Brace et al

Auditory responses in a rodent model of Attention Deficit Hyperactivity Disorder

Louise R. BraceA, Igor KraevA, Claire L. RostronA, Michael G. StewartA, Paul G. OvertonB

and Eleanor J. DommettA,C*

ADepartment of Life, Health and Chemical Sciences, The Open University, Milton Keynes.

MK7 6AA. U.K.

BDepartment of Psychology, University of Sheffield, Western Bank, Sheffield. S10 2TN.

U.K.

CDepartment of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s

College London, London. SE1 3QD. U.K.

* Corresponding Author

Department of Psychology,

Institute of Psychiatry, Psychology and Neuroscience,

King’s College London,

9th Floor, Capital House,

Guy's Campus,

42 Weston Street,

London.

SE1 3QD.

U.K.

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Email: Eleanor.dommett@kcl.ac.uk

Tel: 0207 848 6928

Keywords:

Spontaneously Hypertensive Rat; Superior Colliculus; Orienting; Distractibility; Hearing

loss; Validity

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Abstract

A central component of Attention Deficit Hyperactivity Disorder (ADHD) is increased

distractibility in response to visual and auditory stimuli, which is linked to the superior

colliculus (SC). Furthermore, there is now mounting evidence of altered collicular

functioning in ADHD and it is proposed that a hyper-responsive SC could mediate symptoms

of ADHD, including distractibility. In the present study we conducted a systematic

characterisation of the intermediate and deep layers of the SC in the most commonly used

and well-validated model of ADHD, the spontaneously hypertensive rat (SHR), building on

prior work showing increased distractible behaviour in this strain using visual distractors. We

examined collicular-dependent orienting behaviour, local field potential (LFP) and multiunit

activity (MUA) in response to auditory stimuli in the anaesthetised rat, and morphological

measures, in the SHR in comparison to the Wistar Kyoto (WKY) and Wistar (WIS). We

found no evidence of increased distractibility in the behavioural data but suggest that this

may arise due to cochlear hearing loss in the SHR. Furthermore, the electrophysiology data

indicate that the SC in the SHR may still be hyper-responsive, normalising the amplitude of

auditory responses that would otherwise be reduced due to the hearing impairment. The

morphological measures of collicular volume, cell density and ratios did not indicate this

potential hyper-responsiveness had a basis at the structural level examined. These findings

have implications for future use of the SHR in auditory processing studies and may represent

a limitation to the validity of this animal model.

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1. Introduction

Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental

disorder, affecting 8–12% of children (Biederman and Faraone, 2005), with symptoms often

persisting into adulthood (Spencer et al., 2002). A central feature of ADHD is increased

distractibility (Douglas, 1983; Thorley, 1984), which has long been considered one of the

most common symptoms of ADHD (Barkley and Ullman, 1975) and features in the

inattentive and combined presentations of ADHD under DSM-5 (APA, 2013).

Distractibility can be measured by examining changes in task performance or ongoing

behaviour as a result of the presentation of an extraneous or distracting stimulus (Bremer and

Stern, 1976). Dykman et al. (Dykman et al., 1970) presented a loud unpredictable tone (90 dB

SPL) during a reaction time task in which participants had to press a key in response to a

visual target. They reported greater increases in reaction times from hyperactive children in

comparison to non-hyperactive children, indicating increased distractibility to an auditory

stimulus in this group. Similar results were found by Bremer and Stern (1976) who assessed

distractibility during a reading task by repeated presentation of one of two types of distracting

stimuli: a telephone ringing (65 dB SPL) and flashing or a sinusoidal oscilloscope display

with an accompanying sound (75 dB SPL). They found that both types of multimodal stimuli

elicited similar reactions with hyperactive children significantly more distracted by the

stimulus. Hyperactive children showed orienting responses to a greater number of

consecutive stimuli and prolonged duration of response in comparison to the non-hyperactive

group. More recently, an increased distractibility in children with ADHD has been supported

by further behavioural findings and electrophysiological measures. Gumenyuk et al

(Gumenyuk et al., 2005) showed that a variety environmental ‘distractor’ sounds (e.g. rain,

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car horn; 200 dB SPL, 200 ms) enhanced the number of response omissions on a visual

discrimination task significantly more in ADHD children than controls and altered evoked

potential response components (P3a and LN) in such a way that the authors suggest there

could be a deficit in control of involuntary attention in ADHD. Finally, Cassuto et al (Cassuto

et al., 2013) showed that, on a visual continuous performance task (CPT), children with

ADHD were significantly distracted by visual distractors, auditory distractors and multimodal

(auditory and visual) distractors, whilst those without ADHD were only significantly

distracted by the multimodal distractors. The authors suggest that this difference in

distractibility on CPT could be a useful diagnostic tool for ADHD.

Behavioural evidence suggests that distractibility is intimately linked with the superior

colliculus (SC), a subcortical structure that is highly conserved across species (Ingle, 1973).

The SC is involved in detecting and responding to novel, unexpected and salient stimuli

across a range of modalities (Dean et al., 1989). In particular, it is responsible for orienting

head and eye movements (Grantyn et al., 2004) and covert attention towards such stimuli

(Rizzolatti et al., 1987). Work in a range of species has shown that collicular lesions cause a

decrease in distractibility (Goodale et al., 1978; Milner et al., 1978; Sprague and Meikle,

1965) whilst removal of prefrontal cortex inhibitory control of the colliculus leads to an

increase in distractibility in humans (Gaymard et al., 2003). This suggests that the SC

remains important in the neural basis of distractibility in humans.

Furthermore, there is now mounting evidence indicative of a role for the SC in ADHD.

Firstly, people with ADHD have difficulty inhibiting saccades (Klein et al., 2003; O'Driscoll

et al., 2005) and shifts in covert attention (Swanson et al., 1991), consistent with collicular

dysfunction (Ignashchenkova et al., 2004; Katyal et al., 2010; Robinson and Kertzman,

1995). Secondly, collicular dysfunction has been reported in rodent models of ADHD. For

example, in the spontaneously hypertensive rat (SHR), the most commonly used rodent

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model of ADHD, altered height dependency of air righting reflexes has been found (Dommett

and Rostron, 2011) which is linked to collicular dysfunction (Pellis et al., 1989; Pellis et al.,

1991; Yan et al., 2010). More recently, orienting behaviour to a repeated visual stimulus has

been shown to be increased in the SHR (Robinson and Bucci, 2014). In addition, in the New

Zealand Genetically Hypertensive (GH) rat, a proposed model of ADHD, increased

responsiveness to whole field light flashes has been found in the superficial layers of the

colliculus (Clements et al., 2014). Thirdly, amphetamine which is used to treat ADHD,

decreases the responsiveness of cells in the superficial layers of the colliculus to visual

stimuli (Clements et al., 2014; Gowan et al., 2008) and is associated with increased Fos-like

immunoreactivity in the intermediate/deeper layers (Hebb and Robertson, 1999a; Hebb and

Robertson, 1999b). Amphetamine also reduces distractibility in healthy rats (Agmo et al.,

1997) and humans both with (Brown and Cooke, 1994; Spencer et al., 2001) and without

ADHD (Halliday et al., 1990). Finally, the colliculus is known to modulate ascending

dopaminergic systems within the basal ganglia (Dommett et al., 2005) via a direct connection

from the colliculus to midbrain dopaminergic neurons (Coizet et al., 2003; Comoli et al.,

2003) and, therefore, alterations in collicular functioning could cause the dopaminergic

abnormalities seen in ADHD (Sagvolden et al., 2005a; Solanto, 2002; Viggiano et al., 2003).

