GABAergic and glutamatergic effects on nigrostriatal and mesolimbic dopamine release in the rat

Susanne Nikolaus; Markus Beu; Hans-Jörg Wittsack; Anja Müller-Lutz; Christina Antke; Hubertus Hautzel; Yuriko Mori; Eduards Mamlins; Gerald Antoch; Hans-Wilhelm Müller

doi:10.1515/revneuro-2019-0112

Publicly Available Published by De Gruyter July 3, 2020

GABAergic and glutamatergic effects on nigrostriatal and mesolimbic dopamine release in the rat

Susanne Nikolaus , Markus Beu , Hans-Jörg Wittsack , Anja Müller-Lutz , Christina Antke , Hubertus Hautzel , Yuriko Mori , Eduards Mamlins , Gerald Antoch and Hans-Wilhelm Müller

From the journal Reviews in the Neurosciences

https://doi.org/10.1515/revneuro-2019-0112

Abstract

In this review, a series of experiments is presented, in which γ-amino butyric acid (GABA)ergic and glutamatergic effects on dopamine function in the rat nigrostriatal and mesolimbic system was systematically assessed after pharmacological challenge with GABA_A receptor (R) and and N-methyl d-aspartate (NMDA)R agonists and antagonists. In these studies, [¹²³I]iodobenzamide binding to the D_2/3R was mesured in nucleus accumbens (NAC), caudateputamen (CP), substantia nigra/ventral tegmental area (SN/VTA), frontal (FC), motor (MC) and parietal cortex (PC) as well as anterior (aHIPP) and posterior hippocampus (pHIPP) with small animal SPECT in baseline and after injection of either the GABA_AR agonist muscimol (1 mg/kg), the GABA_AR antagonist bicuculline (1 mg/kg), the NMDAR agonist d-cycloserine (20 mg/kg) or the NMDAR antagonist amantadine (40 mg/kg). Muscimol reduced D_2/3R binding in NAC, CP, SN/VTA, THAL and pHIPP, while, after amantadine, decreases were confined to NAC, CP and THAL. In contrast, d-cycloserine elevated D_2/3R binding in NAC, SN/VTA, THAL, frontal cortex, motor cortex, PC, aHIPP and pHIPP, while, after bicuculline, increases were confined to CP and THAL. Taken together, similar actions on regional dopamine levels were exterted by the GABA_AR agonist and the NMDAR antagonist on the one side and by the GABA_AR antagonist and the NMDAR agonist on the other, with agonistic action, however, affecting more brain regions. Thereby, network analysis suggests different roles of GABA_ARs and NMDARs in the mediation of nigrostriatal, nigrothalamocortical and mesolimbocortical dopamine function.

Keywords: amantadine; bicuculline; D2/3 receptor; D-cycloserine; GABAA receptor; muscimol; NMDA receptor; network analysis

Introduction

The dopamin(DA)ergic system is under excitatory glutamat(GLU)ergic and inhibitory γ-amino butyric acid (GABA)ergic control (Figure 1). In the nigrostriatal system, the caudateputamen (CP) receives DAergic input from the pars compacta of the substantia nigra (SNc; Gerfen et al. 1987) and GLUergic input from neocortex and thalamus (THAL; Jayaraman 1985; Smeal et al. 2007). A further DAergic projection runs from the SNc to the neocortex, in particular to the frontal cortex (FC; Lindvall and Björklund 1974, 1978). The CP sends GABAergic efferents to the internal and the external part of the globus pallidus (GPi and GPe, respectively; Graybiel and Ragsdale 1979), and to the pars reticulata of the substantia nigra (SNr; Bolam et al. 1981). Via the direct pathway, GPi and SNr provide GABAergic input to the THAL (Albin et al. 1989). In the indirect pathway, the GPe sends GABAergic efferents to the subthalamic nucleus (STN), which, in turn, routes GLUergic fibers back to the GPe as well as to GPi and SNr (Carpenter et al. 1981; Albin et al. 1989). Further GABAergic projections run from CP and GP back to the SN (Hattori et al. 1975). Moreover, GLUergic fibers extend from THAL to neocortex (Kharazia and Weinberg 1994), from neocortex back to the THAL (Bromberg et al. 1981) and from either region back to the STN (Graybiel and Ragsdale 1979; Monakow et al. 1978).

Figure 1:

Diagram of relevant dopaminergic (black), GABAergic (blue) and glutamatergic connections (red) between substantia nigra (pars compacta [SNc], pars reticulata [SNr]), VTA, caudateputamen (CP), globus pallidus (internus [GPi], externus [GPe]), ventral pallidum (VP), nucleus subthalamicus (STN), nucleus accumbens (NAC), thalamus (THAL), hippocampus (HIPP) and neocortex.

The chart is based on the original reports given in the Introduction.

In the mesolimbic system, the nucleus accumbens (NAC) receives DAergic input from ventral tegmental area (VTA; Lisman and Grace 2005), GLUergic input from THAL (Jayrayaman 1985), prefrontal cortex (PFC; Berendse et al. 1992) and limbic regions including the HIPP (Walaas and Fonnum 1980a) as well as GABAergic input from VTA (Brown et al. 2012) and PFC (Lee et al. 2014). It sends GABAergic efferents back to the VTA (Walaas and Fonnum 1980b), to the SN (Walaas and Fonnum 1980b) and to the ventral pallidum (VP; Nauta and Cole 1978). The VP, in turn, receives GLUergic input from the STN (Turner et al. 2001) and provides GABAergic input to the THAL (Kuo and Carpenter 1973) and back to the VTA (Lisman and Grace 2005). Moreover, the VTA receives GLUergic input from the PFC (Carr and Sesack 2000) and sends DAergic efferents to neocortex (Lindvall and Björklund 1974, 1978) and HIPP (Gasbarri et al. 1991) as well as GABAergic efferents (Carr and Sesack 2000) to the neocortex. From the neocortex, GABAergic fibers run back to CP, NAC and limbic regions (Lee et al. 2014).

Dysfunctions of GABA_A receptor (R), NMDAR and D₂R-like binding sites have been implied in a variety of neurological and neuropsychiatric disorders including Parkinson’s disease, Huntington’s disease, Alzheimer’s disease, anxiety disorder, major depressive disorder and schizophrenia (for review see Nikolaus et al. 2009a,b, 2010, 2012, 2014a,b). Hence, it is of major importance to assess the functional relationships between DA and the GABAergic as well as GLUergic system. The present review, firstly, presents a series of small animal in vivo imaging studies, in which the interactions between DA and GABA_AR as well as NMDAR function were systematically investigated in relevant regions of the nigrostriatal and mesolimbic system including NAC, CP, THAL, SN/VTA, FC, motor cortex (MC), parietal cortex (PC), anterodorsal HIPP (aHIPP) and posterior HIPP (pHIPP) using small animal SPECT and MRI (Nikolaus et al. 2017, 2018a,b, 2019a,b). For all of these regions, autogradiography studies have confirmed the presence of D₂R-like binding sites (Bouthennet et al. 1987, Seeman and Grigoriadis 1987, Morelli et al. 1990).

In our investigations, [¹²³I]S-3-iodo-N-(1-ethyl-2-pyrrolidinyl) methyl-2-hydroxy-6-methoxy benzamide ([¹²³I]IBZM) binding to the D_2/3R-subtype was measured in baseline and after pharmacological challenge with either muscimol (MUS; 5-aminomethyl-isoxazol-3-ol), bicuculline (BIC; (6R)-6-[(5 S)-6-methyl-5,6,7,8-tetrahydro[1,3]dioxolo[4,5-g]isoquinolin-5-yl]furo[3,4-e][1,3]benzodioxol-8(6H)-one)), d-cycloserine (DCS; D-4-amino-isoaxazolidinon) or amantadine (AMA; 1-amino-adamantane). Due to the competition between endogenous DA and the administered D₂R-like radioligand, this approach allows to gauge regional alterations of DA availability as induced by the respective pharmacological treatment in relation to baseline (for review see Laruelle 2000). In the present survey, the pool of regional D_2/3R binding data was subjected to between-group comparisons, principal component analysis and network analysis in order to assess the specific impact of the individual challenges on regional DA function and DAergic interactions between regions.

Study 1: D_2/3R binding after GABA_AR agonistic treatment

Aim

MUS is as highly selective GABA_AR agonist (inhibition constant [K_i] = 2.7 nM; Negro et al. 1995). We had previously shown that D₂R-like imaging can be employed in order to estimate alterations of synaptic DA upon challenge with compounds such as methylphenidate and l-DOPA, which modify DA availability in the synaptic cleft (for review see Nikolaus et al. 2011). So far, effects of MUS on cerebral DA concentrations had not been assessed using in vivo small animal imaging methods. Therefore, in our first in vivo imaging study of DA and GABA interaction we set out to determine the effect of MUS on nigrostriatal and mesolimbic D_2/3R-like binding (Nikolaus et al. 2017, 2018a,b).

