Neuro And Hearing Discussion

Provide a brief overview of the main points and conclusions of the article. Only pertinent points should be stated – there is no need to include details of methods unless directly related to a main conclusion. Every result does not need to be included, if not pertinent to the thrust of your discussion. However, there must be sufficient explanation to demonstrate an understanding of the paper. Simply summarizing the abstract is not acceptable.

What is the main novelty of this article? Why are the results novel? What was previously known, and how do the results expand on previous knowledge? Why are these results an addition to the current field? Do they clarify something that was previously unknown? This section MUST incorporate outside research and not just recapitulate the authors’ discussion.

What is/are the outstanding question(s) stemming from this article? This section MUST incorporate outside research that puts the current study in perspective of the field. It must go beyond the discussion of the current article being reviewed, and not just summarize their discussion points. Based on the main novelty of the article, what is the next step? What missing information still needs to be researched? Make sure you focus your discussion of outstanding questions around the main novel results.

I need 2 discussion posts and I included the instructions below. One i based off of the article labeled “neuro”, the second is based off the article labeled “2hearing”. They are separate discusions but use the same three questions and prompts I have shown above.

NeuroImage: Clinical 26 (2020) 102211
Contents lists available at ScienceDirect
NeuroImage: Clinical
journal homepage: www.elsevier.com/locate/ynicl
Neocortical morphometry in Huntington’s disease: Indication of the
coexistence of abnormal neurodevelopmental and neurodegenerative
processes
T
Jean-Francois Mangina, Denis Rivièrea, Edouard Duchesnaya, Yann Cointepasa,
Véronique Gaurab, Christophe Vernyc, Philippe Damierd, Pierre Krystkowiake,
Anne-Catherine Bachoud-Lévif, Philippe Hantrayeg, Philippe Remyb, Gwenaëlle Douaudh,⁎
a
Université Paris-Saclay, CEA, CNRS, Baobab, Neurospin, Gif-sur-Yvette, France
Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), Département des Sciences du Vivant (DSV), Institut d’Imagerie Biomédicale (I2BM), MIRCen,
France
c
Centre national de référence des maladies neurogénétiques, Service de neurologie, CHU, 49000 Angers, France, UMR CNRS 6214 – INSERM U1083, France
d
CHU Nantes, INSERM, CIC 0004, France
e
Neurologie, CHU Amiens-Picardie, France
f
AP-HP, Hôpital Henri Mondor, Centre de Référence-Maladie de Huntington, France
g
MIRCen, Institut d’Imagerie Biomédicale, Direction de la Recherche Fondamentale, Commissariat à l’Energie Atomique et aux Energies Alternatives, France
h
Functional Magnetic Resonance Imaging of the Brain (FMRIB) Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences,
University of Oxford, United Kingdom
b
ARTICLE INFO
ABSTRACT
Keywords:
Huntington’s disease
MRI
Cortical morphometry
Sylvian fissure
Neurodevelopment
Asymmetry
Huntington’s disease (HD) is an inherited, autosomal dominant disorder that is characteristically thought of as a
degenerative disorder. Despite cellular and molecular grounds suggesting HD could also impact normal development,
there has been scarce systems-level data obtained from in vivo human studies supporting this hypothesis. Sulcusspecific morphometry analysis may help disentangle the contribution of coexisting neurodegenerative and neurodevelopmental processes, but such an approach has never been used in HD. Here, we investigated cortical sulcal
depth, related to degenerative process, as well as cortical sulcal length, related to developmental process, in earlystage HD and age-matched healthy controls. This morphometric analysis revealed significant differences in the HD
participants compared with the healthy controls bilaterally in the central and intra-parietal sulcus, but also in the left
intermediate frontal sulcus and calcarine fissure. As the primary visual cortex is not connected to the striatum, the
latter result adds to the increasing in vivo evidence for primary cortical degeneration in HD. Those sulcal measures
that differed between HD and healthy populations were mainly atrophy-related, showing shallower sulci in HD.
Conversely, the sulcal morphometry also revealed a crucial difference in the imprint of the Sylvian fissure that could
not be related to loss of grey matter volume: an absence of asymmetry in the length of this fissure in HD. Strong
asymmetry in that cortical region is typically observed in healthy development. As the formation of the Sylvian
fissure appears early in utero, and marked asymmetry is specifically found in this area of the neocortex in newborns,
this novel finding likely indicates the foetal timing of a disease-specific, genetic interplay with neurodevelopment.
1. Introduction
gyrification allowing for the neocortical surface to increase and become
more complex in the last three months of development. These historical
observations prefigured a theory that poses the stability of these sulcal
“roots” across individuals, something which was further observed in vivo in
newborns (Regis et al., 2005; Dubois et al., 2008a; Dubois et al., 2008b).
Furthermore, it has been known since the beginning of the 19th century
that various developmental abnormalities leading to cortical sulci
Cortical sulcal analysis has for long solely relied on the empirical description of the cortical foldings investigated post mortem (Dareste, 1852;
Broca, 1878). At the antenatal stage, two fundamental steps, now thought
to be common to the higher order mammals, had been observed: first, the
operculisation of the insula at 6 months, followed by a progressive
⁎
Corresponding author.
E-mail address: gwenaelle.douaud@ndcn.ox.ac.uk (G. Douaud).
https://doi.org/10.1016/j.nicl.2020.102211
Received 6 June 2019; Received in revised form 5 February 2020; Accepted 12 February 2020
Available online 13 February 2020
2213-1582/ © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
NeuroImage: Clinical 26 (2020) 102211
J.-F. Mangin, et al.
malformation are associated with sensorimotor, cognitive or behavioural
disorders (Bruce, 1889; Cameron, 1907). Investigating sulcal morphometry might thus capture abnormalities emerging during neocortical development, either chronologically coinciding to the formation of sulcal
roots, or later during cortical maturation process.
Huntington’s disease (HD) is a fatal autosomal dominant, neurodegenerative disorder resulting from an expansion of a CAG repeat within
the IT15 gene on chromosome 4. While the striatum is the most atrophied structure in HD, there is evidence that the cortical atrophy is
more widespread than previously thought based on post mortem observations, this loss of volume sometimes appearing even before the
onset of symptoms (Rosas et al., 2002; Thieben et al., 2002; Rosas et al.,
2005; Douaud et al., 2006; Rosas et al., 2008). Importantly, two recent
in vivo studies of global anthropometric measures in asymptomatic
subjects carrying the mutated gene also point at a developmental aspect
in HD (Nopoulos et al., 2011; Lee et al., 2012). These are, to our
knowledge, the only human studies showing results supporting the
thought-provoking idea that degeneration in some disorders of possible
genetic aetiology, including HD and Alzheimer’s disease, might be the
consequence of abnormal development, with certain populations of
neuronal cells made more vulnerable to late life stressors (Mehler and
Gokhan, 2000; Molero et al., 2009; Marder and Mehler, 2012).
Here, we carried out for the first time in HD a sulcal morphometry
analysis using a tool that automatically reconstructs and labels sulci
from T1-weighted images (Riviere et al., 2002; Mangin et al., 2004).
This approach has revealed for instance significant phylogenetic differences in a language-related sulcal area (Leroy et al., 2015), or alterations in sulcal shape in ageing (Kochunov et al., 2005) – with, for
instance, a reduced sulcal depth related to adjacent gyral atrophy – as
well as in mild cognitive impairment and Alzheimer’s disease
(Reiner et al., 2012; Hamelin et al., 2015). Furthermore, differences in
sulcal length have been recently consistently related to (abnormal)
developmental processes (Auzias et al., 2014; Cachia et al., 2014;
Muellner et al., 2015).
We thus expected that sulcal morphometry analysis might reveal
evidence for coexisting abnormal degenerative and developmental
processes, in line with the duality, observed for the mutant protein, of
both gain-of-function and loss-of-function (effects which are in turn
thought to play a distinct role in brain degeneration and abnormal
development respectively) (Marder and Mehler, 2012). As this exploratory, yet region-of-interest based approach provides information
on the shape of sulci complementary to information obtained with
voxelwise techniques, we anticipated that it should in particular detect
subtle abnormalities not identified using an approach such as VBM
(Mangin et al., 2004; Douaud et al., 2006) and that it might, crucially,
reveal novel abnormalities related to altered neurodevelopment in HD.
Table 1
Clinical variables for the HD participants.
Clinical Variable
Mean±std
Range
CAG repeat
Total Functional Capacity
Disease Burden
Motor UHDRS
Behavioural UHDRS
Functional Assessment
Independence Scale
Verbal Fluency (P, R, V) – 1min
Verbal Fluency (P, R, V) – 2min
Digit Symbol
Stroop (Words)
Stroop (Colour)
Stroop (Interference)
46±4
11±1
409±73
35±14
12±10
27±2
88±9
27±10
37±13
26±9
63±21
46±15
26±9
40–57
8–13
239–538
16–61
0–36
25–31
70–100
7–43
14–62
14–48
29–103
24–76
10–43
8–13). 18 healthy controls (HC, 14 males, 4 females, 2 left-handed)
matched for age (41 ± 8 years) to the HD patients underwent the same
imaging protocol. Each HD patient was examined using the Unified
Huntington’s Disease Rating Scale (UHDRS, 1996) in each hospital and
the scores for each subscale (motor, behavioural, functional and neuropsychological) were collected (Table 1).
2.2. Data acquisition
Whole-brain anatomical MRI was acquired in all 41 participants
with a 1.5 T Signa imager (General Electric Healthcare, Milwaukee, WI)
with a standard 3D T1-weighted inversion recovery fast spoiled gradient recalled (IR-FSPGR) sequence with the following parameters:
axial orientation, matrix 256 × 256, 124 slice locations,
0.9375 × 0.9375 mm2 in-plane resolution, slice thickness 1.2 mm, TI/
TE/TR (inversion/echo/repetition time) 600/2/10.2 ms, flip angle (α)
10°, read bandwidth (RBW) 12.5 kHz.
2.3. Image processing
Here is a brief description of the main steps implemented in
BrainVISA for the reconstruction of the sulci http://brainvisa.info
(Mangin et al., 2004).
First, T1-weighted images were corrected for inhomogeneities and a
brain mask (grey matter GM and white matter WM) was created for
each image, based on the analysis of the histogram and a morphological
opening, before being segmented into left and right hemispheres, as
well as cerebellum. Next, the complement of the white matter, defined
as the space between the brain envelope (identified using a morphological closing) and the GM/WM boundary (identified from the intensities of the two tissues), was skeletonised to create a 3D print of
each sulcus. We thus obtained the 3D reconstruction of sulci for each of
the 23 HD patients and 18 healthy controls.
Various sulcal features can then be analysed, but here we focused on
two that are easily interpretable: depth and length of the sulcus.
