Magnetic Resonance Im

Pharmacological modulation of functional connectivity: the correlation

structure underlying the phMRI response to d-amphetamine modified by

selective dopamine D3 receptor antagonist SB277011A

Adam J. Schwarza,4, Alessandro Gozzia, Torsten Reesea,

Christian A. Heidbrederb, Angelo Bifonea

aDepartment of Neuroimaging, Psychiatry Centre of Excellence in Drug Discovery, GlaxoSmithKline, Via Fleming 4, 37135 Verona, ItalybDepartment of Neuropsychopharmacology, Psychiatry Centre of Excellence in Drug Discovery, GlaxoSmithKline, Via Fleming 4, 37135 Verona, Italy

Accepted 11 January 2007

Abstract

Pharmacological MRI (phMRI) experiments utilise fMRI time series methods to map the central effect of pharmaceutical compounds. The

typical univariate maps may, however, integrate the effects of several different neurotransmitter systems or underlying mechanisms. The

results may thus be spatially and/or mechanistically nonspecific. Intersubject correlation analysis based on the phMRI response amplitude can

more directly identify patterns of functional connectivity underlying the central effects of an acutely administered compound. In this article,

we extend this approach to experiments where the effects of one compound in modulating the response to another are of interest. Specifically,

we show a modulation of the correlation structure of a probe compound (d-amphetamine) by pretreatment with the selective dopamine D3

receptor antagonist SB277011A in the rat. The strongest modifications in the correlation patterns occurred in connection with the ventral

tegmental area, the source of mesolimbic dopamine projections and a key substrate in the reward system.

D 2007 Elsevier Inc. All rights reserved.

Keywords: Pharmacological MRI; d-Amphetamine; SB277011A

1. Introduction

Pharmacological MRI (phMRI) experiments utilise fMRI

time series methods to examine the direct effect of

pharmaceutical compounds on the central haemodynamic

response [1,2]. This approach has been used to map the

central effects of numerous compounds in both humans and

laboratory animals [3–20]. The simplest phMRI experi-

ments involve a comparison between the acute response to a

drug and its vehicle. Alternatively, the action of a compound

in modulating the response to another dprobeT drug can be

assessed, by comparing the probe responses in subjects

pretreated with the compound of interest vs. those in control

subjects. Such dantagonist–agonistT experiments are partic-

ularly useful when the compound of interest does not yield a

clear phMRI response per se. Most commonly, the analysis

of phMRI experiments involves massively univariate

0730-725X/$ – see front matter D 2007 Elsevier Inc. All rights reserved.

doi:10.1016/j.mri.2007.02.017

4 Corresponding author. Tel.: +39 045 821 9067; fax: +39 045 821

8375.

E-mail address: adam.j.schwarz@gsk.com (A.J. Schwarz).

comparisons of some measure of the magnitude of the

postinjection signal change. This can identify brain regions

implicated in the response to the drug, but provides only

indirect information regarding the functional coupling

between the different structures involved. Even in the case

of relatively selective pharmacology, there may be cross-talk

between neurotransmitter systems and recruitment of neural

substrates other than those directly targeted by the drug [21].

Moreover, different neurovascular coupling mechanisms or

confounding contributions may also potentially contribute

to the signal changes detected. Univariate phMRI group

maps may thus integrate the effects of several different

neurotransmitter systems or underlying mechanisms. An

analysis of which brain regions are correlated in their

response to the pharmacological challenge can thus provide

more direct information regarding the relationship between

the different brain structures involved in the central action of

the drug.

