Received: 1 April 2022 Revised: 1 August 2022 Accepted: 1 September 2022
DOI: 10.1002/brb3.2773
OR I G I N A L A RT I C L E
Brain structure correlates with auditory function in children
diagnosedwith auditory neuropathy spectrum disorder
Hannah E. Cooper1,2,3 Lorna F. Halliday4 Doris-Eva Bamiou2,5 Kshitij Mankad6
Christopher A. Clark1
1Developmental Imaging and Biophysics
Section, UCLGreat Ormond Street Institute of
Child Health, London, UK
2Faculty of Brain Sciences, UCL Ear Institute,
University College London, London, UK
3Audiology Department, Royal Berkshire NHS
Foundation Trust, Reading, UK
4MRCCognition and Brain Sciences Unit,
University of Cambridge, Cambridge, UK
5National Institute of Health Research (NIHR)
University College LondonHospitals
Biomedical Research Centre, University
College London, London, UK
6Department of Neuroradiology, Great
Ormond Street Hospital for Children, London,
UK
Correspondence
Hannah Cooper, UCL Ear Institute, University
College London, London, UK.
Email: Hannah.cooper@ucl.ac.uk
Funding information
NIHR/CSOHealthcare Science Doctoral
Research Fellowship, Grant/Award Number:
NIHR-HCS-D12-03-05; NIHR Biomedical
Research Centre at Great Ormond Street
Hospital and the Great Ormond Street
Institute for Child Health
Abstract
Introduction:Auditory neuropathy spectrumdisorder (ANSD) is a term for a collection
of test results which indicate disruption of the auditory signal at some point along the
neural pathway. This results in a spectrum of functional outcomes, ranging from rea-
sonably normal hearing to profound hearing loss. This study assessed brain structure
changes and behavioral correlates in children diagnosedwith ANSD.
Methods: Seventeen children who had previously been diagnosed with ANSD were
recruited to the study and underwent a battery of behavioral measures of hear-
ing, language, and communication, along with structural MR imaging. Analysis of
cortical thickness of temporal lobe structures was carried out using FreeSurfer. Tract-
based spatial statistics were performed on standard diffusion parameters of fractional
anisotropy and diffusivity metrics. The control group comprised imaging data taken
from a library of MRI scans from neurologically normal children. Control images were
matched as closely as possible to the ANSD group for age and sex.
Results:Reductions in right temporal lobe cortical thicknesswere observed in children
with ANSD compared to controls. Increases in medial diffusivity in areas including the
corpus callosum and in the right occipital white matter were also seen in the group
with ANSD compared to controls. Speech perception abilities, both in quiet and in
noise, were correlatedwith cortical thicknessmeasurements for several temporal lobe
structures in children with ANSD, and relationships were also seen between diffusion
metrics andmeasures of auditory function.
Conclusion: This study shows that children with ANSD have structural brain differ-
ences compared to healthy controls. It also demonstrates associations between brain
structure and behavioral hearing abilities in children diagnosed with ANSD. These
results show that there is a potential for structural imaging to be used as a biomarker
in this population with the possibility of predicting functional hearing outcome.
KEYWORDS
auditory neuropathy spectrum disorder, diffusion, hearing, MRI
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2022 The Authors. Brain and Behavior published byWiley Periodicals LLC.
Brain Behav. 2022;e2773. wileyonlinelibrary.com/journal/brb3 1 of 14
https://doi.org/10.1002/brb3.2773
2 of 14 COOPER ET AL.
1 INTRODUCTION
Auditoryneuropathy spectrumdisorder is a term for a collectionof test
results characterized by evidence of cochlear outer hair cell function
(as shown by present otoacoustic emissions (OAEs) and/or cochlear
microphonics (CM)) togetherwith absent or grossly abnormal auditory
brainstem response (ABR) indicating disruption of the signal at some
point along the auditory neural pathway.
Pediatric cases of ANSD are generally detected shortly after birth
via newborn hearing screening programs, with an estimated preva-
lence of around 10% of children with permanent hearing loss, which
equates to around 1/10,000 of the general population (Feirn et al.,
2013; Rance et al., 1999). The most significant risk factors are hyper-
bilirubinemia, hypoxia, infant respiratory distress syndrome, prema-
turity, low birth weight, intracranial hemorrhage, meningitis, sepsis,
and gentamicin or vancomycin treatment (Beutner et al., 2007; Coen-
raad et al., 2011; Dowley et al., 2009; Morimoto et al., 2010). Up to
40% of cases of ANSD have a genetic origin including both syndromic
and nonsyndromic causes (see Manchaiah et al., 2011, for review).
