Is subcortical–cortical midline activity in depression mediated by glutamate and GABA? A cross-species translational approach

Abstract

Major depressive disorder has recently been characterized by abnormal resting state hyperactivity in anterior midline regions. The neurochemical mechanisms underlying resting state hyperactivity remain unclear. Since animal studies provide an opportunity to investigate subcortical regions and neurochemical mechanisms in more detail, we used a cross-species translational approach comparing a meta-analysis of human data to animal data on the functional anatomy and neurochemical modulation of resting state activity in depression. Animal and human data converged in showing resting state hyperactivity in various ventral midline regions. These were also characterized by abnormal concentrations of glutamate and γ-aminobutyric acid (GABA) as well as by NMDA receptor up-regulation and AMPA and GABA receptor down-regulation. This cross-species translational investigation suggests that resting state hyperactivity in depression occurs in subcortical and cortical midline regions and is mediated by glutamate and GABA metabolism. This provides insight into the biochemical underpinnings of resting state activity in both depressed and healthy subjects.

Full text

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Review
Is subcortical–cortical midline activity in depression mediated by glutamate
and GABA? A cross-species translational approach
Antonio Alcaroa, Jaak Pankseppb, Jan Witczakc, Dave J. Hayesc, Georg Northoffc,*
aSanta Lucia Foundation, European Centre for Brain Research (CERC), Via del Fosso di Fiorano 65, 00143 Rome, Italy
bDepartment of VCAPP, College of Veterinary Medicine, Washington State University, Pullman, WA 99164-6520, USA
cMind, Brain Imaging and Neuroethics Unit, Institute of Mental Health Research, Royal Ottawa Health Care Group, University of Ottawa, 1145 Carling Ave., Ottawa,
ON KIZ 7K4, Canada
Contents
1.
2.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. Regional changes and neurochemical modulation of resting state activity in humans. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1.Literature search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.2.Exclusion criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.3. Activation likelihood estimation (ALE) meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2. Regional changes and neurochemical modulation of resting state activity in animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1.Literature search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2.Exclusion criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.Regional hyperactivity in the resting state in humans and animals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.2.Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.3.Comparison between human and animal findings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.Regional hypoactivity in the resting state in humans and animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.1.Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.2.Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3. Comparison between human and animal findings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3. Glutamatergic modulation of resting state activity in humans and animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.1.Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3.
Neuroscience and Biobehavioral Reviews 34 (2010) 592–605
A R T I C L EI N F O
Article history:
Received 12 August 2009
Received in revised form 28 October 2009
Accepted 26 November 2009
Keywords:
Meta-analysis
Depression
Glutamate
GABA
Resting state
Translational
A B S T R A C T
Major depressive disorder has recently been characterized by abnormal resting state hyperactivity in
anterior midline regions. The neurochemical mechanisms underlying resting state hyperactivity remain
unclear. Since animal studies provide an opportunity to investigate subcortical regions and
neurochemical mechanisms in more detail, we used a cross-species translational approach comparing
a meta-analysis of human data to animal data on thefunctional anatomyand neurochemical modulation
of resting state activity in depression. Animal and human data converged in showing resting state
hyperactivity in various ventral midline regions. These were also characterized by abnormal
concentrations of glutamate and g-aminobutyric acid (GABA) as well as by NMDA receptor up-
regulation and AMPA and GABA receptor down-regulation. This cross-species translational investigation
suggests that resting state hyperactivity in depression occurs in subcortical and cortical midline regions
and is mediated by glutamate and GABA metabolism. This provides insight into the biochemical
underpinnings of resting state activity in both depressed and healthy subjects.
Crown Copyright ? 2009 Published by Elsevier Ltd. All rights reserved.
* Corresponding author at: Mind, Brain Imaging and Neuroethics, Canada Research Chair, EJLB-Michael Smith Chair for Neuroscience and Mental Health, Royal Ottawa
Healthcare Group, University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Room 6467, Ottawa, ON K1Z 7K4, Canada. Tel.: +1 613 722 6521x6959;
fax: +1 613 798 2982.
E-mail address: georg.northoff@rohcg.on.ca (G. Northoff).
Contents lists available at ScienceDirect
Neuroscience and Biobehavioral Reviews
journal homepage: www.elsevier.com/locate/neubiorev
0149-7634/$ – see front matter. Crown Copyright ? 2009 Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.neubiorev.2009.11.023