In phMRI experiments, the postinjection signal changes

that embody the drug response typically last tens of minutes

or more, even with intravenous administration, with a

aging 25 (2007) 811–820

A.J. Schwarz et al. / Magnetic Resonance Imaging 25 (2007) 811–820812

relatively fast signal increase followed by a slower decrease

toward baseline. These changes usually occur in many brain

regions. As a result, intrasubject analyses examining

temporal correlations (characteristic of fMRI studies of

dresting stateT functional connectivity [22–25]) may be

nonspecific within individual phMRI subjects and insensi-

tive when generalised to the group level. However, we have

recently shown that an intersubject analysis of correlations

in the phMRI response amplitude can convincingly identify

functional connectivity patterns underlying its central effect

[26–28]. This approach is akin to correlation-based analyses

of positron emission tomography or 2-deoxyglucose data

[29,30] and harnesses differences in the spatial profile of the

response to the drug between individuals. We have

previously described functional connectivity relationships

underlying the phMRI response to the prototypical drugs d-

amphetamine and fluoxetine [27]. In this article, we extend

these results by providing an example of how a modulation

of functional connectivity relationships can be detected

within an dantagonist–agonistT framework, where the effects

of a compound of interest are assessed by examining how it

modifies the response to a probe compound.

d-Amphetamine both enhances dopamine release and

blocks its reuptake presynaptically, and in the anaesthetised

rat gives rise to a relatively widespread central phMRI

response. As a result, it has been used as in phMRI studies

as a dopaminergic stimulus, providing a quantifiable probe

signal suitable for modulation by more selective ligands in

antagonist–agonist experiments [7,11,12,31]. These studies

have shown that localised increases or decreases in the d-

amphetamine response amplitude can be detected, depend-

ing on the pharmacological profile of the antagonist used.

In particular, we have previously shown that acute

pretreatment with the selective dopamine D3 antagonist

SB277011A in the rat potentiates the amplitude of the

relative cerebral blood volume (rCBV) response to d-

amphetamine in a regionally specific manner [7]. The D3

receptor has a particularly localised distribution in the

brain, with high levels of expression in dopaminergically

innervated mesolimbic brain structures, including the

nucleus accumbens, involved in reward-related processes.

Increasing evidence with selective D3 receptor antagonists

suggests that this mechanism may be highly effective in

the treatment of addiction, especially in the prevention of

drug-seeking behaviour [32]. These findings suggest that

the cue-, drug- and stress-triggered reinstatement circuits

may have a common final pathway and that the efficacy of

selective dopamine D3 receptor antagonists is due to the

selective blockade of D3 receptors in this pathway.

Alternatively, D3 receptors might be located at critical

junctures in the pathways underlying drug-seeking behav-

iours. Accordingly, in the present study we examined how

correlations in the rCBV response to d-amphetamine

between different brain regions — an indication of

functional connectivity — are modulated by pretreatment

with SB277011A.

2. Methods

2.1. Animal preparation

All experiments were carried out in accordance with

Italian regulations governing animal welfare and protection.

Protocols were also reviewed and consented to by a local

animal care committee, in accordance with the guidelines of

the Principles of Laboratory Animal Care (NIH publication

86-23, revised 1985).

Male Sprague-Dawley rats [N =36, weight (mean-

FS.E.M.) 289F6 g] were surgically prepared and moni-

tored as detailed previously [7,33] and imaged under 0.8%

halothane maintenance anesthesia, neuromuscular blockade

and artificial ventilation. The ventilation parameters were

adjusted for each animal such that its blood gas values

remained within physiological range. Baseline peripheral

blood pressure was 93F2 mm Hg and remained within the

autoregulatory range associated with halothane anaesthesia

[33,34]. As in the previous study [7], postinjection blood

pressure increases were not significantly different between

the two amphetamine challenge groups.

2.2. MRI Acquisition

MR data were acquired using a Bruker Biospec 4.7-T

system with a cylindrical volume coil for RF transmit and a

Bruker quadrature drat brainT surface receive coil. For each

subject, a T2-weighted anatomical image volume was

acquired using the RARE sequence with RARE factor 8,

matrix 256�256, FOV 40 mm, 16 contiguous 1-mm slices,

TReff=5500 ms, TEeff=76 ms. The images were acquired in

the coronal plane, centred 8 mm caudal from the posterior

edge of the olfactory bulb. This was followed by a time series

acquisition using the same sequence with a reduced image

matrix of 128�128 and RARE factor 32, TReff=2700 ms,

TEeff=100 ms, but with the image volume co-centred with

that of the anatomical images and identical spatial coverage.