Methods

Fifteen adult male Wistar rats (ZETT, Heinrich-Heine University, Düsseldorf, Germany; weight: 439 ± 23 g [mean ± standard deviation {SD}]) underwent SPECT measurements in baseline (no pre-treatment) and after challenge with MUS (Sigma-Aldrich, Taufkirchen, Germany; dose: 1 mg/kg). The dose of 1 mg/kg had been previously shown to be behaviorally active at 10 to 30 min after systemic application (e. g., Corbett et al. 1991, Oakley et al. 1991). Hence, at 30 min post-challenge, animals were injected [¹²³I]IBZM (GE Healthcare, München, Germany; mean activity in baseline and post-challenge: 26 ± 4 MBq) into the lateral tail vein. [¹²³I]IBZM exhibits a high affinity for the D₂R-like subtype (D₂: K_i = 1.6 nM, D₃: K_i = 2.2 nM; Videbaek et al. 2000). Furthermore, the benzamide analogue [¹¹C]raclopride displays similar affinities for the high- and low-affinity state of the D₂R-like subtype (Seneca et al. 2006), which implies that also [¹²³I]IBZM binds to D₂R-like binding sites in both configurations and that the regional binding potentials (BPs) obtained in the present studies reflect the regional densities of D_2/3Rs as such, irrespective of the individual contributions of either affinity state.

In our series of studies, we employed a two-modality imaging approach, in which regional volumes of interest (VOIs) were defined on overlays of functional SPECT and morphological MRI data sets. D_2/3R binding data were acquired with the “TierSPECT” (field of view: 90 mm; spatial resolution: 3.4 mm for ¹²³I; Schramm et al. 2000) from 45 to 105 min after radioligand administration, corresponding to 75–135 min post-challenge and coinciding with the time of maximum striatal DA levels (75 to 195 min min post-challenge) upon MUS administration (Santiago and Westerink. 1992). Radioligand administration and SPECT measurements were performed in animals anaesthetized with ketaminehydrochloride (Ketavet^®, Pharmacia GmbH, Erlangen, Germany; dose: 100 mg/kg) and xylazinehydrochloride (Rompun^® Bayer, Leverkusen, Germany; dose: 5 mg/kg i.p.). Morphological images of the rat heads were obtained with a dedicated small animal MRI (MRS3000 Pre-clinical MRI, 3.0 T, MR Solutions, Guildford, UK; coil diameter: 54 mm, acquisition time: 553 s, spatial resolution: 0.25 × 9.25 × 0.69 mm).

Findings

Figure 2A shows characteristic coronal images of regional [¹²³I]IBZM accumulations in baseline and after challenge with MUS at different positions from Bregma (Paxinos and Watson 2014) obtained in the same rat.

Figure 2:

[¹²³I]IBZM binding to D_2/3R in baseline and after challenge with the GABA_AR agonist muscimol (1 mg/kg i.p.) and the GABA_AR antagonist bicuculline (1 mg/kg i.p.) at different positions from Bregma (Paxinos and Watson, 2014).

The white arrows indicate the significant reductions after challenge relative to baseline. All images show BPs (ratios of specific binding to non-specific binding in the cerebellum [CER]). Data were evaluated with PMOD (version 3.5, PMOD Technologies Ltd., Zürich, Switzerland). Regional VOIs (NAC, CP, SN/VTA, THAL, FC. MC. PC, aHIPP, pHIPP) were defined on overlays of SPECT images of [¹²³I]IBZM binding (formerly coregistered with MRI) with the standard Paxinos rat brain MRI (Schiffer et al., 2006). Abbreviations: NAC, nucleus accumbens; CP, caudateputamen; SN, substantia nigra/ventral tegmental area; THAL, thalamus; FC, frontal cortex; MC, motor cortex; PC, parietal cortex; aHIPP, anteriodorsal hippocamus; pHIPP, posterior hippocampus; CER, cerebellum; BP, binding potential.

MUS significantly (non-parametric Wilcoxon signed rank test for paired samples, two-tailed, α ≤ 0.05) reduced D_2/3R BPs in NAC (median: 74% of baseline, 25-percentile: 52%, 75-percentile: 99%), CP (median: 82% of baseline, 25-percentile: 74%, 75-percentile: 92%), SN/VTA (median: 74% of baseline, 25-percentile: 54%, 75-percentile: 84%), THAL (median: 81% of baseline, 25-percentile: 62%, 75-percentile: 102%) and pHIPP (median: 77% of baseline, 25-percentile: 68%, 75-percentile: 100%; Figure 4).

Previous in vivo microdialysis studies have shown that MUS infusions (0.1–40 μM) into the PFC (Matsumoto et al. 2003) and into the VTA (Westerink et al. 1996, 1998) reduced DA concentrations in CP (Matsumoto et al. 2003; Westerink et al. 1996) and PFC (Westerink et al. 1998). In contrast, however, MUS infusions (10–100 µM) into SN (Santiago and Westerink 1992) and GP (Cobb and Abercrombie 2002, 2003) augmented DA concentrations in CP and SN, respectively. Regional alterations of DA availability are commonly assessed in vivo by two measurements of D₂R-like binding – once before and once after the administration of compounds, which augment DA concentration in the synaptic cleft (for review see Laruelle 2000). The principle underlying this approach is the competition between endogenous DA and the administered D₂R-like radioligand. Thus, our finding of reduced D_2/3R binding in NAC, CP, SN/VTA, THAL and pHIPP can be interpreted to reflect increased availability of DA in these regions, which is in agreement with the precedent findings of elevated DA levels in CP (Santiago and Westerink 1992) and SN (Cobb and Abercrombie 2002, 2003) upon administration of MUS into SN and GP, respectively. Interestingly, however, our results after systemic treatment as well as the findings obtained after infusion into SN (Santiago and Westerink 1992) and GP (Cobb and Abercrombie 2002, 2003) contradict the reports of Westerink et al. (1996, 1998) and Matsumoto et al. (2003), who found reductions of striatal and prefrontal DA levels upon administration of MUS into PFC and VTA, respectively. This inconsistency highlights the region-specificy of intracerebral MUS action.

Study 2: D_2/3R binding after GABA_AR antagonistic treatment

Aim

BIC is a highly selective GABA_AR antagonist (K_i = 3.04 nM; Ito et al. 1988). So far, effects of BIC on cerebral DA concentrations had not been assessed using in vivo small animal imaging methods. Hence, in our second study, we set out to determine the effect of BIC on nigrostriatal and mesolimbic D_2/3R-like binding (Nikolaus et al. 2017, 2018a).

Methods

Sixteen adult male Wistar rats (weight: 433 ± 28 g) underwent SPECT measurements in baseline and after challenge with BIC (Sigma-Aldrich, Taufkirchen, Germany; dose: 1 mg/kg). This dose had been previously shown to be subconvulsant (Girardi et al. 1987), but behaviorally active after systemic application (Zhang and Cranney 2008). Therefore, at 30 min post-challenge, animals were injected [¹²³I]IBZM (mean activity in baseline and post-challenge: 26 ± 5 MBq) into the lateral tail vein. Again, D_2/3R binding data were acquired with the “TierSPECT” (see above) from 45 to 105 min after radioligand administration), corresponding to 75–135 min post-challenge and coinciding with the time of maximum striatal DA levels (75–195 min min post-challenge) upon administration of BIC (Santiago and Westerink. 1992).

Findings

Figure 2B shows characteristic coronal images of regional [¹²³I]IBZM accumulations in baseline and after challenge with BIC at different positions from Bregma (Paxinos and Watson 2014) obtained in the same rat.

BIC significantly (non-parametric Wilcoxon signed rank test for paired samples, two-tailed, α ≤ 0.05) elevated BPs in CP (median: 112% of baseline, 25-percentile: 98%, 75-percentile: 121%) and THAL (median: 116% of baseline, 25-percentile: 98%, 75-percentile: 131%; Figure 4).

Previous in vivo microdialysis studies have shown that BIC infusions (20–100 µM) into SN (Santiago and Westerink 1992; Westerink et al. 1992), VTA (Ikemoto et al. 1997) and PFC (Karreman and Moghaddam 1996; Matsumoto et al. 2003) increased DA levels in SN and CP (Santiago and Westerink 1992; Westerink et al. 1992) as well as NAC (Ikemoto et al. 1997) and PFC (Karreman and Moghaddam 1996; Matsumoto et al. 2003). Thus, if the data obtained in the present study after BIC are interpreted to reflect reductions of endogenous DA, the obtained findings do not agree with the precedent in vivo microdialysis studies. This inconsistency, may be accounted for by differences in applied doses (2.7 mM in our study vs 10–100 μM [Aono et al. 2008; Ikemoto et al. 1997; Karreman and Moghaddam 1996; Matsumoto et al. 2003; Santiago and Westerink 1992; Smolders et al. 1995; Westerink et al. 1992; Yan et al. 1999]) and mode of application (systemic approach vs intracerebral injection).