Decrease of depth of sulci has been consistently reported in case of
neurodegeneration (with healthy ageing and Alzheimer’s disease), as
the sulci become more shallow as adjacent gyri degenerate
(Kochunov et al., 2005; Reiner et al., 2012; Hamelin et al., 2015). In
contrast, differences in length of the sulci are thought to relate to abnormal developmental processes (Auzias et al., 2014; Cachia et al.,
2014; Muellner et al., 2015).
As there is a substantial inter-subject variability in the shape and
location of the sulci, making a non-linear warping to standard space
approach not appropriate, the strategy here was to use the automatic
recognition of the sulci based on supervised learning from a database
created by neurosurgeons and using neural networks (Riviere et al.,
2002). This process relies on energy minimisation and in this specific
case three successive annealings, where we selected the one which
2. Methods
This study was part of the MIG-HD project (Multicentric
Intracerebral Grafting in Huntington’s Disease) and was approved by
the ethics committee of Henri Mondor Hospital in Créteil. All subjects
gave written informed consent.
2.1. Participants
Twenty-three HD patients (14 males, 9 females, 2 left-handed, aged
42 ± 8 years, range 25–54) were included from four different hospitals
(Nantes, Angers, Lille and Créteil). All were scanned using the same
scanner, in the same imaging centre in Orsay. To meet inclusion criteria, all had genetically proven HD, with an abnormal number of CAG
repeats ranging from 40 to 57 (46 ± 4). None had juvenile HD. They all
had clinical symptoms for at least 1 year and 15 were at stage I of the
disease according to their total functional capacity score (TFC ≥ 11)
(Shoulson and Fahn, 1979), i.e., they were autonomous and could
function fully both at work and at home (on average 10.9 ± 1.4, range
2
NeuroImage: Clinical 26 (2020) 102211
J.-F. Mangin, et al.
minimised best the system’s energy.
To create an additional variable, we manually delineated the striatal
regions on each axial plane of each individual T1-weighted scan, after
all the images were rigidly reoriented so that the anterior and posterior
commissures were located in the same axial plane (Douaud et al.,
2006). The accuracy of delineation was further checked in both sagittal
and coronal planes, and each striatal region was reconstructed in 3D to
control for the shape of each volume created. We then calculated the
asymmetry index of the striatal regions to further correlate with possible results showing a marked unilateral effect.
statistical model, as well as after normalising for intracranial volume
3.1. Results consistent with voxel-based findings of cortical atrophy
In line with the literature and our previous voxel-based results
based on the same HD population (18 out of 23 HD participants in
common) (Douaud et al., 2006), we found the strongest difference in
the left central sulcus, with a significant reduction of depth of more
than 8.8% in the HD patients (see Table 2, Figs. 1 and 2). The right
central sulcus depth was also found significantly reduced in HD
(−6.6%). The other sulcus significantly different bilaterally in the patients compared with the healthy controls was the intra-parietal sulcus,
which was shallower on the left by 8.3%, and on the right by 8.7%
(Table 2, Figs. 1 and 2).
2.4. Statistical analysis
We carried out an ANCOVA to compare sulci between the two populations, with diagnosis, age, and age by diagnosis interaction as
covariates to make the results easily comparable with a previous voxelbased study in this population (18 out of 23 HD patients in common)
(Douaud et al., 2006). Results were considered significant for P < 0.05 (two-tailed), corrected for false discovery rate (FDR) across all sulci (n = 57). We additionally checked that our sulcal results held when: 1. adding sex and handedness as additional covariates, 2. normalising for intracranial volume by calculating the residuals for depth and length after the linear contribution of the intracranial volume to the power 1/3 was removed (Sanfilipo et al., 2004). We further ensured that our results showing differences in the length of the sulci – presumably of developmental nature – were in fact not associated with disease burden ((nCAG-35.5) × age) or disease stage (TFC). To this effect, we calculated the correlation coefficient within the HD group between these two clinical measures and our imaging measures of length showing significant group differences. In addition, we investigated within the HD group whether any of our significant findings might be correlated a posteriori with their behavioural and clinical scores (Table 1) using Pearson correlation (with and without age added as a covariate of no interest), as well as with their striatal volumetric asymmetry for asymmetric finding. To account for multicollinearity of these scores, we reduced the set of clinical scores to those that did not share more than 50% of explained variance. Normality of the data was tested in R for every statistical analysis (using the Datamind software of BrainVISA) (Duchesnay et al., 2007). 3.2. Further cortical atrophy findings This individual measure approach further revealed significantly shallower left intermediate frontal sulcus, decreased by 12.9% in the patients, in line with the consistent cortical post mortem observation of dorso-lateral prefrontal cortex atrophy (Table 2, Figs. 1 and 3). The depth of right subparietal sulcus (in the precuneus) and left superior temporal sulcus were also found significantly decreased in the patients (Table 2). Remarkably, we also found a strong decrease in depth of the left calcarine fissure of 20.6% in the HD patients, despite this cortical area not projecting onto the basal ganglia (Table 2, Figs. 1 and 3). 3.3. Evidence for abnormality of neurodevelopment in HD Beyond these consistent findings of reduced sulcal depth pointing at cortical degeneration, the sulcal analysis also revealed a strong difference in the length of the posterior Sylvian fissure, with a length increased by 18.9% for the HD participants compared with healthy controls (Table 2, Figs. 1 and 4). This measure of the sulcal length, on the contrary to that of sulcal depth which is a probably marker of colocalised atrophy, is more likely the hallmark of an altered developmental process during the formation of the sulcal roots (Auzias et al., 2014; Cachia et al., 2014; Muellner et al., 2015). Investigating further the measure of length in the Sylvian fissure, it is clear that, in healthy controls, this fissure is in fact considerably shorter in the left hemisphere than the right (Fig. 5). Rather than simply seeing the finding in the left Sylvian fissure as a mere longer sulcus in the patients, it can thus be interpreted more appropriately as an almost complete absence of asymmetry for this sulcus in HD, asymmetry that is normally found in healthy participants (Fig. 5). The left Sylvian fissure is indeed shorter than the right by 18.8% in the healthy participants, compared with only 5.5% in HD. This absence of asymmetry is further maintained at the single-subject level, as the asymmetry index between left and right Sylvian fissure length, AI=(R-L)/0.5*(R + L), is significantly decreased (towards 0) in the HD group (P = 0.02, n = 41). 3. Results Several sulci were significantly abnormal in the HD patients (Table 2, Fig. 1, FDR-corrected). While most of the measures that differed between the two populations were atrophy-related, showing shallower sulci in HD (8 out of 9 of the significant findings), one measure could not be related to loss of grey matter volume seen in this neurodegenerative disorder: the length of the left posterior Sylvian fissure. These results held when adding sex and handedness to the Table 2 All significant (FDR-corrected) sulcal differences between HD (n = 23) and healthy controls (HC, n = 18). Sulci Side Feature HC Mean±std HD Mean±std P-value P-value*(Sex+Handedness) P-value⁎⁎(ICV) Central Sulcus L R L R L L R L L Depth Depth Depth Depth Depth Depth Depth Depth Length 22.6 ± 1.3 23.0 ± 1.2 25.1 ± 1.5 24.4 ± 1.8 17.1 ± 2.1 31.4 ± 5.9 14.3 ± 2.9 24.7 ± 2.4 282.7 ± 42.3 21.2 ± 1.1 21.0 ± 1.3 23.0 ± 1.8 22.2 ± 1.5 14.9 ± 1.5 24.9 ± 6.4 11.4 ± 2.8 22.7 ± 4.0 335.9 ± 58.1 4.0 × 10−4 4.6 × 10−6 2.9 × 10−5 3.0 × 10−4 3.5 × 10−5 1.5 × 10−4 2.0 × 10−4 7.3 × 10−4 3.2 × 10−4 1.9 × 10−3 3.0 × 10−5 1.3 × 10−4 4.4 × 10−4 2.6 × 10−2 1.1 × 10−3 5.6 × 10−3 1.6 × 10−3 8.9 × 10−4 1.8 × 10−2 2.1 × 10−3 5.2 × 10−4 2.1 × 10−3 3.2 × 10−2 1.2 × 10−2 5.5 × 10−3 2.6 × 10−2 1.4 × 10−3 Intra-parietal sulcus Intermediate frontal sulcus Calcarine fissure Subparietal sulcus Superior temporal sulcus Sylvian (lateral) fissure ⁎ ⁎⁎ Same analyses carried out adding sex and handedness as two additional covariates of no interest. Same analyses carried out on the residuals obtained after partialling out the effect of intracranial volume (ICV). 3 NeuroImage: Clinical 26 (2020) 102211 J.-F. Mangin, et al. Fig. 1. Visual representation of some of the sulci found the most different between healthy and HD participants (5 of 7). We show the sulci in the left hemisphere of one randomly selected healthy control: left, opaque cortex; right, partially transparent cortex to visualise the 3D conformation of the sulci, and those on the medial surface. The central sulcus appears in red, the intra-parietal sulcus in green, the posterior lateral fissure in dark blue, the intermediate frontal sulcus in light blue, and by transparency, the calcarine fissure in brown. While the results in the central sulcus and intra-parietal sulcus were bilateral, differences in the posterior lateral fissure, intermediate frontal sulcus and calcarine fissure were left-lateralised. Fig. 2. Bilateral results consistent with cortical atrophy in HD: shallower central and intra-parietal sulcus in HD. Top: Central Sulcus. Left, 3D rendering of the left central sulcus in one healthy subject. Middle and Right, maximal depth of the right and left central sulcus in the healthy controls (HC, n = 18, in blue circles, average in dark blue), and in the HD participants (n = 23, in magenta triangles, average in dark magenta) (a.u.). Bottom: Intra-Parietal Sulcus. Same representation as above. As the length of the left posterior Sylvian fissure seemed to be a hallmark of abnormal asymmetry in HD, we further investigated within this group if it was associated with their striatal volumetric asymmetry, as measured using careful manual segmentation of the subcortical structures (Douaud et al., 2006). We found that it was significantly correlated with such subcortical asymmetry (r23 = 0.49, 24% of variance explained, P = 0.017, Supplementary Figure 1). Finally, we established that the abnormal length of the Sylvian fissure in HD was not correlated with either disease burden (r21 = 0.08, P = 0.38) or TFC (r20 = 0.19, P = 0.21). 3.4. Post-hoc correlations with clinical scores We first reduced the set of scores to those that did not share more than 50% of explained variance (r > 0.70). This allowed us to assess
correlations between the significant sulcal findings of depth and length
with the Motor UHDRS, Behavioural UHDRS, Stroop Interference
(highly correlated with Stroop Word and Colour, and Digit Symbol),
Functional Assessment (highly correlated with Independence Scale),
TFC and Sum Fluency (where we summed the two runs, and which was
highly correlated with the MATTIS). Associations are summarised in
4
NeuroImage: Clinical 26 (2020) 102211
J.-F. Mangin, et al.
