Progress in Neuro-Psychopharmacology & Biological Psychiatry 56 (2015) 174–184

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Progress in Neuro-Psychopharmacology & BiologicalPsychiatry

Neuronal activity of the prefrontal cortex is reduced in rats selectivelybred for deficient sensorimotor gating

Mesbah Alam, Svilen Angelov, Meike Stemmler, Christof von Wrangel, Joachim K. Krauss, Kerstin Schwabe ⁎Department of Neurosurgery, Hannover Medical School, Carl-Neuberg-Str.1, D- 30625 Hannover, Germany

Abbreviations:ASR, acoustic startle response; AP, anunits; DA, dopamine; ECG, electrocardiographic; EEGentopeduncular nucleus; LFPs, local field potentials; mML, mediolateral; NAC, nucleus accumbens; PPTg, pedunPPI, prepulse inhibition; SU, single unit; SPL, sound predrome; V, ventral.⁎ Corresponding author. Tel.: +49 511 532 2862; fax: +

E-mail address: schwabe.kerstin@mh-hannover.de (K

http://dx.doi.org/10.1016/j.pnpbp.2014.08.0170278-5846/© 2014 Elsevier Inc. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 1 October 2013Received in revised form 8 August 2014Accepted 15 August 2014Available online 16 September 2014

Keywords:Entopeduncular nucleusLocal field potentialsNeuropsychiatric disordersNucleus accumbensPrepulse inhibition

Rats selectively bred for deficient prepulse inhibition (PPI), an operantmeasure of sensorimotor gating inwhich aweak prepulse stimulus attenuates the response to a subsequent startling stimulus, may be used to study certainpathophysiological mechanisms and therapeutic strategies for neuropsychiatric disorders with abnormalities ininformation processing, such as schizophrenia and Tourette's syndrome (TS). Little is known about neuronalactivity in the medial prefrontal cortex (mPFC) and the nucleus accumbens (NAC), which are involved in themodulation of PPI. Here, we examined neuronal activity in these structures, and also in the entopeduncularnucleus (EPN), since lesions of this region alleviate the PPI deficit.Male rats with breeding-induced high and low expression of PPI (n= 7, each) were anesthetized with urethane(1.4 mg/kg). Single-unit activity and local field potentials were recorded in the mPFC, the NAC and in the EPN.In themPFC discharge rate, measures of irregularity and burst activity were significantly reduced in PPI low com-pared to PPI high rats (P b 0.05), while analysis in theNAC showed approximately inverse behavior. In the EPN nodifference between groups was found. Additionally, the oscillatory theta band activity (4–8 Hz) was enhancedand the beta band (13–30 Hz) and gamma band (30–100 Hz) activity was reduced in the NAC in PPI low rats.Reduced neuronal activity in the mPFC and enhanced activity in the NAC of PPI low rats, together with alteredoscillatory behavior are clearly associated with reduced PPI. PPI low rats may thus be used to study the patho-physiology and therapeutic strategies for neuropsychiatric disorders accompanied by deficient sensorimotorgating.

© 2014 Elsevier Inc. All rights reserved.

1. Introduction

Neuropsychiatric disorders are increasingly recognized as networkdisorders with abnormal neuronal activity in cortico-subcortical loops.Understanding the abnormalities in the firing patterns and synchronyof neuronal activity that underlie specific behavioural disturbanceswould be useful to develop and improve therapeutics to attenuatesuch pathological processes (Carlson et al., 2006; Kopell and Greenberg,2008).

Sensorimotor gating mechanisms, which allow the nervous systemto suppress or “gate” responding to external stimuli and internallygenerated signals or impulses, are disturbed in certain neuropsychiatricdisorders (Swerdlow and Geyer, 1998; Braff et al., 2001). Suchgating mechanisms have been operationalized in measures of prepulse

terior-posterior; AU, arbitrary, electroencephalogram; EPN,PFC, medial prefrontal cortex;culopontine tegmental nucleus;ssure level; TS, Tourette's syn-

49 511 532 3960.. Schwabe).

inhibition (PPI) of the acoustic startle response (ASR), i.e., the reductionof the ASR when the startling noise pulse is shortly preceded by a weakprepulse (Koch et al., 2000; Swerdlow et al., 2001). Deficient PPI hasbeen demonstrated in schizophrenia, Tourette's syndrome (TS), andobsessive compulsive disorder (Swerdlow and Sutherland, 2006; Braffet al., 2001; Kohl et al., 2013), and experimentally-induced PPI deficitsin rodents are used as a common endophenotype to model this basicdeficiency in these disorders (Cadenhead et al., 2002; Braff and Light,2005).

Selective breeding in Wistar rats for high and low PPI leads to asegregation of two rat lines with significantly different PPI (Schwabeet al., 2007). The antipsychotic dopamine (DA) receptor antagonisthaloperidol alleviated the breeding-induced PPI-deficit (Hadamitzkyet al., 2007). Additionally, behavioral deficits and epigenetic factors inPPI low rats corroborate clinical findings seen in clinical practice(Dieckmann et al., 2007; Freudenberg et al., 2007; Rhein et al., 2013).