Consequently, a number of sites of abnormal function have been iden-
tified along the auditory pathway, including presynaptic areas such as
the inner hair cells and ribbon synapses, postsynaptic regions such as
unmyelinated and myelinated auditory nerve dendrites and auditory
ganglion cells, and auditory brainstem areas (Rance& Starr, 2015). This
variation, while leading to similar test results on ABR and OAE/CM,
leads to a spectrum of functional outcomes, which range from reason-
ably normal hearingwith someminor difficulties hearing in background
noise, through to profound hearing loss (Berlin et al., 2003).
It is particularly challenging to gain insight into neural process-
ing in ANSD. ABR is unhelpful in evaluating the site of disfunction
in cases of ANSD as it is, by definition, absent or grossly abnormal.
Cortical auditory evoked potentials may give some information about
degree of hearing, even where the ABR is absent or grossly abnormal
(Gardner-Berry et al., 2015). However, this technique is limited in its
ability to assess central auditory structures and evaluate microstruc-
tural integrity and connectivity. Rance and Starr (2015) proposed using
diffusion tensor imaging (DTI) to examine central auditory systems
in ANSD in order to potentially identify site of lesion and evaluate
changes in central auditory structures. A recent study of adults with X-
linked auditory neuropathy used fixel-based analysis to evaluate fiber
density of structures in the brainstem and showed reduced fiber den-
sity in both cranial nerve VIII and the auditory brainstem tracts (Zanin
et al., 2020).
DTIhasbeenused in several studies to lookat relationshipsbetween
white matter microstructure and functional auditory abilities in adults
andchildren,with associationsbeingobservedbetweenvarious speech
discrimination and auditory processing tasks. Schmithorst et al. (2011)
showed numerous brain areas with both positive and negative cor-
relations between fractional anisotropy (FA) and speech-in-noise and
filtered word scores in various white matter tracts bilaterally in nor-
mally hearing children aged 9–11 years, but did not examine mean
diffusivity (MD) which may have given further insight into white mat-
termicrostructure by indicating areas of increased or decreasedwater
diffusion.Atypical left ear advantage (LEA), sometimesusedasabehav-
ioral marker for auditory processing problems, has also been examined
using DTI in children age 7–14 years with listening difficulties as
reported by their parents. Lower FA values were observed in frontal
white matter regions in the children with atypical LEA compared to
controls (Farah et al., 2014). Correlations between temporal process-
ing abilities, assessed using tone detection in noise measures, and
diffusion metrics have also been evaluated in normally hearing adults
and showed strong negative associations between tone detection in
noise scores, and measures of diffusivity including MD and axial dif-
fusivity (AD), particularly at the superior olivary complex (Wack et al.,
2014). However, this study found higher diffusivity values with better
signal detection levels suggesting decreasingwhitemattermicrostruc-
tural densitywith improvingperformance, anunexpected result. FAhas
been shown to be related to ABR latencies and wave intervals with
increasedFAat the inferior colliculus demonstrated in pretermor very-
low-birth-weight infants with shorter latencies for some waveforms.
FA at the inferior colliculus was also positively correlated with wave V
amplitude (Reiman et al., 2009).
It has also been suggested that diffusion metrics may be useful for
estimating behavioral outcomes in children with hearing loss. Children
with good outcome following cochlear implantation have higher FA
values in brain areas associated with auditory and language function
compared to those with poor outcome, with some authors suggesting
that FA values may thus represent predictive biomarkers of cochlear
implant outcome (Chang et al., 2012; Huang et al., 2015; Wu et al.,
2014). Furthermore, a study of children with unilateral hearing loss
evaluated using DTI suggested that better educational attainment was
more likely with higher FA values in auditory regions (Rachakonda
et al., 2014).
Cortical thickness measurements may also be useful in evaluat-
ing the impact of the distorted auditory input seen in ANSD on brain
development, and there is evidence of reduced cortical volumes in
the auditory regions in those with hearing loss independent of their
language experience (Olulade et al., 2014).
There is a paucity of research evaluating the central consequences
of the distorted auditory input experienced by children diagnosedwith
ANSD. The sensitivity of current diagnostic tools for ANSD is poor,
giving little ability to evaluate disease processes and potential neu-
ral pathway alterations in this cohort. MRI methods including DTI
and cortical thickness analysis may help to identify gross structural
changes in neural anatomyandmay lead tomore targeted andeffective
management strategies for children with ANSD.
In this exploratory study, the hypotheses were as follows:
1. Children and adolescents diagnosed with ANSD show differences
in cortical thickness in auditory areas compared to controls.
2. Children and adolescents diagnosed with ANSD show reductions
in the density of white matter pathways as measured by diffusion
parameters comparedwith age-matched controls.
3. Brain structures (as measured by cortical thickness and diffu-
sion metrics) correlate with clinical scores, including, pure tone
COOPER ET AL. 3 of 14
audiometry, and speech detection in quiet and in noise in children
and adolescents diagnosedwith ANSD.