Fig. 3. Additional left-lateralised results consistent with cortical atrophy in HD: shallower intermediate frontal sulcus and calcarine fissure.Top:
Intermediate Frontal Sulcus. Left, 3D rendering of the left intermediate frontal sulcus in one healthy subject. Right, maximal depth of the left intermediate frontal
sulcus in the healthy controls (HC), and in the HD patients (a.u.). Bottom: Calcarine Fissure. Same representation as above.
Supplementary Table 1, but briefly: these showed an association between Stroop Interference and depth of the right intra-parietal sulcus
(r20 = 0.40, 16% of variance explained, P = 0.04), Functional
Assessment and depth of the left intermediate frontal sulcus
(r20 = −0.46, 21% of variance explained, P = 0.02), and Behavioural
UHDRS and depth of the left calcarine fissure (r20 = −0.52, 28% of
variance explained, P = 0.009). After regressing age out, we also found
an association between the sum of the fluency scores and the depth of
the left intermediate frontal sulcus (Supplementary Table 1).
Rosas et al., 2005). A previous global morphological study found a global
decrease of sulcal depth in HD (Nopoulos et al., 2007). Here, our results
might explain this global effect by showing a clear, localised decrease in
depth of the central and intra-parietal sulcus in both hemispheres, right
sub-parietal sulcus, and left intermediate frontal sulcus, calcarine fissure
and superior temporal sulcus. Second, as this sulcal morphometry approach may be able to differentiate underlying degenerative and developmental processes, it was further motivated by two recent in vivo studies
in gene carriers showing the first signs of abnormal development using
anthropometric measurements (Nopoulos et al., 2011; Lee et al., 2012).
Remarkably, our sulcal analysis revealed a substantial difference in the
imprint of the posterior Sylvian fissure, namely an absence of asymmetry
in the HD population between left and right hemispheres, suggesting a
very early insult to the developing neocortex.
A shallower central sulcus in our HD participants can be easily related
to the most consistent loss of cortical grey matter in the precentral and
postcentral gyri observed in a meta-analysis in HD (Dogan et al., 2013),
4. Discussion
This is the first study of sulcal morphology carried out in HD. The
motivation for this study was two-fold. First, it was prompted by a string of
published evidence that has established early cortical degeneration in HD,
whether in the same early HD population (18 out of 23 in common):
(Douaud et al., 2006), or even in premanifest HD: (Thieben et al., 2002;
5
NeuroImage: Clinical 26 (2020) 102211
J.-F. Mangin, et al.
Fig. 4. Evidence for abnormality of neurodevelopment in HD: longer left posterior Sylvian (lateral) fissure in HD. Left, 3D rendering of the left posterior
Sylvian fissure in one healthy subject. Right, length of the left posterior Sylvian fissure in the healthy controls (HC, n = 18, turquoise circles), and in the HD
participants (n = 23, mauve triangles) (a.u.).
not least in the same patients (Douaud et al., 2006). The depth of the
intraparietal sulcus was also significantly decreased bilaterally in the HD
participants, particularly so in the left hemisphere (Fig. 3). The left intraparietal sulcus is, together with the premotor and primary sensorimotor
cortex, the cortical region found to also discriminate best between premanifest and manifest HD in a meta-analysis (Dogan et al., 2013). In addition, the right intraparietal sulcus depth correlated in the patients with
the Stroop Interference (r20==0.40, Supplementary Table 1), a measure
of selective attention whose functional network is centred on the intraparietal sulcus (Hedden et al., 2012). When we also investigated, as an
additional analysis, the surface measure of the sulci, we found that the
strongest differences were found bilaterally in the intraparietal sulcus
(Supplementary Table 2). While mainly redundant (and less sensitive)
than the measure of sulcal depth, the surface of sulci solely revealed a
significant difference in the left olfactory sulcus, which might be linked to
the smell deficits consistently observed in HD (Paulsen et al., 2017).
Findings of a left-lateralised degeneration around the intermediate
frontal sulcus concur with the wealth of post mortem evidence on the
injury to the dorso-lateral prefrontal cortex e.g., (Hedreen et al., 1991;
Halliday et al., 1998). As it was not detected using VBM (Douaud et al.,
2006), this suggests that the method used here might be sensitive to
detect very early signs of prefrontal degeneration, which are typically
seen at later stages of HD (Rosas et al., 2008). For instance, total
functional capacity score (TFC) ranging from 1 to 13 was found to
correlate with left prefrontal areas (Rosas et al., 2008). In our predominantly stage I HD population, where TFC range was more limited,
Fig. 5. Evidence for abnormality of neurodevelopment in HD: absence of asymmetry in the posterior Sylvian fissure in HD. A. There is a natural asymmetry
between left and right length of the posterior Sylvian fissure in healthy controls (HC) (a.u.). In HC, the left fissure is shorter on average by almost 20%. By contrast,
there is almost no difference on average in the HD patients. For the patients, the left Sylvian fissure is only shorter by less than 6% on average. B.This absence of
asymmetry is also found at the single-subject level: the asymmetry index of the Sylvian fissure length is close to 0 in the HD patients.
6
NeuroImage: Clinical 26 (2020) 102211
J.-F. Mangin, et al.
we found similar associations specifically between the depth of the
intermediate frontal sulcus and the UHDRS measures o Functional Assessment (r20 = −0.46), and at a trend level with TFC (r20 = 0.31)
(Supplementary Table 1).
Decrease in depth of the left calcarine fissure could seem surprising at
first, as this part of the brain is not connected to the striatum. But it is in
fact a result consistent with in vivo surface-based studies of HD, where
degeneration was found in the occipital lobe and in particular around the
left calcarine fissure (Rosas et al., 2002; Rosas et al., 2008), as well as with
post mortem studies (Halliday et al., 1998). Indeed, while cortical degeneration in HD had been initially thought to be a secondary event due to the
striatal degeneration, it is more likely that both primary and secondary
degenerative processes co-exist in the cortex (Rub et al., 2015). This is
further supported by histopathological findings showing damage to layer
VI of the cortex that does not project to the striatum (Hedreen et al.,
1991). Of note, the decrease in depth in the calcarine fissure is the
strongest in terms of effect size (more than 20%) compared with all other
sulci found shallower in HD. Intriguingly, the depth of the left calcarine
fissure was correlated with the behavioural UHDRS score (r20 = −0.52,
Supplementary Table 1). This association might perhaps be related to the
association observed between behavioural symptoms – visual hallucinations and depression – and this specific region of the brain also seen in
Parkinson’s disease (Matsui et al., 2006; Hu et al., 2015).
Interestingly, this sulcal analysis also revealed an increase of nearly
20% in the length of the left posterior Sylvian fissure in HD compared with
the healthy participants. The consistent decrease of depth found in various
sulci are consistent with a neurodegenerative process, and thus mainly
consistent with volume-based and surface-based findings. A significant
difference in the length of one sulcus, on the contrary, is more difficult to
be interpreted, especially in light of the absence of colocalised atrophy,
and the lack of association with disease stage or burden, and age. As such,
it is more likely related to an altered development. This left peri‑Sylvian
region is for instance well known to be associated with functional language lateralisation and specialisation, although it did not correlate with
verbal fluency (Table 1), the only language-related measure available in
our HD population. Morphological anomalies in this brain region have
been found in population with neurodevelopmental disorders, such as
stuttering and in children with dyslexia (Foundas et al., 2004; Kibby et al.,
2004; Cykowski et al., 2008). It is also connected by white matter tracts
that are the only fibre bundles showing the effect of genetic associations
with handedness (Wiberg et al., 2019). However, as shown in the Results
section, healthy development typically leads to a strong asymmetry between the two hemispheres – in fact the strongest asymmetry found across
the entire cortex, as demonstrated for instance in preterm newborns
(Dubois et al., 2010). Our result in the posterior Sylvian fissure therefore
demonstrates an absence of asymmetry in HD, compared with normal development. Interestingly, differences in sulcal asymmetry have recently
demonstrated to be key in understanding differences in developmental
processes (Kloppel et al., 2010; Cachia et al., 2014; Leroy et al., 2015).
This altogether suggests that the abnormal length of the left posterior
Sylvian fissure in HD might bear the hallmark of an early, altered developmental process. As the formation of the Sylvian fissure appears early in
utero, and marked asymmetry is specifically found in this region in preterm newborns (Dubois et al., 2010), this likely indicates the foetal timing
of a disease-related genetic interplay with neurodevelopment. In our HD
population, the length of the left posterior Sylvian fissure was further
significantly associated with the striatal volumetric asymmetry, as measured using careful manual segmentation of the subcortical structures
(r23 = 0.49, 24% of variance explained, P = 0.017, Supplementary
Figure 1) (Douaud et al., 2006). Such striatal asymmetry in turn explains a
substantial part of the variance in two fundamental UHDRS measures in
our cohort: TFC (r20 = −0.49, 24% of variance explained, P = 0.027) and
Independence Scale (r19 = −0.59, 35% of variance explained,
P = 0.0075). It could thus be that the subcortical volume asymmetry seen
in the striatum of HD patients is both a combination of developmental and
degenerative processes.
Compared with a technique such as VBM, this specific sulcal approach cannot show precisely where some of the abnormalities might
be localised along a sulcus (e.g., dorsal vs. ventral part of the central
sulcus). Newest developments might be able to resolve these limitations
(Coulon et al., 2015). In any case, it revealed in the same HD population
(18 out of 23 in common), and using the same statistical model, significant differences in areas where the VBM analysis had failed to detect
a loss of volume or morphology: the right precuneus, as well as the left
dorso-lateral prefrontal cortex, primary visual cortex, superior temporal
cortex and peri‑Sylvian region (Douaud et al., 2006). Other VBM studies, possibly because of larger sample size or more advanced HD population, have in some cases demonstrated voxelwise differences in
those cortical regions where only our sulcal approach revealed abnormalities (Muhlau et al., 2007; Scahill et al., 2013; Minkova et al.,
2018). The sample size of this study is also limited, but we made sure to
only present in the main manuscript sulcal group differences surviving
correction for multiple comparisons (as an indication, top 20 results in
Supplementary Table 3). This relatively small sample size also meant
large effect sizes for our significant results, such as a difference of 19%
in length of the Sylvian fissure, or of 21% in the depth of the calcarine
fissure. Finally, another clear limitation is that our participants were
already symptomatic. Especially for the findings in the Sylvian fissure, a
study on (ideally young) gene carriers far from the onset of symptoms such as done in Lee et al. (2012) – should confirm the pre-existing
nature of this sulcal abnormality, and in particular of its distinctive
asymmetry in HD.