Furthermore, connections to the basal ganglia, purported to act as a central selection device

(Redgrave et al., 1999) would enable the SC, amongst other structures, to specify actions by

putting ‘bids’ into the basal ganglia. Therefore, heightened activity within the SC may have

the effect of strengthening the bid to the basal ganglia and increasing the likelihood of the bid

‘winning’ over competing action choices and generating an output, such as an orienting

towards a stimulus (Grantyn et al., 2004).

In light of the mounting evidence supporting a role for the SC in ADHD and the previous

work in the SHR and the GH rat focussing on visual processing, we conducted a detailed

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characterisation of the SC, examining the auditory-responsive intermediate and deep layers,

in the SHR model of ADHD. Specifically, we hypothesized that the SHR would show

increased distractibility as measured by an auditory orienting task and increased

responsiveness to auditory stimuli at the neuronal level in the colliculus. Furthermore, we

hypothesized that there would be changes in the underlying morphology (collicular volume,

cell densities and neuron-glia ratio) of the colliculus.

2. Results

2.1 There were no strain differences in behavioural responses to auditory stimuli.

The majority of animals (89% SHR, 100% WKY and 88 % WIS) responded to the auditory

stimulus on the first presentation, as expected for a novel stimulus. Responsiveness of the

different strains remained similar across the consecutive presentations (Figure 1A) with the

survival analysis showing no significant difference in median survival time (SHR=9.58;

WKY=10.00; WIS=9.50) between strains (U(2)=25.0; p=0.882). In the 5 second periods

either side of the stimulus tone being on, animals were not responsive to the stimulus object

and this remained the case for all stimulus presentations. In terms of response duration, all

three strains spent a similar amount of time responding to the first stimulus presentation

(Figure 1B; SHR: 50.67±11.21% of total time or 2.53±0.56 s; WIS: 48.25±10.93% of total

time or 2.41±0.58 s; WKY: 52.00±7.48% of total time or 2.60±0.37 s). Repeated measures

ANOVA with STIMULUS PRESENTATION as the within-subjects factor and STRAIN as

the between-subjects factor was conducted using the percentage of overall time responding to

the stimulus as the dependent variable. There was a significant main effect of STIMULUS

PRESENTATION (F (5.61, 129.13)=8.01; p<0.001), with all animals spending significantly

less time responding to the stimulus at later stimulus presentations, with significant decreases

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in response duration compared to the first stimulus beginning at the sixth stimulus (F (1,

23)=10.51; p=0.004). By the final stimulus there was a highly significant time difference in

response duration relative to the initial response (F (1, 23)=65.54; p<0.001). There was no

main effect of STRAIN (F (2, 23)=1.05; p=0.365) or STIMULUS PRESENTATION x

STRAIN interaction (F(11.23, 129.13)=0.583; p=0.843).

Repeated measures ANOVA with TIME as the within-subjects factor and STRAIN as the

between-subjects factor was used to analyse locomotor activity for the four different

measures (distance travelled, vertical activity, average velocity and stereotypic activity Figure

2) in order to be sure that locomotor activity did not confound measures of distractible

behaviour. There was no main effect of STRAIN for average velocity (F(2, 23)=0.66;

p=0.528) or stereotypic activity (F(2, 23)=0.44; p=0.650). However, there was a main effect

of STRAIN for distance travelled (F(2, 23)=4.10; p=0.030), with post hoc (Tukey HSD)

analysis revealing that there was a trend towards the WKY moving significantly less distance

than the WIS (p=0.052) and SHR (p=0.056), and no significant difference between the WIS

and SHR (p=0.994). There was also a main effect of STRAIN on vertical activity (F(2,

23)=4.12; p=0.029), with post hoc (Tukey HSD) analysis showing that the SHR were

significantly more vertically active than WKY (p=0.023) but not the WIS (p=0.480). There

were no significant differences between WIS and WKY (p=0.265). As may be expected for

locomotor activity in a confined space, there was a main effect of TIME, with parameters

decreasing with increasing time within the chamber as the environment became familiar

through exploration for distance travelled (F (5, 115)=67.46; p<0.001), stereotypic activity

(F(5, 115)=31.57; p<0.001) and vertical activity (F(3.07, 70.71)=11.02; p<0.001). There was

no main effect of TIME on average velocity (F(3.63, 83.41)=2.38; p=0.064). There were no

significant TIME x STRAIN interactions for average velocity (F(7.25, 83.41)=1.02;

p=0.423), stereotypic activity (F(5, 115)=1.63; p=0.125) and vertical activity (F(6.15,

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70.71)=2.08; p=0.070). There was a TIME x STRAIN interaction for the distance travelled

(F(10, 115)=5.15; p<0.001). Restricted ANOVAs revealed this significant interaction to be

due to differences between the WIS and the other two strains in the first ten minutes with the

WIS showing a greater decrease during this period.

2.2 The SHR is less likely to respond to low intensity auditory stimulation and has lower

amplitude LFP responses and greater onset latency in multiunit responses.

Eighty-three auditory responses were recorded from the intermediate and deep layers of the

SC (Figure 3); 46 were positioned in the Intermediate Grey (InG; 14 SHR; 15 WIS; 17

WKY), 3 were recorded from the Intermediate White (InW; 1 SHR; 1 WIS; 1 WKY), with

the remaining 34 responses were recorded in the deep layers (Deep Grey, DpG; 10 SHR; 12

WIS; 10 WKY; Deep White DpWh; 2 SHR). Chi-square analysis showed there was no

significant association between strain and the layer from which recordings were made

(χ2(6)=4.58; p=0.60).

The percentage of animals showing responses at each of the five stimulus intensities is shown

by strain in Table 1. Chi-square analysis revealed a significant association between strain and

responsiveness for the lowest stimulus intensity in both local field potential responses

(χ2(2)=9.902; p=0.0.007) and multiunit activity (χ2(2)=6.696; p=0.035). Examination of the

effect size of these analyses showed that the association between strain and responsiveness

had a larger effect size for the LFP responses (φ=0.345) than the MUA (φ=0.284). Restricted

Fisher’s Exact tests showed that for the LFP response the WKY was more likely to respond

than the SHR (p=0.003) but not the WIS (p=0.282) and there was no significant strain

associations when only the SHR and WIS (p=0.089) were compared. The same pattern was

observed for the multiunit responses with the WKY more likely to respond than the SHR

(p=0.026) but not the WIS (p=0.102) and with no differences between the WIS and SHR

(p=0.758) in terms of responsiveness.

