72 (0.42 to 1.23), 0.23 1.08 (0.78 to 1.51), 0.65 Omitted* 0.51 (0.18 to 1.44), 0.20
NNNS Quality
of Movement
1.07 (0.94 to 1.21), 0.31 1.07 (0.87 to 1.32), 0.51 1.02 (0.78 to 1.34), 0.88 0.44 (0.11 to 1.70), 0.23 1.45 (0.97 to 2.16), 0.07
NNNS
Excitability
0.93 (0.83 to 1.05), 0.25 0.87 (0.71 to 1.06), 0.17 1.05 (0.83 to 1.35), 0.67 0.20 (0.03 to 1.50), 0.12 0.85 (0.57 to 1.27), 0.43
NNNS Lethargy
1.20 (1.01 to 1.43), 0.04 1.44 (1.09 to 1.89), 0.01 0.78 (0.47 to 1.27), 0.32 Omitted* 2.22 (1.38 to 3.59), <0.01
NNNS Non-
optimal
Reflexes
1.23 (1.07 to 1.41), <0.01 1.30 (1.06 to 1.59), 0.01 1.33 (1.03 to 1.72), 0.03 1.12 (0.36 to 3.53), 0.85 1.47 (0.97 to 2.23), 0.07
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Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
NNNS Asymmetrical
Reflexes
0.86 (0.69 to 1.08), 0.20 0.89 (0.63 to 1.26), 0.52 0.83 (0.50 to 1.36), 0.45 Omitted* 0.43 (0.18 to 1), 0.05
NNNS
Hypertonicity
1.07 (0.92 to 1.24), 0.40 1.17 (0.91 to 1.52), 0.23 1.07 (0.78 to 1.47), 0.68 Omitted* 0.99 (0.58 to 1.68), 0.97
NNNS Hypotonicity
1.05 (0.93 to 1.19), 0.40 1.21 (1 to 1.46), 0.05 0.92 (0.67 to 1.27), 0.62 Omitted* 1.08 (0.67 to 1.73), 0.75
NNNS Stress 1.10 (0.98 to 1.22), 0.11 1.15 (0.96 to 1.38), 0.12 1.05 (0.84 to 1.32), 0.66 0.90 (0.37 to 2.16), 0.81 1.35 (0.91 to 1.99), 0.13
HNNE Total
Score
1.19 (1.05 to 1.33), <0.01 1.29 (1.06 to 1.57), 0.01 1.16 (0.91 to 1.49), 0.23 0.68 (0.23 to 2.02), 0.49 1.61 (1.09 to 2.38), 0.02
HNNE Tone
Optimality
1.10 (0.97 to 1.23), 0.14 1.14 (0.94 to 1.38), 0.17 1 (0.77 to 1.29), 0.97 1.38 (0.58 to 3.29), 0.46 1.55 (1.02 to 2.34), 0.04
HNNE Tone
Patterns
1.13 (0.98 to 1.31), 0.10 1.09 (0.86 to 1.37), 0.48 1.22 (0.87 to 1.70), 0.25 0.54 (0.09 to 3.06) 0.48 1.50 (0.91 to 2.46), 0.11
HNNE
Reflexes
1.17 (1.03 to 1.33), 0.01 1.37 (1.09 to 1.71), 0.01 1.05 (0.80 to 1.39), 0.72 0.38 (0.05 to 2.76), 0.34 1.41 (0.94 to 2.12), 0.10
HNNE
Spontaneous
Movement
1.11 (0.98 to 1.25), 0.09 1.26 (1.04 to 1.52), 0.02 1.04 (0.81 to 1.35), 0.74 0.42 (0.10 to 1.88), 0.26 1.15 (0.77 to 1.72), 0.50
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Suboptimal
neurobehaviour
Global brain abnormality
score
OR (95%CI), p-value
Cerebral WM brain
abnormality score
OR (95%CI), p-value
Cortical GM abnormality
score
OR (95%CI), p-value
Deep Nuclear GM
abnormality score
OR (95%CI), p-value
Cerebellar abnormality
score
OR (95%CI), p-value
HNNE Abnormal
Signs
1.14 (1.01 to 1.28), 0.03 1.12 (0.93 to 1.34), 0.24 1.23 (0.94 to 1.61), 0.13 1.09 (0.44 to 2.69), 0.86 1.34 (0.88 to 2.06), 0.18
HNNE
Behaviour
1.11 (0.90 to 1.36), 0.32 1.20 (0.89 to 1.63), 0.23 1.02 (0.66 to 1.58), 0.94 Omitted* 1.52 (0.84 to 2.74), 0.16
Abnormal GMs 1.12 (0.99 to 1.26) 0.07 1.21 (1.00 to 1.46) 0.05 1.15 (0.91 to 1.45) 0.24 0.92 (0.41 to 2.05) 0.84 1.04 (0.71 to 1.53) 0.83
NNNS: Neonatal Intensive Care Unit Network Neurobehavioral Scale; HNNE: Hammersmith Neonatal Neurological Examination; WM: white matter; OR:
odds ratio; GM: grey matter; CI: confidence interval; GMs: General Movements; *Result omitted as too few numbers with suboptimal neurobehavior and
deep nuclear GM abnormality to analyze.