In summary, we used for the first time a detailed analysis of sulcal
morphology in HD. This approach, which precisely targets cortical
features, offers complementary sources of information, not only to
conventional voxel- and vertex-wise approaches, but also in how they
relate to different underlying physiopathological processes, and could
help detect subtle neurodevelopmental abnormalities that would
otherwise go unnoticed in other degenerative disorders with a genetic
susceptibility. It revealed in HD abnormalities consistent with a neurodegenerative process, but also importantly with an altered neurodevelopment. While the atrophy found in the left visual cortex adds to the
increasing wealth of data indicative of a primary cortical degeneration
in HD, this study provides, to the best of our knowledge, the first in vivo
indication of an interplay between disease and neocortical development.
CRediT authorship contribution statement
Jean-Francois Mangin: Conceptualization, Formal analysis,
Software, Writing – original draft. Denis Rivière: Formal analysis,
Software, Writing – original draft. Edouard Duchesnay: Formal analysis, Software. Yann Cointepas: Software. Véronique Gaura:
Resources. Christophe Verny: Resources. Philippe Damier:
Resources. Pierre Krystkowiak: Resources. Anne-Catherine
Bachoud-Lévi: Resources, Funding acquisition. Philippe Hantraye:
Supervision, Funding acquisition. Philippe Remy: Supervision,
Resources,
Funding
acquisition.
Gwenaëlle
Douaud:
Conceptualization, Resources, Investigation, Formal analysis, Funding
acquisition, Writing – original draft, Writing – review & editing.
Declaration of Competing Interest
The authors declare no competing financial interests.
Acknowledgements and Funding
G.D. is supported by the UK Medical Research Council (MR/
K006673/1). This study was part of the MIG-HD trial coordinated by A.C.B.-L. (Principal investigator) and granted through PHRCs AOM00139
and AOM 04021 from the DRCD (Assistance Publique- Hôpitaux de
Paris). We would like to thank the patients and their families.
7
NeuroImage: Clinical 26 (2020) 102211
J.-F. Mangin, et al.
Supplementary materials
L., Nopoulos, P., 2012. Measures of growth in children at risk for Huntington disease.
Neurology 79, 668–674.
Leroy, F., Cai, Q., Bogart, S.L., Dubois, J., Coulon, O., Monzalvo, K., Fischer, C., Glasel, H.,
Van der Haegen, L., Benezit, A., Lin, C.P., Kennedy, D.N., Ihara, A.S., Hertz-Pannier,
L., Moutard, M.L., Poupon, C., Brysbaert, M., Roberts, N., Hopkins, W.D., Mangin,
J.F., Dehaene-Lambertz, G., 2015. New human-specific brain landmark: the depth
asymmetry of superior temporal sulcus. Proc. Natl. Acad. Sci. U.S.A. 112, 1208–1213.
Mangin, J.F., Riviere, D., Cachia, A., Duchesnay, E., Cointepas, Y., Papadopoulos-Orfanos,
D., Scifo, P., Ochiai, T., Brunelle, F., Regis, J., 2004. A framework to study the cortical
folding patterns. Neuroimage 23 (Suppl 1), S129–S138.
Marder, K., Mehler, M.F., 2012. Development and neurodegeneration: turning HD pathogenesis on its head. Neurology 79, 621–622.
Matsui, H., Nishinaka, K., Oda, M., Hara, N., Komatsu, K., Kubori, T., Udaka, F., 2006.
Hypoperfusion of the visual pathway in parkinsonian patients with visual hallucinations. Mov. Disord. 21, 2140–2144.
Mehler, M.F., Gokhan, S., 2000. Mechanisms underlying neural cell death in neurodegenerative diseases: alterations of a developmentally-mediated cellular rheostat.
Trends Neurosci. 23, 599–605.
Minkova, L., Gregory, S., Scahill, R.I., Abdulkadir, A., Kaller, C.P., Peter, J., Long, J.D.,
Stout, J.C., Reilmann, R., Roos, R.A., Durr, A., Leavitt, B.R., Tabrizi, S.J., Kloppel, S.,
2018. Cross-sectional and longitudinal voxel-based grey matter asymmetries in
Huntington’s disease. Neuroimage. Clin. 17, 312–324.
Molero, A.E., Gokhan, S., Gonzalez, S., Feig, J.L., Alexandre, L.C., Mehler, M.F., 2009.
Impairment of developmental stem cell-mediated striatal neurogenesis and pluripotency genes in a knock-in model of Huntington’s disease. Proc. Natl. Acad. Sci.
U.S.A. 106, 21900–21905.
Muellner, J., Delmaire, C., Valabregue, R., Schupbach, M., Mangin, J.F., Vidailhet, M.,
Lehericy, S., Hartmann, A., Worbe, Y., 2015. Altered structure of cortical sulci in
gilles de la Tourette syndrome: further support for abnormal brain development.
Mov. Disord. 30, 655–661.
Muhlau, M., Weindl, A., Wohlschlager, A.M., Gaser, C., Stadtler, M., Valet, M., Zimmer,
C., Kassubek, J., Peinemann, A., 2007. Voxel-based morphometry indicates relative
preservation of the limbic prefrontal cortex in early Huntington disease. J. Neural
Transm. 114, 367–372.
Nopoulos, P., Magnotta, V.A., Mikos, A., Paulson, H., Andreasen, N.C., Paulsen, J.S.,
2007. Morphology of the cerebral cortex in preclinical Huntington’s disease. Am. J.
Psychiatry 164, 1428–1434.
Nopoulos, P.C., Aylward, E.H., Ross, C.A., Mills, J.A., Langbehn, D.R., Johnson, H.J.,
Magnotta, V.A., Pierson, R.K., Beglinger, L.J., Nance, M.A., Barker, R.A., Paulsen,
J.S., 2011. Smaller intracranial volume in prodromal Huntington’s disease: evidence
for abnormal neurodevelopment. Brain 134, 137–142.
Paulsen J.S., Miller A.C., Hayes T., Shaw E. (2017) Cognitive and behavioral changes in
Huntington disease before diagnosis. 144:69–91.
Regis, J., Mangin, J.F., Ochiai, T., Frouin, V., Riviere, D., Cachia, A., Tamura, M., Samson,
Y., 2005. “Sulcal root” generic model: a hypothesis to overcome the variability of the
human cortex folding patterns. Neurol. Med. Chir. (Tokyo) 45, 1–17.
Reiner, P., Jouvent, E., Duchesnay, E., Cuingnet, R., Mangin, J.F., Chabriat, H., 2012.
Sulcal span in Azheimer’s disease, amnestic mild cognitive impairment, and healthy
controls. J. Alzheimer’s Dis. 29, 605–613.
Riviere, D., Mangin, J.F., Papadopoulos-Orfanos, D., Martinez, J.M., Frouin, V., Regis, J.,
2002. Automatic recognition of cortical sulci of the human brain using a congregation
of neural networks. Med. Image Anal. 6, 77–92.
Rosas, H.D., Hevelone, N.D., Zaleta, A.K., Greve, D.N., Salat, D.H., Fischl, B., 2005.
Regional cortical thinning in preclinical Huntington disease and its relationship to
cognition. Neurology 65, 745–747.
Rosas, H.D., Liu, A.K., Hersch, S., Glessner, M., Ferrante, R.J., Salat, D.H., van der Kouwe,
A., Jenkins, B.G., Dale, A.M., Fischl, B., 2002. Regional and progressive thinning of
the cortical ribbon in Huntington’s disease. Neurology 58, 695–701.
Rosas, H.D., Salat, D.H., Lee, S.Y., Zaleta, A.K., Pappu, V., Fischl, B., Greve, D., Hevelone,
N., Hersch, S.M., 2008. Cerebral cortex and the clinical expression of Huntington’s
disease: complexity and heterogeneity. Brain 131, 1057–1068.
Rub, U., Seidel, K., Vonsattel, J.P., Lange, H.W., Eisenmenger, W., Gotz, M., Del Turco, D.,
Bouzrou, M., Korf, H.W., Heinsen, H., 2015. Huntington’s Disease (HD): neurodegeneration of Brodmann’s primary visual area 17 (BA17). Brain Pathol 25, 701–711.
Sanfilipo, M.P., Benedict, R.H., Zivadinov, R., Bakshi, R., 2004. Correction for intracranial
volume in analysis of whole brain atrophy in multiple sclerosis: the proportion vs.
residual method. Neuroimage 22, 1732–1743.
Scahill, R.I., Hobbs, N.Z., Say, M.J., Bechtel, N., Henley, S.M., Hyare, H., Langbehn, D.R.,
Jones, R., Leavitt, B.R., Roos, R.A., Durr, A., Johnson, H., Lehericy, S., Craufurd, D.,
Kennard, C., Hicks, S.L., Stout, J.C., Reilmann, R., Tabrizi, S.J., 2013. Clinical impairment in premanifest and early Huntington’s disease is associated with regionally
specific atrophy. Hum. Brain Mapp. 34, 519–529.
Shoulson, I., Fahn, S., 1979. Huntington disease: clinical care and evaluation. Neurology
29, 1–3.
Thieben, M.J., Duggins, A.J., Good, C.D., Gomes, L., Mahant, N., Richards, F., McCusker,
E., Frackowiak, R.S., 2002. The distribution of structural neuropathology in preclinical Huntington’s disease. Brain 125, 1815–1828.
Wiberg, A., Ng, M., Al Omran, Y., Alfaro-Almagro, F., McCarthy, P., Marchini, J., Bennett,
D.L., Smith, S., Douaud, G., Furniss, D., 2019. Handedness, language areas and
neuropsychiatric diseases: insights from brain imaging and genetics. Brain 142,
2938–2947.
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.nicl.2020.102211.
References
Auzias, G., Viellard, M., Takerkart, S., Villeneuve, N., Poinso, F., Fonseca, D.D., Girard,
N., Deruelle, C., 2014. Atypical sulcal anatomy in young children with autism
spectrum disorder. Neuroimage Clin. 4, 593–603.
Broca, P., 1878Broca P.. Anatomie comparée des circonvolutions cérébrales. Le Grand
Lobe Limbique et la scissure limbique dans la sèrie des Mammifères. Rev. Anthropol
1, 385–498.
Bruce, A., 1889Bruce A.. On the absence of the corpus callosum in the human brain, with
the description of a new case. Brain 12, 171–190.
Cachia, A., Borst, G., Vidal, J., Fischer, C., Pineau, A., Mangin, J.F., Houde, O., 2014. The
shape of the ACC contributes to cognitive control efficiency in preschoolers. J. Cogn.