Four successive excitations were averaged to yield a final

effective time resolution of 80 s and a total of 64 image

volumes per subject. A total of 2.67 ml/kg of the blood pool

contrast agent Endorem (Guerbet, France) was injected

following five reference time points so that subsequent signal

changes would reflect alterations in rCBV [5,35].

The compound administration protocol consisted of an

intraperitoneal injection of 20 mg/kg SB277011A or vehicle

30 min after contrast agent injection, followed by an

intravenous injection of either 1 mg/kg d-amphetamine

(Sigma, Milan, Italy) or vehicle (saline) a further 30 min

later. Subsequent signal changes were tracked for 20 min,

capturing the robust initial rCBV changes following

intravenous injection [7]. This dose of SB277011A has

been shown to attenuate significantly and selectively the

action of a number of addictive drugs in various paradigms

[32]. The study comprised three arms: vehicle/amphetamine

(n=17), SB277011A/amphetamine (n=12) and vehicle/

vehicle (n =7). Note that, at the 20 mg/kg dose,

SB277011A per se does not elicit significant central rCBV

A.J. Schwarz et al. / Magnetic Resonance Imaging 25 (2007) 811–820 813

changes [7]. Intravenous injections were administered via

the femoral vein over a period of 1 min.

2.3. MR Image preprocessing

Anatomical and time series data were converted to

Analyze (AVW 7.5) format, and signal intensity changes

in each time series were transformed into fractional rCBVon

a pixel-wise basis, using a constrained exponential model of

the gradual elimination of contrast agent from the blood

pool to provide a robust prediction of postinjection

background signal and remove the worst effects of this

systematic trend in the resulting rCBV data [36]. Data for

each subject were then spatially normalized to a stereotaxic

rat brain template [37] by computing a nine degree-of-

freedom affine transform for the anatomical image and

applying the resulting transformation matrix to the accom-

panying rCBV time series (FSL/FLIRT v. 5.2). Finally, the

rCBV data were multiplied by a brain parenchyma mask to

remove extracranial and CSF contributions.

2.4. Time series analysis

Image-based time series analysis of the rCBV response in

individual subjects was carried out in the framework of the

general linear model (GLM) using FSL/FEAT v. 5.43 with

0.8-mm FWHM spatial smoothing. The design matrix

comprised a signal model identified by study-level wavelet

cluster analysis reflecting the typical temporal rCBV profile

of the amphetamine response, along with the temporal

derivative of this regressor and a postinjection linear ramp

(both orthogonalised to the regressor of interest) [38]. The

signal model regressor coefficients obtained from the GLM

analysis thus provided rCBV response amplitude maps for

subsequent intersubject correlation calculations.

2.5. Volumes of interest

Volumes of interest (VOIs) corresponding to selected

structures were delineated automatically using a 3D

volumetric reconstruction of the Paxinos and Watson [39]

rat brain atlas co-registered with the rat brain template [37].

VOIs from 48 brain regions, providing a tractable yet

reasonably complete anatomical parcellation of the brain

covered in the MRI acquisition, were extracted (see

Appendix A). All VOIs were defined as bilateral, with

voxels from both brain hemispheres combined together —

no strongly unilateral effects were hypothesized nor

observed in this study. For each subject, a single rCBV

time course from each VOI was calculated by averaging the

signal value from all voxels lying within the VOI at each

time point. A GLM analysis of the VOI time courses, using

the same design matrix as described above, yielded a vector

of response amplitudes across subjects for each selected

brain structure. These vectors were used to construct VOI

correlation matrices and also used to generate reference

regressors for use in image-based correlation analyses.