Within the neuronal circuitry that regulates PPI, the medial prefron-tal cortex (mPFC) and the nucleus accumbens (NAC) play key roles(Swerdlow et al., 2001; Pothuizen et al., 2005). Abnormalneurofunctional coupling of themPFC andNAC has been found in differ-ent animal models for deficient sensorimotor gating (Miller et al., 2010;Arime et al., 2012; Li et al., 2013; Swerdlow et al., 2013). Lesions or deep

175M. Alam et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 56 (2015) 174–184

brain stimulation of the entopeduncular nucleus (EPN), i.e., the equiva-lent to the human globus pallidus internus (GPi), alleviate breeding- orapomorphine-induced deficient PPI in rats, indicating that dysfunctionof neuronal activity may be altered as well in this region (Schwabeet al., 2009; Lütjens et al., 2011; Posch et al., 2012). Notably, deepbrain stimulation of the GPi is clinically used to improve tics in TS(Houeto et al., 2005; Shahed et al., 2007; Servello et al., 2008).

We hypothesize that specific neuronal firing and oscillatory activityin themPFC, NAC and EPN is altered in sensory gating abnormalities.Wetherefore examined the spontaneous neuronal activity in these regionsin PPI high and low rats.

2. Material and methods

2.1. Subjects

Rats with either breeding-induced reduced (PPI low) or increasedPPI (PPI high) were housed in groups of four in standard MacrolonType IVS cages (Tecniplast, Hohenpeissenberg, Germany) under a14-h light/10-h dark cycle (on at 07:00 h) at a room temperature of22±2 °C, with food andwater ad libitum. All experimentswere carriedout in accordancewith the EU directive 2010/63/EU andwere approvedby the local animal ethic committee.

2.2. Breeding selection for PPI high or low

The parental generation for our PPI high and low lines consisted of 23male and 27 female rats (outbred adult Hannover-strain Wistar ratsfrom Harlan-Winkelmann, Borchen, Germany). The TSE StartleResponse SystemTM (Bad Homburg, Germany) was used to test rats forPPI, i.e., the percent decrease of the startle response in pulse-alone(20 ms white noise pulse at 105 dB sound pressure level (SPL))compared to the startle response in prepulse-pulse trials (80 dB SPL,10 kHz pure tone pulse, 20 ms duration followed by pulse 100 ms afterprepulse onset). Two females andmaleswith the highest and the lowestlevel of PPI, respectively, were chosen for selective breeding of two lineswith either high or low level of PPI. After the 10th generation the startleresponse system of San Diego instruments was used for testing of PPI asdescribed before, but with 68 dB white noise as prepulse. For this studywe used PPI low and PPI high rats (n= 7 each) from the 11th and 12thgeneration, which differed in their PPI measures (normally distributed;8.38 ± 3.9% vs. 66.7 ± 2.0%, Student's t-test, P b 0.001), but not intheir ASR measures (not normally distributed; 1945 ± 304 AU vs.1762 ± 344 AU, MannWhitney U test, P = 0.563).

2.3. Single-unit and local field potential recording procedures

In vivo neuronal activitymeasurementswere carried out on a total of14 rats, i.e., high PPI (N = 7) and low PPI (N = 7), one to two weeksafter PPI assessment.

Rats were anaesthetized with urethane (1.4 g/kg, i.p. ethyl carba-mate, Sigma; with additional doses as needed, depth of anaesthesiawas checked by the foot pinch), which has been widely used as ananesthetic in animal experiments because it can be administrated read-ily, produces a long-lasting steady level of anesthesia with minimaleffects on autonomic and cardiovascular systems (Hara and Harris,2002; Li et al., 2012). Body temperature was kept at 37.5 ± 0.5 °Cwith a heating pad. Electrocardiographic (ECG) activity was monitoredconstantly to ensure the animals' wellbeing. Rats were placed in a ste-reotaxic frame and craniotomiesweremade over the target coordinates,relative to bregma (flat skull position). For all regions we used two tra-jectories within the following coordinates in millimeter scale; for themPFC: anterior-posterior (AP), +3.2 and +2.2; mediolateral (ML),±0.5 and ±0.8; ventral (V), −3.2 and 4.5; for the NAC core: AP +1.7and +1.2, ML ±1.5 and ±1.7, V 6.5 and 7.8, and for the EPN: AP−2.3 and −2.8, ML ±2.4 and ±2.6, V 7.4 and 8.0. At the end of all

recordings the electrode tip was used to coagulate the tissue alongeach of the trajectories in 200 μm steps with bipolar current of 10 μAfor 10 s to verify recordings in the targets after sacrifice of the animals(Fig. 1).