Study 3: D_2/3R binding after NMDAR agonistic treatment

Aim

DCS is an NMDAR agonist, which binds with high affinity to the glycine_B NMDAR subunit (K_i = 2.33; Monahan et al. 1989). So far, effects of DCS on cerebral DA concentrations had not been assessed using in vivo small animal imaging methods. In our next study (Nikolaus et al. 2019a) we set out to determine the effect of DCS on nigrostriatal and mesolimbic D_2/3R-like binding.

Methods

Fifteen adult male Wistar rats (weight: 374 ± 40 g) underwent SPECT measurements in baseline and after challenge with DCS (Sigma-Aldrich, Taufkirchen, Germany; dose: 20 mg/kg). This dose had been previously shown to be behaviorally active after systemic application (Zlomuzica et al. 2007). At 30 min post-challenge, animals were injected [¹²³I]IBZM (mean activity in baseline and post-challenge: 28 ± 4 MBq) into the lateral tail vein. D_2/3R binding data were acquired with the “TierSPECT” (see above) from 45 to 105 min after radioligand administration, corresponding to 75 to 135 min post-challenge and coinciding with the time of maximum striatal DA levels (80 to 160 min min post-challenge after administration of DCS (Gandolfi et al. 1994).

Findings

Figure 3A shows characteristic coronal images of regional [¹²³I]IBZM accumulations in baseline and after challenge with DCS at different positions from Bregma (Paxinos and Watson 2014) obtained in the same rat.

DCS significantly (non-parametric Wilcoxon signed rank test for paired samples, two-tailed, α ≤ 0.05) elevated BPs in NAC (median: 112% of baseline, 25-percentile: 104%, 75-percentile: 127%), SN/VTA (median: 127% of baseline, 25-percentile: 104%, 75-percentile: 152%), THAL (median: 116% of baseline, 25-percentile: 93%, 75-percentile: 135%), MC (median: 119% of baseline, 25-percentile: 104%, 75-percentile: 152%), PC (median: 110% of baseline, 25-percentile: 101%, 75-percentile: 142%), aHIPP (median: 119% of baseline, 25-percentile: 97%, 75-percentile: 150%) and pHIPP (median: 127% of baseline, 25-percentile: 93%, 75-percentile: 153%; Figure 4).

So far, the effect of systemic DCS (3 and 30 mg/kg) had only been assessed in the rat CP, where either a significant elevation (Bennett and Gronier 2005) or no effect (Gandolfi et al. 1994) on DA efflux was observed. The former contradicts our findings, which did not show an alteration of D_2/3R binding in the CP after 20 mg/kg DCS. Likely reasons for this inconsistency are the differences in methods (in vivo SPECT in our study vs assessment of striatal homogenates with high pressure liquid chromatography), mode of application (systemic approach vs incubation of striatal slices) and age of subjects (adult rats with a mean weight of 397 ± 49 g in our study vs adolesent animals, weighing between 250 and 350 g).

Study 4: D_2/3R binding after NMDAR antagonistic treatment

Aim

The NMDAR antagonistic AMA binds to the phencyclidine NMDAR binding site (K_i = 10 μM; Kornhuber et al. 1991, 1993). After chronic treatment with AMA (200 mg/day for at least 10 days), two in vivo imaging studies of striatal D₂R-like binding have been conducted on Parkinsonian patients with [¹¹C]raclopride as radioligand (Moresco et al. 2002; Volonte et al. 2001). In both investigations, striatal D₂R-like binding was significantly elevated, implying that, at least in individuals suffering from Parkinson’s disease, the AMA-induced elevation of endogenous DA was not high enough to elicit a detecable competition with the exogenous radioligand. Therefore, in our next study (Nikolaus et al. 2019a,b) we set out to determine the effect of AMA on nigrostriatal and mesolimbic D_2/3R-like binding in healthy rats, which–contrasting with the human subjects of Volonte et al. (2001) as well es Moreso et al. (2002) - had not been inflicted Parkinsonian lesions.

Methods

Twenty adult male Wistar rats (weight: 414 ± 48 g) underwent SPECT measurements in baseline and after challenge with AMA (Sigma-Aldrich, Taufkirchen, Germany; dose: 40 mg/kg). The 40 mg/kg dose had formerly been shown to be behaviorally active in rats after systemic application (Maj et al. 1972). At 30 min post-challenge, animals were injected [¹²³I]IBZM (mean activity in baseline and post-challenge: 28 ± 4 MBq) into the lateral tail vein. D_2/3R binding data were acquired with the “TierSPECT” (see above) from 45 to 105 min after radioligand administration, corresponding to 75–135 min post-challenge and coinciding with the time of maximum striatal DA levels (60 to 90 min post-challenge) after administration of AMA (Takahashi et al. 1996).

Findings

Figure 3B shows characteristic coronal images of regional [¹²³I]IBZM accumulations in baseline and after challenge with AMA at different positions from Bregma (Paxinos and Watson 2014) obtained in the same rat.

AMA significantly (non-parametric Wilcoxon signed rank test for paired samples, two-tailed, α ≤ 0.05) diminished BPs in NAC (median: 90% of baseline, 25-percentile: 70%, 75-percentile: 101%), CP (median: 82% of baseline, 25-percentile: 72%, 75-percentile: 106%) and THAL (median: 79% of baseline, 25-percentile: 71%, 75-percentile: 106%; Figure 4).

So far, the effect of systemic AMA on DA had only been assessed in the CP. The present finding of elevated DA in this region is consistent with the results obtained in various studies after AMA doses between 40 and 100 mg/kg i.p. or s.c. (Scatton et al. 1970, Quack et al. 1995, Takahashi et al. 1996). They are not in agreement, however, with the findings of Maj et al. (1972) as well as Bak et al. (1972), who did not observe changes in striatal DA after 10 to 100 mg/kg AMA i.p. Again, this may be accounted for by differences in methods: the precedent investigations either employed invasive in vivo methods such as microdialysis (Quack et al. 1995, Takahashi et al. 1996) or ex vivo methods involving the preparation of striatal tissue extracts (Bak et al. 1972, Maj et al. 1972, Scatton et al. 1970). Scatton et al. (1970), who found an increase of striatal DA, sacrificed their rats 2 h post-challenge, whereas Bak et al. (1972) as well as Maj et al. (1972), who observed no significant effects, killed their animals only 1 h post-challenge. Thus, it may be concluded that a time of 1 h post-challenge is not sufficient to detect changes in DA levels, if combined with ex vivo methods, precluding AMA action to take place in living compartments.

Comparisons of regional D_2/3R binding between treatments

Regional D_2/3R binding (D_2/3R BP after challenge expressed as percentage of baseline) was compared between treatment groups (MUS, BIC, DCS, AMA). Since in neither treatment group data were uniformly normally distributed (Kolmogorov-Smirnov test, 0001 ≤ p ≤ .20), groups were compared with non-parametric Kruskal-Wallis analysis of variance (two-tailed, α ≤ 0.05) and Mann-Whitney U tests (two-tailed, α ≤ 0.05). No corrections were made for multiple comparisons. Tests were performed with IBM SPSS Statistics 23 (IBM SPSS Software Germany, Ehningen, Germany).

Figure 3 shows the regional D_2/3R BPs after challenge with MUS, BIC, DCS and AMA expressed as percentages of baseline. The Kruskal-Wallis test yielded significant differences of BP percentages of baseline between treatment groups in all brain regions (NAC: p ≤ 0.0001, CP: p = 0.001, SN/VTA: p ≤ 0.0001, THAL: p = 0.001, FC: p = 0.052, MC: p = 0.004, PC: p = 0.001, aHIPP: p = 0.010, pHIPP: p = 0.009). Individual comparisons between treatment groups with the Mann-Whitney U test yielded (1) significant decreases of BP percentages of baseline after MUS relative to BIC in NAC (p = 0.037), CP (p = 0.004), SN/VTA (p = 0.001), THAL (p = 0.006), PC (p = 0.044) and aHIPP (p = 0.044); (2) significant decreases of BP percentages of baseline after MUS relative to DCS in NAC (≤ 0.0001), CP (p ≤ 0.0001), SN/VTA (p ≤ 0.0001), THAL (p = 0.004), FC (p = 0.026), MC (p = 0.010), PC (p ≤ 0.0001), aHIPP (p = 0.002) and pHIPP (p = 0.003); (3) significant decreases of BP percentages of baseline after MUS relative to AMA in SN/VTA (p = 0.047) and PC (p = 0.037); (4) significant decreases of BP percentages of baseline after BIC relative to DCS in PC (p = 0.013); (5) significant decreases of BP ratios after BIC relative to AMA in NAC (p = 0.013), CP (p = 0.017), THAL (p = 0.002) and MC (p = 0.050); (6) significant decreases of BP percentages of baseline after DCS relative to AMA in NAC (p ≤ 0.0001), CP (p = 0.012), SN/VTA (p = 0.017), THAL (p = 0.003), FC (p = 0.045), MC (p = 0.001), PC (p = 0.025), aHIPP (p = 0.012) and pHIPP (p = 0.009).