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When an interaction term was included in the model to explore whether the
relationship differed according to the preterm infants’ gestational age group,
statistically significant relationships between brain MRI abnormality scores and
suboptimal neurobehavior on the NNNS and HNNE were similar in both the PT<30
and MLPT groups (all p values for interaction >0.05). The relationships between
brain MRI abnormality scores and abnormal GMA were stronger in the PT<30 group
for global brain (OR 1.39; 95% CI 1.04, 1.86; p=0.03) and cortical grey matter (OR
1.75; 95% CI 1.05, 2.93; p=0.03) abnormality scores.
DISCUSSION
This study demonstrates the continuum of neurobehavior across gestational age
groups, demonstrating more infants with suboptimal neurobehavior for many items on
the HNNE, NNNS and GMA with decreasing gestational age. This study extends
previous research that has shown differences in neurobehavior between VPT and
term-born infants at term-equivalent age by exploring brain-behavior relationships on
concurrent neurobehavioral assessment and brain MRI.
Regulation and Behavioral Domains of the NNNS and HNNE
The current study demonstrated that suboptimal regulation on the NNNS (which
assesses the infant’s capacity to organize motor activity, physiology and state during
the examination)24
was more common with decreasing gestational age, and is
consistent with other reports describing VPT infants as more irritable with poorer
self-regulation at term-equivalent age than their term-born peers.33
Compromises to
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the development of preterm infants’ self-regulation may influenced by the ‘chaotic
context’ of the neonatal intensive care unit environment,34
which includes perinatal
care experiences associated with pain and stress to a system undergoing rapid growth
and organization.35
Previous studies have demonstrated a relationship between lower
gestational age and greater exposure to stress in the neonatal period,36
which may
explain the current study’s finding of increasing rates of suboptimal regulation at
term-equivalent age in infants born at lower gestations.
Poor regulation is often exhibited through infant behaviors known as stress cues.37
Accordingly, it is not surprising that the current study also demonstrated greater
suboptimal levels of stress-related behaviors on the NNNS with decreasing
gestational age. The NNNS stress scale provides information about the infant’s
capacity to regulate and organize multiple systems (e.g. visual, state, physiological,
motor) in response to handling and interaction demands of their environment.
Interestingly, a significant proportion of the MLPT infants showed suboptimal stress
responses on the NNNS (36%), suggesting that despite their greater gestational age,
MLPT infants, like their PT<30 infant peers, are vulnerable to environmental stressors
and may require external support to regulate their developing regulatory systems.38
Influenced in part by an infant’s regulatory capacity, it is not surprising that compared
with their term-born peers, both PT<30 and MLPT infants had higher rates of
suboptimal arousal and excitability on the NNNS, however, the relationship between
these subscale scores and their dependence on gestational age did not reach statistical
significance (P<0.05). This may be explained by the bidirectional nature of these
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scales, with higher scores representing over-arousal or over-excitability and lower
scores reflecting under-arousal or under-excitability. The suboptimal score does not
differentiate the direction of the pattern.