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Local field potential responses: In order to analyse the impact of stimulus intensity on local

field potential responses (example shown in Figure 4A) onset latency, peak-to-peak

amplitude and duration, data from the animals that responded to the highest three stimulus

intensities were analysed (SHR n=22; WKY n=24; WIS n=22) using repeated measures

ANOVA with STIMULUS INTENSITY as the within-subjects factor and STRAIN as the

between-subjects factor. These analyses revealed a significant main effect of STIMULUS

INTENSITY (F(2, 130)=5.44; p=0.005) for onset latency, with the only significant difference

being a decrease in latency between the 65 and 70 dB SPL (F(1, 65)=10.24; p=0.002). There

was no significant main effect of STRAIN (F(2, 65)=2.79; p=0.069) or STIMULUS

INTENSITY x STRAIN interaction (F (4, 130)=0.94; p=0.442) (Figure 4B). Analysis of

peak-to-peak amplitude revealed no significant main effect of STIMULUS INTENSITY

(F(1.78, 115.48)=2.67; p=0.080), but a significant main effect of STRAIN (F(2, 65)=8.46;

p=0.001). Post hoc Tukey (HSD) analyses revealed that the SHR had significantly smaller

responses than both the WKY (p=0.001) and WIS (p=0.012). The WKY and WIS did not

differ from each other (p=0.513). There was no significant STIMULUS INTENSITY x

STRAIN interaction (F (3.55, 115.48)= 1.99; p=0.108) (Figure 4C). The duration of the

responses is shown in Figure 4D. As with amplitude, there was no significant main effect of

STIMULUS INTENSITY (F(1.73, 112.59)=0.067; p=0.913), but there was also no

significant main effect of STRAIN (F(2, 65)=0.598; p=0.553) or STIMULUS INTENSITY x

STRAIN interaction (F(3.46, 112.59)=2.35; p=0.067).

Multiunit activity responses: As with the local field potential responses, in order to analyse

the impact of stimulus intensity on multiunit activity responses (example shown in Figure

5A) stimulus parameters of onset latency, peak amplitude and duration, data from the animals

that responded to the highest three stimulus intensities were analysed (SHR n=22; WIS n=22;

WKY n=24) using repeated measures ANOVA with STIMULUS INTENSITY as the within-

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subjects factor and STRAIN as the between-subjects factor. For onset latency, there was a

significant main effect of STIMULUS INTENSITY (F(1.56, 101.30)=6.372; p=0.002), with

significant decreases in latency with each consecutive increase in stimulus intensity (65-70

dB SPL: F(1, 65)=4.50; p=0.038, 70-75 dB SPL: F(1, 65)=8.82; p=0.004). There was also a

significant main effect of STRAIN (F(2, 65)=5.87; p<0.001). Post hoc (Tukey HSD) analysis

showed that the only significant differences where between the SHR and the WKY (p=0.010)

and the WIS (p=0.013) with the SHR have a greater onset latency in both cases. There was a

significant STIMULUS INTENSITY x STRAIN interaction (F(3.12, 101.30)=3.05; p=0.030)

(Figure 5B). Restricted ANOVAs with post-hoc Tukey (HSD) tests indicate that this

interaction is due to a reduction in strain differences with increasing stimulus intensity, such

that at the 65 dB SPL intensity there are significant strain differences (F(2)=5.08; p=0.009)

with the SHR having greater onset latency than the WKY (p=0.011) and the WIS (p=0.039),

which did not differ from each other. At the 70 dB SPL strain differences remained

(F(2)=5.18; p=0.008) but this time the SHR only differed from the WIS (p=0.007) and by the

final stimulus intensity of 75 dB SPL, there were no strain differences (F(2)=0.954);

p=0.391). For peak amplitude there was a significant main effect of STIMULUS

INTENSITY (F(2, 130)=6.29; p=0.002), with significant increases in amplitude with each

consecutive intensity (65-70 dB SPL: F(1, 65)=4.86; p=0.031, 70-75 dB SPL: F(1, 65)=7.93;

p=0.006; Figure 5C), but no significant main effect of STRAIN (F(2, 65)=1.89; p=0.159) or

STIMULUS INTENSITY x STRAIN interaction (F(4, 130)=0.589; p=0.671). The duration

of the responses is shown in Figure 5D. As with the LFP responses there was no significant

main effect of either STIMULUS INTENSITY (F(2, 130)=2.86; p=0.061) or STRAIN (F(2,

65)=0.234; p=0.792) and no significant interaction (F(4, 130)=2.12; p=0.082).

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2.3 There were no strain differences in morphological measures once volume was

controlled for.

Despite no significant differences in body weight between the three strains for these

experiments, analysis of whole brain volume using a One-Way ANOVA revealed a

significant difference between the three strains (F(2)=6.77; p=0.016), with post hoc Tukey

HSD analysis revealing that the SHR had significantly reduced brain volume relative to both

the WKY (p=0.046) and the WIS (p=0.018), making it necessary to normalise collicular

measures to whole brain volume. A One-Way ANOVA using volume fraction to assess the

volume of the intermediate and deep layers of the superior colliculus revealed that there was

no significant difference between strains (F(2)=1.19 p=0.35; Figure 6A). Analysis of the

absolute number of neurons and glia (Figure 6B) revealed a significant difference in the

number of neurons between strains (F(2)=4.89; p=0.025) and glia (F(2)=3.73; p=0.05). Post

hoc Tukey HSD analysis showed that for both cell types the significant differences were

between the WKY and the SHR (neuron p=0.029; glia p=0.041), with the WKY having a

higher number in both cases. However, these measures would have been confounded by the

differences in volume between the strains and therefore density and ratio were analysed. This

analysis showed that there was no significant differences in t the glia:neuron ratio (F(2)=1.02;

p=0.398 Figure 6C) or the density of neurons (F(2)=0.276; p=0.765) and glia (F(2)=0.136;

p=0.875) (Figure 6D).

3. Discussion

We hypothesized that the SHR would show increased distractibility as measured with a

simple orienting task using a repeated auditory stimulus. However, our results do not appear

to support this hypothesis, instead showing no significant differences between the three

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strains. Moreover, there were no effects within the locomotor activity that suggest altered

activity levels between the strains could have masked any changes in the orienting behaviour.