Figure 3:

[¹²³I]IBZM binding to D_2/3R in baseline and after challenge with the NMDAR agonist d-cycloserine (20 mg/kg i.p.) and the NMDAR antagonist amantadine (40 mg/kg i.p.) at different positions from Bregma (Paxinos and Watson, 2014).

The white arrows indicate the significant reductions after challenge relative to baseline. All images show BPs (ratios of specific binding to non-specific binding in the cerebellum [CER]). Data were evaluated with PMOD (version 3.5, PMOD Technologies Ltd., Zürich, Switzerland). Regional VOIs (NAC, CP, SN/VTA, THAL, FC. MC. PC, aHIPP, pHIPP) were defined on overlays of SPECT images of [¹²³I]IBZM binding (formerly coregistered with MRI) with the standard Paxinos rat brain MRI (Schiffer et al., 2006). Abbreviations: NAC, nucleus accumbens; CP, caudateputamen; SN, substantia nigra/vental tegmental area; THAL, thalamus; FC, frontal cortex; MC, motor cortex; PC, parietal cortex; aHIPP, anteriodorsal hippocamus; pHIPP, posterior hippocampus; CER, cerebellum; BP, binding potential.

Figure 4:

Binding potentials (BPs; expressed as ratios to baseline) after challenge with 1 mg/kg muscimol (red), 40 mg/kg amantadine (pink), 1 mg/kg bicuculline (blue) and 20 mg/kg d-cycloserine (light blue).

Estimations of regional D_2/3R binding potentials (BPs) for baseline and challenges were obtained according to the simplified reference tissue model (Ichise et al., 2001) by computing ratios of radioactvity counts obtained in the specifically-bound compartments (nucleus accumbens [NAC], caudateputamen [CP], thalamus [THAL], substantia nigra/ventral tegmental area [SN/VTA], frontal cortex [FC], motor cortex [MC], parietal cortex [PC], anterodorsal hippocampus [aHIPP], posterior hippocampus [pHIPP]) to radioactivity counts in the cerebellum (CER). Rendered are medians and 25-/75-percetiles (boxes). The circles represent the individual animals. For significant between-group differences, the respective p values are given (Wilcoxon Mann Whitney U test for unrelated samples, two-tailed, a = .05). Calculations were performed with IBM SPSS Statistics 23 (IBM SPSS Software Germany, Ehningen, Germany).

Principal component analysis

Using an orthogonal transformation, principal component analysis converts a set of possibly correlated variables (in the present analysis: BP percentages relative to baseline in NAC, CP, SN/VTA, THAL, FC, MC, PC, aHIPP and pHIPP with all treatments pooled) into a set of linearly uncorrelated variables, the so-called principal components. In the resulting new coordinate system, the greatest (second greatest, third greatest etc.) variance of the data lies on the first (second, third etc.) coordinate, yielding the first (second, third etc.) PrC, which, thus, accounts for the greatest (second greatest, third greatest etc.) variability in the data set. Here, the principal component analysis was performed according to Hotelling (1933), yielding eigenvalues (the PrCs) and the eigenvector components contributing to the individual principal components. The principal component analysis was performed using Rapid Miner (version 5.3., Rapid-I GmbH, Dortmund, Germany).

Principal component analyis revealed that 56% of the cumulative variance of the whole data set (D_2/3R BPs expressed as percentage of baseline with all treatments pooled) could be explained by the first principal component (PrC1), while PrC2 to PrC9 added up to 72.9, 80.7, 85.6, 90.1, 94.2, 96.7%, 98,8 and 100%, respectively, of the cumulative variance (Table 1). The eigenvector component with the highest contribution (38.4%) to PrC1 was the D_2/3R BP (expressed as percentage of baseline) in the CP. The major eigenvector components of the other principal components were the BP percentages of baseline in SN/VTA (43.2%, PrC2 and 54.2%, PrC6), FC (39.7%, PrC3), THAL (41.3%, PrC4), PC (75.6%, PrC5), MC (50%, PrC7 and 49.7%, PrC8) and NAC (58.5%, PrC9).

Table 1:

Main results of principal component analysis.

PrCs	% of VAR accounted for by PrCs	Regional eigenvectors with the highest contribution to PrCs	% of VAR accounted for by the eigenvectors with the highest contribution to PrCs
PrC1	56.0	CP	38.4
PrC2	17.0	SN/VTA	43.2
PrC3	7.8	FC	39.7
PrC4	4.9	THAL	41.3
PrC5	4.5	PC	76.0
PrC6	4.1	SN/VTA	54.2
PrC7	2.5	MC	50.0
PrC8	2.0	MC	49.7
PrC9	1.2	NAC	58.5

PrC, principal component; VAR, variance.

Network analysis

Network analysis permits to analyse the network structure of variables - pre-defined so-called “nodes” - by estimating path coefficients, which describe the “strength” of the individual connections. Here, we assessed the associations between D_2/3R binding in the individual brain regions (CP, NAC, THAL, SN/VTA, FC, MC, PC, aHIPP and pHIPP), based (1) on the regional D_2/3R BPs in baseline with all four treatments groups pooled, (2) on the regional D_2/3R BPs (expressed as percentage of baseline) obtained after the individual challenges with MUS, AMA, DCS and BIC, and, (3) on the regional D_2/3R BPs (expressed as percentage of baseline) with either GABA_AR agonistic (MUS) and NMDAR antagonistic (AMA) or GABA_AR antagonistc (BIC) and NMDAR agonistic (DCS) challenges pooled. Covariance matrices were estimated with pairwise Mixed Graphical Models (MGMs; Haslbeck and Waldorp 2015, Yang et al. 2014). Graphical L₁ (lasso) regularized regression was employed in order to increase matrix sparsity (Friedman et al. 2008). In baseline and after each treatment, missing binding data were replaced by the respective average BP. MGMs were computed using JASP (version 0.10.2.0, JASP Team 2019, ©2013-2019 University of Amsterdam).

Network analysis of baseline BPs yielded 14 out of 36 possible connections (Figure 5A; sparsity: 0.611). Table 2 shows the individual path coeffients (c) with the strongest associations between CP and NAC (c = 0.566), FC and MC (c = 0.414), CP and THAL (c = 0.366), THAL and aHIPP (c = 0.348), SN/VTA and pHIPP (c = 0.330), MC and aHIPP (c = 0.242) and MC and PC (c = 0.211).

Figure 5:

Connections between nucleus accumbes (NAC), caudeputamen (CP), thalamus (THAL), substantia nigra/ventral tegmental area (SN/VTA), frontal cortex ( FC), motor cortex (MC), parietal cortex (PC), anterodorsal hippocampus (aHIPP) and posterior hippocampus (pHIPP).

(A) Connections obtained in baseline. (B) Connections obtained after the individual challenges with muscimol, bicuculline, amantadine or d-cycloserine. Positive and negative association are represented by green and red lines, respectively. The size of the lines indicates the strength of the individual connections. Models were estimated with JASP (version 0.10.2.0, JASP Team, 2019, ©2013–2019 University of Amsterdam).

Table 2:

Path coefficient matrix in baseline obtained with mixed graphical modelling of all D_2/3R binding potentials irrespective of treatment assignment. D_2/3R binding was assessed in the following brain regions: nucleus accumbens (NAC), caudateputamen (CP), thalamus (THAL), substantia nigra/ventral tegmental area (SN/VTA), frontal cortex (FC), motor cortex (MC), parietal cortec (PC), anterodorsal hippocampus (aHIPP) and posterior hippocampus (pHIPP).