2 PARTICIPANTS AND METHODS
2.1 Group characteristics
Participants were recruited as part of a larger study, which entailed
behavioral, electrophysiological and psychophysical measures of audi-
tory function, as well as structural MR imaging. The study was
approved by the Joint Research Ethics committee of GOSH/UCL Insti-
tute of Child Health andwritten informed consent was given by partic-
ipants’ parents with assent/consent from the participants themselves
as appropriate. A convenience sample of seventeen children with a
diagnosis ofANSDwere recruited fromaudiology clinics at several par-
ticipant identification centers (PICs) throughout England. The study
was also promoted via social media. PICs were asked to identify chil-
dren age 6–16 years who had been diagnosed with ANSD on the basis
of present cochlear function, demonstrated by OAEs and/or cochlear
microphonic, and absent or grossly abnormal ABR bilaterally. Origi-
nal evoked potential raw data was not available for all participants.
Fourteen of the ANSD group were diagnosed through the UK new-
born hearing screening (NHSP) care pathway for babies in the neonatal
intensive care unit/special care baby unit. Twelvewere premature (ges-
tational age<37weeks); 13 required treatment for jaundicewith eight
receiving phototherapy and five receiving exchange transfusion. Two
participants had no complications at birth but were referred for diag-
nostic audiology testing following no clear response onOAE screening.
One participantwas diagnosed at the age of 13 years following referral
from their general practitioner due to difficulties hearing.
The control group comprised MRI data from neurologically normal
children which was taken from the Developmental Imaging and Bio-
physics Section database. This database consists of MRI data from
other studies for which consent has been given for data to be used in
further investigations. Demographic information available for controls
included age at scanning and sex. There was no explicit information
about hearing or language abilities available for the control data. Con-
trol data was matched as closely as possible to ANSD participants for
age and sex.
2.2 Behavioral measures
Behavioral measures were conducted with the ANSD group only.
Unaided pure tone audiograms (PTA) were acquired bilaterally for all
children for at least four frequencies (0.5, 1, 2, and 4 kHz) bilater-
ally with results shown in Figure 1. Nonverbal IQ was assessed using
the Block Design subtest of theWechsler Abbreviated Scale of Intelli-
gence (WASI; Wechsler, 1999), as IQ has been shown to be associated
with diffusionmetrics (Schmithorst et al., 2005), and is known to affect
speech and language abilities (Rice, 2016). The Bamford-Kowal-Bench
(BKB) sentence lists (Bench et al., 1979) were used to examine speech
perception in quiet. The sentences were presented diotically through
TDH39 headphones using recordings developed by UCL and the Med-
ical Research Council Institute of Hearing Research (MRC/IHR) and
were spoken by a British female speaker (Faulkner, 1998). Participants
were asked to repeat what they heard in order to score each sentence.
Amodified version of the guidelines of the American Academy of Audi-
ology (American Academy of Audiology, 2012) was used to calculate
speech reception threshold. The starting presentation level was cho-
sen to be above threshold based on interaction with participant and
PTA results. If the participant was unable to comfortably identify the
first three sentences the level was increased by 20 dB until the opti-
mal level was reached. A bracketing procedurewith step-sizes of 20 dB
down and 10 dB upwas used until the 50% speech reception threshold
(SRT) was determined.
Speech-in-noise abilities were measured using the Children’s Coor-
dinate Response Measure (CCRM; Messaoud-Galusi et al., 2011), an
adaptive, nonstandardized test based on an adult version (Bolia et al.,
2000; Brungart, 2001). During this test, participants heard a series of
low-context sentences using the carrier phrase “show the dog where
the [color] [number] is” with colors being black, white, green, red, blue,
or pink, and numbers being between one and nine, excluding bisyl-
labic seven. A speech-shaped noise masker was used to add energetic
masking. Participants were required to indicate what they heard via a
response panel on a computer screen. Sentences were presented via
Sennheiser HD25SPII headphones, with both speech and noise pre-
sented diotically. The output level of the test was kept constant at
70 dB SPL and a three-up, one-down adaptive procedure was used to
vary the signal-to-noise ratio (SNR) and track 79.4% correct on the
psychometric function (Levitt, 1971). The signal was audible to all par-
ticipants, including thosewithmore severe degrees of hearing loss. The
taskwas carried out twice in succession and themeanof the two scores
used in the analyses. A higher score indicated poorer ability to hear
speech in the presence of background noise. Scores for nonverbal IQ,
speech-in-quiet and speech-in-noise are shown in Figure 2.