Neurosci. 26, 96–106.
Cameron, A., 1907Cameron J.. A brain with complete absence of the corpus callosum. J.
Anat 41, 93–101.
Coulon O., Lefevre J., Kloppel S., Siebner H., Mangin J.-.F. (2015) Quasi-isometric length
parameterization of cortical sulci: application to handedness and the central sulcus
morphology. doi:10.1109/isbi.2015.7164105.
Cykowski, M.D., Kochunov, P.V., Ingham, R.J., Ingham, J.C., Mangin, J.F., Riviere, D.,
Lancaster, J.L., Fox, P.T., 2008. Perisylvian sulcal morphology and cerebral asymmetry patterns in adults who stutter. Cerebral Cortex 18, 571–583.
Dareste, C., 1852Dareste C.. Mémoire sur les circonvolutions du Cerveau chez les
mammifères. Ann Sci. Nat. 3eme Ser. Zool. 17, 34–54.
Dogan, I., Eickhoff, S.B., Schulz, J.B., Shah, N.J., Laird, A.R., Fox, P.T., Reetz, K., 2013.
Consistent neurodegeneration and its association with clinical progression in
Huntington’s disease: a coordinate-based meta-analysis. Neurodegener. Dis. 12,
23–35.
Douaud, G., Gaura, V., Ribeiro, M.J., Lethimonnier, F., Maroy, R., Verny, C., Krystkowiak,
P., Damier, P., Bachoud-Levi, A.C., Hantraye, P., Remy, P., 2006. Distribution of grey
matter atrophy in Huntington’s disease patients: a combined ROI-based and voxelbased morphometric study. Neuroimage 32, 1562–1575.
Dubois, J., Benders, M., Borradori-Tolsa, C., Cachia, A., Lazeyras, F., Ha-Vinh Leuchter,
R., Sizonenko, S.V., Warfield, S.K., Mangin, J.F., Huppi, P.S., 2008a. Primary cortical
folding in the human newborn: an early marker of later functional development.
Brain 2028–2041 131.
Dubois, J., Benders, M., Cachia, A., Lazeyras, F., Ha-Vinh Leuchter, R., Sizonenko, S.V.,
Borradori-Tolsa, C., Mangin, J.F., Huppi, P.S., 2008b. Mapping the early cortical
folding process in the preterm newborn brain. Cerebral Cortex 18, 1444–1454.
Dubois, J., Benders, M., Lazeyras, F., Borradori-Tolsa, C., Leuchter, R.H., Mangin, J.F.,
Huppi, P.S., 2010. Structural asymmetries of perisylvian regions in the preterm
newborn. Neuroimage 52, 32–42.
Duchesnay, E., Cachia, A., Roche, A., Riviere, D., Cointepas, Y., Papadopoulos-Orfanos,
D., Zilbovicius, M., Martinot, J.L., Regis, J., Mangin, J.F., 2007. Classification based
on cortical folding patterns. IEEE Trans. Med. Imag. 26, 553–565.
Foundas, A.L., Bollich, A.M., Feldman, J., Corey, D.M., Hurley, M., Lemen, L.C., Heilman,
K.M., 2004. Aberrant auditory processing and atypical planum temporale in developmental stuttering. Neurology 63, 1640–1646.
Halliday, G.M., McRitchie, D.A., Macdonald, V., Double, K.L., Trent, R.J., McCusker, E.,
1998. Regional specificity of brain atrophy in Huntington’s disease. Exp. Neurol. 154,
663–672.
Hamelin, L., Bertoux, M., Bottlaender, M., Corne, H., Lagarde, J., Hahn, V., Mangin, J.F.,
Dubois, B., Chupin, M., Cruz de Souza, L., Colliot, O., Sarazin, M., 2015. Sulcal
morphology as a new imaging marker for the diagnosis of early onset Alzheimer’s
disease. Neurobiol. Aging 36, 2932–2939.
Hedden, T., Van Dijk, K.R., Shire, E.H., Sperling, R.A., Johnson, K.A., Buckner, R.L., 2012.
Failure to modulate attentional control in advanced aging linked to white matter
pathology. Cerebral cortex 22, 1038–1051.
Hedreen, J.C., Peyser, C.E., Folstein, S.E., Ross, C.A., 1991. Neuronal loss in layers V and
VI of cerebral cortex in Huntington’s disease. Neurosci. Lett. 133, 257–261.
Hu, X., Song, X., Yuan, Y., Li, E., Liu, J., Liu, W., Liu, Y., 2015. Abnormal functional
connectivity of the amygdala is associated with depression in Parkinson’s disease.
Mov. Disord. 30, 238–244.
Huntington study group, 1996. Unified Huntington’s disease rating scale: reliability and
consistency. Mov. Disord. 11, 136–142.
Kibby, M.Y., Kroese, J.M., Morgan, A.E., Hiemenz, J.R., Cohen, M.J., Hynd, G.W., 2004.
The relationship between perisylvian morphology and verbal short-term memory
functioning in children with neurodevelopmental disorders. Brain Lang 89, 122–135.
Kloppel, S., Mangin, J.F., Vongerichten, A., Frackowiak, R.S., Siebner, H.R., 2010.
Nurture versus nature: long-term impact of forced right-handedness on structure of
pericentral cortex and basal ganglia. J. Neurosci 30, 3271–3275.
Kochunov, P., Mangin, J.F., Coyle, T., Lancaster, J., Thompson, P., Riviere, D., Cointepas,
Y., Regis, J., Schlosser, A., Royall, D.R., Zilles, K., Mazziotta, J., Toga, A., Fox, P.T.,
2005. Age-related morphology trends of cortical sulci. Hum. Brain Mapp. 26,
210–220.
Lee, J.K., Mathews, K., Schlaggar, B., Perlmutter, J., Paulsen, J.S., Epping, E., Burmeister,
8
MSCI 650
DISCUSSION ASSIGNMENT INSTRUCTIONS
The student will complete 2 Discussions in this course. The student will post one thread of at
least 750 words then post 2 replies of at least 350 words. For each thread, students must support
their assertions with at least 2 scholarly citations in APA format. Each reply must incorporate at
least 1 scholarly citation(s) in APA format. Any sources cited must have been published within
the last five years. Acceptable sources include the textbook, the Bible, and peer review journal
articles.
Behavioural Brain Research 372 (2019) 112069
Contents lists available at ScienceDirect
Behavioural Brain Research
journal homepage: www.elsevier.com/locate/bbr
Hearing loss as a risk factor for cognitive impairment and loss of synapses in
the hippocampus
Munyoung Changa,1, Haeng Jun Kimb,1, Inhee Mook-Jungb,c, Seung-ha Ohc,d,
T
⁎
a
Department of Otorhinolaryngology-Head and Neck Surgery, Chung-Ang University College of Medicine, 102 Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea
Department of Biomedical Sciences, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
c
Department of Biochemistry, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
d
Department of Otolaryngology-Head and Neck Surgery, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
b
A R T I C LE I N FO
A B S T R A C T
Keywords:
Hearing loss
Alzheimer’s disease
Dementia
Amyloid-β
Hippocampus
Although epidemiological studies have identiﬁed an association between hearing loss and cognitive impairment,
there is a lack of biological evidence detailing the mechanisms underlying this association. The present study
investigated the eﬀects of hearing loss on cognitive impairment using an at-risk model. In this animal model,
amyloid-β (Aβ) was administered to the brain to such an extent that it did not cause cognitive impairments but
made the brain vulnerable to risk factors. This study included four experimental groups based on hearing level
and Aβ administration. Behavioral tests were conducted to evaluate cognitive function, and synaptic protein
levels were measured in the hippocampus and prefrontal cortex. The group with hearing loss and Aβ administration showed signiﬁcantly greater deﬁcits on cognitive tests associated with the hippocampus than the other
three groups (only Aβ administration, only hearing loss, and without hearing loss or Aβ administration). The
hearing loss and Aβ administration group also had signiﬁcantly lower levels of synaptic proteins in the hippocampus than the other groups. The present results suggest that hearing loss may act as a risk factor for cognitive
impairment in Alzheimer’s disease. Additionally, the present ﬁndings indicate hearing loss may cause hippocampal synapses to be more vulnerable to Aβ-induced damage.
1. Introduction
In 2016, approximately 43.8 million people suﬀered from dementia
worldwide. Furthermore, the worldwide death rate associated with
dementia was 2.4 million people, which made it the ﬁfth leading cause
of death [1]. The leading cause of dementia is Alzheimer’s disease (AD)
[2] and, therefore, there is an urgent need for the development of
treatments for AD. Although much research has been conducted in this
area, the currently available treatments for AD have yet to achieve
signiﬁcant clinical eﬃcacy in that they can partially stabilize the
symptoms of this disease but not correct it [3].
It is also important to identify risk factors for AD, as this information
will allow us to develop methods preventing AD development or
slowing disease progression. Age, family history, and heredity are the
most important risk factors of AD [4] and can be used to predict its
occurrence. However, these factors cannot be modiﬁed and, thus,
cannot contribute to the prevention of AD. Recent epidemiological
evidence suggests that there is an association between hearing loss and
cognitive impairment [5–8] and other studies have shown that hearing
loss may be a potentially modiﬁable risk factor of AD [9]. Approximately one-third of elderly people 65 years of age and older have
hearing loss, which can be ameliorated by hearing aids and cochlear
implants. Therefore, if hearing loss is a risk factor of cognitive impairment and its mechanisms can be identiﬁed, then the treatment of
hearing loss can contribute to the prevention of AD. However, the
causal relationship between hearing loss and AD remains controversial.
For example, it has been suggested that the association between hearing
loss and AD exists due to diﬃculties in cognitive function tests that
patients with hearing loss experience due to poor verbal communication. Furthermore, the biological mechanisms that underlie this association have yet to be elucidated.
Thus, the present study employed animal models to investigate
Abbreviations: Aβ, amyloid-β; AD, Alzheimer’s disease; OPT, object-in-place task; OLT, object location task; NOR, novel object recognition task; ABR, auditory
brainstem response; NH-SA, normal hearing-subthreshold amyloid-β; deaf-SA, deaf-subthreshold amyloid-β; NH-NA, normal hearing-non amyloid-β; deaf-NA, deafnon amyloid-β
⁎
Corresponding author.
E-mail address: shaoh@snu.ac.kr (S.-h. Oh).
1
These authors contributed equally to this work.
https://doi.org/10.1016/j.bbr.2019.112069
Received 1 May 2019; Received in revised form 9 June 2019; Accepted 1 July 2019
Available online 02 July 2019
0166-4328/ © 2019 Published by Elsevier B.V.