	NAC	CP	THAL	SN/VTA	FC	MC	PC	aHIPP	pHIPP
NAC	0.000	0.566	0.000	0.114	0.000	0.000	0.000	0.000	0.146
CP		0.000	0.366	0.000	0.000	0.000	-0.098	0.000	0.000
THAL			0.000	0.062	0.000	0.000	0.000	0.346	0.046
SN/VTA				0.000	0.000	0.000	0.000	0.000	0.330
FC					0.000	0.414	0.081	0.000	0.000
MC						0.000	0.211	0.242	0.000
PC							0.000	0.000	0.000
aHIPP								0.000	0.076
pHIPP									0.000

When the regional D_2/3R BPs obtained after the individual challenges were subjected to network analysis, five out of 36 (sparsity: 0.861) and nine out of 36 possible connections (sparsity: 0.750) were obtained for MUS and AMA, respectively. Moreover, network analysis yielded four out of 36 (sparsity: 0.0889) and three out of 36 possible connections (sparsity: 0.917) for BIC and DCS, respectively (Figure 5B, Table 3). After treatment with the GABA_AR agonistic MUS, the strongest associations were found between MC and PC (c = 0.971), CP and THAL (c = 0.459) and SN/VTA and pHIPP (c = 0.314). Treatment with the GABA_AR antagonistic BIC yielded the strongest associations between CP and NAC (c = 0.744), SN/VTA and THAL (c = 0.386), FC and aHIPP (c = 0.321) and MC and PC (c = 0.235). After treatment with the NMDAR agonistic DCS, the strongest associations were found between FC and MC and CP and NAC (c = 0.595, each). After the NMDAR antagonistic AMA, the strongest associations existed between FC and MC (c = 0.626), CP and NAC (c = 0.472), THAL and aHIPP (c = 0.445) and SN/VTA and pHIPP (c = 0.311).

Table 3:

Path coefficient matrix after challenge with muscimol (MUS), bicuculline (BIC), d-cycloserine (DCS) and amantadine (AMA) obtained with mixed graphical modelling of D_2/3R binding potentials (expressed as percentage of baseline). D_2/3R binding was assessed in the following brain regions: nucleus accumbens (NAC), caudateputamen (CP), thalamus (THAL), substantia nigra/ventral tegmental area (SN/VTA), frontal cortex (FC), motor cortex (MC), parietal cortec (PC), anterodorsal hippocampus (aHIPP) and posterior hippocampus (pHIPP).

	NAC	CP	THAL	SN/VTA	FC	MC	PC	aHIPP	pHIPP
NAC
MUS	0.000	0.323	0.000	0.000	0.000	0.000	0.000	0.000	0.000
BIC	0.000	0.744	0.000	0.000	0.000	0.000	0.000	0.000	0.000
DCS	0.000	0.396	0.000	0.000	0.000	0.000	0.000	0.000	0.000
AMA	0.000	0.472	0.000	0.122	0.000	0.000	0.000	0.000	0.000
CP
MUS		0.000	0.459	0.000	0.000	0.000	0.000	0.000	0.000
BIC		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
DCS		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
AMA		0.000	0.175	0.163	0.000	0.000	0.000	0.000	0.000
THAL
MUS			0.000	0.000	0.000	0.000	0.000	0.106	0.000
BIC			0.000	0.386	0.000	0.000	0.000	0.000	0.000
DCS			0.000	0.000	0.000	0.000	0.000	0.198	0.000
AMA			0.000	0.000	0.000	0.000	0.000	0.445	0.177
SN/VTA
MUS				0.000	0.000	0.000	0.000	0.000	0.314
BIC				0.000	0.000	0.000	0.000	0.000	0.000
DCS				0.000	0.000	0.000	0.000	0.000	0.000
AMA				0.000	0.000	0.000	0.000	0.000	0.311
FC
MUS					0.000	0.000	0.000	0.000	0.000
BIC					0.000	0.000	0.000	0.321	0.000
DCS					0.000	0.595	0.000	0.000	0.000
AMA					0.000	0.628	0.000	0.000	0.000
MC
MUS						0.000	0.971	0.000	0.000
BIC						0.000	0.235	0.000	0.000
DCS						0.000	0.000	0.000	0.000
AMA						0.000	0.000	0.115	0.000
PC
MUS							0.000	0.000	0.000
BIC							0.000	0.000	0.000
DCS							0.000	0.000	0.000
AMA							0.000	0.000	0.000
aHIPP
MUS								0.000	0.000
BIC								0.000	0.000
DCS								0.000	0.000
AMA								0.000	0.000
pHIPP
MUS									0.000
BIC									0.000
DCS									0.000
AMA									0.000

When regional D_2/3R BPs were subjected to network analysis with either GABA_AR agonistic and NMDAR antagonistic (MUS/AMA) or GABA_AR antagonistc and NMDAR agonistic (BIC/DCS) challenges pooled, six out of 36 possible connections for both MUS/AMA and BIC/DCS (sparsity: 0.833, each; Figure 6A and B, respectively, Table 4). After MUS and AMA combined, the strongest associations existed between FC and MC (c = 0.483), CP and THAL (c = 0.394) and MC and aHIPP (c = 0.222). After BIC and DCS combined, CP and NAC (c = 0.508), MC and PC (c = 0.475) and MC and FC (c = 0.284) were most strongly associated.

Figure 6:

Connections between nucleus accumbes (NAC), caudeputamen (CP), thalamus (THAL), substantia nigra/ventral tegmental area (SN/VTA), FC, MC, parietal cortex (PC), anterodorsal hippocampus (aHIPP) and posterior hippocampus (pHIPP).

(A) Connections obtained after pooling of either GABA_AR agonistic (muscimol) and NMDAR antagonistic challenge (amantadine) or (B) GABA_AR antagonistic (bicuculline) and NMDAR agonistic challenge (d-cycloserine). Positive associations are represented by green lines. No negative associations were obtained. The size of the lines indicates the strength of the individual connections. Models were estimated with JASP (version 0.10.2.0, JASP Team, 2019, ©2013–2019 University of Amsterdam).

Table 4:

Path coefficient matrix after pooling of GABA_AR agonistic and NMDAR antagonistic (muscimol [MUS], amantadine [AMA]) as well as GABA_AR antagonistc and NMDAR agonistic challenges (bicuculline [BIC], d-cycloserine [DCS]) obtained with mixed graphical modelling of D_2/3R binding potentials (expressed as percentage of baseline). D_2/3R binding was assessed in the following brain regions: nucleus accumbens (NAC), caudateputamen (CP), thalamus (THAL), substantia nigra/ventral tegmental area (SN/VTA), frontal cortex (FC), motor cortex (MC), parietal cortec (PC), anterodorsal hippocampus (aHIPP) and posterior hippocampus (pHIPP).

	NAC	CP	THAL	SN/VTA	FC	MC	PC	aHIPP	pHIPP
NAC
MUS/AMA	0.000	0.156	0.000	0.000	0.000	0.000	0.000	0.000	0.017
BIC/DCS	0.000	0.508	0.000	0.000	0.000	0.000	0.000	0.000	0.000
CP
MUS/AMA		0.000	0.394	0.000	0.000	0.100	0.000	0.000	0.000
BIC/DCS		0.000	0.208	0.077	0.000	0.000	0.000	0.000	0.000
THAL
MUS/AMA			0.000	0.000	0.000	0.000	0.000	0.000	0.000
BIC/DCS			0.000	0.087	0.000	0.000	0.000	0.000	0.000
SN/VTA
MUS/AMA				0.000	0.000	0.000	0.000	0.000	0.000
BIC/DCS				0.000	0.000	0.000	0.000	0.000	0.000
FC
MUS/AMA					0.000	0.483	0.000	0.000	0.000
BIC/DCS					0.000	0.284	0.000	0.000	0.000
MC
MUS/AMA						0.000	0.000	0.222	0.000
BIC/DCS						0.000	0.475	0.000	0.000
PC
MUS/AMA							0.000	0.000	0.000
BIC/DCS							0.000	0.000	0.000
aHIPP
MUS/AMA								0.000	0.000
BIC/DCS								0.000	0.000
pHIPP
MUS/AMA									0.000
BIC/DCS									0.000

Discussion

Summary of findings

After treatment with the GABA_AR agonistic MUS, D_2/3R BP percentages of baseline were the lowest, followed by the NMDAR antagonistic AMA (with significant differences only in SN/VTA and PC). In turn, D_2/3R BP percentages of baseline after treatment with the NMDAR agonistic DCS were the highest, followed by the GABA_AR antagonistic BIC (with a significant difference only in PC). This implies that GABA_AR agonist and NMDAR antagonist on one side and GABA_AR antagonist and NMDAR agonist on the other induced similar effects on D_2/3R binding and DA levels.

If we regard the significant differences relative to baseline after the individual pharmacological challenges, a pattern of region-specifity becomes apparent: while MUS reduced D_2/3R binding (and increased DA levels) throughout the nigrostriatal and mesolimbic system (NAC, CP, SN/VTA, THAL and pHIPP), AMA-induced reductions of D_2/3R binding (and elevations of DA) were confined to NAC, CP and THAL. Contrarily, while DCS augmented D_2/3R binding (and decreased DA levels) throughout the nigrostriatal and mesolimbocortical (NAC, SN/VTA, THAL, FC, MC. PC, aHIPP and pHIPP), BIC-induced elevations of D_2/3R binding (and decreases of DA) were limited to CP and THAL.