2.3 MRI measures
MRI investigation was carried out on a 1.5-Tesla Siemens Magne-
tom Avanto system (Siemens, Erlangen, Germany) and included T1-
weighted three-dimensional fast low-angle shot (FLASH) sequence
(flip angle = 15◦; TR = 11 ms; TE = 4.94 ms; voxel size = 1 mm
isotropic; number of slices= 176); constructive interference in steady-
state (CISS) temporal bone sequence (flip angle = 70◦; TR = 10.3 ms;
TE = 5.1 ms; slice thickness = 0.70 mm; number of slices = 48); and
diffusion tensor imaging consisting of a twice-refocused spin echo
diffusion-weighted echo planar imaging (EPI) sequencewith 60 unique
gradient directions (b = 1000 s/mm2), interleaved with three images
without diffusion weighting (b= 0 s/mm2) (TR= 7300ms; TE= 81ms;
voxel size = 2.5 mm isotropic; number of slices = 60 axial). Con-
trol group MRI measures were carried out on the same system with
the same parameters and software version. A consultant pediatric
neuroradiologist (KM) evaluated all images from the ANSD group to
determine the status of the internal auditory meatus and whether any
gross abnormalities were present.
4 of 14 COOPER ET AL.
F IGURE 1 Speech-frequency pure tone average thresholds as a function of frequency. Thin blue lines represent individual participant with
thick blue lines representingmean values
F IGURE 2 Performance on the nonverbal IQ, speech-in-quiet and speech-in-noise tasks. For nonverbal IQ, higher scores represent better
performance. For speech-in-quiet (measured in dBHL) and speech-in-noise (measured in dB), higher scores represent poorer performance. The
boxplot represents the 25th, 50th, and 75th percentiles for the group. The violin plot shows the kernel probability density, with the width of the
plot representing the proportion of the data at that point
2.4 Region-of-interest analysis
Cortical reconstruction and volumetric segmentation of T1-weighted
images was carried out using FreeSurfer image analysis suite v5.2 for
MacOSX (Fischl, 2012). Technical details have been described in detail
in the literature (Dale et al., 1999) therefore only a brief description is
given here. Preliminary stages includingmotion correction and averag-
ing, intensity normalization, automated Talairach transformation, and
COOPER ET AL. 5 of 14
skull stripping were applied to the T1-weighted volumetric images.
The next steps included volume registration and removal of the neck,
followed by segmentation of white matter and subcortical grey mat-
ter volumes. Inflation was then used to reveal topological defects in
the segmentation. Neuroanatomical labels were applied to each loca-
tion on the cortical surface parcellating the cortex into 68 regions of
interest based on gyral and sulcal structure. The pial and white matter
surfaces were reconstructed by FreeSurfer in order to estimate corti-
cal thickness which was calculated as the closest distance between the
grey/white matter boundary and the grey matter/CSF (cerebrospinal
fluid) boundary at each vertex. All registrations and segmentations
were checked visually tominimizemethodological errors.
Analysis focused on cortical thickness of temporal lobe structures
that contain the auditory cortex and areas involved in language pro-
cessing including bilateral transverse temporal gyri, superior temporal
gyri, middle temporal gyri and inferior temporal gyri. Total intracra-
nial volume measures were also made for use in statistical analyses.
Structures were tested for significant deviations from the normal dis-
tribution using the Shapiro–Wilk test. ANCOVA analysis was used
to compare groups, controlling for age, sex and intracranial volume.
For the ANSD group, partial Pearson’s correlations were carried out
between volumes of temporal lobe structures and speech discrim-
ination measures (both in quiet and in noise), corrected for total
intracranial volume.
2.5 DTI preprocessing
DTI data were visually inspected to check for the presence of motion
artifacts. Volumeswith artifacts presentwere removed.Datawerepre-
processed using TractoR version 2.6 (Clayden et al., 2011) and FMRIB
Software Library (FSL) version 5.0 (Jenkinson et al., 2012). In brief,
brain extraction was performed on a reference b = 0 volume for each
subject (Smith, 2002). Diffusion-weighted imageswere then registered
to this reference volume to correct for eddy current distortions. At
each voxel, a diffusion tensorwas derived using aweighted linear least-
squares process to calculate voxel-wise measurements of FA, MD, AD,
and radial diffusivity (RD).
2.6 Tract-based spatial statistics (TBSS) whole
brain analysis
TBSS (Smith et al., 2006) was used to analyze the DTI data and was
carried out using FSL version 5.0 (Jenkinson et al., 2012). Each sub-
ject’s data were aligned to every other using nonlinear registration
and the “most representative” image was identified as the target. This
was achieved by registering each subject’s data to every other sub-
ject, then summarizing every warp field by its mean displacement, and
finally choosing the “most representative” subject as the one with the
smallest mean distance to every other subject (Smith et al., 2006).