A single microelectrode for extracellular recordings (quartz coatedelectrode with a platinum-tungsten alloy core (95%–5%), diameter80 μm, impedance 1–2MΩ) was connected to theMini Matrix 2 channelversion drives headstage (Thomas Recording, Germany). The electrodewas guided stereotaxically through the guide cannula towards the tar-get coordinates in the mPFC, NAC or EPN, respectively. The microelec-trode signal was passed through a headstage with unit gain and thensplit to separately extract the single unit (SU) and the local field poten-tials (LFPs) components. For SU recording signals were bandpass-filtered between 500 and 5000 Hz and amplified from × 9,500 to19,000. The LFP signals were filtered to pass frequencies between 0.5and 140Hz, before being amplified and digitized at 1 kHz. Datawere ac-quired using the CED 1401 A/D interface (Cambridge Electronic Design,Cambridge, UK).

2.4. Data analysis

Action potentials arising from a single neuronwere discriminated bythe template-matching function of the spike-sorting software (Spike2;Cambridge Electronic Design, Cambridge, UK). For analyses of spontane-ous activity, one epoch of 300 s with simultaneously recorded spikingand LFP activities that was free of artefacts was used from every record-ed neuron.

2.4.1. Firing rates and burst detectionThe firing rate was calculated with the firing rate histograms

produced in NeuroExplorer version 4 (NEX Technologies, NC). Theterm ‘burst’ is usually defined as a cluster of spikes from a single neuronthat differs from other spikes in a particular way, usually being moreclosely spaced in time than neighboring spikes thus having a higherdischarge rate than the surrounding spike trains (Lobb, 2014). Oneproblem with any definition of a burst, however, is the comparison oftwo groups of spike trains inwhich there is a difference in the referenceactivity. Additionally, non-stationarities in spike trains may complicateburst analysis.

Basically, there are two different approaches to define and describebursts, which both have certain advantages and disadvantages: a meth-od based on specification of interspike interval (ISI) parameters, whichdepends on user-defined parameters (e.g., Harris et al., 2001 andChiappalone et al., 2005). This method, however, does not allow identi-fication of individual burst events and is strongly influenced by thebackground activity of the neuron. The second method, which is basedon Poisson distribution of ISI, is able to identify individual burst eventson the base of poissonian surprise (e.g. shown in Alam et al., 2014).Here, the choice of the threshold does not affect burst detection(Grace and Bunney, 1984; Legéndy and Salcman, 1985) and it alsoleads to less detection of artificial ‘burst’ as discussed by Chen et al.(2009). In the present study, the Poissonian surprise method ofLegéndy and Salcman (1985) is used for bursts and burst related statis-tics. This method is implemented in NeuroExplorer and has beenfrequently used for analysis of spontaneous discharge of neurons byus and other groups (e.g., Jackson et al., 2004; Homayoun andMoghaddam, 2006; Rumpel et al., 2013).

2.4.2. Asymmetry indexVariations to the Gaussian distributionwere evaluated by determin-

ing the asymmetry index, which is the ratio of themode to themean ISI.An asymmetry index close to 1 reveals a relatively regularfiring pattern,whereas the more the index differs from unity, the more irregular thespike trains. A ratio of less than 1 reflects an asymmetrical shape, indi-cating a larger fraction of short interspike intervals (positively skewed),as is expected when there is bursting activity.

mPFC NAC EPN

Fig. 1. Histological verification showing the localization of a recording electrode by electric coagulation (white arrows) in hematoxylin and eosin (H&E) stained coronal sections of themedial prefrontal cortex (mPFC), nucleus accumbens (NAC) and entopeduncular nucleus (EPN). Subregions within histological photographs are indicated with the following abbrevia-tions: M1 – motorcortex region; Cg 1 – cingulate cortex; PrL – prelimbic cortex; IL – infralimbic cortex.

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2.4.3. Complexity analysisThe non-linear signal-processing metric Lempel-Ziv complexity

(cLZ) was used to evaluate occurrence and recurrence of patternsalong electrophysiological data series. This index quantifies dynamicfeatures of time series especially for highly correlated and shortsequences (Lesne et al., 2009). The algorithm to compute cLZ isexplained in detail in Hu et al. (2006). The spike trains were binned byusing a 1 millisecond bin size. The number 0 or 1 is assigned to eachbin, according to whether it contains no spike (0) or at least one spike(1) (Szczepański et al., 2004). The binary representation of electrophys-iological data was finally analyzed for cLZ by using the original algo-rithm proposed by Lempel and Ziv (1976). The resulting cLZ was thennormalized to the length of spike train, to overcome known complexitydependence from block size; in general, LZ complexity measures therate of generation of new patterns along a sequence and in the case ofrandom or stochastic processes is closely related to the entropy rate ofthe source (Amigó et al., 2004). A decrease in the complexity (γ) hasbeen attributed to the presence of non-random patterns in given data.cLZ calculations were performed by in-house developed proceduresusing C++ and Mat lab software.

2.4.4. Regularity of spikes eventsTo determine three distinct patterns: regular, irregular and bursting

activity we used a modified approach of the discharge density histo-gram of Kaneoke and Vitek (1996). The estimated density histogramsd (λ) were compared to reference probability density function px(λ)by means of a mathematical distance.