Behavioural Brain Research 372 (2019) 112069
M. Chang, et al.
weeks after surgery and the Y-maze test was performed every 2 weeks
in all rats starting at 7 weeks after the surgery. The results of the ﬁrst
stage were used to determine the timepoints at which hearing loss induced a signiﬁcant eﬀect on cognitive impairment.
In the second stage, 26 rats were randomly divided into four experimental groups: a normal hearing-non Aβ group (NH-NA; n = 6)
that underwent a sham surgery but not infusion of subthreshold Aβ, a
normal hearing-subthreshold Aβ group (NH-SA; n = 6) that underwent
a sham surgery and the infusion of subthreshold Aβ, a deaf-non Aβ
group (deaf-NA; n = 7) that underwent bilateral cochlear ablation but
not infusion of subthreshold Aβ, and a deaf-subthreshold Aβ group
(deaf-SA; n = 7) that underwent bilateral cochlear ablation and the
infusion of subthreshold Aβ. The infusion of subthreshold Aβ for two
weeks began 9 weeks after surgery and cognitive tests including the Ymaze test, object-in-place task (OPT), object location task (OLT), and
novel object recognition task (NOR), were performed to all rats 11
weeks after surgery. After the cognitive function tests, tissue samples
were harvested from the hippocampus and prefrontal cortex. One animal in the deaf-SA group exhibited postural asymmetry when picked
up after the bilateral cochlear ablation and was excluded from the experiment. During the breeding period, one animal in the NH-SA group
and one animal in the deaf-SA group died. Ultimately, the NH-NA, NHSA, deaf-NA, and deaf-SA groups consisted of 6, 5, 7, and 5 animals,
respectively. We performed an additional experiment using another
nine rats to assess whether the animals preferred familiar or novel
objects in the NOR.
whether hearing loss would be a risk factor for AD and to assess the
mechanisms by which hearing loss may act as a risk factor. Because
several empirical cases and other evidence indicates that hearing loss
alone does not lead to cognitive impairment [10], a subthreshold
amyloid-β (Aβ) model of AD [11] was used in the present study. In this
model, Aβ is administered to the brain to such an extent that it does not
cause cognitive impairments but makes the brain vulnerable to risk
factors so that it might be possible to verify whether hearing loss would
be a risk factor for cognitive impairment.
2. Methods
2.1. Experimental design
This study was approved by the Institutional Animal Care and Use
Committee of Chung-Ang University (2016-00086) and Seoul National
University Hospital (16-0133-C1A0) and all experiments were conducted in accordance with relevant guidelines and regulations. Sevenweek-old male Wistar rats (200–250 g) were used and all animals were
adapted to laboratory conditions for 1 week prior to the start of the
experiment and housed in a temperature- and humidity-controlled
room with a 12 -h light:dark cycle with food and water available ad
libitum. Auditory brainstem response (ABR) recordings and surgical
procedures were performed under anesthesia induced by the intraperitoneal administration of ketamine hydrochloride (100 mg/kg;
Ketamine®, Yuhan Co.; Seoul, Korea) mixed with xylazine (10 mg/kg;
Rompun®, Bayer-Korea; Seoul, Korea).
The present study consisted of two stages: determining the time
course of cognitive decline following hearing loss and then evaluating
changes in cognitive function and synaptic protein levels after induction of the hearing loss (Fig. 1). In the ﬁrst stage, 10 rats were randomly
divided into two groups: a pilot-normal hearing-subthreshold Aβ group
(pilot-NH-SA; n = 5) that underwent a sham surgery and the infusion of
subthreshold Aβ and a pilot-deaf-subthreshold Aβ group (pilot-deaf-SA;
n = 5) that underwent bilateral cochlear ablation and infusion of subthreshold Aβ. The infusion of subthreshold Aβ for 2 weeks began 3
2.2. ABR recordings
ABR recordings were conducted in all rats before surgery and 1
week, 6 weeks, and 11 weeks after surgery to measure hearing levels.
ABRs on the left side were recorded with subdermal needle electrodes
between the left mastoid and the nape of the neck with the right
mastoid as the return while ABRs on the right side were measured by
reversing the direction of the electrodes. ABRs were recorded with highfrequency transducers (HFT9911–20–0035) and software (ver. 2.33)
Fig. 1. Experimental ﬂow of the ﬁrst (a) and second (b) stage.
Aβ, amyloid-β; NH-SA, normal hearing-subthreshold amyloid-β; deaf-SA, deaf-subthreshold amyloid-β; NH-NA, normal hearing-non amyloid-β; deaf-NA, deaf-non
amyloid-β.
2
Behavioural Brain Research 372 (2019) 112069
M. Chang, et al.
2.6.2. OPT, OLT, and NOR
The OPT, OLT, and NOR were conducted by modifying a previously
reported method [16]. Beginning 4 days before the tests, the rats were
placed in an open ﬁeld box (58 × 42 × 35 cm) without stimuli for
10–15 min daily. Each session consisted of familiarization and test
phases and either the type or location of the stimulus objects in the test
phase was diﬀerent from that in the familiarization phase. In the familiarization phase, the rats explored stimulus objects in the open ﬁeld
box for 5 min and were then returned to their home cage for a ﬁxed
amount of time (5 min for OPT and OLT and 3 h for NOR). Then, the
rats were placed in the box again and allowed to explore the stimulus
objects during the test phase. The experiment was video recorded in a
room without the experimenter and the recorded video was analyzed
later. Exploratory behavior was deﬁned as directing the nose toward an
object at a distance of less than 2 cm or touching the object with the
nose or paws. A discrimination ratio was calculated as follows: (exploration time with the changed object – exploration time with the
unchanged object) / (total exploration time with the changed and unchanged object). When exploration time was shorter than 15 s during
the familiarization phase or shorter than 10 s in the test phase, the data
were excluded from the analysis.
The test conditions are shown in Fig. S1. For the OPT familiarization
phase, four diﬀerent stimulus objects were placed in the corners of the
box (10 cm from the wall). During the OPT test phase, the positions of
two of the objects (which were both on the left or right of the box) were
switched. For the OLT familiarization phase, two identical objects were
placed in the corners of the box. During the OLT test phase, one object
was repositioned to the corner adjacent to its original position; thus, the
two objects were diagonal to each other. For the NOR familiarization
phase, two identical objects were placed in the corners of the box.
During the NOR test phase, one object was changed to a novel object.
Before the NOR, we performed an additional experiment to assess object bias. After adaptation to the open ﬁeld box, another nine rats explored the two objects (a familiar and a novel object) to be used in the
NOR test phase for 5 min. The durations of time spent exploring each
object were measured and compared.
from SmartEP (Intelligent Hearing Systems; Glenvar Heights, FL, USA)
and the responses were ampliﬁed (100,000×), band pass-ﬁltered
(100–1500 Hz), and averaged over 512 stimulus repetitions. Tone pips
of 8, 16, and 32 kHz were used as sound stimuli (5-ms duration, cos
shaping, 21 Hz) and stimulus intensity was reduced in 5 dB SPL decrements. Two researchers, blind to the experimental conditions, determined the lowest stimulus intensity that evoked a recognizable response, and that was regarded as the threshold.
2.3. Cochlear ablation
Cochlear ablation was performed on both sides as previously described [12]. Brieﬂy, after a retroauricular incision, the external auditory canal was opened and the tympanic membrane and ossicles, except
for the stapes, were removed. Then, a small hole was made on the bony
wall of the cochlea and the contents of the cochlea were ablated with a
dental pick. A small amount of soft tissue was packed into the small
hole on the bony wall of the cochlea. In the sham surgery, the same
operative procedure was performed before the point of opening the
external auditory canal.
2.4. Behavioral tests for vestibular deﬁcits
To exclude the eﬀects of vestibular function deterioration during
cochlear ablation, the behavioral test for vestibular deﬁcits was performed the day and week after surgery as previously described [13].
Brieﬂy, the behavioral scoring for vestibular deﬁcits consisted of three
components: postural asymmetry, head roll tilt, and nystagmus (Table
S1). If any deﬁcits were found in any of these three components, the
animal was excluded from the experiment.
2.5. Infusion of subthreshold Aβ
The Aβ peptide solution was continuously administered into the
intracerebroventricular space (160 pmol/day) for 2 weeks using a brain
infusion cannula (Brain Infusion Kit 2, Alzet; Cupertino, CA, USA) that
was connected to a mini-osmotic pump (Osmotic Pump 2002, Alzet).
The infusion cannula was implanted into the right cerebral lateral
ventricle (AP: −0.3, L: 1.2, V: 4.5) according to the coordinates of
Paxinos and Watson (2006) [14]. The composition of the Aβ peptide
solution, which does not induce cognitive impairment, has been described previously [11]. Brieﬂy, a Aβ1-42 peptide solution (AnaSpec
Inc.; San Jose, CA, USA) was dissolved in 35% acetonitrile/0.1% triﬂuoroacetic acid. The mini-osmotic pump was removed 2 weeks after
implantation, and the remaining volume of Aβ1-42 peptide solution
measured to conﬁrm that the expected volume had been delivered; we
subtracted the residual from the initial volume.
2.7. Western blot analysis
After completion of the behavioral tests, all animals were euthanized and brain tissue samples were harvested from the hippocampus and prefrontal cortex based on the coordinates of Paxinos and
Watson (2006) [14]. For the Western blot analyses, tissues from the
hippocampus and prefrontal cortex of all groups were lysed in a
radioimmunoprecipitation assay buﬀer (RIPA) buﬀer (iNtRON Biotechnology; Seoul, Korea) containing a protease inhibitor cocktail
(Sigma; St. Louis, MO, USA), protein phosphatase inhibitor cocktail (AG
Scientiﬁc; San Diego, CA, USA), and phenyl-methylsulfonyl ﬂuoride
(PMSF; Sigma). Then, the brain lysates were sonicated to ensure thorough lysis. The concentrations of the protein lysates were determined
with a BCA assay and an identical amount of protein from each sample
was electrophoretically separated by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) in 4–12% Bis–Tris gels and
then transferred to polyvinylidene diﬂuoride (PVDF) membranes. The
membranes were blocked in 5% non-fat dry milk in Tris-buﬀered saline
(TBS) and 0.1% Tween-20 (TBS-T) and then incubated with the following primary antibodies at 4℃ overnight: postsynaptic density protein 95 (PSD95; ab18258, Abcam; Cambridge, UK), synaptophysin
(mab268, Millipore; Burlington, MA, USA), Ca2+/calmodulin-dependent protein kinase II (CaMKII; ab52476, Abcam), phosphorylated
CAMKII (pCaMKII; 3361 s, Cell Signaling Technology; Danvers, MA,
USA), N-methyl D-aspartate receptor subtype 2B (NR2B; 06–600, Millipore), and α-tubulin (05–829, Millipore). Next, the membranes were
washed with TBS-T for 30 min and incubated with secondary IgG-HP
antibodies against each primary antibody for 1 h. Then, the membranes
were washed with TBS-T and incubated with an ECL chemiluminescent
2.6. Cognitive testing
2.6.1. Y-maze test
Cognitive function was assessed by recording spontaneous alternation behavior in a single session in the Y-maze; the protocol for this task
has been previously reported [15]. Brieﬂy, each arm of the maze was
40 cm long, 30 cm high, and 15 cm wide and converged in a central
triangle area. None of the animals had ever experienced a Y-maze before. All arms were brushed with 10% ethanol prior to each session to
remove the possible eﬀects of odor cues and the experimenter was not
in the room during testing. Each rat was placed on one arm tip of the Ymaze and then allowed to walk around the maze for 7 min without
restriction. Each session in the Y-maze was video recorded and analyzed
later. The rat was considered to have entered the arm when its hind
paws entered the arm and alternation was deﬁned as successive entries
into three arms based on overlapping triplets. The alternation percentage was calculated as follows: actual alternations / possible alternations (total number of arm entries minus two).