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3.3.2.
3.3.3.
GABAergic modulation of resting state activity in humans and animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Comparison between humans and animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3.4.1.
3.4.2.
3.4.3.
Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Comparison between humans and animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.Resting state hyper- and hypoactivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.Resting hyperactivity and glutamatergic and GABAergic modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.
1. Introduction
Major depressive disorder (MDD) is a psychiatric disorder
characterized by depressive symptoms like anhedonia, poor
motivation, psychomotor retardation, and ruminations including
an increased self-focus (see Thase, 2005; Northoff, 2007). Recent
imaging studies demonstrated consistently elevated resting state
activity in various cortical and subcortical midline regions like the
sub- and perigenual anterior cingulate cortex (PACC), medial
prefrontalcortex(PFC),theventral striatum(VS),and thethalamus
(Th) (see reviews and meta-analyses in Fitzgerald et al., 2006,
2007; Greicius et al., 2007; Grimm et al., 2009a,b; Mayberg, 2002,
2003; Phillips et al., 2003). Unfortunately, the exact role of
subcortical regions remains unclear due to the limited resolution
in human imaging. Moreover, the exact neurochemical mechan-
isms mediating abnormal resting state activity also remain
unclear.
Animal models of depression provide an excellent opportu-
nity to investigate subcortical regions related to primary process
emotions and their neurochemical mechanisms in greater
anatomical detail compared to human imaging studies (Pank-
sepp, 1998, 2005). Recent animal studies focus on various
subcortical regions like the ventral tegmental area (VTA), locus
coeruleus (LC), periaqueductal grey (PAG), hypothalamus (Hyp),
habenula (Hab), various nuclei of the amygdala (Amyg), bed
nucleus of stria terminalis (BNST), dorsal raphe nuclei (DR),
nucleus of the solitary tract (NST), basal ganglia, especially the
nucleus accumbens (NACC) and caudate-putamen (CP), septum,
and thalamic nuclei like the pulvinar and the mediodorsal
thalamus (MDT) (see Krishnan and Nestler, 2008; Ressler and
Mayberg, 2007; Shumake and Gonzalez-Lima, 2003 for recent
reviews). Interestingly, many of these regions show abnormal
resting state activity in animal models of depression which may
be convergent with human imaging findings. Though one must
be cautious when comparing structural and functional neuro-
anatomy across species, nonetheless there is evidence for many
subcortical and cortical homologies across mammals (Cenci
et al., 2002; Dalley et al., 2004; Robbins, 1998). A potential
relationship of abnormal resting state activity between humans
and animals, however, remains to be demonstrated in systematic
translational analyses.
Moreover, animal models provide some evidence of GABA and
glutamate abnormalities in the very same brain regions showing
resting state hyperactivity (see below for details). This raises the
question of whether resting state hyperactivity in depression may
be due to glutamatergic and GABAergic abnormalities. Though
recent animal models have clarified genetic contributions to
depression (Cryan and Slattery, 2007; Krishnan and Nestler, 2008;
McArthur and Borsini, 2006), neurochemical data in animals may
needtobecomplementedbyhumandatainordertobridgethegap
to human clinical issues. This makes it necessary to translate the
animalrestingstateandneurochemicalfindingsintothecontextof
human imaging findings. More specifically, there is a need to
merge the observations of abnormal resting state activity in both
animals and humans into a common neurochemical model (see
Stone et al., 2008; Krishnan and Nestler, 2008 for reviews).
The general aim of this investigation was to develop a cross-
species translational pathophysiological model of abnormal
resting state activity in MDD. More specifically, our first aim
was to directly compare human and animal data on resting state
activity in order to yield a common subcortical–cortical network.
With this common anatomical model in place, we then aimed to
characterize this abnormal subcortical–cortical resting state
network in neurochemical terms drawing again on both animal
and human data. We hypothesized that increased resting state
activity in a ventral anterior subcortical–cortical network, includ-
ing many limbic regions, may be related to abnormal activity in
both glutamatergic and GABAergic metabolism.
To pursue this hypothesis, we performed a two-step investiga-
tion.In the first step, weused a systematic meta-analysisof human
positron emission tomography (PET) imaging studies in the resting
state. The regions identified were then compared with those
observed to be abnormal in resting state data of human fMRI
studies and animal models; the overall aim was to identify
anatomical similarities in the direction of resting state activity
showing either hypo- or hyper-activity. The second step consisted
of searching for neurochemicalabnormalities in those subcortical–
cortical regions. Since glutamatergic and GABAergic substances
canbe investigated inboth animalsand humans,and haverecently
been shown to be therapeutically effective in human MDD (see
Northoff et al., 1997; Zarate et al., 2006), we then focused on those
neurotransmitters. This analysis sheds light on one important
aspect of the pathophysiology of depression (i.e. resting state
hyperactivity), and thereby may increase our understanding of the
biochemical modulation of resting state activity in the default-
modenetwork (Buckner et al., 2008;Raichle et al., 2001;Vincent et
al., 2007) in both humans and animals.
2. Materials and methods