The lack of dependence on gestational age for some variables in the current study
may be explained by the age of term infants at assessment. Compared with the MLPT
infants, term infants had higher proportions of suboptimal lethargy and hypotonicity
on the NNNS. These infants were often assessed before hospital discharge, when the
physiological changes that occur in the first days after birth may have influenced the
presentation of lethargy and apparent hypotonicity.39
The NNNS handling scale
includes the strategies used to obtain focused attention and the non-significant
relationship across groups is somewhat surprising. Term infants did not require as
much supportive handling, as expected, but MLPT infants required more handling
than the PT<30 infants, which may reflect their different responses to stress. On the
HNNE, the proportions of suboptimality in the abnormal signs domain were similar
between PT<30 and MLPT infants and the behavior domain had low levels of
suboptimality in all three groups.
Despite the effects of gestational age at birth on regulation at term-equivalent age, all
three groups demonstrated similar orientation and attentional responses to visual and
auditory stimuli, although perhaps at the cost of the PT<30 and MLPT infants’ stress
and regulation performance.
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Neurological Domains of the NNNS and HNNE, and the General Movements
Assessment
Having spent longer in physiological flexion in-utero, term-born infants often present
with greater flexion and smoother movement patterns compared with preterm
peers.16,40,41
Higher rates of tone abnormalities, both hypertonicity and hypotonicity,
are reported in preterm infants at term-equivalent age.16
Consistent with this pattern,
the current study demonstrated higher rates of suboptimal hypertonicity on the
NNNS, suboptimal tone optimality and suboptimal tone patterns on the HNNE with
decreasing gestational age. Similarly, there was poorer quality of movement with
decreasing gestational age on all three neurobehavioral assessments. On the HNNE,
PT<30 infants demonstrated substantially higher rates of suboptimal spontaneous
movement (41%) compared with MLPT and term infants (14% and 4%, respectively),
and there were higher rates of abnormal GMA with decreasing gestational age.
Reduced quality of movement at lower gestational ages may reflect differences in
central nervous system integrity and the extra-uterine environment, including limited
opportunities for movement.
Similarly, the reflex scales of the NNNS and HNNE also reflect maturation of the
infants’ central nervous system with increasing suboptimal reflexes on the NNNS and
HNNE with decreasing gestational age. Infants born PT<30 had a substantially
higher rate of suboptimal reflexes (32%) compared with MLPT and term-born infants
(9% and 7%, respectively). Furthermore, the rates of suboptimal total scores on the
HNNE, a reflection of an infant’s overall performance on the more neurologically-
based assessment, increased significantly with decreasing gestational age,
highlighting the impact of preterm birth on the maturation and integrity of the central
nervous system. This finding contrasts with other studies having reported that motor
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reflexes do not seem to be differentially affected by a short gestation, severity of
illness, or brain injury.42
Brain behavior relationships
A number of relationships between brain MRI abnormality scores and suboptimal
neurobehavior were identified in the current study. These brain behavior
relationships were more common on the HNNE than the NNNS, which may reflect its
greater neurological focus.9
On the NNNS, various brain abnormality scores were associated with greater odds of
non-optimal reflexes and suboptimal lethargy. In particular, the global, cerebral white
matter, and cortical grey matter abnormality scores were associated with suboptimal
reflexes and the global, cerebral white matter and cerebellar with suboptimal lethargy
on the NNNS. This is consistent with the study by Brown,17
who also reported both
white matter signal abnormality and delayed gyral maturation to be associated with
worse performance on the NNNS non-optimal reflex scale.
Previous studies have linked poor quality of movement with cerebral injury, in
particular, white matter abnormality and poor outcome.10
Similarly, the current study
demonstrated a significant relationship between white matter abnormality and
suboptimal performance on the HNNE spontaneous movement scale. This is
consistent with the study by Brown et al who reported an association between a worse
grade of white matter abnormality and suboptimal HNNE spontaneous movement
scores. In keeping with the current study’s findings, previous studies found no
relationship between brain abnormality scores and the NNNS quality of movement
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scale.16,17
No other regional abnormality scores were associated with suboptimal
movement in contrast with two earlier studies that reported delayed gyral maturation
and higher grey matter abnormality were associated with lower HNNE spontaneous
movement scores.17,43
On the GMA, however, global brain abnormality and cortical
grey abnormality, a composite of signal abnormality, delayed gyration and dilated
extra-cerebral space, was associated with abnormal GMA.