All subject data were then transformed to MNI (Montreal Neurolog-
ical Institute) space. A mean FA skeleton was created by aligning the
FA images of all subjects to the most typical subject and thresholding
(at FA = 0.2) to suppress areas of high intersubject variability or low
mean FA. Each subject’s aligned FA image was then projected onto the
mean FA skeleton and voxel-wise statistics were carried out on the
skeleton FA data across subjects. Other diffusion parameters from the
various models analyzed were projected onto the skeleton in a similar
manner and these values used for voxel-wise analysis. AD was defined
as the first eigenvalue of the diffusion tensor and RD was calculated
as the mean of the second and third eigenvalues. TBSS was carried
out using nonparametric testing (5000 permutations). Threshold-Free
Cluster Enhancement was used as per the TBSS protocol to find clus-
ters in data by comparing neighboring voxels to identify similarities,
thereby increasing confidence that the results in each voxel have not
occurred by chance. Family wise error (FWE) correction was used to
reduce the likelihood of type I error. The locations of significant clus-
ters were determined using FSL atlas tools (FSL, n.d). Age and sexwere
includedas covariates in the analysis.Diffusionmetrics havepreviously
been shown to be affected by gestational age (see Pandit et al., 2013,
for a review) and therefore this was also included as a covariate in the
analysis.
2.7 Missing data
One participant completed behavioral testing but declined to be
scanned. Scans were visually inspected for artifacts and one partici-
pant was excluded due to poor quality data caused by excessive head
motion, leaving 15 T1-weighted data sets from the ANSD group for
analysis and 15 age and sex matched controls. Twelve of the 16 partic-
ipants who completed T1-weighted scanning went on to also complete
diffusion weighted imaging. Scans were visually inspected for artifacts
and two participants were excluded due to poor quality data caused by
excessive head motion. This left 10 diffusion weighted data sets avail-
able fromANSD participants for DTI analysis and ten controls (age and
sex matched where possible). Table 1 describes the demographics of
the final groups. For regionof interest (ROI) analyses, calculationswere
carriedout both including andexcludingparticipantswhose scanswere
reported as abnormal by the neuroradiologist but the results did not
substantially change for any evaluations. No participants with scans
reported as abnormal by the neuroradiologist were included in the DTI
analysis.
All participants were able to complete IQ and speech-in-quiet
testing. However, five participants were unable to complete speech-in-
noise testing, despite the signal being audible for all participants.
3 RESULTS
3.1 Radiological assessment
Sixteen participants diagnosed with ANSD completed T1-weighted
scanning. For these, abnormalities were reported in three, those being
reduced brain volume overall (one participant), features of white mat-
6 of 14 COOPER ET AL.
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ities had cerebral palsy. All participants had intact vestibulocochlear
nerve bilaterally.
3.2 Region-of-interest analysis
3.2.1 Group comparisons
Results of ANCOVA testing (controlling for age, sex, and total intracra-
nial volume) showed significantly reduced cortical thickness of the
right hemisphere in the ANSD group compared to controls (see
Table 2). Reductions in cortical thickness in middle and superior right
temporal lobe for the ANSD group compared to controls were also
observed when controlling for age, sex, and total intracranial volume
with strong effect sizes; however, these differences did not survive
Bonferroni correction for multiple comparisons. No differences in
cortical thickness were seen in the left temporal lobe.
3.2.2 Correlations with auditory testing
Partial Pearson’s correlations were performed between cortical thick-
ness measures of the temporal lobes and both speech-in-quiet and
speech-in-noise scores controlling for age, sex, IQ, and total intracranial
volume for the ANSD group with results shown in Table 3. Signifi-
cant correlations were seen for the speech-in-quiet scores for several
temporal lobe structures including left superior temporal thickness
(rp = −0.78, p = .005) and right inferior temporal (rp = −0.76, p =
.007) and superior temporal (rp = –0.70, p = .010) thicknesses, which
all survived Bonferroni correction for multiple comparisons. Plots of
significant relationships are shown in Figure 3. Significant correlations
were also shown for speech-in-noise scores in the left superior tempo-
ral thickness (rp = −0.78, p = .038) and the right transverse temporal
lobe cortical thickness (rp =−0.86, p= .010). The relationship with the
right transverse temporal thickness survived correction for multiple
comparisons. Plots of significant relationships are shown in Figure 4.
3.3 Diffusion tensor imaging
3.3.1 Group comparisons
All ANSD participants who successfully completed DTI scanning had
T1-weighted scans reported as normal by the neuroradiologist. The
results of TBSS analysis comparing ANSD participants and controls
are shown in Figure 5. After controlling for the effects of age and sex
therewere no differences in FA, AD or RD.MDwas significantly higher
in the ANSD group (p < .05) in the corpus callosum and in the right
occipital white matter.