In order to identify brain regions associated with changes

in functional connectivity between the two d-amphetamine

groups, Pearson linear correlation coefficients summarizing

the correlation between the responses in each pair of VOIs

were calculated for each group. These were then converted

to z-statistics using Fisher’s r-to-z transformation, with a

varianceffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1= Nsub � 3ð Þ

pcorresponding to the Nsub inde-

pendent samples in the group: z ¼ ln 1þ r=1� rð Þð Þ=2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1= Nsub � 3ð Þ

p� �. This resulted in a 48�48 matrix of the

z-statistics for each group corresponding to the pair-wise

correlations between responses in each pair of VOIs. Direct

comparison of the z-statistics corresponding to each cell in

the matrix then indicated the pairs of brain regions in which

the correlation differed between the two groups.

2.6. Group comparison and correlation maps

Maps of group mean response and correlated responses

across subjects were calculated within a GLM framework at

the higher level using FSL/FEAT v. 5.43 with ordinary least

squares simple mixed effects inference [40]. Univariate

intragroup differences in response amplitude were calculat-

ed as contrasts between the group mean responses in the

respective cohorts. For the correlation maps, the data matrix

comprised first-level amphetamine response amplitude

maps from SB277011A- and vehicle-pretreated groups.

The design matrix comprised columns describing the mean

signal and the vector of de-meaned response amplitudes

across subjects from the reference region, for each group.

The reference VOI was selected a priori for each map,

analogous to the dseed voxelT approach [25,30,41]. Z

statistic images were calculated via contrasts capturing

positive or negative correlations with the reference response

in each group and the difference in correlation between

groups. All group maps were thresholded using clusters

determined by zN2.3 and a corrected cluster significance

threshold of P=.05 [42]. All contrasts corresponded to one-

sided t-tests.

3. Results

d-Amphetamine per se produced widespread rCBV

increases, involving most cortical regions as well as many

limbic structures. The strongest effects were associated with

consistent responses observed in the insular, piriform and

auditory cortex (Fig. 1A). The largest magnitude rCBV

increases occurred on average in these and other cortical

regions, but more intersubject variability was present in the

more dorsal motor and sensory cortices. A direct univariate

group comparison between the responses to d-amphetamine

in SB277011A pretreated and control (vehicle pretreated)

groups evidenced a regionally specific potentiation of the

response amplitude in the treated group (Fig. 1), consistent

with the previous study [7].

The correlation patterns underlying the response to d-

amphetamine described a rich structure of brain regions

functionally coupled in their response to the drug [27,28].

This included a focal network of brain regions corres-

ponding to mesolimbic dopamine projections from the

Fig. 1. (A) d-Amphetamine per se gave rise to widespread rCBV increases. When pretreated with SB277011A, the d-amphetamine response was increased in a

region-specific manner, here illustrated at the level of the nucleus accumbens (Acb). (B) Potentiated regions included the nucleus accumbens, dorsolateral

thalamus (DLthal) and ventral hippocampus (vhc). Other brain regions, such as the medial prefrontal cortex (mPFC) and VTAwere not significantly modulated

in amplitude. (C) Group mean time courses from the Acb and mPFC.

A.J. Schwarz et al. / Magnetic Resonance Imaging 25 (2007) 811–820814

ventral tegmental area (VTA) to ventral forebrain and dorsal

thalamic structures. In the present study, we were particu-

larly interested in how the functional connectivity relation-

ships were modified by the presence of SB277011A. Neural

circuits thought to underlie the process of addiction involve

limbic structures including the accumbens, prefrontal cortex

and thalamus along with midbrain regions including the

VTA [43,44]. A simplified schematic of these brain regions,

and the correlation relationships between them found in the

present study, is shown in Fig. 2. The correlations between

d-amphetamine responses in these structures were modified

in the SB277011A-treated group, with the strongest effects

being reductions in correlation with the VTA.