Both principal component and network analysis indicate intense functional relations between regions: the former identified D_2/3R BPs (expressed as percentage of baseline) in CP (PrC1), SN/VTA (PrC2, PrC6), FC (PrC3), THAL (PrC4), PC (PrC5), MC (PrC7, PrC8) and NAC (PrC9) as the most prominent eigenvector components, which represented the very “nodes”, between which the latter identified the majority of non-zero edges with path coefficients up to 0.971.

This poses the questions, how the DA-reducing effects of MUS and AMA and the DA-raising effects of BIC and DCS, their region-specifity and the apparent interactions between regions can be accounted for by the agonstic and antagonistic properties of these compounds.

D_2/3R binding in baseline

In baseline, we assessed “normal” D_2/3R binding in the individual brain regions without infliction of GABAergic or GLUergic challenges on DA function. As outlined above, the CP receives DAergic input from the SNc (Gerfen et al. 1987), GLUergic input form THAL and neocortex (Jayrayaman 1985; Smeal et al. 2007) and GABAergic input from the neocortex (Lee et al. 2014), while it sends GABAergic efferents to GPi, GPe and SN (Hattori et al. 1975; Graybiel and Ragsdale 1979; Bolam et al. 1981). Via the direct and indirect pathway, GPi and GPe provide GABAergic input to THAL and STN (Albin et al. 1989). The THAL sends further GLUergic efferents to the neocortex (Kharazia and Weinberg 1994), whereas the STN routes GLUergic fibers to the VP, as well as back to GP and SN (Carpenter et al. 1981; Albin et al. 1989; Turner et al. 2001). The NAC receives DAergic input from VTA (Lisman and Grace 2005), GLUergic input from THAL, PFC and HIPP (Walaas and Fonnum 1988a0; Jayrayaman 1985; Berendse et al. 1992) and GABAergic input from VTA and PFC (Brown et al. 2012; Lee et al. 2014), while it sends GABAergic efferents to VP and SN as well as back to the VTA (Nauta and Vole 1978; Walaas and Fonnum 1980b). The VP, in turn, provides GABAergic input to the THAL (Kuo and Carpenter 1973) and back to the VTA (Lisman and Grace 2005).

The network analysis of regional BPs in baseline yielded positive associations between D_2/3R binding (and DA levels) in CP and NAC, CP and THAL, THAL and aHIPP, NAC and SN/VTA, NAC and pHIPP, SN/VTA and pHIPP, aHIPP and MC, MC and PC, MC and FC and FC and PC. Since all of these regions are either directly or indirectly linked (see above), the model obtained by network analysis of D_2/3R binding in baseline provides a suitable standard for the subsequent analyses of DA function after the indivdual GABAergic and GLUergic challenges.

D_2/3R binding after the individual GABAergic and GLUergic challenges

MUS After treatment with the GABA_AR agonistic MUS, network analysis of the regional BPs (expressed as percentage of baseline) yielded positive associations between D_2/3R binding in CP and THAL, CP and NAC, SN/VTA and pHIPP and MC and PC. Thus, percentual reductions of D_2/3R binding (and increases of DA) relative to baseline in CP, SN/VTA and MC were accompanied by percentual reductions of D_2/3R binding (and increases of DA) in THAL, NAC, pHIPP and PC, respectively, and vice versa. This direct relationship is plausible, since the effects of MUS on D_2/3R binding (and DA levels) are unidirectional and lie in the same order of magnitude across regions.

In order to account for these findings, we propose the following mechanism of action: the increased GABA_AR agonistic action in the CP may be conceived to inhibit the SN via striatonigral GABAergic projections. Increased inhibition of GPi and GPe via GABAergic striatopallidal fibers lead to a disinhibition of SN and THAL. The consequence is an increase of nigral and thalamic DA, as reflected by the reduction of D_2/3R binding under the above experimental conditions.

From the THAL, GLUergic efferents run to the neocortex (Jarayaman 1985), which, in turn, sends GLUergic projections back to the CP (Kharazia and Weinberg 1994). Disinhibition of the THAL elevates GLUergic excitation of the neocortex, enhancing the excitatory input of corticostriatal fibers. Excitatory GLUergic afferents likely compensate the GABA_AR agonistic action in the CP, leading to the elevation of neostriatal DA concentrations reflected by the observed reduction of D_2/3R binding.

The NAC receives inhibitory GABAergic afferents from PFC (Lee et al. 2014) and VTA (Brown et al. 2012) and sends GABAergic efferents back to the VTA (Watabe-Uchida et al. 2012) as well as to the VP (Nauta and Cole 1978), which, in turn provides GABAergic input to THAL (Kuo and Carpenter 1973) and VTA (Lisman and Grace 2005). In line with the action of MUS on the CP (incurring elevated inhibition of the SN), the action of MUS on the NAC may be conceived to enhance the inhibition of the VTA. However, in analogy to the CP, the disinhibition of the THAL increases GLUergic input to the neocortex and, via GLUergic efferents, back to the NAC, leading to the elevation of ventrostriatal DA concentrations as reflected by the observed reduction of D_2/3R binding. Moreover, the disinhibition of the VTA may incur a net increase of endogenous DA as reflected by the observed reduction of D_2/3R binding.

The HIPP receives DAergic neurons originating in the VTA (Gasparri et al. 1991). Since the increased excitation of the NAC can be assumed to enhance the DAergic input back to the VTA, the likely consequence is also an increase of DAergic input to the HIPP, leading to the observed reduction of D_2/3R binding.

BIC After treatment with the GABA_AR antagonistic BIC, network analysis of the regional BPs (expressed as percentage of baseline) yielded positive associations between D_2/3R binding in CP and NAC, SN/VTA and THAL, FC and aHIPP and MC and PC. Thus, percentual elevations of D_2/3R binding (and decreases of DA) relative to baseline in CP, SN/VTA, FC and MC are accompanied by percentual elevations of D_2/3R binding (and decreases of DA) in NAC, THAL, aHIPP and PC, respectively, and vice versa.

In vivo imaging studies did not show alterations of D_2/3R binding (and DA) in SN/VTA, NAC, neocortex and HIPP. Hence, the DAergic input to the CP via ascending nigrostriatal fibers can be assumed to be normal. Based on the observed augmentation of D_2/3R binding in the CP, however, it may be inferred that GPi and GPe receive increased GABA_AR antagonistic input from the CP, leading to the inhibition of the THAL. This, for one, may account for the decrease of thalamic DA levels reflected by the observed increase of D_2/3R binding in this region.

The elevation of inhibitory GABAergic input to the THAL, secondly, can be surmised to diminish not only the direct GLUergic input to the CP, but also the excitatory input to the neocortex, which sends GLUergic efferents back to the CP. It may be hypothesized that the increase of DA efflux effected by the elevation of the GABA_AR antagonistic action in the CP is outweighed by the decline of excitatory input from THAL and neocortex, leading to a net decrease of available DA as reflected by the observed increase of D_2/3R binding.

It is interesting that network analysis did not yield a direct connection between CP and THAL after BIC. Rather, positive associatons between CP and NAC as well as THAL and SN/VTA were obtained, with D_2/3R binding (and DA levels) in NAC and SN/VTA, however, not affected by BIC challenge. This, likewise, holds for MC and PC, as well as FC and aHIPP, which were positively associated, but also failed to show alterations of D_2/3R binding after treatment with BIC. It may be concluded that the decreases of nigrostriatal and thalamic DA were functionally related to decreases of DA levels in SN/VTA, NAC, neocortex and aHIPP, which were, however, too low to induce measurable changes of D_2/3R binding under the present experimental conditions.

DCS After challenge with the NMDAR agonistic DCS, network analysis of the regional BPs (expressed as percentage of baseline) yielded positive associations between D_2/3R binding in CP and NAC, THAL and aHIPP and FC and MC. Consequently, percentual increases of D_2/3R binding (and reductions of DA) relative to baseline in CP, THAL and FC are accompanied by percentual increases of D_2/3R binding (and reductions of DA) in NAC, aHIPP and MC, respectively, and vice versa.

DCS elevates GABA release in the mouse whole brain (Polc et al. 1986, Scotto et al. 1963). Moreover, decreased GLU levels were observed in the rat amygdala (Lehner et al. 2010) as well as in the mouse whole brain (Polc et al. 1986), whereas no effect was detected in the rat FC (Fujihira et al. 2007). Thus, after DCS, increased GABAergic input from striato- as well as pallidonigral fibers may be inferred, which, together with decreased GLUergic input from the STN, results in a net inhibition of the SN. Additionally, the VTA receives increased GABAergic input from the NAC, which, in turn, is subject to decreased GLUergic input from both neocortex and HIPP. In sum, this may ensue in a decrease of DA concentrations in SN/VTA, as reflected by the observed increase of D_2/3R binding in this target region.

The decline of DA levels in SN and VTA diminishes the DAergic input to both CP and NAC. In the NAC, this - together with the reduced GLUergic input from both neocortex and limbic system – may be expected to incur a net reduction of available DA, as reflected by the augmentation of D_2/3R binding in the present study.