The discharge density of a regular neuron is expected to follow aGaussian distribution PG(λ) with mean equal to 1 and variance equalto 0.5. An irregular neuron is expected to follow a Poisson distribu-tion PP(1)(λ) with mean equal to 1, a bursting neuron with meanequal to 0.5. The estimated density histogram d(λ) is compared tothe reference probability density function PDF px (λ) by means of amathematical distance. In our analysis, we have chosen the 2-normdistances test, since both neuronal and simulation data have shownthat this measure provides more reliable results than the 1-norm,and the Kullback Leibler infinity-norm distances (Labarre et al.,2008).

Because of the reduced number of samples forming d(λ) and thepossibility to take values very close to zero, we chose the 2-normdistance to determine the goodness-of-fit. The distribution withthe smallest distance determines to which class the neuron is assigned(Labarre et al., 2008). Differences in the incidence of discharge patternswere evaluated by χ2 test.

2.4.5. Spectral power analysis of local field potentialsFor analysis of LFPs, each recording segment was detrended to

remove any slow DC components and padded with zeros to increase

frequency resolution. Spectra were determined for the total 300 srecordings and signal notch (50 Hz) and low pass filtered (100 Hz).Autospectra of LFPs was derived by discrete Fourier transformationwith 1024 blocks. The relative power of epochs of 300 s, from thesame recording section like the spike trains, was analyzed (θ =4–8 Hz, α = 8–13 Hz, β = 13–30 Hz, γ = 30–100 Hz), averaged, andcompared between PPI high and PPI low rats.

2.5. Statistical analysis

Data are represented asmeans± standard error of themeans (SEM)for single unit activity and as median values for the percentage of rela-tive power of theta, alpha, beta and gamma oscillatory activity of LFPs.Because the neuronal activities differ between regions (i.e., mPFC, NACand EPN), we chose to compare PPI high and low neuronal activity aspairs within one region. We first tested the distribution of high andlow PPI neuronal activity in the mPFC, NAC and EPN for normalityusing 1-sample Kolmogorov-Smirnov analysis for each group of dataset. Except for cLZ all data were not normally distributed and weretherefore compared within one region by the Mann–Whitney U test,while cLZ was compared by unpaired t-test with a P b 0.05 representingsignificance. Additionally, we performed a correlation analysis betweenfiring rate and other linear and nonlinear measures (burst parameters,asymmetry index and complexity) of each neuron. Since measuringthe overall firing rate pooled from all neurons may hide the changesrelated to neurons displaying different firing patterns, we additionallyanalyzed the firing rates and cLZ of neurons categorized as “bursty”,“irregular”, and “regular”.

3. Results

All neuronswere analyzed after histological verification of recordingsites in the different regions (Fig. 1). From a total of 450 recordedneurons 96 neuronswere not included into the analysis due to unstableneuronal activity. Thus, 354 neurons were analyzed from both high andlow PPI groups. The total number of neurons in PPI high rats in themPFCwas n = 58, in the NAC core n = 67, and in the EPN n = 38. In the PPIlow rats the number of neurons in the mPFC was n = 84, in the NACn = 60, and in the EPN n = 47.

3.1. Firing rate and bursts

In comparison to PPI high rats themean firing rate in the PPI low ratswas lower in the mPFC (10.87 ± 0.90 vs. 5.31 ± 0.40; P b 0.01) andhigher in the NAC (2.85 ± 0.40 vs. 4.76 ± 0.84; P b 0.05; Fig. 2a), butnot different in the EPN.

In comparison to PPI high rats PPI low rats had a lower percentage ofspikes in bursts in the mPFC (94.34 ± 1.06 vs. 76.44 ± 2.76; P b 0.01;

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Fig. 2. The mean firing rates (a), the percentage of spikes in bursts (b), the total number of bursts (c), the mean burst duration (d), the asymmetry index (e) and the mean Lempel-Zivcomplexity (cLZ) values (f) of neurons in PPI high rats (white bars) in the mPFC (n = 58), the NAC (n = 67) and the EPN (n = 38), and PPI low rats (hatched bars) in the mPFC (n =84), the NAC (n = 60) and the EPN (n = 77). Bars represent mean ± SEM. Differences between PPI high rats and PPI low rats are shown as asterisks (** P b 0.01; * P b 0.05; unpairedt-test for cLZ and Mann-Whitney U test for all other comparison).

177M. Alam et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 56 (2015) 174–184

Fig. 2b), a lower total number of bursts (191.62 ± 9.57 vs. 150.25 ±8.07; P b 0.01; Fig. 2c), and a lower mean burst duration (7.44 ± 5.24vs. 0.41 ± 0.03; P b 0.01; Fig. 2d). No differences were found betweengroups in the NAC and the EPN.