A more systematic analysis of interregion correlations

between VOIs based on 48 brain structures covering the

anatomical range of the acquisition confirmed that modified

interregion correlations were particularly strongly associated

with the VTA (Fig. 3). At the significance level of Pb.01

shown in Fig. 3, six of the 12 significantly modified

connections represent a reduced correlation with the VTA;

specifically, these involved the ventral subiculum and

ventral CA3 fields in the hippocampus, dorsolateral and

midline dorsal thalamus, piriform cortex and superior

colliculi. (The reductions in correlation between the VTA

and the AcbC and PrL regions, shown in Fig. 2, were in the

range 0.01bPb.05). Interestingly, the analysis in Fig. 3 also

indicated increased correlations between responses in dorsal

regions of the hippocampus — specifically, between the

dorsal anterior and posterior parts and the posterior fields of

the dentate gyrus.

These region-specific changes in correlation between the

two groups are further illustrated by the scatterplots shown

in Fig. 4. In the midline dorsal thalamus (Fig. 4A), the

relationship is altered from a significantly positive correla-

tion with the VTA to a significantly negative one. In the

ventral subiculum, a weak positive trend becomes a negative

correlation with the VTA (Fig. 4B). As suggested by the

many blank cells in Fig. 3, not all relationships were

significantly altered; Fig. 4C illustrates a pair of VOIs in

which the correlation is very similar (weak, nonsignificant

positive trend) in both groups. Fig. 4D illustrates the

significant increase in correlation between responses mea-

sured in the anterodorsal and posterior dentate gyrus regions

of the hippocampus.

Changes in correlation can also be visualised on a pixel-

wise basis. Correlation maps referenced to the VTA for each

group show that, when pretreated with SB277011A, the

correlation pattern for d-amphetamine changes considerably

(Fig. 5A,B). In the control group, responses correlating with

the VTA describe a very focal pattern coincident with the

ig. 2. Correlations between the d-amphetamine responses in key brain regions involved in the reward circuit for (A) the control vehicle/amphetamine group

nd (B) the SB277011A/amphetamine group. (C) Changes in correlation between these brain regions.

A.J. Schwarz et al. / Magnetic Resonance Imaging 25 (2007) 811–820 815

F

a

main mesolimbic dopamine projections [26,27]. In the

SB277011A-treated group, only negative correlations were

evident, particularly in dorsal thalamic and ventral hippo-

campal regions, consistent with the VOI analysis above. The

lack of significant correlation between the VTA and its

constituent pixels was due to a greater heterogeneity

amongst these pixels (observable at less stringent statistical

thresholds). The direct pixel-wise comparison between the

VTA correlation maps in these two groups revealed a pattern

of significantly decreased correlation with the VTA includ-

ing voxels in the retrosplenial cortex, mediodorsal and

dorsolateral thalamus, ventral hippocampus, septum and

mPFC (Fig. 5C).

4. Discussion

The correlation relationship underlying the phMRI

response to d-amphetamine was modified by the presence

of SB277011A. Interpreting correlated responses as an

indication of functional connectivity [41], this finding

suggests that selective blockade of D3 receptors affects a

modification of the functional connectivity between brain

regions involved in the response to a dopaminergic

stimulus. These modulations were particularly strongly

associated with the VTA and the mesolimbic reward circuit.

The strongest effects observed were inverted correlations

with the VTA in the thalamus and ventral hippocampus.

While strong responses in the VTA were associated with

strong responses in the mediodorsal thalamus in control

animals, this association was reversed in the presence of the

D3 antagonist. These pathways are key components of the

reward system implicated in drug-seeking behaviour [45].

For example, the mediodorsal thalamus receives direct

afferents from the amygdala, medial prefrontal cortex,

ventral pallidum and VTA [46]. Furthermore, the projection

pattern of the mediodorsal thalamus in the rat prefrontal

cortex has been proposed to be homologous to the human

orbitofrontal cortex, which in turn projects to key subcor-

tical associative learning nodes such as the basolateral

amygdala and nucleus accumbens [46–48]. Drug-induced

changes in these pathways have been related to dysfunc-

tional decision making that is a key component of drug

addiction [49]. The previously reported potentiation of the

phMRI response in these regions following d-amphetamine

challenge, a finding replicated in the present study, suggests

that SB277011A may exert its efficacy in animal models of

Fig. 3. Correlation matrix capturing significant VOI to VOI correlation differences between vehicle/amphetamine and SB277011A/amphetamine groups

(thresholded at P b.01). Cells are coloured according to the z-statistic corresponding to the difference in correlation coefficients between the two groups. The

red/yellow colour scale indicates positive modulations (r011/ampN rveh/amp), while the blue colour scale indicates negative modulations (r011/ampb rveh/amp). The

VOIs are defined in Appendix A.