The CP not only receives DAergic afferents from the SNc, but also GLUergic fibers from PFC, FC, MC, somatosensory cortex and THAL (for review see Afifi et al. 1994a,b). The DCS-induced decline of GLU efflux results in a decrease of GLUergic input (except for the FC [Fujihira et al. 2007]). Together with the increased availability of inhibitory GABA in the neostriatal microcircuits (Groves 1983), this can be hypothesized to reduce DA levels in the CP, in analogy to the NAC. However, thalamic disinhibition exerted by increased GABA levels in the direct and indirect pathway, apparently, fascilitates GLUergic stimulation of the CP, leading to a compensatory enhancement of DA release, and, ultimately, to unaltered neostriatal D_2/3R binding in the CP under the present experimental conditions.

Since GLUergic input from the THAL to the neocortex may be decreased, a likely consequence is the decline of neocortical DA levels, as reflected by the observed decreases of D_2/3R binding in FC, MC and PC. In turn, in the THAL, decreased GLUergic input from the neocortex may outweigh the disinhibition exerted via the pallidothalamic pathway, leading to a reduction of DA levels, as reflected by the increase of D_2/3R binding in this region.

The decline of GLU release in neocortical and limbic regions as well as the decrease of DAergic input to the HIPP via ventral tegmental afferents may be inferred to diminish DAergic input to the limbic system, leading to a decrease of hippocampal DA levels, as reflected by the observed increase of D_2/3R binding in aHIPP and pHIPP.

Strikingly, in spite of the increase of D_2/3R binding (and decrease of DA) throughout the nigrostriatal and mesolimbic system and the possibility to account for these findings by the known interactions between DA, GLU, GABA in the individual brain regions, network analysis merely yielded direct connections between CP (whose D_2/3Rs were not measurably affecteld by DCS) and NAC, THAL and aHIPP as well as FC and MC. Evidently, the NMDAR agonistic DCS elicited regional alterations of DA levels, which were not strictly interdependent and presumably involved neurotransmitter actions beyond DA, GLU and GABA, which werely solely considered in our model.

AMA After challenge with the NMDAR antagonistic AMA, network analysis of the regional BPs (expressed as percentage of baseline) yielded positive associations between D_2/3R binding in CP and NAC, CP and THAL, CP and SN/VTA, NAC and SN/VTA, THAL and aHIPP, THAL and pHIPP, SN/VTA and pHIPP, FC and MC as well as MC and aHIPP. Thus, percentual reductions of D_2/3R binding (and increases of DA) relative to baseline in CP are accompanied by percentual reductions of D_2/3R binding (and increases of DA) in NAC, THAL and SN/VTA and vice versa. Likewise, percentual reductions of D_2/3R binding (and increases of DA) relative to baseline in NAC, THAL and SN/VTA are directly related to percentual reductions of D_2/3R binding (and increases of DA) in SN/VTA and both aHIPP and pHIPP, respectively. Furthermore, percentual reductions of D_2/3R binding (and increases of DA) relative to baseline in FC and MC are accompanied by percentual reductions of D_2/3R binding (and increases of DA) relative to baseline in MC and aHIPP, respectively.

AMA enhances both GABA (Bak et al. 1972) and GLU efflux (Takahashi et al. 1996) in the CP. Hence, for one, the increased availability of inhibitory GABA in the neostriatal microcircuits (Groves 1983) may be conceived to incur a reduction of DA efflux in the CP. Moreover, it can be surmised that AMA fascilitates the release of GABA in the NAC, reducing available DA also in the latter region. On the other hand, however, both CP and NAC receive GLUergic afferents, and it may be hypothesized that the augmentation of GLUergic input outweighs the action of GABA, resulting in a net elevation of DA levels in CP and NAC, as reflected by the decline of D_2/3R binding observed in these regions. Thereby, however, DA levels in both CP and NAC, after AMA, fall short by one order of magnitude compared to MUS. This may be accounted for, firstly, by different extents of GABAergic action exerted by MUS and AMA, and, secondly, by the lack of DA increase in SN/VTA after AMA, likely incurring less DAergic input into the dorso- and ventrostriatal target regions of nigral and ventral tegmental projections.

As outlined above for MUS, the AMA-induced increase of GABA levels in the CP enhances the inhibition of GPi and GPe via striatopallidal GABAergic projections. Increased inhibition of the GP ensues in a net disinhibition of the THAL, resulting in the increase of thalamic DA reflected by the reduction of D_2/3R binding under the present experimental conditions. With DA levels in the CP lower after AMA compared to MUS, it may be argued that, on account of this fact, also thalamic DA levels are lower.

It is remarkable that network analysis not only yielded connections between CP, NAC and THAL but also between these regions and SN/VTA, aHIPP and pHIPP. Also here, it may be implied that the NMDAR antagonistic AMA elicited regional alterations of DA levels, which, however, were not sufficient to induce measurable reductions of D_2/3R binding.

D_2/3R binding after pooling of of treatments

MUS and AMA After combination of the GABA_AR agonistic MUS and the NMDAR antagonistic AMA, network analysis yielded positive associations between D_2/3R binding in CP and THAL, CP and NAC, CP and MC, NAC and pHIPP, MC and FC as well as MC and pHIPP. Thus, percentual reductions of D_2/3R binding (and increases of DA) relative to baseline in CP were accompanied by percentual reductions of D_2/3R binding (and increases of DA) in THAL, NAC and MC. Likewise, percentual reductions of D_2/3R binding (and increases of DA) relative to baseline in NAC and MC were related to percentual reductions of D_2/3R binding in pHIPP and in CP, aHIPP and FC, respectively.

Thus, appararently, many regulatory effects on DA levels are common to MUS and AMA. Interestingly, however, after pooling of MUS and AMA, the connections between SN/VTA and pHIPP as well as SN/VTA and pHIPP and both dorsal and ventral striatum were obliterated, which formerly had been obtained by separate analysis of binding data after MUS and AMA challenge, respectively. This is primarily due to the fact that, in contrast to MUS, AMA did not increase DA levels in SN/VTA. After MUS, increased inhibition of GPi and GPe lead to a disinhibition of the SN, resulting in the increase of nigral DA. Likewise, the disinhibition of the THAL increases GLUergic input to the neocortex and, via GLUergic efferents, back to the NAC, leading to an enhancement of DA efflux in the VTA. After AMA, however, synaptic DA is lower in CP, NAC and THAL compared to MUS. As mentioned above, this, for one, may be due to different extents of GABAergic action after MUS and AMA. Furthermore, the NMDAR antagonst, in contrast to MUS, is known to facilitate neostriatal GLU release (Takahashi et al. 1996). Apparently, in sum, the ensuing effects balance each other, leaving D_2/3R binding in SN/VTA unaltered relative to baseline.

Furthermore, in contrast, to MUS, AMA did not increase DA levels in pHIPP. As outlined above, after MUS, the increased excitiation of the VTA likely enhances the DAergic input to the HIPP. Since challenge with AMA, however, incurs no alterations of synaptic DA levels in SN/VTA, also no alterations of hippocampal DA levels are likely to occur.

BIC and DCS After combination of GABA_AR antagonistic and NMDAR agonistic treatment, network analysis yielded positive associations between D_2/3R binding in CP and NAC, CP and THAL, CP and SN/VTA, THAL and SN/VTA, MC and FC as well as MC and FC. Thus, percentual reductions of D_2/3R binding (and increases of DA) relative to baseline in CP were accompanied by percentual reductions of D_2/3R binding (and increases of DA) in NAC, THAL and SN/VTA. Likewise, percentual reductions of D_2/3R binding (and increases of DA) relative to baseline in SN/VTA and MC were related to percentual reductions of D_2/3R binding in CP and THAL as well as FC and PC, respectively. When comparing these connections with the ones obtained after separate analyses of binding data after BIC and DCS challenge, respectively, it becomes obvious that a connection between SN/VTA and CP was added, while connections between FC and aHIPP THAL and pHIPP were obliterated. Again, this may be accounted for by the differing effects of BIC and DCS on regional D_2/3Rs.

In contrast to BIC, DCS increased D_2/3R binding (and decreased DA) thoughout the nigrostriatal and mesolimbic system except for the CP. DCS elevates GABA release in the mouse whole brain (Polc et al. 1986, Scotto et al. 1963), hence, for DCS, it may be assumed that the availability of inhibitory GABA is increased in the neostriatal microcircuits (Groves 1983), which ought to diminish DA levels in the CP, but is outweighed by thalamic disinhibition exerted by increased GABA levels in the direct and indirect pathway, leading to a compensatory enhancement of DA release, and, ultimately, to unaltered neostriatal D_2/3R binding in the CP. Contrarily, for BIC, it may be hypothesized that the increase of DA efflux effected by the elevation of the GABA_AR antagonistic action in the CP is outweighed by the decline of excitatory input from THAL and neocortex, leading to a net decrease of available DA and the observed elevation of D_2/3R binding.