3.2. Asymmetry index

In the mPFC the asymmetry index was enhanced in PPI high rats ascompared to PPI low rats (0.32 ± 0.03 vs. 0.124 ± 0.01; P b 0.01),which corroborates the reduced burst activity in PPI low rats. In contrast,in theNAC the asymmetry indexwas reduced in PPI high as compared to

PPI low rats (0.11 ± 0.01 vs. 0.16 ± 0.02; P b 0.05), which indicateshigher burst activity of PPI low rats in this region (Fig. 2e).

3.3. Estimation of complexity

The averaged cLZ complexity estimation showed low complexityvalues in the PPI low rats in the mPFC and high complexity values inthe NAC as compared to PPI high rats (0.038 ± 0.002 vs. 0.074 ±0.005; P b 0.001 and 0.035 ± 0.005 vs 0.023 ± 0-002; P b 0.05; un-paired t test). In the EPN, no difference was found between groups(P = 0.23; unpaired t test; Fig. 2f).

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0102030405060708090

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% o

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atte

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burst irregular regular

Fig. 3. Examples of the three different firing patterns of neuronal activity: (a) a regularly firing neuron, in which discharge density histogram (DDH) represents a Gaussian bell shape dis-tribution and compared to Gaussian with a mean of 1 and variance equal to 0.5; (b) an irregularly firing neuronal pattern, where DDH comparison was made with a Poisson distributionwith a mean of 1.00; (c) a bursting pattern where DDH comparison was made with a Poisson distribution with a mean of 0.5. In (d) the percentage of bursty firing pattern neurons(hatched bar), irregular firing pattern (cross hatched bar) and regular firing pattern (white bar) is shown in the mPFC, NAC and EPN in both groups of rats.

178 M. Alam et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 56 (2015) 174–184

A correlation analysis between firing rate and other linear and nonlin-ear measures (burst parameters, asymmetry index and complexity) witheither Spearman's rank correlation analysis, or the Pearson correlationtest showed no correlation between two variables (all P-values N0.05).

Table 1Firing rate and Lempel-Ziv complexity (cLZ) of neurons with different firing patterns in the mecleus (EPN) of PPI high and low rats.

Burst firing pattern Irregular firin

Percent (%) Firing rate cLZ Percent (%)

mPFCPPI high (N = 58) 71% 9.89 ± 0.8 0.07 ± 0.005 29%PPI low (N = 84) 55%* 5.91 ± 0.5** 0.042 ± 0.002 ** 45%*

NACPPI high (N = 67) 35% 2.28 ± 0.6 0.017 ± 0.003 62%PPI low (N = 60) 36% 6.79 ± 2.0* 0.021 ± 0.003 56%

EPNPPI high (N = 38) 15% 18.46 ± 6.8 0.101 ± 0.028 70%PPI low (N = 47) 27% 14.85 ± 3.5 0.098 ± 0.017 46%**

Neurons classified as bursty, irregular and regular are shown in percentage. Firing rates and cLZ oregions (paired two-sample t-tests*P b 0.05 and **P b 0.01) compared between PPI high and l

3.4. Firing patterns

Examples of regular, irregular and bursty firing patterns are shownin Fig. 3a-c. The percentage of firing patterns in the mPFC, the NAC

dial prefrontal cortex (mPFC), the nucleus accumbens (NAC) and the entopeduncular nu-

g pattern Regular firing pattern

Firing rate cLZ Percent (%) Firing rate cLZ

11.33 ± 2.0 0.081 ± 0.011 0% - -4.50 ± 0.7** 0.034 ± 0.004** 0% - -

2.47± 0.30 0.024 ± 0.003 3% 3.85 ± 0.8 0.048 ±0.044.60 ± 1.06* 0.035 ± 0.006 8% 2.61 ± 0.9 0.107 ± 0.03

28.61 ± 2.03 0.167 ± 0.011 15% 18.5 ± 5.6 0.098 ± 0.01*24.48 ± 2.55 0.144 ± 0.013 27% 29.64 ± 2.8 0.143 ± 0.01

f neurons classified for these different patterns are given asmean ± S.E.M for thedifferentow.

179M. Alam et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 56 (2015) 174–184

and the EPN for PPI high and low groups are shown in Fig. 3d. Analysisrevealed a lower percentage of irregular spikes and a higher percentageof burst patterns in the PPI high rats as compared to PPI low rats in themPFC (irregular: 29% vs. 45%; P b 0.05 and bursty: 71% vs. 55%;P b 0.01 two sample t-test between percent). No regular firing patternwas observed in the mPFC in both groups. In the NAC the percentageof irregular firing of PPI high and low rats was 62% vs. 56%, of burst firing35% vs. 36%, and of regular firing 3% vs. 8%, which did not statisticallydiffer between groups. Further, in the EPN of PPI high rats the percent-age of irregular firing was enhanced compared to PPI low rats (70% vs.46%; P b 0.05). There was also a tendency of low percentage of burstyand regular firing neurons in PPI high compared to PPI low rats (bursty:15% vs. 27% and regular: 15% vs. 27% respectively), which also did notreach the level of significance.