COOPER ET AL. 7 of 14
F IGURE 3 Partial Pearson’s correlations are shown between speech-in-quiet scores and (a) left superior temporal thickness, (b) right inferior
temporal thickness, and (c) right superior temporal thickness. Higher speech-in-quiet scores represent poorer performance. Age, sex, IQ, and total
intracranial volumewere included as covariates. The shaded panel represents standard error
F IGURE 4 Partial Pearson’s correlations are shown between speech-in-noise scores and (a) left superior temporal thickness and (b) right
transverse temporal thickness. Higher speech-in-noise scores represent poorer performance. Age, sex, IQ, and total intracranial volumewere
included as covariates. The shaded panel represents standard error
F IGURE 5 Whole brain group comparison TBSS results showing the cohort’s meanwhichmatter skeleton in green. Red voxels indicate areas
whereMDwas significantly higher in ANSD participants compared to controls (p< .05, FWE corrected)
8 of 14 COOPER ET AL.
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COOPER ET AL. 9 of 14
F IGURE 6 TBSS correlations with
gestational age in weeks. (a) Red/yellow voxels
show areas where gestational age and FAwere
significantly positively correlated (p< .05, FWE
corrected). (b) Blue voxels show areas where
gestational age andMDwere significantly
negatively correlated. (c) Blue voxels show
areas where gestational age and RDwere
significantly negatively correlated. (d) Blue
voxels show areas where gestational age and
ADwere significantly negatively correlated
3.3.2 TBSS correlations with clinical scores
Relationships betweendiffusionmetrics and gestational age are shown
in Figure 6. A strong positive relationship between FA and gesta-
tional age was seen in the left frontal lobe. There was a signifi-
cant negative relationship between gestational age and MD in the
right fornix/hippocampus. A significant negative relationship was seen
between gestational age and RD in the splenium of the corpus callo-
sum.Therewasa significant negative relationshipwithAD in the fornix.
As significant relationships were found between gestational age and
all diffusion metrics, gestational age was subsequently controlled for
in the subsequent analyses.
Results of TBSS correlations betweenwhitematter diffusionparam-
eters and speech-frequency PTA for the ANSD group only, and fol-
lowing the removal of one outlier (with speech-frequency PTA that
was substantially better than the rest of the group), are shown in
Figure 7. After controlling for the effects of age, sex, IQ, and gestational
age, a small volume with a significant negative relationship was found
between speech-frequency PTA and FA in the left frontal lobe. There
was a significant positive relationship between speech-frequency PTA
and RD in the corpus callosum. Mean raw values of FA and RD from
the ROIs with significant correlations were extracted and significant
correlations were found (FA: rp = 0.77, p< .001).
Results of TBSS correlations betweenwhitematter diffusionparam-
eters and speech-in-quiet scores for the ANSD group are shown in
Figure 8. After controlling for the effects of age, sex, IQ, and gestational
age, a significant positive relationship was seen between speech-in-
quiet scores and RD in the corpus callosum. Mean raw values of RD
from the ROI with significant correlations were extracted and a highly
significant correlation was found (rp = 0.90, p < .001). No participants
were identified as outliers in this analysis. There were no significant
relationships seen for FA,MD, or AD.
4 DISCUSSION
The aims of this study were first, to examine differences in brain
structure in children diagnosed with ANSD compared to controls, and
second, to examine the relationships between brain structure and
behavioral auditory function in children diagnosed with ANSD. We
10 of 14 COOPER ET AL.
F IGURE 7 TBSS correlations with speech-frequency pure tone audiometry (PTA). Blue voxels indicate areas where FA and PTAwere
significantly negatively correlated (p< .05, FWE corrected). Plot showsmean values of diffusion data from region-of-interest shown on the panels
against speech-frequency PTA
F IGURE 8 TBSS correlations with speech-in-quiet scores. Red/yellow voxels indicate areas where there was a positive correlation between
speech-in-quiet scores and RD (p< .05, FWE corrected). Plot showsmean RD value from region-of-interest shown on the panels against
speech-in-quiet
identified reductions in cortical thickness in the right temporal lobe as
well as reducedwhite matter microstructural integrity in childrenwith
ANSD compared to controls. Increases in MD suggest a reduction in
thedensity ofwhitemattermicrostructure in childrenwithANSDcom-
pared to controls, particularly in areas including the corpus callosum
and in the right occipital white matter. Moreover, relationships were
observed between brain structure and auditory abilities.