A.J. Schwarz et al. / Magnetic Resonance Imaging 25 (2007) 811–820816

drug dependence by normalising the attenuated dopamine

release observed in drug-dependent individuals [50]. Fur-

thermore, the thalamus and hippocampus are also important

components of a limbic brain circuit thought to be involved

in drug addiction [43], providing modulatory inputs to the

prefrontal cortex, an important brain region involved in

goal-directed behaviour. The present findings, of a reversed

correlation between responses in the VTA and the dorsal

thalamus, and a reduced correlation with the ventral

striatum, suggest that a modified functional connectivity

within this circuit may also play a key role in the efficacy of

selective dopamine D3 receptor antagonists in attenuating

drug-seeking behaviour [32].

Methodologically, this study extends the applicability of

intersubject functional connectivity analyses of phMRI data

to antagonist–agonist experiments, where modulations in

the correlation structure underlying the response to a probe

signal may be detected. However, this requires that the

signal response amplitude be accurately quantifiable in each

group. The present experiment is appropriate for such an

analysis since it was known that the pretreatment affected a

region-specific increase in the amplitude of the response to

Fig. 4. Correlations between d-amphetamine responses in selected pairs of VOIs for both vehicle/amphetamine (filled circles) and SB277011A/amphetamine

(open circles) groups. Linear regression fits with 95% confidence bands are shown for each group in black and grey, respectively. (A) MDthal vs. VTA,

illustrating significant negative modulation of correlation. Regression equation for control group: MDthal=0.015+0.36�VTA, r =0.60, P=.011. Regression

equation for SB277011A-treated group: MDthal=0.17–0.60�VTA, r =�0.64, P= .026. Change: Dr =�1.24, P= .00075. (B) hcVS vs. VTA. Control group:

hcVS=�0.033+0.20�VTA, r =0.30, P=.24. SB277011A-treated group: hcVS=0.12–0.71�VTA, r =�0.67, P=.018. Change: Dr =�0.97, P=.0088. (C) VP

vs. VTA, an example of no significant change in correlation. Control group: VP=0.050+0.20�VTA, r =0.41, P=.10. SB277011A-treated group:

VP=0.091+0.35�VTA, r =0.51, P=.09. Change: Dr =0.10, P= .77. (D) hcAD vs. hcDGp, illustrating significant positive modulation of correlation. Control

group: hcDGp=�0.0000065+0.18�hcAD, r =0.22, P= .39. SB277011A-treated group: hcDGpS=�0.032+0.79�hcAD, r =0.90, P b.0001. Change:

Dr =0.68, P= .0034.

A.J. Schwarz et al. / Magnetic Resonance Imaging 25 (2007) 811–820 817

d-amphetamine challenge. Experiments in which the probe

signal response is substantially suppressed, for example,

may be less suitable for such analyses. We have presented

both VOI-based and image-based analyses of correlation

Fig. 5. Correlation maps referenced to the VTA for each group, and the group comp

Vehicle/amphetamine group, showing a focal pattern of correlated responses c

amphetamine group. (C) Map of changes in correlation (011/amp�veh/amp).

changes between the two groups. AVOI decomposition is a

useful means of reducing the data to manageable propor-

tions, and VOIs based on anatomical brain regions are

conceptually tractable. Moreover, changes in correlation

arison. (A) Position reference of the three slices shown in Panels (B–D). (B)

orresponding to the mesolimbic dopamine projections. (B) SB277011A/

A.J. Schwarz et al. / Magnetic Resonance Imaging 25 (2007) 811–820818

between all pairs of VOIs can be easily visualised via the

correlation or modulation matrix such as that displayed in

Fig. 3. However, such a reduction may mask heterogene-

ities within each VOI. The image-based correlation map

approach allows changes to be interrogated at a higher

spatial resolution, but is unwieldy to perform for all VOIs

or all pixels in the image. It may be thus best suited to

testing hypotheses either formulated a priori or arising

from a VOI analysis.