Overall, neuronal firing rates in neurons classified as bursty andirregular reflect our findings shown in Fig. 2. In the mPFC the firingrate and cLZ in bursty and irregular neurons was higher in PPI highrats as compared to PPI low rats (both P b 0.01), while in the NAC thefiring rate of neurons classified as bursty and irregular was reduced inPPI high rats (P b 0.05; P b 0.01, respectively). The cLZ did not differbetween groups in the NAC. In the EPN no differences were found inthe mean firing rate of bursty and irregular neurons. In neurons classi-fied as regular the firing rate did not differ between groups in eitherregion, but the cLZ was higher in PPI low as compared to PPI high rats(Table 1).

3.5. Local field potentials: Schematic representations of spectral estimates ofoscillatory power from the mPFC, NAC and EPN in PPI high and low rats areshown in Fig. 4

3.5.1. Theta (4–8 Hz)The relative power of theta bands in theNAC and EPNwere higher in

PPI low as compared to PPI high rats (P b 0.01 and P b 0.01, respectively;Fig. 5a), while in themPFC no difference was observed between groups.

3.5.2. Alpha (8–13 Hz)In the PPI low rats in the EPN the percentage of relative power of

alpha band activity was lower as compared to PPI high rats (P b 0.01;Fig. 5b), while no difference was observed in the mPFC or in the NACin both groups.

3.5.3. Beta (13–30 Hz)In PPI low rats the percentage of relative power of beta band activity

was decreased in all regions (mPFC: P b 0.05, NAC: P b 0.01 and EPN:P b 0.01; 5c).

3.5.4. Gamma (30–100 Hz)The relative power of gamma band activity was decreased in the PPI

low rats in the NAC (P b 0.01; Fig. 5d), while no difference was found inthe mPFC and EPN (P = 0.55 and P = 0.64) respectively.

4. Discussion

Our data sheds new light on the possible interplay between neuro-nal activity in cortical and subcortical regions involved in deficientsensorimotor gating processes. In PPI low rats the firing rate and theburst behavior of mPFC neurons is reduced as compared to PPI highrats. This is corroborated by a reduced asymmetry index and a reducedcLZ, which indicates less functional complexity and dynamic features inthe firing pattern of mPFC neurons.

The PFC cortex is involved in higher-order executive tasks, such aslearning, working memory, and behavioral flexibility. In humans,dysfunction of prefrontal cortical areas, especially its dorsolateral part,contributes to a decline in cognitive performance in neuropsychiatricdisorders (Heidbreder and Groenewegen, 2003). In patients withschizophrenia ‘hypofrontality’ has been reported (Sabri et al., 1997),

which has also been related to sensorimotor gating deficits (Hazlettand Buchsbaum, 2001; Hazlett et al., 1998; Tregellas et al., 2007). Also,functional brain imaging studies found reduced perfusion within thedorsolateral PFC and the anterior cingulate cortex in TS patients(Moriarty et al., 1995). It has been suggested that increase in burstsenhance neurotransmitter release and information processing, where-as, reduced burst activity attenuates the signal transmission efficiencyof PFC neurons and diminishes cortical mediation of normal behavior(Jackson et al., 2004).

Anatomical, electrophysiological and behavioural evidence supportsthe view that the rat medial PFC, although at a rudimentary level,combines elements of the primate anterior cingulate cortex and dorso-lateral PFC. Within the medial PFC the prelimbic PFC has been targetedfor electrophysiological recordings in the present study, since it hasbeen described as a developmental homologue to the dorsolateralregion of the primate PFC and critical for attentional processes, behav-ioral flexibility, and the encoding of action outcome (Heidbreder andGroenewegen, 2003; Uylings et al., 2003; Seamans et al., 2008). Morerecent studies, however, show that projections of the rat prelimbiccortex occupy a large portion of the NAC core, NAC shell and medialparts of the dorsal striatum, showing important zones of overlap withother regions of the medial PFC, especially the dorsal cingulate cortex(Mailly et al., 2013).

Disturbed mPFC function detected with electrophysiologicalmethods has been associated with sensorimotor gating deficits mea-sured as reduced PPI in different animalmodels. By using the ratmater-nal immune activation (MIA) model extracellular recordings showed asignificant reduced mPFC-hippocampus coherence that correlatedwith decreased PPI. Interestingly, a putative population of theta-modulated, gamma-entrained mPFC neurons showed reduced firing inthese rats (Dickerson et al., 2010). However, in brain slices ofpostpubertal rats with neonatal ventral hippocampal lesions, a modelwhich also lead to reduced PPI, patch clamp recordings of layer 5mPFC neurons revealed that lesions possibly shifted basal synaptic ac-tivity toward increased excitation (Ji and Neugebauer, 2012). Althoughthese studies, did not differentiate between different mPFC subregions,inspection of schematic drawings showed that recordings were donemainly in the prelimbic cortex.