3
Behavioural Brain Research 372 (2019) 112069
M. Chang, et al.
reagent. Finally, peroxidase activity was detected with LAS 4000 (GE
Healthcare Life Science; Marlborough, MA, USA); the optical densities
were normalized with a standard protein.
2.8. Statistical analyses
IBM SPSS software version 21.0 (IBM; New York, NY, USA) was
used for all statistical analyses. ABR thresholds were analyzed with oneway analysis of variance (ANOVA) tests. Scores on the cognitive tests in
the ﬁrst stage of the experiment were analyzed with repeated measures
ANOVA tests and scores at each timepoint were analyzed with unpaired
two-tailed Student’s t-test. Scores on the cognitive tests in the second
stage of the experiment were analyzed with one-way ANOVAs and
paired t-tests. The results of the Western blot analyses were analyzed
with one-way ANOVAs. All post hoc testing was performed using
Tukey’s tests.
3. Results
3.1. ABR recordings
Prior to surgery, the baseline ABR thresholds at 8, 16, and 32 kHz
ranged from 20 to 35 dB SPL in all animals; these values did not diﬀer
signiﬁcantly among the groups (p > 0.05). At 1 week, 6 weeks, and 11
weeks after surgery, the ABR thresholds at 8, 16, and 32 kHz ranged
from 20 to 35 dB SPL in the NH group but were higher than 80 dB SPL
in the deaf group (Fig. 2).
3.3. Time course of cognitive decline following hearing loss
Fig. 3. Cognitive test results. (a) Time course of cognitive decline following
hearing loss. Y-maze scores were signiﬁcantly lower in the pilot-deaf-SA group
compared to the pilot-NH-SA group at 11 weeks after surgery. (b) In the Ymaze, OPT, and OLT tests, the deaf-SA group had signiﬁcantly lower scores than
the other three groups in the second stage of the experiment. All data are
presented as a mean ± SEM. (a) Unpaired two-tailed Student’s t-test at each
timepoint. (b, c) One-way ANOVA followed by Tukey’s post-hoc test.
*P < 0.05, **P < 0.01, ***P < 0.001. SA, sub-amyloid-β; NH, normal hearing; OPT, object-in-place task; OLT, object location task; NA, non-amyloid-β. In the ﬁrst stage of the experiment, the time course of cognitive decline following hearing loss was evaluated using the results of the Ymaze test (Fig. 3a). The inﬂuence of hearing loss was explored with a repeated measures ANOVA using the Y-maze scores across time as a repeated measure (7, 9, and 11 weeks after surgery) and the groups as ﬁxed factors. Mauchly’s test of sphericity indicated that the assumption 3.2. Dose of Aβ1-42 peptide solution delivered The daily volumes of delivered Aβ1-42 peptide solution ranged from 12.0 to 11.9 μL, corresponding to 161.0 to 159.0 pmoL/day of the Aβ142 peptide, similar to the anticipated volumes. The daily doses did not diﬀer signiﬁcantly between the groups (p > 0.05).
Fig. 2. ABR thresholds before surgery and 1 week, 6 weeks, and 11 weeks after surgery. (a) pilot-NH-SA group. (b) pilot-deaf-SA group. (c) NH-NA group. (d) NH-SA
group. (e) deaf-NA group. (f) deaf-SA group. Error bars indicate standard deviation.
ABR, auditory brainstem response; SPL, sound pressure level; NH, normal hearing; SA, sub-amyloid-β; NA, non-amyloid-β.
4
Behavioural Brain Research 372 (2019) 112069
M. Chang, et al.
hearing loss would act as a risk factor for AD and to identify the mechanisms underlying this association.
When planning the present experiments, it was important to consider that cognitive dysfunction will not be induced when only hearing
loss is present. The results of a follow-up study investigating cognitive
function in subjects who developed hearing loss in childhood reported
that long-term sensory impairment alone has a negligible eﬀect on one’s
overall level of cognitive function [10]. Therefore, the present study
compared cognitive function in animals with hearing loss and normal
hearing using a model of subthreshold Aβ, which has been published
under the name of the at-risk model [11]. This model is intended to
represent individuals with a predisposition for Aβ buildup but normal
cognitive function. Thus, it is possible to investigate whether certain
factors may be risk factors of AD.
In the present study, four experimental groups based on hearing
level and the subthreshold administration of Aβ were formed and
cognitive tests known to be related to speciﬁc brain regions were conducted. Cognitive tests associated with the hippocampus, such as the Ymaze, OPT, and OLT [16], revealed signiﬁcant decreases in cognitive
function in the deaf-SA group after hearing loss, as compared to the
other groups. However, there were no signiﬁcant diﬀerences among the
groups in the NOR. The hippocampus may aﬀect NOR results when the
time between the familiarization and test phases is extended [19,20].
However, others have reported that the hippocampus does not inﬂuence NOR results regardless of the time interval between the two phases
[16,21–24]. The discrepancies may be attributable to diﬀerences in the
methods used to eliminate hippocampal function and the experimental
conditions under which NOR was performed. A study that evaluated
NOR exactly as we did reported that the hippocampus did not aﬀect the
results [16]. Therefore, in our experiment, the hippocampus may not
aﬀect NOR results. Taken together, these results suggest that hearing
loss aﬀected the hippocampus and may be a risk factor for cognitive
impairment.
Comparisons of synaptic protein levels in the hippocampus between
the NH-NA and NH-SA groups revealed no signiﬁcant diﬀerences. These
results indicate that the subthreshold administration of Aβ did not affect synaptic protein levels in the hippocampus in normal hearing animals. The changes in synaptic protein levels in the hippocampus after
hearing loss mirrored the results of the cognitive testing: the deaf-SA
group exhibited a signiﬁcant decrease in synaptic proteins compared to
the other three groups. These data indicate that cognitive impairment
may be accelerated by the synergistic eﬀects of hearing loss and Aβ due
to synaptic loss. In the case of prefrontal cortical synaptic protein levels,
some proteins in the deaf-SA group exhibited a reduction but these
changes were not consistent and were not likely to be aﬀected by
hearing loss.
The present study demonstrated that hearing loss might act as a risk
factor for cognitive impairment in AD patients and that hearing loss
may cause hippocampal synapses to be more vulnerable to brain pathology. This ﬁnding indicates that there are connections between the
central auditory pathway and the hippocampus, which has been proposed in previous studies. For example, there are changes in the hippocampus following sound exposure [25–29] and the use of anterograde tracers revealed that the hippocampus receives signals from the
auditory cortex via the entorhinal cortex [30]. Therefore, degeneration
in the central auditory pathway induced by hearing loss [31,32] may
cause the degeneration of hippocampal synapses or make these synapses more vulnerable to damage. This hypothesis is supported by
ﬁndings showing that focal cortical infarction of brain regions that are
remote but connected to the hippocampus induce neuronal loss in the
hippocampus [33]. Further studies are needed to obtain solid conclusions.
The present study has several limitations that should be noted. First,
the development of hearing loss and Aβ deposition in the animal
models used in this study diﬀer from those in actual humans. In most
humans, hearing loss and Aβ deposition progress slowly and, therefore,
of sphericity for time had been violated (p = 0.453) and, therefore, the
results for time are reported using the Greenhouse-Geisser correction
(ε = 0.832). The Y-maze scores changed over time (p = 0.046) and
there was a signiﬁcant interaction between time and group (p = 0.032);
thus, the main eﬀects for group are reported at each timepoint. The Ymaze scores of the pilot-NH-SA and pilot-deaf-SA groups did not signiﬁcantly diﬀer at 7 or 9 weeks after surgery (p = 0.624 and p = 0.208,
respectively) but the Y-maze scores of the pilot-deaf-SA group were
signiﬁcantly lower than those of the pilot-NH-SA group at 11 weeks
after surgery (p = 0.014).
3.4. Cognitive function and synaptic maker protein levels after hearing loss
The cognitive testing results in the second stage of the experiment
are displayed in Fig. 3b and Table S2 and S3. The time spent by the
animals in exploration exceeded 15 s during the familiarization phases
and 10 s during the test phases of the OPT, OLT, and NOR. No animal
was excluded from the analysis. The total time spent exploring objects
during the familiarization and test phases of the OPT, OLT, and NOR
did not diﬀer among the groups (Table S2). During the familiarization
phases of the OPT and OLT, no signiﬁcant diﬀerences in the time spent
exploring objects that were switched and those not switched during the
test phases were apparent (Table S3). This was also the case for the
additional experiment of the NOR (21.8 ± 3.3 and 21.6 ± 4.0 s respectively, p = 0.852, paired t-test).