Limitations of the current study are important to consider in the interpretation of the
findings. In particular, the low rates of brain abnormality scores may have influenced
the power to detect relationships between brain abnormality and suboptimal
neurobehavioral outcomes. Also, not all infants were able to have all assessments.
CONCLUSION
The current study demonstrated a clear continuum of neurobehavior across three
gestational age groups, with poorer behavior regulation, increased stress response,
greater tone abnormality, abnormal reflexes, and poorer quality of movement with
decreasing gestational age. A number of relationships between brain abnormality
scores and suboptimal neurobehavior were found, particularly the global, white
matter, and cerebellar abnormality scores with the more neurologically focused
domains of the NNNS and HNNE, evidence that the neurobehavioral assessment can
provide insight into the integrity of the developing brain and can both supplement
imaging findings when available, or, in the absence of MRI, provide guidance to
clinicians who are identifying high risk infants for early intervention services.
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Contributorship Statement:
Dr Eeles collected data, carried out the analyses, drafted the initial manuscript, and
reviewed and revised the manuscript.
Dr Walsh collected data, drafted the initial manuscript, and reviewed and revised the
manuscript.
Dr Olsen collected data and reviewed and revised the manuscript.
Dr Cuzzilla critically reviewed and revised the manuscript.
Dr Thompson, Professor Anderson, Professor Doyle, A/Professor Cheong and
A/Professor Spittle conceptualized and designed the study and critically reviewed and
revised the manuscript.
All authors approved the final manuscript as submitted and agree to be accountable
for all aspects of the work.
Competing Interests: All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for
the submitted work; no financial relationships with any organisations that might have
an interest in the submitted work in the previous three years; no other relationships or
activities that could appear to have influenced the submitted work.
Funding source:
This work was supported in part by the Australian National Health and Medical
Research Council (NHMRC) (Project Grant ID 1028822); Centre of Clinical
Research Excellence Grant ID 546519; Centre of Research Excellence Grant ID
1060733; Senior Research Fellowship ID 1081288 to PJA.; Early Career Fellowship
ID 1053787 to JLYC, ID 1053767 to AJS; Career Development Fellowship ID
1108714 to AJS; Australian Postgraduate Scholarship to JEO, Murdoch Children’s
Research Institute, Clinical Sciences Theme Grant, the Victorian Government
Operational Infrastructure Support Program.
Financial Disclosure: The authors have no financial relationships relevant to this
article to disclose.
We attest that we have obtained appropriate permissions and paid any required fees
for use of copyright protected materials.
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254x190mm (96 x 96 DPI)
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Supplementary Table 1a: Differences in infant characteristics for those infants with and without HNNE data across three gestational age groups
VPT
with
HNNE
data
VPT
no
HNNE
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
HNNE
data
MLPT
no
HNNE
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
HNNE
data
TERM
no
HNNE
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=133 n=10 n=196 n=5 n=186 n=15