Before discussing possible reasons for the lack of differences in orienting behaviour, it is

pertinent to comment briefly on what the locomotor activity data reveals about the SHR in

terms of demonstrable hyperactivity and appropriate choice of control strain, a matter that has

been extensively debated (Sagvolden et al., 2009). Despite the generally well-accepted

validity of the SHR in displaying hyperactivity, it has been argued that they only show

hyperactivity when compared to the WKY (van den Bergh et al., 2006) because the latter

strain are particularly inactive and at certain ages. We found only a few significant strain

differences in locomotor activity. The WKY did cover less distance than the SHR and the

WIS, giving some support to the idea that the WKY is showing abnormally less activity

rather than the SHR showing abnormally more. However, we also found that the SHR

showed great vertical activity than the WKY, with the WIS having intermediate levels of

activity, which did not differ significantly from either of the other two strains. Therefore the

present findings do not allow any firm conclusions to be made regarding the presentation of

hyperactivity or the usefulness of one control strain over another but do support the need for

careful interpretation of data and consideration of using more than one control strain in

studies with the SHR.

Returning now to the main behavioural finding that there were no strain differences in

orienting behaviour; we suggest a number of reasons for this. The previous studies conducted

using people with ADHD (Bremer and Stern, 1976; Cassuto et al., 2013; Dykman et al.,

1970; Gumenyuk et al., 2005) all had participants engaged in a specific visual task prior to

exposure to an auditory or multimodal distractor stimulus. In the present study the SHR were

freely moving around a circular arena and therefore engaging in spontaneous rather than task

directed behaviour. Furthermore, the animals remained in the arena almost one hour (10

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consecutive stimuli with a 5 minute inter-stimulus interval) meaning it is likely that the

general exploratory behaviour would have decreased quite quickly, reducing any activity

from which the animal could be distracted. Our locomotor activity recorded in the activity

monitoring chambers shows a plateauing of activity within around 15 minutes and similar

familiarisation to the environment would have been likely in the orienting test arena.

Although the lack of a focussed task, visual or otherwise, from which the animal may be

distracted is a possibility, it does seem unlikely to fully explain the lack of effects, because

we have recently shown, using the same paradigm but with a visual distractor, that the SHR is

significantly more distracted by the stimulus than both control strains (Brace et al., 2015)

indicating heightened distractibility (both in terms of frequency of orienting and duration of

response) even when no specific task is being conducted. Furthermore, the same study found

corresponding changes in collicular physiology and morphology.

Another possible reason for the lack of effects is the animals used in the present study. They

were 15-20 weeks of age at the start of the study and it is possible that this impacted

negatively on the study because this is older than is often used in studies relating to ADHD

with SHR at this age will having significant hypertension (Bell et al., 2004; Gray, 1984;

Kokubo et al., 2005; van den Buuse and de Jong, 1988). However, despite much work being

done in juvenile SHR, there is strong evidence that at the age used in the present study the

SHR still models ADHD. For example, evidence shows increased activity levels can persist

into adult hood with one study showing that this is still present at 12 weeks of age (Ferguson

and Cada, 2003). Moreover, whilst there is evidence that alterations in impulsivity and

attention are present substantial beyond the aged used in the present study with data from

SHR at 5-7 months and (De Bruin et al., 2003), 15 months (Evenden and Meyerson, 1999).

Finally, and critically for the present study, we have already shown increased distractibility to

visual stimuli in animals of the same age to those used in the present study (Brace et al.,

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2015). Therefore, it seems unlikely that the age of the animals has impact negatively on the

ability to model ADHD in general or distractibility to other types of stimulus. However it is

possible that the age of the animals has resulted in an auditory specific deficit with the lack of

increased responsiveness in the SHR caused by the detection of auditory stimuli being

impaired due to hypertension-induced hearing loss in the SHR which can enhance age

induced hearing loss – something that has been reported in a healthy rat strain from as young

as 20 weeks of age (Li et al., 2003) and therefore may be present in the animals used in the

current study.

A number of studies using rats ranging from 12 to 48 weeks of age have found that SHR have

reduced cochlear sensitivity in contrast to both WKY and WIS, which is thought to be due to

a reduced number of viable sensory hair cells in the cochlear (Li et al., 2003; McCormick et

al., 1982; Rarey et al., 1986; Tachibana et al., 1984). If the SHR is less sensitive to auditory

stimuli in general it is likely that this lack of sensitivity could mask any increase in

responsiveness present in this strain which has previously been shown to be more distractible

in response to visual stimuli on a similar task (Robinson and Bucci, 2014). This is, in part,

supported by a comparison between our study and previous work examining SHR

behavioural responses to auditory stimuli. The present study found no strain differences

whilst the limited number of previous studies has found reduced responsiveness. Hard et al

(Hard et al., 1985) compared auditory startle in the three strains employed in the present

study. They found that the SHR showed reduced auditory startle in comparison to both

control strains and interpreted this as a lack of fear in the SHR. Similar findings and

interpretation were shown by Sutterer et al (Sutterer et al., 1988) who introduced a strain

intermediate to the SHR and WKY called Borderline Hypertensive Rats (BHR) and found

that the SHR and BHR showed reduced auditory startle in comparison to the WKY. In both

cases, it is possible to that the startle response was confounded by hearing loss. The fact that

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we did not find reduced responsiveness on the behavioural task allows us to tentatively

speculate that the effect of hearing loss which would have caused a reduction in

responsiveness may have been partially mitigated by increased distractibility.

If the above speculation were correct, we would expect to observe differences in the SHR

responses collected using LFP and MUA relative to control strains. Specifically we would

expect to see a reduction in LFP response in the SHR because this is thought to best represent

peri-synaptic activity, including post-synaptic potentials (Ekstrom et al., 2009; Logothetis,

2008) and therefore would be decreased due to the reduced cochlear sensitivity. In contrast

we would expect to find no significant difference in MUA in the SHR, indicative of a hyper-

responsive SC compensating for the reduced input. Our findings partially support this

because we did show that the SHR were less likely to produce an LFP response to a low

intensity stimulus (55 dB SPL) in contrast to the WKY, but not the WIS, and had reduced

peak-to-peak amplitude across all intensities in comparison to both control strains. However,

we also found a reduction in likelihood of response in the MUA, albeit to a lesser extent, as

indicated by the effect size (at 55 dB SPL). In addition, there were no strain differences in

amplitude of MUA response. This indicates that any hyper-responsiveness in SC did not

offset any reduced input in the SHR in terms of whether a response is produced to low

intensity stimuli but where responses are produced with comparable likelihood to control

strains, it may compensate for the reduced input and give a ‘normal’ collicular response in

terms of amplitude. It is noteworthy to clarify that the behavioural paradigm used a 70 dB

SPL stimulus and the differences described for the electrophysiology paradigm were only

found at the lower stimulus intensity of 55 dB SPL and not the 70 dB SPL which produced

normal LFP data implying normal auditory input at this higher level. This could suggest that

any hypertension induced hearing loss is not relevant to our behavioural results, however, it is

important to recognise that the two different paradigms cannot be directly compared in terms

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of precise stimulus intensities because of the different proximity with which the auditory

stimulus was applied; in the behavioural paradigm the stimulus is transmitted within an arena,

whilst in the electrophysiological one it is applied directly into the auditory canal via

modified ear bars. Therefore the lower intensity stimuli within the electrophysiology

paradigm are likely comparable to the higher stimulus in the behavioural paradigm.