Moreover, after BIC, DA levels in SN/VTA remained normal, also resulting in normal neocortical D_2/3R binding. In contrast, after DCS, a decline of GLU efflux (Polc et al. 1986) may have incurred a net reduction of neocortical DA levels, as reflected by the observed decreases of D_2/3R in FC, MC and PC.

After BIC, normal DA levels in SN/VTA also incurred a normal DAergic input to the limbic system. In contrast, after DCS, DA levels in SN/VTA were diminished, which, together with the DCS-induced reduction of GLU release in neocortical and limbic regions may have incurred the decrease of hippocampal DA levels, as reflected by the observed increase of D_2/3R binding in aHIPP and pHIPP.

Conclusions

The reductions of D_2/3R binding after MUS and AMA (in the present doses) indicate increased DA levels in the nigrostriatal and mesolimbic system, whereas, contrarily, the increases of D_2/3R binding after DCS and BIC (in the present doses) reflect reductions of available DA in the nigrostriatal and throughout the mesolimbocortical system, respectively.

Network analysis yielded an association between D_2/3R binding (and DA levels) in CP and THAL, which is in line with the known neuronal connections between these regions. The obtained association was strongest in baseline, moderate after challenge with the GABA_AR agonistic MUS and weakest after challenge with the NMDAR antagonistic AMA, which may be conceived to reflect the decline of DA release elicited by the increase of inhibitory GABA_AR as well as the decrease of excitatory NMDAR action. Interestingly, the association between CP and THAL is unhinged by treatment with both the GABA_AR antagonistic BIC and the NMDAR agonistic DCS, which both induce a decline of thalamic DA as evidenced by the elevation of D_2/3R binding in this region.

Moreover, network analysis demonstrated a functional association between D_2/3R binding (and DA levels) in CP and NAC, which was strongest in baseline and after challenge with BIC, moderate after challenge with AMA and DCS and weakest after challenge with MUS. Apparently, DA efflux in CP and NAC still correlate after GABA_AR antagonistic treatment as much as they do in the untreated animal, while this connection, interestingly, deteriorates after pharmacologically induced alterations of GLUergic neurotransmission as well as after GABA_AR agonistic treatment.

SN/VTA, the site of origin of DAergic fibers, and its neostriatal target region, interestingly, exhibit a strong association after challenge with the NMDAR antagonistic AMA. The apparent correlation between DA efflux in SN/VTA and CP, however, neither exists in baseline, where GLUergic neurotransmission is not one-sidedly challenged, nor after treatment with the NMDAR agonistic DCS or the GABAergic compounds MUS and BIC. In contrast, SN/VTA and NAC exhibit a low association not only after challenge with AMA, but also in baseline, which, however, is likewise disrupted after treatment with the NMDAR agonistic DCS and the GABAergic compounds MUS and BIC.

Another striking feature is the strong association between SN/VTA and THAL after challenge with the GABA_AR antagonistic BIC, which – in accordance with the connection between SN/VTA and CP - is not present in baseline. In contrast to the connection between SN/VTA and CP, however, it is disrupted by treatment with the GABA_AR agonistic MUS and the NMDAergic compounds AMA and DCS.

The association between THAL and aHIPP was strongest after challenge with AMA, moderate after challenge with DCS, but unhinged in baseline and after challenge with the GABAergic compounds. Network analysis, furthermore, yielded a strong association between D_2/3R binding (and DA levels) in MC and FC, which was strongest after treatment with AMA and DCS, slighly weaker in baseline and completely disrupted after challenge with the GABAergic compounds. In contrast, after MUS, a strong association was observed between MC and PC, which was weaker in baseline and after treatment with BIC, but completely disrupted after challenge with the GLUergic compounds.

Altogether, this underlines that the effects of GABA_AR and NMDAR agonists and antagonists on DA function are region-specific. Although GABA_AR agonist and NMDAR antagonist on one side and GABA_AR antagonist and NMDAR agonist on the other side uniformly affect D_2/3R binding (and DA levels), the extent of involved brain regions varies with a significant impact beyond ventral and/or dorsal striatum plus THAL only after (either GABA_AR or NMDAR) agonistic treatment.

In the present series of experiments, the maximum VOI diameters were either in the range of or beyond the spatial resolution of the “TierSPECT” (Nikolaus et al. 2018a). It must be taken into consideration, however, that in those portions of VOIs, whose diameters were smaller than the full width at half maximum, the quantification of D_2/3R binding may have been confounded by partial volume effects leading to underestimations of radioligand accumulation. On the other hand, spill-over from regions with high radioligand accumulation to adjacent VOIs may have caused overestimations of regional radioligand binding. However, since this held for SPECT measurements both in baseline and post-challenge, the exactitude of (semi)quantitative values in either condition, but not the comparability of data between baseline and challenge may have been biassed. Another pitfall of the present studies may have been the employment of the NMDAR antagonist ketamine as anaesthetic. Since ketamine may increase DA release in rats (Onoe et al. 1994), it can not be dismissed that increased amounts of DA due to ketamine action actually diminished regional D_2/3 receptor binding after challenge with MUS, BIC, DCS and AMA. Effects on neostriatal and/or ventrostriatal DA, however, are exerted by practically all known anaesthetics (for review see Müller et al. 2011), and since the same anaesthetic was used in baseline and post-challenge, data basically remain comparable not also between baseline and challenge but also between the individual pharmacological treatments.

The most important limitation, however, is that in the present imaging studies only the action of GABAergic and GLUergic challenges on DA have been assessed. Since also effects of serotonin (Saito et al. 1996), acetylcholine (Marshall et al. 1997) and/or substance P (Hasenöhrl et al. 2000) on cerebral DA are widely known - not to mention their interactions with GABA and GLU (see, e. g., Aghajanian and Marek 1999; Cathala et al. 2019; Hu et al. 2004; Howe et al. 2016; Xia et al. 2010; Yousefi et al. 2012) - the performance of further receptor (beyond D_2/3Rs) and transporter imaging studies is mandatory, in which neurotransmitter systems beyond GLU and GABA are challenged.

Corresponding author: Susanne Nikolaus, Clinic of Nuclear Medicine, University Hospital Düsseldorf, Moorenstr. 5, D-40225, Düsseldorf, Germany, E-mail: susanne.nikolaus@uni-duesseldorf.de

Susanne Nikolaus and Markus Beu: These authors contributed equally to this article.

Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2019-12-02

Accepted: 2020-02-01

Published Online: 2020-07-03

Published in Print: 2020-08-27

GABAergic and glutamatergic effects on nigrostriatal and mesolimbic dopamine release in the rat

Abstract

Introduction

Study 1: D_2/3R binding after GABA_AR agonistic treatment

Aim

Methods

Findings

Study 2: D_2/3R binding after GABA_AR antagonistic treatment

Aim

Methods

Findings

Study 3: D_2/3R binding after NMDAR agonistic treatment

Aim

Methods

Findings

Study 4: D_2/3R binding after NMDAR antagonistic treatment

Aim

Methods

Findings

Comparisons of regional D_2/3R binding between treatments

Principal component analysis

Network analysis

Discussion

Summary of findings

D_2/3R binding in baseline

D_2/3R binding after the individual GABAergic and GLUergic challenges

D_2/3R binding after pooling of of treatments

Conclusions

References

Journal and Issue

Articles in the same Issue

GABAergic and glutamatergic effects on nigrostriatal and mesolimbic dopamine release in the rat

Abstract

Introduction

Study 1: D2/3R binding after GABAAR agonistic treatment

Aim

Methods

Findings

Study 2: D2/3R binding after GABAAR antagonistic treatment

Aim

Methods

Findings

Study 3: D2/3R binding after NMDAR agonistic treatment

Aim

Methods

Findings

Study 4: D2/3R binding after NMDAR antagonistic treatment

Aim

Methods

Findings

Comparisons of regional D2/3R binding between treatments

Principal component analysis

Network analysis

Discussion

Summary of findings

D2/3R binding in baseline

D2/3R binding after the individual GABAergic and GLUergic challenges

D2/3R binding after pooling of of treatments

Conclusions

References

Journal and Issue

Articles in the same Issue

Study 1: D_2/3R binding after GABA_AR agonistic treatment

Study 2: D_2/3R binding after GABA_AR antagonistic treatment

Study 3: D_2/3R binding after NMDAR agonistic treatment

Study 4: D_2/3R binding after NMDAR antagonistic treatment

Comparisons of regional D_2/3R binding between treatments

D_2/3R binding in baseline

D_2/3R binding after the individual GABAergic and GLUergic challenges

D_2/3R binding after pooling of of treatments