In the Y-maze, OPT, and OLT tests, the deaf-SA group had signiﬁcantly lower scores than the other three groups (p < 0.05, Fig. 3b). There were no signiﬁcant diﬀerences among the other three groups on those three tests and no signiﬁcant diﬀerences among all four groups in the NOR test. The present study also investigated molecular changes in the hippocampus and prefrontal cortex of all groups by quantifying synaptic protein levels with Western blot analyses. In the hippocampus, there were signiﬁcant decreases in NR2B and PSD95, which are post-synaptic markers, and synaptophysin, which is a pre-synaptic marker, levels in the deaf-SA group (Figs. 4a–d and S2) but no signiﬁcant changes in the other three groups. Additionally, there were no signiﬁcant changes in the phosphorylation levels of CaMKII (Fig. 4a and e). In the prefrontal cortex, PSD95 levels signiﬁcantly decreased in the deaf-SA group compared to the NH-NA and deaf-NA groups (Fig. 4f and i). Synaptophysin levels signiﬁcantly decreased in the NH-SA and deaf-SA groups compared to the NH-NA and deaf-NA group showed decreasing trends (Fig. 4f and h). The phosphorylation levels of CaMKII decreased in all other groups compared to the NH-NA group (Fig. 4f and j). NR2B levels in the prefrontal cortex did not signiﬁcantly diﬀer among the groups (Fig. 4f and g). 4. Discussion Although several epidemiological studies have suggested that hearing loss is a risk factor for cognitive decline [6–8,17], the underlying mechanisms remain unclear. Three representative hypotheses have been presented; they involve the eﬀects of hearing impairments on cognitive load and brain structure and decreased social engagement [18]. The cognitive load hypothesis suggests that auditory perceptual processing requires more cognitive resources when the auditory signal is degraded, which results in the degradation of other cognitive processes, such as working memory. Another hypothesis proposes that impaired auditory signals and reduced stimulation from an impaired cochlea cause changes in brain structure. This would make the brain more vulnerable to brain pathology-causing factors, such as Aβ accumulation, neuroﬁbrillary tangles, and microvascular disease, and lead to an increased risk of dementia. The third hypothesis suggests that cognitive function is degraded by social isolation due to hearing loss. However, few studies have provided evidence supporting these hypotheses. Thus, the present study attempted to determine whether 5 Behavioural Brain Research 372 (2019) 112069 M. Chang, et al. Fig. 4. Synaptic marker proteins are altered by hearing loss and Aβ infusion in the rat brain. Pre- and post-synaptic marker protein levels in the hippocampus decreased following hearing loss and Aβ infusion. (a) Representative images and (b–e) quantiﬁcational graphs (n = 5–7). Some pre- and post-synaptic marker protein levels in the prefrontal cortex decreased following hearing loss and Aβ infusion. (f) Representative images and (g–j) quantiﬁcational graphs (n = 5–7). All data are presented as a mean ± SEM. One-way ANOVA followed by Tukey’s post-hoc test. *P < 0.05, **P < 0.01, ***P < 0.001. NA, non-amyloid-β; SA, sub-amyloid-β; Aβ, amyloid-β. it will be necessary to develop a novel animal model in which hearing loss and Aβ deposition progress in a manner similar to that of humans. Second, the present study showed that there was a decrease in hippocampal synapses following hearing loss. However, the locations and roles of the degenerated synapses could not be identiﬁed and further research will be necessary to clarify these ﬁndings. (2019) 88–106. [2] C. Ballard, S. Gauthier, A. Corbett, C. Brayne, D. Aarsland, E. Jones, Alzheimer’s disease, Lancet 377 (2011) 1019–1031. [3] J. Cao, J. Hou, J. Ping, D. Cai, Advances in developing novel therapeutic strategies for Alzheimer’s disease, Mol. Neurodegener. 13 (2018) 64. [4] F. Panza, V. Solfrizzi, G. Logroscino, Age-related hearing impairment-a risk factor and frailty marker for dementia and AD, Nat. Rev. Neurol. 11 (2015) 166–175. [5] S. Behrman, L. Chouliaras, K.P. Ebmeier, Considering the senses in the diagnosis and management of dementia, Maturitas 77 (2014) 305–310. [6] J. Gallacher, V. Ilubaera, Y. Ben-Shlomo, A. Bayer, M. Fish, W. Babisch, P. Elwood, Auditory threshold, phonologic demand, and incident dementia, Neurology 79 (2012) 1583–1590. [7] K.M. Kiely, B. Gopinath, P. Mitchell, M. Luszcz, K.J. Anstey, Cognitive, health, and sociodemographic predictors of longitudinal decline in hearing acuity among older adults, the journals of gerontology, Series A, Biological sciences and medical sciences 67 (2012) 997–1003. [8] F.R. Lin, K. Yaﬀe, J. Xia, Q.L. Xue, T.B. Harris, E. Purchase-Helzner, S. Satterﬁeld, H.N. Ayonayon, L. Ferrucci, E.M. Simonsick, Hearing loss and cognitive decline in older adults, JAMA Intern. Med. 173 (2013) 293–299. [9] G.A. Gates, J.H. Mills, Presbycusis, Lancet 366 (2005) 1111–1120. [10] M. Vernon, Fifty years of research on the intelligence of deaf and hard-of-hearing children: a review of literature and discussion of implications, J. Deaf Stud. Deaf Educ. 10 (2005) 225–231. [11] T.T. Tran, M. Srivareerat, K.A. Alkadhi, Chronic psychosocial stress triggers cognitive impairment in a novel at-risk model of Alzheimer’s disease, Neurobiol. Dis. 37 (2010) 756–763. [12] S.K. Mun, K.H. Han, J.T. Baek, S.W. Ahn, H.S. Cho, M.Y. Chang, Losartan prevents maladaptive auditory-somatosensory plasticity after hearing loss via transforming growth factor-beta signaling suppression, Clin. Exp. Otorhinolaryngol. 12 (2019) 33–39. [13] M.Y. Chang, S. Park, J.J. Choi, Y.K. Kim, M.W. Suh, J.H. Lee, S.H. Oh, M.K. Park, Micro R.N.As 218a-5p, 219a-5p, and 221-3p regulate vestibular compensation, Sci. 5. Conclusions The present study showed that hearing loss may act as a risk factor for cognitive impairment in AD. Furthermore, hearing loss may make synapses in the hippocampus more vulnerable to damage that can result in brain pathology. Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT)(No. NRF-2016R1C1B2007131 to M.C.). References [1] E. Nichols, C.E.I. Szoeke, S.E. Vollset, N. Abbasi, F. Abd-Allah, Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016, Lancet Neurol. 18 6 Behavioural Brain Research 372 (2019) 112069 M. Chang, et al. temporal lobe, J. Neurosci. 24 (2004) 5901–5908. [25] F. Angelucci, M. Fiore, E. Ricci, L. Padua, A. Sabino, P.A. Tonali, Investigating the neurobiology of music: brain-derived neurotrophic factor modulation in the hippocampus of young adult mice, Behav. Pharmacol. 18 (2007) 491–496. [26] T.J. Goble, A.R. Moller, L.T. Thompson, Acute high-intensity sound exposure alters responses of place cells in hippocampus, Hear. Res. 253 (2009) 52–59. [27] K.S. Kraus, S. Mitra, Z. Jimenez, S. Hinduja, D. Ding, H. Jiang, L. Gray, E. Lobarinas, W. Sun, R.J. Salvi, Noise trauma impairs neurogenesis in the rat hippocampus, Neuroscience 167 (2010) 1216–1226. [28] D. Liu, W. Teng, Z. Shan, X. Yu, Y. Gao, S. Wang, C. Fan, H. Wang, H. Zhang, The eﬀect of maternal subclinical hypothyroidism during pregnancy on brain development in rat oﬀspring, Thyroid 20 (2010) 909–915. [29] B. Meng, S. Zhu, S. Li, Q. Zeng, B. Mei, Global view of the mechanisms of improved learning and memory capability in mice with music-exposure by microarray, Brain Res. Bull. 80 (2009) 36–44. [30] A. Mohedano-Moriano, P. Pro-Sistiaga, M.M. Arroyo-Jimenez, E. Artacho-Perula, A.M. Insausti, P. Marcos, S. Cebada-Sanchez, J. Martinez-Ruiz, M. Munoz, X. Blaizot, A. Martinez-Marcos, D.G. Amaral, R. Insausti, Topographical and laminar distribution of cortical input to the monkey entorhinal cortex, J. Anat. 211 (2007) 250–260. [31] M. Groschel, D. Basta, A. Ernst, B. Mazurek, A.J. Szczepek, Acute noise exposure is associated with intrinsic apoptosis in murine central auditory pathway, Front. Neurosci. 12 (2018) 312. [32] M. Groschel, R. Gotze, A. Ernst, D. Basta, Diﬀerential impact of temporary and permanent noise-induced hearing loss on neuronal cell density in the mouse central auditory pathway, J. Neurotrauma 27 (2010) 1499–1507. [33] J. Zhang, Y. Zhang, S. Xing, Z. Liang, J. Zeng, Secondary neurodegeneration in remote regions after focal cerebral infarction: a new target for stroke management? Stroke 43 (2012) 1700–1705. Rep. 7 (2017) 8701. [14] G. Paxinos, C. Watson, The Rat Brain in Stereotaxic Coordinates, Academic Press, Inc, Burlington, MA, USA, 2006. [15] A. Nitta, R. Murai, N. Suzuki, H. Ito, H. Nomoto, G. Katoh, Y. Furukawa, S. Furukawa, Diabetic neuropathies in brain are induced by deﬁciency of BDNF, Neurotoxicol. Teratol. 24 (2002) 695–701. [16] G.R. Barker, E.C. Warburton, When is the hippocampus involved in recognition memory? J. Neurosci. 31 (2011) 10721–10731. [17] F.R. Lin, E.J. Metter, R.J. O’Brien, S.M. Resnick, A.B. Zonderman, L. Ferrucci, Hearing loss and incident dementia, Arch. Neurol. 68 (2011) 214–220. [18] F.R. Lin, M. Albert, Hearing loss and dementia - who is listening? Aging Ment. Health 18 (2014) 671–673. [19] K.B. Baker, J.J. Kim, Eﬀects of stress and hippocampal NMDA receptor antagonism on recognition memory in rats, Learning & memory, Cancer Cells 9 (2002) 58–65. [20] S.J. Cohen, A.H. Munchow, L.M. Rios, G. Zhang, H.N. Asgeirsdottir, R.W. Stackman Jr., The rodent hippocampus is essential for nonspatial object memory, Curr. Biol. 23 (2013) 1685–1690. [21] S.E. Forwood, B.D. Winters, T.J. Bussey, Hippocampal lesions that abolish spatial maze performance spare object recognition memory at delays of up to 48 hours, Hippocampus 15 (2005) 347–355. [22] M.A. Good, P. Barnes, V. Staal, A. McGregor, R.C. Honey, Context- but not familiarity-dependent forms of object recognition are impaired following excitotoxic hippocampal lesions in rats, Behav. Neurosci. 121 (2007) 218–223. [23] R.F. Langston, E.R. Wood, Associative recognition and the hippocampus: diﬀerential eﬀects of hippocampal lesions on object-place, object-context and objectplace-context memory, Hippocampus 20 (2010) 1139–1153. [24] B.D. Winters, S.E. Forwood, R.A. Cowell, L.M. Saksida, T.J. Bussey, Double dissociation between the eﬀects of peri-postrhinal cortex and hippocampal lesions on tests of object recognition and spatial memory: heterogeneity of function within the 7

Order an Essay Now & Get These Features For Free:

Turnitin Report

Formatting

Title Page

Citation

Outline

Place an Order