In the NAC of PPI low rats the firing rate was significantly increased,together with enhanced asymmetry index and cLZ estimation, which,according to Chen et al. (2011), indicates more regular or tonic activity,and a more complex firing pattern as compared to that of PPI high rats.Overall, except for burst activity, the firing behavior of NAC neurons ofPPI low rats was quite the opposite of that found in the mPFC. Severalneuroanatomical and neurophysiological studies suggest a functionalrelationship between the mPFC and the NAC (Tzschentke, 2001;Sesack et al., 2003). Abnormal neurofunctional coupling betweenthese regions has been found in different animal models for deficientsensorimotor gating measured as PPI, e.g. after neonatal ventral hippo-campus lesions (Swerdlow et al., 2013), after infusion of NMDA into theventral temporal lobe (Miller et al., 2010), after maternal separation (Liet al., 2013), or in dopamine transporter (DAT) knockout (KO) mice(Arime et al., 2012). It has been suggested that experimental manipula-tions that decrease mPFC DA “tone” in rats lead to deficient PPI viadisinhibition of descending glutamatergic fibers, which cause increasedNAC dopamine transmission (Koch and Bubser, 1994; Ellenbroek et al.,1996). However, the relationship between cortical and subcortical DArefers to the DA inputs, not the output neurons, which mainly have in-hibitory D2 receptors. An increased DA tone in the NAC should thus de-crease firing in the NAC. Nevertheless, how exactly DA contributes toinformation processing within the NAC is still debated since theactions of DA depend on a complex interplay of cellular and synapticproperties and DA can either excite of inhibit NAC neurons (Nicolaet al., 2000; Bennay et al., 2004).

On the other hand, although altered dopaminergic signallingthrough basal ganglia structures are regarded critical determinants in

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several neuropsychiatric disorders, including TS and schizophrenia(Albin et al., 2003; Albin and Mink, 2006; Wang et al., 2011), there isalso clinical and preclinical evidence that other transmitter systems,e.g., noradrenergic transmission, are involved (cf. Leckman et al., 2010;Swerdlow, 2013). With regard to our rat model, although haloperidolrestored the breeding-induced PPI deficit, indicating disturbed DA func-tion in these rats (Hadamitzky et al., 2007), the atypical antipsychoticsclozapine and risperidone had similar, albeit non-significant effects,suggesting that other transmitter systems may be affected as well.

Finally, the NAC is functionally heterogeneous, with core and shellsubregions characterized by distinct neurochemical, anatomical andbehavioral properties. Both, the core and the shell have been shown to

be involved in the regulation of PPI, but possibly in a different mannerwith regard to interaction between the dopamine and glutamate trans-mitter systems (Swerdlowet al., 2001). Electrolytic lesions of the core aswell as the shell significantly reduce PPI in rats. In our study, however,we only selected the NAC core for recording of neuronal activity, sinceit has been demonstrated that manipulations of themPFC or basolateralamygdala reduce PPI via direct effects on subcortical DA transmission inthe NAC core, which is innervated by both regions (cf. Groenwegenet al., 1991).

With regard to the finding that in PPI low rats burst activity in themPFC was enhanced along with increased firing rate, while in the NAConly firing rate was decreased, without changes in burst activity, it

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Fig. 5. The box plots illustrate the summary changes in the percentage of relative power in the theta (a), alpha(b) beta (c) and gamma (d) oscillatory activity band from 300 s of LFP data inthemPFC,NAC and EPNof PPIhigh (white box) and PPI low rats (hatchedbox). The central line of the boxplots represents themedian, the 25–75% (interquartile) range, and the edge of thewhiskers show the 5–95% of the overall distribution of theta, alpha, beta and gammabands activity in themPFC, NAC andEPN.Differences between PPI high rats and PPI low rats are shownas asterisks (** P b 0.01; * P b 0.05; Mann–Whitney U test).

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should bementioned, that different studies have suggested that the rateand patterns of bursts in neurons can bemodulated independently, con-sistentwith some independence of themechanisms generating baselinefiring rates and bursts (Charlety et al., 1991; Overton and Clark, 1992;Hyland et al., 2002). Notably, higher bursts in spike trains do not neces-sarily denote higher firing rates because high firing rate in bursty neu-rons depends on burst parameters such as duration of bursts or burstintervals, and the average frequency of bursts in a spike train.

The EPN is considered the equivalent to the human GPi, which hasbeen used for functional neurosurgery to treat movement disorders,e.g., Parkinson's disease and dystonia (Krauss et al., 2004; Andradeet al., 2009; Fasano et al., 2012), andwhich is currently under investiga-tion for neurosurgical treatment of TS (Houeto et al., 2005; Shahed et al.,2007). We recently showed that lesions or DBS of the EPN alleviate PPIin PPI low rats and counteract deficient PPI induced by the dopaminereceptor agonist apomorphine (Schwabe et al., 2009; Lütjens et al.,2011; Posch et al., 2012). Nevertheless, except for enhanced complexityin regular firing neurons of PPI low rats, both linear and nonlinearmeasures of firing activity did not differ between groups in the EPN.This may be explained by anatomical segregation, since the majorprojection from the NAC is to the ventral pallidum, while the majorinput into the EPN comes from the dorsal striatum and may thus bepart of a different circuitry than the mPFC and the NAC. However, onelimitation of our work is that electrophysiological recordings wereperformed in anaesthetized rats, which may have affected neuronalactivity. Nevertheless, it can be considered that the effect of anaesthesia

influenced the neuronal activity in a similar manner in both high andlow PPI groups.