In summary, we have shown that modulations in the

correlation structure underlying the strong central response

to d-amphetamine, due to the presence of a more selective

dopaminergic ligand, can be detected within the framework

of an intersubject functional connectivity analysis. This

demonstrates the feasibility of extending this approach to

phMRI experiments of an antagonist–agonist design. The

results obtained with this particular compound, the selective

dopamine D3 antagonist SB277011A, included strong

effects in connection with the VTA, the source of

mesolimbic dopamine projections to the forebrain and a

key substrate in the reward system. A modulation of activity

in these pathways is consistent with the efficacy of this

compound in blocking a range of drug-seeking behaviour in

the rat.

Appendix A. VOI definitions and abbreviations

ON olfactory nuclei

OT olfactory tubercle

AcbC shell sub-region of the nucleus accumbens

AcbSh shell sub-region of the nucleus accumbens

CPu caudate putamen

LGP lateral globus pallidus

Sept septum

VP ventral pallidum

BNST bed nucleus of the stria terminalis

hcAD anterodorsal region of the hippocampus (regions of

hippocampus dorsal to a line 5.5 mm ventral from

bregma, Figs. 25–35 in Ref. [39]

hcPD postero-dorsal region of the hippocampus (regions

of hippocampus dorsal to a line 5.5 mm ventral

from bregma, Figs. 36–45 in Ref. [39], excluding

subiculum and DG region in Fig. 45)

hcV ventral hippocampus (regions of hippocampus

greater than 5.5 mm ventral from bregma, Figs.

36–45 in Ref. [39], excluding subiculum and DG

region in Fig. 45)

hcDGp posterior layers of the dentate gyrus (DG and

PoDG regions, Figs. 45–47 in Ref. [39])

hcCA3v ventral CA3 fields (regions within the closed

ventral CA3 contours, Figs. 36–38 in Ref. [39])

hcDS dorsal subiculum (regions of the subiculum dorsal

to a line 5.5 mm ventral from bregma, Figs. 40–45

in Ref. [39])

hcVS ventral subiculum (regions of the subiculum

greater than 5.5 mm ventral from bregma, Figs.

40–45 in Ref. [39])

hcSTr transition area of the subiculum (posterior regions

of the subiculum in Figs. 46–49 in Ref. [39],

nomenclature following Ref. [51].

thalDL dorsolateral thalamus

thalMD mediodorsal thalamus

thalVM ventromedial thalamus

LH lateral hypothalamus

PVN paraventricular hypothalamic nucleus

IL infralimbic cortex

PrL prelimbic cortex

ctxCing cingulate cortex

ctxOF orbitofrontal cortex

ctxRS retrosplenial cortex

ctxM motor cortex

ctxA auditory cortex

ctxSS somatosensory cortex

ctxV visual cortex

ctxEnt entorhinal cortex (includes ectorhinal and perirhi-

nal areas)

ctxPir piriform cortex (includes both Pir layer and

adjacent tissue)

ctxIns insular cortex

ctxPtA parietal association cortex

ctxTeA temporal association cortex

VTA ventral tegmental area

SN substantia nigra

Raphe raphe nuclei

MedGenmedial geniculate nuclei

Mes mesencephalic region

PAG periaqueductal grey

SupCol superior colliculi

Pons pons

BLA basolateral amygdaloid nucleus

BMA basomedial amygdaloid nucleus

CeA central nucleus of the amygdala

MeA medial amygdaloid nucleus

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