In addition to differences in responsiveness, the SHR MUA responses also showed delayed

onset latency, relative to both other strains. This delayed response onset could be considered

consistent with ADHD being a developmental disorder because it is known that during

development transmission within visual afferent input to the rat (Reece and Lim, 1998) and

human (Crognale et al., 2001) SC increase in speed, giving rise to quicker onset latencies

within the SC as this system develops. To date, however, there is no research into whether the

same may be true of afferent auditory inputs which arise predominantly from the inferior

colliculus in the rat (Cadusseau and Roger, 1985; Edwards et al., 1979; Van Buskirk, 1983)

and it is acknowledged that findings from other species cannot be used to deduce

development patterns of these afferents due to significant inter-species differences (Nodal et

al., 2005). We found no differences in any morphological measures once differences in

overall brain volume were accounted for and therefore if the SC is hyper-responsive in the

SHR it cannot be explained by features at this level. Within the superficial layers of the

superior colliculus, serotonin serves to modulate visual responses (Mooney et al., 1996).

However, it is less clear if any single transmitter fulfils this function in the intermediate and

deep layers for auditory stimuli, although GABA is known to be critical in the development

and maintenance of the auditory map (Brandao et al., 2005; Ingham et al., 1998) within the

colliculus and therefore alterations to GABAergic transmission may be useful starting point

in elucidating the alterations in the SHR. Another, and not necessary mutually exclusive,

mechanism worthy of investigation builds on work investigating the impact of cochlear

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ablation on the superior colliculus in rats (Alvarado et al., 2009) which has found rapid

increases in calretinin, a calcium-binding protein important for the homeostasis of

intracellular calcium concentration in the auditory pathway. The authors suggest that the

increase in calretinin, which is known to be activity dependent within the colliculus, could be

due to an increase in the anatomical connections between the colliculus and auditory

afferents. Future research is therefore needed to examine some of these possibilities.

An alternative explanation for the lack of clear evidence for increased distractibility in the

SHR could be that the model lacks validity in modelling this specific element of ADHD,

although it should be noted that any lack of validity could arise from the hearing loss

described meaning that it could relate only to auditory stimuli, indeed this would be

supported by our recent work showing heightened distractibility to visual stimuli (Brace et

al., 2015) . Studies to date indicate good face validity of the SHR with this strain exhibiting

core behaviours of ADHD: hyperactivity in familiar environments that diminishes with age

(Arime et al., 2011; Sagvolden et al., 1992; Sagvolden et al., 2005b), long lasting impulsivity

and inattention (Bizot et al., 2007; De Bruin et al., 2003; Evenden and Meyerson, 1999; Fox

et al., 2008; Jentsch, 2005; Sagvolden et al., 2005b) and increased distractibility in response

to visual distractors (Brace et al., 2015; Robinson and Bucci, 2014). Furthermore, in the SHR

– as in ADHD - the development of these behaviours is poorly correlated and they are

affected differently by medications, indicating that they are separate behavioural dimensions

(Lahey and Willcutt, 2010; Sagvolden et al., 2005b; Sagvolden, 2006; Sagvolden and Xu,

2008). Therefore, a failure to show distractibility following auditory distractors could be

viewed as a very specific challenge to the face validity of this model, but not one that need

prevent its use to model other elements of the condition. The SHR displays good construct

validity with abnormalities in the systems thought to be dysfunctional in ADHD, the

dopaminergic and noradrenergic systems (Biederman, 2005): reduced TH expression in the

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neostriatum and nucleus accumbens (Akiyama et al., 1992); reduced electrically or K+-

stimulated dopamine release in the striatum and frontal cortex (de Villiers et al., 1995;

Linthorst et al., 1990; Russell et al., 1998; Russell, 2000; van den Buuse et al., 1991), reduced

dopamine reuptake in the same areas (Fan et al., 2012), alterations to the D2-autoreceptor

(Linthorst et al., 1990; Russell, 2000; van den Buuse et al., 1991), increased basal release of

noradrenalin (de Villiers et al., 1995), reduced noradrenergic autoreceptor inhibitory

feedback(Russell, 2002; Tsuda et al., 1990), decreased noradrenalin efflux in the frontal

cortex and increased noradrenalin reuptake (Myers et al., 1981). The weakest area of validity

in the SHR to date is that of predictive validity i.e. the amelioration of signs of the disorder

by medications effective in ADHD were some studies support validity (Myers et al., 1982;

Sagvolden et al., 1992; Sagvolden and Xu, 2008; Wultz et al., 1990) and others do not

(Dommett, 2014; Turner et al., 2013; van den Bergh et al., 2006). The present findings do not

impact on the construct and predictive validity of the model but they do indicate that

researcher’s conducting behavioural tests using the SHR need to carefully consider modality

of stimulus presented. This, when combined with previous strain-specific issues identified for

working with this strain (Dommett and Rostron, 2013; Dommett, 2014) in the context of

ADHD, indicate that great care is needed when employing the SHR as am ADHD model.

4. Conclusion

In summary, the present study has not found conclusive evidence for increased distractible

behaviour or physiological responses following the presentation of an auditory stimulus in the

SHR model of ADHD, with reference to two control strains. This contrasts with what would

be expected based on previous studies on individuals with ADHD (Bremer and Stern, 1976;

Cassuto et al., 2013; Dykman et al., 1970; Gumenyuk et al., 2005) and work in rodent models

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using visual distractor stimuli (Brace et al., 2015; Clements et al., 2010; Robinson and Bucci,

2014). We suggest that this discrepancy may arise due to the reduced cochlear sensitivity in

the SHR, something that is likely to confound any studies employing auditory stimuli in this

strain and therefore should be carefully considered in any future studies. Irrespective of the

exact cause of the lack of increased distractibility, the results of the present study also

represent a challenge, albeit it one focussed on auditory processing, to the validity of the SHR

as a model for ADHD. This challenge, combined with previous challenges to the validity of

the SHR, do not negate the value of this model but highlight the need to recognise its

limitations.