Altered oscillatory activity and synchrony in different frequencybands reflects compromised neuronal information processing not onlyin movement disorders but also in neuropsychiatric disorders(Whittington, 2008; Uhlhaas and Singer, 2010). Abnormalities of neuraloscillations, for example, are found essentially in all frequency bands inschizophrenia patients (Moran and Hong, 2011).

In PPI low rats, main findings were enhanced oscillatory theta bandand reduced beta band activity in all regions tested, while changes ingamma and alpha band activity were confined to only one region. Theenhanced theta band activity is similar to findings in the centromediannucleus of the thalamus and the GPi of TS patients (Marceglia et al.,2010; Kühn et al., 2011). Different studies have linked increased thetaactivity to a general tendency for impulsive, disinhibited behaviourand poor attention with high distractibility, andwhich together impliesdeficits in inhibitory functions (Alonso-Frech et al., 2006; Hong et al.,2008; Messerotti Benvenuti et al., 2011).

Beta band activity was reduced in PPI low rats in all regions. Bothanimal and clinical studies have shown that changes in beta activityare influenced by net dopamine levels in the cortico-basal ganglialoop. Reduced DA level and increased oscillatory beta band activityhave been related to hypokinetic symptoms in PD, while DAmedicationreduced beta activity (Hammond et al., 2007; Dejean et al., 2009).

To date, the functional relationship of neuronal single unit activity andoscillatory activity has not been well established in neuropsychiatric

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disorders with deficient sensorimotor gating. Surface electroenceph-alogram (EEG) recording showed that gating of total beta andgamma frequency power was decreased in patients with schizophre-nia (Smucny et al., 2013), which is in agreement with previous workdemonstrating that sensory gating abnormalities in schizophreniaare associated with altered gamma and beta frequencies (Honget al., 2004; Hall et al., 2011). Interestingly, a recent study that useda paired pulse paradigm to assess sensorimotor gating in a mousemodel for schizophrenia found that gating-related total beta andgamma frequency power measured in the hippocampus was de-creased in α7 heterozygotic mice compared to wild type C3H mice.Within the context that beta oscillations have been related to infor-mation processing over long distances and the impact of the stimulus(Haenschel et al., 2000; Kopell et al., 2000), while gamma oscilla-tions have been associated with local processing and encoding of adiscrete object with recognizable features (Eckhorn et al., 1988;Tallon-Baudry and Bertrand, 1999), reduced gamma and betapower during sensorimotor gating may indicate a reduction in stim-ulus salience and encoding to help prevent repeated stimuli frombeing consciously processed.

Localfield potentials (LFPs) reflect synaptic potentials and accordinglyshould correlatewith neuronal discharge. In our study, however, the rela-tion between oscillatory activity and single unit activity was complex.While in the PFC of PPI high rats, e.g., high beta activity wasaccompanied by high firing rate and burst activity, in the NAC, low firingrate was accompanied by enhanced beta oscillatory activity. Such a com-plex relationship has also been observed in patients with movement dis-orders. In PD high oscillatory beta band is related to increased firing andburst activity in the STN and GPi, while in dyskinesias and dystonia,high burst activity in the GPi was accompanied by reduced beta band ac-tivity (Silberstein et al., 2003; Alonso-Frech et al., 2006).

Whether the altered neuronal activity found in PPI low rats is one ofthe pathophysiological mechanisms leading to reduced PPI or whether itis subsequent to its occurrence, is unclear. Several disorders that are ac-companied by deficient sensorimotor gating, such as schizophrenia, TSand obsessive compulsive disorder, are associated with overlapping pat-terns of abnormal neural oscillations and synchrony, which may thus benon-specific features of diverse pathophysiological processes (Swerdlowet al., 1996). However, different clinical and experimental studies sug-gest an overlap between brain disturbances in neuropsychiatric disor-ders and the neurobiology of PPI. The experimental association ofreduced PPI and neuropsychiatric disorders accompanied by deficientsensorimotor gating has thus been regarded useful for testing hypothe-ses and potential etiologies, and even for developing novel therapeutics(Swerdlow, 2013).

5. Conclusions

Our study reveals that neuronal activity in a rat model with breeding-induced PPI deficit showsfindings similar to those seen in neuropsychiat-ric disorders accompanied by disturbed sensorimotor gating. This modelmay be used to further investigate and characterize the neuronal basis ac-tivity in these circuits to better understand underlying mechanisms ofthese disorders.

Acknowledgements

This work was supported by the Tourette Syndrome Association. Theauthors wish to thank Ms. Armstrong for proofreading of the article andMr. JürgenWittek andMsAchilles for expert assistance inhistochemistry.

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