4.1 Group comparisons
Mean total cortical thickness was reduced on the right in the ANSD
group in this study. This is in common with a previous study which
showed reducedmeanwhole brain cortical thickness but no significant
reduction in grey matter volume in children and adolescents with sen-
sorineural hearing loss (SNHL) compared to hearing controls (Li et al.,
2012). ROI cortical thickness analysis showed differences in the right
temporal lobe in childrenwithANSD compared to controlswith signifi-
cantly reduced thickness being observed in right middle and superior
temporal gyri prior to correction for multiple comparisons. This is in
contrast to the literature on SNHL which generally shows a preser-
vation of cortical thickness or volume in the temporal lobes (Leporé
et al., 2010; Shibata, 2007), as well as preserved leftward asymmetry
of temporal lobe grey matter (Li et al., 2013; Shibata, 2007), although
one study showed increased grey matter volume in the right superior
temporal gyrus (Emmorey et al., 2003). However, all of these studies
looked at participants with profound prelingual hearing loss who pri-
marily communicated using signed language unlike the participants in
the current study. The controls in previous studies were all oral lan-
guage users and therefore used a different mode of communication to
the deaf participants, introducing confounding factors into the studies.
The results of the current studymay suggest that differentmechanisms
contribute to cortical development in children with residual hearing
who use spoken language to communicate, and in this case, with a diag-
nosis of ANSD, to those with profound hearing loss who communicate
with signed language.
DTI analysis using TBSS showed significant differences in white
matter structure in ANSD participants compared to controls with sig-
nificantly increased MD seen in the mid body/isthmus of the CC and
in the right occipital lobe as well as a trend towards increased RD in
the region of the superior longitudinal fasciculus suggesting less dense
white matter microstructure in these regions. A previous study of chil-
dren with profound SNHL showed increases in white matter volume in
the visual cortex, particularly on the right (Leporé et al., 2010), while
other studies have shown decreases in white matter volume in the
temporal lobes of profoundly deaf participants (Emmorey et al., 2003;
Shibata, 2007) when manually drawing ROIs. The isthmus of the CC is
thought to include connections from the superior temporal cortex and
COOPER ET AL. 11 of 14
is therefore likely to be associated with language function (Witelson,
1989). Adolescents with profound hearing loss have also been shown
to exhibit reductions in FA in areas including the splenium of the CC as
well as bilateral areas of the temporal lobe on TBSS (Miao et al., 2012).
The main differences between previous studies and the current study
is that the children in this study all had usable residual hearing and all
used spoken English as their primary mode of communication. Previ-
ous studies have concentrated on deaf individuals who use a signed
language for communication. The results of the present study suggest
that children diagnosed with ANSD who use spoken language have
disrupted white matter microstructure in some brain areas.
4.2 Relationships between brain structure and
auditory function
Negative relationships between speech discrimination scores and cor-
tical thickness in left and right temporal lobe structureswere observed
in the ANSD group in this study, showing greater cortical thickness
with better speech discrimination. Significant associations between
speech-in-quiet scores and left superior temporal thickness as well as
right inferior and superior temporal lobe thickness were observed fol-
lowing correction for multiple comparisons. There were also negative
relationships between speech-in-noise scores and cortical thickness
(equivalent to positive relationships between task performance and
cortical thickness) in the left superior temporal gyrus and the right
transverse temporal gyrus with the right transverse temporal gyrus
relationship surviving correction for multiple comparisons. The left
superior temporal gyrus contains the primary auditory cortex and is
also an important cortical area for speech processing. Previous stud-
ies have shown that the right temporal lobe plays a crucial role in
pitch perception particularly for complex stimuli (Zatorre, 1988) and
has a stronger sensitivity to voices (Belin et al., 2000) than the left
hemisphere. The ANSD group in the current study had significant dif-
ficulties with simple frequency discrimination tasks and with speech
discrimination and this may have implications for the developing cor-
tex. Also, the two speech discrimination tasks differed in several ways.
The BKB sentences in quiet present an open-set test in which the
listener can use contextual cues to fill in any parts they may have
missed. The CCRM, however, is a closed-set test with low linguistic
content. Therefore, the differences in locations of relationships with
cortical thickness may reflect the different speech discrimination tests
used.
In commonwith previouswork (see Pandit et al., 2013, for a review),
associations were seen between gestational age and diffusion met-
rics. Here, decreases in FA and increases in diffusivity measures were
observed with decreasing gestational age, including in the left frontal
lobe, fornix, hippocampus and corpus callosum. Prematurity is a well-
known risk factor forANSDand71%of theparticipants diagnosedwith
ANSD in this study were born before 37 weeks gestation. In order to
try to limit the influence of gestational age on evaluation of relation-
ships between diffusion metrics and auditory abilities, gestational age
was controlled for in those analyses.
Previous studies have shown relationships between auditory pro-
cessing abilities and FA in both normally hearing children and chil-
dren with auditory processing difficulties. Schmithorst et al. (2013)
suggested that atypical left ear advantage, which is used by some
audiologists as a clinical indicator of auditory processing problems, is
predicted by an increase in AD in the left internal capsule. A further
study by the same group has shown decreases in FA and increases
in MD in those children with left ear advantage compared to those
with right ear advantage with areas of the frontal lobe and the corpus
callosum being implicated (Farah et al., 2014). A previous study of chil-
dren with sensory processing disorders showed associations between
FA and auditory profile score as assessed by parental questionnaire
with significant clusters located in the left and right posterior tha-
lamic radiations and in the corpus callosum (Owen et al., 2013). In
the current study, significant relationships betweendiffusionmeasures
and auditory behavioral scores were observed in the ANSD group.