5. Experimental Procedures

5.1 Animals

All experiments were conducted with the authority of the appropriate U.K. Home Office

Licenses and adhered to guidelines set out in the Animals [Scientific Procedures] Act (1986),

EU Directive 86/609/EEC, and the "Guide for the care and use of Laboratory Animals” (NIH

publication, 8th ed, The National Academies Press, Washington, 2011). Adult male rats

(Harlan Laboratories Ltd, Bicester, U.K) aged 15-20 weeks at the start of testing were housed

within the Biomedical Resource Unit (BRU) at the Open University. All rats were housed in

groups of three (of the same strain) within scantainers held at a constant temperature of 21-23

°C. The holding room was on a 12:12hr reverse light/dark cycle with lights off at 8am. Rats

were given one week to habituate to the BRU prior to use in any experimental procedures.

All procedures were carried out in the dark phase and therefore at the time when rats are most

active. Food and water were available ad libitum throughout. The importance of an

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appropriate control strain for the SHR is widely recognised (Sagvolden et al., 2009), and as

such, we selected both the Wistar Kyoto (WKY), the normotensive control commonly used

but also shown to have some abnormal behaviours in itself (Drolet et al., 2002; van den

Bergh et al., 2006), and the Wistar (WIS) as an outbred albino control strain. The sample

sizes and weight at start of data collection for the different experimental procedures for the

three strains is shown in Table 2.

Despite the animals being the same age at the start of the data collection phases for each of

the experimental procedures there were significant differences in weight for the behavioural

(F(2)=8.39; p=0.002) and physiological measures (F(2)=28.19; p=0.0005). In both cases, post

hoc (Tukey HSD) analysis revealed that the only significant differences were between the

WIS and the WKY (behaviour p=0.002; physiological p=0.0005) and the WIS and the SHR

(behaviour p=0.026; physiological p=0.0005), with the WIS weighing more than both other

strains. There was no significant difference in weight between the strains for the

morphological procedures (volume F(2)=3.14; p=0.092; cell densities and ratios F (2)=1.33;

p=0.311).

5.2 Behavioural testing

Auditory distractibility was measured using an orienting task with an auditory stimulus. Both

the initial response to the stimulus and subsequent habituation to repeated presentation of the

stimulus were examined as has been conducted previously with visual stimuli (Brace et al.,

2015; Clements et al., 2010; Robinson and Bucci, 2014). All testing was carried out between

the hours of 9am and 5pm in a dimly red-lit room in the presence of white noise and with

careful removal of olfactory cues from testing equipment between test sessions to remove any

extraneous cues that could affect behaviour. Prior to testing, animals were habituated to the

experimenter with daily handling for one week. In addition, they were habituated to the

testing space, a circular plastic arena (2.5 m diameter) with a centrally speaker (8 KHz, 70 dB

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SPL) sealed within a clear Perspex cylinder, for two days prior to testing. On each

habituation day the animal was placed in the arena for 15 minutes with the stimulus tone

remaining off for the entire period. Testing began on the third day with the animal placed in

the arena and the video camera started (Samsung VP-HMX20C camcorder). After 5 minutes,

the tone was remotely switched on for a period of 5 seconds. This was repeated for a total ten

stimulus presentations with an inter stimulus interval of 5 minutes. Order of testing was

counterbalanced by strain and the remote control of the paradigm meant that the experimenter

was not present in the room during testing and therefore could not influence behaviour.

Offline video analysis was used to determine whether an animal had responded to the

stimulus. An animal was deemed to have responded if it physically interacted with the

stimulus casing, oriented its head towards the stimulus or stared at the stimulus. Once it was

determined whether the animal had responded, it was possible to calculate the percentage of

animals of each strain that responded for each of the ten consecutive stimulus presentations.

The comparison of interest was between strains and therefore a survival analysis was used to

assess whether any difference in responsiveness across repeated stimulus presentation was

significant. We examined whether there were any strain differences in the median survival

time i.e. the number of stimulus presentations before which 50% of those rats initially

responding showed habituation. In addition to whether a response occurred, the duration of

any response to the stimulus during the 5 seconds in which it was on was measured for each

of the ten stimuli and expressed as a percentage of that time. As well as examining behaviour

within the 5 seconds while the stimulus was on, the 5 second pre- and post-stimulus periods

were also examined to assess whether the animals were affected by the stimulus when it was

not actually on. That is, if their behaviour was a general behaviour directed towards the

stimulus object rather than the actual sensory stimulus (i.e. the tone), that is a result of arousal

rather than attention. The duration data was checked for normality using the Kolmogorov–

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Smirnov test and then repeated measures ANOVA with STIMULUS PRESENTATION as

the within-subjects factor and STRAIN as the between-subjects factor was conducted using

the percentage of overall time distracted by the stimulus as the dependent variable. Where

Mauchly’s test of sphericity was significant in the ANOVAs, the degrees of freedom were

adjusted using Greenhouse–Geisser correction (Greenhouse and Geisser, 1959).

In order to ensure that the measure of orienting behaviour was not confounded by locomotor

activity differences between the three strains, locomotor activity was measured using

automated Activity Monitoring Chambers (Med-Associates, UK). As with the auditory

response task, testing was conducted over three consecutive days. On the first two days,

animals were habituated to the locomotor chambers for 15 minutes each day before an

assessment of locomotor activity during a 30 minute (with 5 minute bins) period on the third

day. The following measurements were used for analysis (i) “distance travelled” - the total

horizontal distance moved in cm; (ii) “average velocity” - average horizontal velocity in

cm/min; (iii) “vertical activity”- the number of continuous vertical beam breaks indicating

rearing and (iv) “stereotypic activity”- the number of partial-body movements that occurred

within a defined space, such as grooming, head-weaving or scratching movements. This

locomotor testing took place within 7 days of the orienting task. All variables were checked

for normality using the Kolmogorov-Smirnov test and repeated measures ANOVA with

TIME as the within-subjects factor and STRAIN as the between-subjects factor was used to

analyse locomotor activity for the four different measures. As with the main behavioural data,

where Mauchly’s test of sphericity was significant in the ANOVAs, the degrees of freedom

were adjusted using Greenhouse–Geisser correction (Greenhouse and Geisser, 1959).

5.3 Electrophysiological Recordings

Animals were anaesthetised by an intraperitoneal injection of 30% urethane solution (1.5

g/Kg given in a volume of 5 ml/kg, Sigma Aldrich, Gillingham, U.K.). Anaesthetic depth for

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surgery was assessed by loss of the pedal reflex and eye blink reflex before the animal was

placed in stereotaxic frame (Kopf Instruments, Tujunga, USA) in the skull flat position using

modified ear bars (Sheffield University, UK) which also served as speakers to deliver an

auditory stimulus. Body temperature was measured throughout the experiment using a rectal

thermometer connected to a thermostatically-controlled heating blanket (Harvard Apparatus

Ltd, UK) to maintain temperature at 36-38 ˚C. Both eyes were sutured open and liquid tear

gel (Viscotears ®, Novartis Pharmaceuticals Ltd., Surry, UK) applied to prevent desiccation.