Postnatal
corticosteroids
n (%)
12 (9) 4 (40) 8.98
(0.003) 0 0 0 0
Multiple birth
n (%) 59 (44) 4 (40) 0.07 (0.79) 71 (36) 3 (60) 1.18 (0.28) 2 (1) 0 (0) 0.16 (0.69)
Caesarean
delivery
n (%)
97 (73) 8 (80) 0.24 (0.63) 133 (68) 4 (80) 0.33 (0.57) 72 (39) 5 (33) 0.17 (0.68)
Gestational age
at birth (weeks)
mean (SD)
27.8 (1.5) 27.2 (1.5)
-0.59 (-1.54
to 0.37),
0.23
34.4 (1.3) 34.1
(0.4)
-0.35 (-1.46 to
0.76), 0.53 39.7 (1.2)
40.2
(1.3)
0.52 (-0.12
to 1.16),
0.11
Birth weight z-
score
mean (SD)
-0.41
(1.02)
-0.97
(1.11)
-0.57 (-1.23
to 0.10),
0.09
-0.35
(1.2)
-0.43
(0.6)
-0.08 (-1.12 to
0.96), 0.88
0.23
(0.82)
0.04
(0.84)
-0.19 (-0.62
to 0.25),
0.39
Male
n (%) 66 (50) 5 (50)
0.0005
(0.98) 94 (48) 2 (40) 0.12 (0.73) 99 (53) 8 (53)
0.0001
(0.99)
Respiratory
distress at birth
n (%)
130 (98) 10 (100) 0.23 (0.63) 44 (22) 1 (20) 0.02 (0.90) 0 0
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Supplementary Table 1b: Differences in infant characteristics for those infants with and without NNNS data across three gestational age groups
VPT
with
NNNS
data
VPT
no
NNNS
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
NNNS
data
MLPT
no
NNNS
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
NNNS
data
TERM
no
NNNS
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=130 n=13 n=187 n=14 n=183 n=18
Postnatal
corticosteroids
n (%)
11 (8) 5 (38) 10.70
(0.001) 0 0 0 0
Multiple birth
n (%) 57 (44) 6 (46) 0.03 (0.87) 65 (35) 9 (64) 4.88 (0.02) 2 (1) 0 (0) 0.20 (0.66)
Caesarean
delivery
n (%)
96 (74) 9 (69) 0.13 (0.72) 128 (68) 9 (64) 0.10 (0.75) 71 (39) 6 (33) 0.21 (0.65)
Gestational age
at birth (weeks)
mean (SD)
27.9 (1.4) 26.8 (1.6)
-1.07 (-1.90
to -0.24),
0.01
34.4 (1.3) 34.3
(0.8)
-0.14 (-0.83 to
0.54), 0.67 39.7 (1.2)
40.3
(1.2)
0.56 (-0.03
to 1.14),
0.06
Birth weight z-
score
mean (SD)
-0.39
(1.01)
-0.98
(1.10)
-0.59 (-1.18
to -0.00),
0.05
-0.32
(1.2)
-0.74
(1)
-0.41 (-1.04 to
0.22), 0.20
0.22
(0.82)
0.16
(0.85)
-0.09 (-0.46
to 0.34),
0.77
Male
n (%) 64 (49) 7 (54) 0.10 (0.75) 88 (47) 8 (57) 0.53 (0.47) 97 (53) 10 (56) 0.04 (0.84)
Respiratory
distress at birth
n (%)
127 (98) 13 (100) 0.31 (0.58) 43 (23) 2 (14) 0.57 (0.45) 0 0
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Supplementary Table 1c: Differences in infant characteristics for those infants with and without GMA data across three gestational age groups
VPT
with
GMA
data
VPT
no GMA
data
Mean
difference
(95% CI),
p-value
or
Chi 2
(p-value)
MLPT
with
GMA
data
MLPT
no
GMA
data
Mean
difference
(95% CI), p-
value
or
Chi 2
(p-value)
TERM
with
GMA
data
TERM
no
GMA
data
Mean
difference
(95% CI),
p-value
or
Chi 2 (p-
value)
n=113 n=30 n=154 n=47 n=142 n=59
Postnatal
corticosteroids
n (%)
14 (12) 2 (7) 0.78 (0.38) 0 0 0 0
Multiple birth
n (%) 47 (42) 16 (53) 1.33 (0.25) 56 (36) 18 (38) 0.06 (0.81) 0 (0) 2 (3) 4.86 (0.03)
Caesarean
delivery
n (%)
79 (70) 26 (87) 3.41 (0.07) 106 (69) 31 (66) 0.14 (0.71) 52 (37) 25 (42) 0.58 (0.45)
Gestational age
at birth (weeks)
mean (SD)
27.7 (1.6) 28.0 (1.1)
0.34 (-0.25
to 0.94),
0.26
34.5 (1.3) 34.1
(1.0)
-0.37 (-0.78 to
0.03), 0.07 39.8 (1.2)
39.6
(1.3)
-0.28 (-0.65
to 0.09),
0.14
Birth weight z-
score
mean (SD)
-0.43
(1.02)
-0.53
(1.10)
-0.10 (-0.52
to 0.32).
0.64
-0.35
(1.2)
-0.36
(1.1)
-0.01 (-0.39 to
0.37), 0.97
0.23
(0.82)
0.17
(0.82)
-0.06 (-0.31
to 0.19),
0.63
Male
n (%) 55 (49) 16 (53) 0.21 (0.65) 73 (47) 23 (49) 0.03 (0.85) 79 (56) 28 (47) 1.12 (0.29)
Respiratory
distress at birth
n (%)
112 (99) 28 (93) 3.86 (0.05) 34 (22) 11 (23) 0.04 (0.85) 0 0
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