Decreasing FA was seen with increasing speech-frequency PTA sug-
gesting a decreasing coherence in white matter microstructure with
poorer hearing in the left frontal lobe and the corpus callosum.
Positive correlations between FA and various speech discrimination
tasks have been shown in children in the absence of hearing problems,
with significant areas being the corpus callosum, prefrontal cortex,
and occipotemporal white matter. Negative correlations of FA with
task performance have also been demonstrated, particularly in the
posterior centrum semiovale (Schmithorst et al., 2011). A positive rela-
tionship between RD and speech-in-quiet scores was seen in the mid
body/isthmus of the corpus callosum in this study, again suggesting
decreasing white matter density in an area associated with language
processing with poorer speech discrimination abilities.
4.3 Methodological considerations
Several limitations should be consideredwhen interpreting our results.
The first is that there was limited information about the control group
particularly in relation to gestational age and IQ which would have
been useful in order to control for these measures in the analy-
ses. Information about the control group’s auditory status would also
have been helpful for ensuring that no hearing deficits were present.
Data was unavailable as control data was drawn from a database of
previously collected information rather than prospectively acquired.
Second, the sample size for the ANSD group was small and, although
therewas some variationwithin the group, themajority of children had
a moderate hearing loss with fewer at the ends of the spectrum. The
study design meant that children with severe-profound ANSD would
necessarily be excluded asmanywill have cochlear implants andwould
therefore be unable to take part in MRI scanning. Third, several of the
children in the ANSD group were born prematurely and several had
other co morbidities apart from ANSD, including cerebral palsy and
visual impairment, which may also be associated with structural brain
changes. However, this would be expected in a typical cohort of chil-
drenwithANSDand thereforemaybemore clinically representative of
the population (Ching et al., 2013). Fourthly, although ABR testing was
12 of 14 COOPER ET AL.
attempted on all participants with ANSD it proved challenging in many
cases due tomovement and issueswith compliance.However, evidence
of neuromaturation was seen in several participants who had visible
waveforms on ABR testing. This may not be uncommon in this popula-
tionwith reports ofmaturation onABR testing ranging from7% to85%
(Attias & Raveh, 2007; Berlin et al., 2003); Uus, 2011). Fifth, over half
(76%) of the ANSD group required treatment for jaundice as neonates
and high levels of unconjugated bilirubin may have an impact on brain
development. Very little information about bilirubin levels was avail-
able for the participants with ANSD (all was collected from parents)
andnonewasavailable for controls. As this is a significant risk factor for
ANSD, more detailed information should be collected and used in any
future analysis (Wisnowski et al., 2014). Finally, there are known limi-
tations of the DTI method, which may result in errors where there are
crossing or kissing fibers, or partial voluming with CSF (Farquharson
et al., 2013).
5 CONCLUSION
To our knowledge, this is the first study to show brain structural differ-
ences in children diagnosed with ANSD compared to healthy controls.
It is also the first study to show associations between brain structure
andbehavioral auditory scores in childrendiagnosedwithANSD.These
results suggest that the auditory difficulties experienced by children
diagnosed with ANSD are related to brain structural abnormalities
and that there may be the potential for structural imaging to be used
as a biomarker for stratifying treatment options in this population,
including prediction of auditory functioning such as speech perception
outcomes.
ACKNOWLEDGMENTS
We would like to thank all the children who participated in this study,
as well as the radiographers at Great Ormond Street Hospital for
their help with MRI acquisitions. We would also like to thank Kiran
Seunarine and Jonathan Clayden for their help with MR and statisti-
cal analyses. This work was supported by an NIHR/CSO Healthcare
Science Doctoral Research Fellowship, Hannah Cooper, NIHR-HCS-
D12-03-05. We also acknowledge funding from the NIHR Biomedical
Research Centre at Great Ormond Street Hospital and the Great
Ormond Street Institute for Child Health, University College London,
as well as at University College LondonHospitals.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the
corresponding author upon reasonable request.
CONFLICT OF INTEREST
All authors declare that they have no conflicts of interest.
ORCID
HannahE. Cooper https://orcid.org/0000-0002-6471-1384
PEER REVIEW
The peer review history for this article is available at: https://publons.
com/publon/10.1002/brb3.2773.
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How to cite this article: Cooper, H. E.., Halliday, L. F., Bamiou,
D. -E., Mankad, K., & Clark, C. A. (2022). Brain structure
correlates with auditory function in children diagnosedwith
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