Following application of local anaesthetic (Ethyl Chloride BP, Cryogesic ®, Acorus

Therapeutics Ltd., UK), scalp retraction, bilaterial craniotomy and durotomy were performed,

creating two 3 mm Ø burr holes exposing the cortex above the superior colliculus (right:-6.3

mm AP to Bregma, and +2 mm ML to the midline; left: -6.3 mm AP to Bregma; and +3.5

mm ML to the midline) to allow for simultaneous recordings from both superior colliculi

with the left electrode angled at 25° to the vertical. In addition, two trepanned holes (1 mm

Ø) were created anterior to the SC burr holes at specific stereotaxic co-ordinates for

electroencephalographic (EEG) recordings (+1 mm anterior, +2 mm lateral; and -4mm

posterior, +3mm lateral, relative to Bregma, Devonshire et al., 2009). Differential and active

EEG electrodes (loop-tipped silver wire, 0.2 mm Ø; Intracel) were placed ~1 mm

subcranially into the rostral and caudal trepanned holes, respectively to obtain continuous

EEG information. Finally, respiration rate was recorded using a three-axis accelerometer IC

(ADXL330KCPZ, Analog Devices, Norwood, MA, USA, device, attached to the animal’s

lateral abdomen (Devonshire et al., 2009). Both EEG and respiration rate were used to

monitor the animal during the recordings and used offline to confirm there were no

differences in anaesthetic depth between the three strains.

Tungsten electrodes (Parylene-C-insulated; 2 MΩ, A-M Systems Inc., Carlsborg, WA, USA)

were positioned directly above the superficial layers of the superior colliculus at the

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coordinates stated above at a depth of – 2.0 mm from the brain surface. The electrodes were

then gradually lowered during presentation of a light stimulus (green LED flashing at 0.5 Hz,

10 ms duration, 20 mcd positioned 5 mm anterior to the contralateral eye) until a strong light

response was detected in both the audio feed from the recording (NL120, The Neurolog

System, Digitimer, UK) and visual feed via Spike2 (CED, Cambridge, U.K.), indicating that

the electrode was positioned in the superficial colliculus. From here the electrodes were

lowered further until the light response was abolished at which point responsiveness to an

auditory stimulus (8 KHz tone sounding at 0.5 Hz, 50 ms duration, 65 dB SPL) was tested.

Once an auditory response was confirmed the electrodes remained in this position throughout

the experimental procedure. Auditory responses to 150 stimulations were then recorded at 5

different stimulus intensities (from minimum to maximum light: 55, 60, 65, 70 and 75 dB

SPL) for offline analysis. Extracellular low frequency (local field potential; LFP) and high-

frequency (multi-unit activity; MUA) was amplified (gain 10,000 and 1000, respectively),

band-pass filtered (LFP: 0.1–500 Hz, MUA: 500–10 kHz) (Logothetis, 2008), digitized at 11

kHz and recorded to PC using a 1401+ data acquisition system (Cambridge Electronic Design

Systems, Cambridge, UK), running Spike2 software (Cambridge Electronic Design Systems,

Cambridge, UK) and saved for offline analysis.

To check that the depth of anaesthesia was comparable in the three strains during testing, the

dominant EEG frequency was obtained using a power spectrum analysis (Spike2) for the

period within which the 150 stimulations where presented. The respiration rate per minute

was calculated during the first and last 30 seconds of this period and then used to calculate an

average rate per minute over the whole recording period. Based on the EEG frequency bands

all animals were found to be in stage III-4, with an EEG frequency of 1-2 Hz during

recordings and have comparable respiration rates using a One-Way ANOVA (F(3)=3.52;

p=0.098) following confirmation of normality of data with a Kolmogorov–Smirnov test.

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Collicular recordings were analysed offline using Spike7, custom-made excel macros

(Sheffield University) and SPSS. All analyses were performed on averaged data where

averages were constructed from the full 5 minute period (150 stimulations) for each of the

five stimulus intensities. The main comparison of interest was between responses in the three

strains across the range of sound intensities. For LFP data a waveform average was created in

Spike7 (1-ms bins, 1 s duration, 0.1 s offset) for each intensity. The waveform average was

exported into the custom-made macro and a response was deemed to have occurred if the

trace extended beyond a pre-determined threshold after stimulus onset. The threshold for

change was set at ±1.96 standard deviations from the mean baseline (i.e. within 95%

confidence levels). This threshold was used to assess three parameters: onset latency, peak-

to-peak amplitude and duration. Onset latency was obtained by recording the time after

stimulus presentation at which the voltage trace extended beyond the threshold. Response

duration was determined by obtaining the time, post-stimulus, when the voltage trace

returned to within baseline levels (i.e. ±1.96 standard deviations of the pre-stimulation mean)

and consistently stayed below this value for 10 ms or 10 bins. The time between onset latency

and the response finishing was then used to calculate duration. Finally, peak-to-peak

amplitude was defined as the voltage difference between the maximum positive peak and the

maximum negative peak in the response period defined by the significant deviation from

baseline. For the MUA, similar measures were utilised following initial extraction of ‘spikes’

from the high-frequency data by thresholding. Individual spikes identified using template

matching (Spike7, CED) on a sample of the data and these used to calculate a threshold

which reliably separated the noise from the spikes. This threshold was used to create a spike

channel and this, in turn was used for analysis. Peri-stimulus time interval histograms

(PSTHs; 1-ms bins, 1 s duration, 0.1 s offset) were constructed from the trial-by-trial spike

counts in this newly created channel within Spike7 and the 100 ms pre-stimulus period was

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defined as baseline activity. An auditory response was deemed to have occurred if, post

stimulus, the activity rose above 1.96 standard deviations of the mean of the baseline period

for at least 5 ms (5 consecutive bins), the first of which was labelled as the onset of a

response. The duration was calculated by measuring when the response fell back to within

the baseline levels for at least 10 ms (10 consecutive bins), the first of which was labelled as

the end of the response. Duration was then given as the difference between onset latency and

the response ending. The amplitude was recorded as the peak value of the response minus the

mean baseline value. Prior to statistical analysis all data were deemed normally distributed

using the Kolmogorov–Smirnov test. Repeated measures ANOVAs with STRAIN as the

between-subjects factor and STIMULUS INTENSITY as the within-subjects factor were

used with a critical p-value of <0.05 considered to be significant. As with the behavioural

data, where Mauchly’s test of sphericity was significant in the ANOVAs, the degrees of

freedom were adjusted using Greenhouse–Geisser correction (Greenhouse and Geisser,

1959).

5.4 Histology

Site reconstruction: Following electrophysiological recordings, animals were transcardially

perfused with physiological saline followed by 4% paraformaldehyde in phosphate buffered

fixative. The brains were the placed in fixative for 24 hours before being transferred to 20%

sucrose for a further 36 hours. They were then frozen to -18 C in isopentane (WWR

International, UK) and cut into 50 µm coronal sections using a cryostat (CM1900, Leica, UK)

with the cutting chamber held at -20 C. The slices were dehydrated with alcohol and Nissl

stained with cresyl violet (0.5%) (Sigma Aldrich, Gillingham, UK), before cover-slipping for

histological verification of recording sites, which were subsequently plotted onto

reconstructed sections from Paxinos and Watson (Paxinos and Watson, 1998) to confirm

location of recording in the superficial layers of the colliculus.

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Collicular volume and cell counts: Animals were given a terminal i.p. dose of sodium

pentobarbitone (Animalcare, York, UK) before being trancardially perfused and the brain

sectioned as described above. For volume analysis of the whole brain and the SC the

Cavalieri principle was used; the first 50 µm section was taken from every 1-in-5 series of

sections throughout the brain were used. For the cell counts, beginning at a random starting

point (between slices 1-5), every 5th section was collected for cell count analysis. For both

measures the slices were dehydrated with alcohol and Nissl stained with cresyl violet (0.5%,

Sigma Aldrich, Gillingham, UK) before cover-slipping for analysis. Images were captured

using a Microfibre digital camera attached to a Nikon Eclipse 80i microscope (Nikon UK

LTD, Kingston-upon-Thames, UK). For volume analysis, images of the section and an

appropriate scale bar were taken at x1 magnification (Nikon Plan UW, 1x/0.04, WD 3.2) and

exported to a freely available reconstruction programme (Reconstruct version 1.1.0.1

http://synapses.clm.utexas.edu/). The whole brain as well as the complete intermediate and

deep layers of the SC, as defined by Paxinos and Watson (Paxinos and Watson, 1998), were

then outlined throughout the slices using the Reconstruct programme. The multiplication of

the cut surface area by the known distance in thickness (250 µm) was calculated to provide

the estimated volume of the examined objects i.e. the whole brain and the intermediate and

deep layers of the colliculus. Factors such as the physical size of the animal influence the

maximum brain size (Raz et al., 1998) and it has therefore been suggested that comparing

solely volumes of intracranial structures between groups would not provide reliable data

(Knutson et al., 2001). As such, the volume fraction of the relevant layers of the SC within

the reference volume (the whole brain) was calculated, to give a proportion of the structure

(i.e. intermediate and deep layers) within the whole brain structure. These data were

confirmed as having a normal distribution using the Kolmogorov–Smirnov test before

analysis was conducted using a One-Way ANOVA to analyse strain differences. For cell

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Brace et al

counts, the images were taken at x40 magnification (Nikon Plan Flor, 40x/0.75, DIC M, WD

0.72). Contours were drawn at low magnification (x1; Nikon Plan UW, 1x/0.04, WD 3.2)

around the region of interest i.e. the superficial layers of the SC, as defined by Paxinos and

Watson (1998). The stereologically unbiased Optical Fractionator method on the Stereo-

Investigator software (MBF Biosciences, Magdeburg, Germany) was used to obtain an

estimate of the total number of cells in the region of interest, as it is not influenced by the

size, shape, spatial orientation, and spatial distribution of the cells studied. . A counting

frame was used (see Figure 2.16) to count cells using the specific rules for counting cells

in the frame (any cell within the frame or touching the top or left lines were included in

the count; similarly any cell touching the bottom or right lines were excluded; if a cell

touched top/left line and the bottom/right line; it was also excluded). The area

sampling fraction can be measured using the formula below;

asf = counting frame areagrid ¿¿¿

The grid spacing and counting frame size were X: 35 µm and Y: 35 µm and constant

throughout the experiment for all animals.. Nuclei from different cell types were

differentiated based on morphological criteria of shape and relative size. Neurons were

identified by their generally larger shape and non-spherical outline, as well as a pale and

uniformly Nissl-stained cytoplasm with a well-marked nucleolus. Glial nuclei were identified

by being generally smaller in size, ovoid shape with the absence of stained cytoplasm, the

presence of a thicker nuclear membrane, and more heterogeneous chromatin within the

nucleus (Cotter et al., 2002). Although we cannot definitively distinguish between glial types,

on the basis of their appearance we believe that the large majority are astrocytes.

Acknowledgments

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Brace et al

The authors would like to thank Steve Walters, Agata Stramek and Karen Evans for their

technical support and care of the animals and Jackie Brown and Paul Gabbott for advice and

guidance on histological techniques.

Financial Disclosures

This work was supported by a PhD studentship provided by the Biomedical Research

Network at the Open University.

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Figure Legends

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Figure 1 The percentage of animals responding to consecutive auditory tones (A) and the

duration of responses as a percentage of the five second period in which the tone was on (B).

There were no significant strain differences in responsiveness. A representative key is shown

in part A.

Figure 2 Locomotor activity for all three strains. There was a main effect of time and strain

on distance travelled (A) and vertical activity (C) but not on average velocity (B). For

stereotypic activity (D) there was a main effect of time but not strain. There was also an

interaction effect for distance travelled. ‘s’ – main effect of strain; ‘t’ main effect of time; ‘x’

interaction. * p<0.05, ** p<0.001. See text for results of posthoc comparisons.

Figure 3 Reconstructed plots of recording sites in the superficial layers of the SC for SHR

(black circles), WKY (grey circles) and WIS (grey triangles). Plots are collapsed onto three

sections through the colliculus (Paxinos and Watson, 1998) with position relative to Bregma

given. There was no significant association between the layer recorded from and strains.

Figure 4 Example of a local field potential auditory response recorded in the SC with a 75 dB

SPL stimulus in an SHR with the stimulus presented at time zero (grey line) (A). The

relationship between LFP response parameters and stimulus intensity is shown for onset

latency (B), peak-to-peak amplitude (C) and duration (D).Overall there was a significant

difference in onset latency for the 65 and 70 dB SPL responses and the SHR had significantly

reduced amplitude in comparison to the control strains. A representative key is shown in part

B. * indicated p<0.05.

Figure 5: Example multiunit auditory response recorded in the SC of an SHR at the 75 dB

SPL intensity The top trace shows a raster plot with a line for each trial whilst the lower trace

is a histogram (1 ms bins) of spike activity with the stimulus presented at time zero (grey

line) (A). The relationship between multiunit response parameters and stimulus intensity is

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shown for onset latency (B), peak-to-peak amplitude (C) and duration (D). Overall there was

a significant main effect of stimulus intensity on onset latency and amplitude. For onset

latency there was also a significant main effect of strain with the SHR having greater onset

latency than control strains although this effect diminished with increasing intensity, creating

a significant interaction between STRAIN and STIMULUS INTENSITY. A representative

key is shown in B.

Figure 6: There was no significant difference between the three strains in terms of superficial

collicular volume (A). However, there were some differences in absolute cell numbers (B),

although these would have been confounded by absolute volume differences between strains.

Independent of volume, glia:neuron ratio (C) and cell density did not differ between strains

(D). *≤0.05.

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