2.1. Regional changes and neurochemical modulation of resting
state activity in humans
2.1.1. Literature search
To form a dataset of coordinates, we conducted multiple
PubMed(http://www.pubmed.gov)searchestoinitiallyidentifyall
imaging studies – positron emission tomography (PET) and
functionalmagneticresonanceimaging(fMRI) – includingpatients
with depressive disorders published from May 1998 to February
2008. The search included the keywords ‘‘depression’’, ‘‘MDD’’,
‘‘PET’’, ‘‘positron emission tomography’’, ‘‘fMRI’’, and ‘‘functional
magnetic resonance imaging’’. In addition, we used the brainma-
p.org data-base of coordinates by utilizing a java-based application
named Sleuth. The Sleuth search parameters were defined as
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‘‘unipolar disorder’’ or ‘‘depression’’ in the category ‘‘Diagnosis’’
combined with ‘‘PET’’ or ‘‘fMRI’’ in the category ‘‘imaging
modality’’. Furthermorewe searched the reference list of identified
articles and several reviews. Although some fMRI studies were
reviewed and discussed, they were not included in the meta-
analysis as there were only four human studies available (Greicius
et al., 2007; Grimm et al., 2009a,b; Walter et al., 2009). The main
problemisthatfMRIstudiescanonlymeasurerestingstateactivity
indirectly, e.g. via the degree of negative BOLD response (NBR) (see
Grimm et al., 2009a; Walter et al., 2009), and this is complicatedby
the fact that NBR is not exclusively found in the resting state. In
contrast, PET studies provide a direct measure of resting state in an
absolute, quantifiable, way. We henceforth refrained from includ-
ing the fMRI studiesin ourmeta-analysis although a comparisonto
other studies was subsequently undertaken.
We then individually screened all the articles for the presence
of Talairach or Montreal Neurological Institute (MNI) coordinates
and tabulated the reported regional foci. We focused on studies
that directly compared disordered adult patients and controls;
only those reporting regional foci with the contrasts MDD > con-
controls or controls > MDD were considered (see Appendix A for a
list of studies).
Neurochemical abnormalities and biochemical metabolism
have been investigated in human neuroimaging predominantly
with magnetic resonance spectroscopy (MRS). The advantage of
MRS is that it is done in the resting state so that the results are
directly comparable to the above-mentioned studies in both
humans and animals. One drawback is that MRS is rather difficult
to conduct in subcortical regions, limiting the animal–human
comparison to cortical regions. In order to account for this
limitation and the wider spectrum of data available in the animal
literature, neurochemical results from human postmortem studies
in MDD that are carried out in subjects without recent
pharmacological exposures were considered. It should be noted,
however, that the postmortem brain is not in a true resting state
and that interpretations of neurochemical measurements are
difficult due to multiple issues (e.g. increases in GABA concentra-
tion following death)—one reason why postmortem studies can be
highly variable (Knorle et al., 1997). Furthermore, given the
information available, we have focused predominantly on those
resting state regions that showed hyperactivity.
Since the number of MRS and postmortem studies is rather low,
we have described the main results without conducting a
quantitative meta-analysis. Search words in PubMed were ‘‘MRS’’,
‘‘spectroscopy’’,‘‘postmortem’’,‘‘suicide’’,‘‘depression’’,and‘‘MDD’’.
2.1.2. Exclusion criteria
Studiesincludingdepressedpatientsinremissionoraeuthymic
state, subjects undergoing additional therapy (e.g. those involving
medication, sleep deprivation or behaviour therapy), patients with
volumetric abnormalities or brain injuries, patients with addition-
al disorders like Alzheimer’s disease, schizophrenia, borderline
disorder, and obsessive–compulsive disorder were excluded. A
large number of studies were excluded due to the absence of
coordinates and/or designs that did not include specific compar-
isons relevant to the current analysis.
2.1.3. Activation likelihood estimation (ALE) meta-analysis
ALE analysis, described by Turkeltaub (Turkeltaub et al., 2002)
and Laird (Laird et al., 2005), was performed with a Java-based
version of ALE software named GingerALE developed by the
Research Imaging Center (http://www.brainmap.org/ale). Each
imported focus wasmodelled
distributions centered at the given coordinates. We calculated
the probability that each focus was located within a particular
voxel using a 3D Gaussian function of 10 mm full-width half-
aslocalizationprobability
maximum (FWHM). The ALE value was computed as the union of
these probabilities in order to create a whole-brain ALE map.
Next, we performed a permutation test of randomly distributed
foci to determine the statistical significance. Using the FWHM
value and number of foci from each respective dataset, five
thousand permutations were computed. The test was corrected
for multiple comparisons using the false discovery rate (FDR)
method with a threshold at p = 0.05. An additional cluster
threshold of 400 mm3(50 voxels) was applied. Anatomical labels
of cluster locations were provided by the Talairach Daemon.
The analysis was performed with the following datasets:
[MDD > controls] with coordinates from resting state studies
and [controls > MDD] with coordinates from resting state
studies.
2.2. Regional changes and neurochemical modulation of
resting state activity in animals
2.2.1. Literature search
We aimed to identify all brain areas that have revealed an
altered metabolism in the various animal models of depression
based on a PubMed analysis of the literature. The search included
the keywords ‘‘depression’’, ‘‘anhedonia’’, ‘‘learned helplessness’’,
‘‘animal’’, ‘‘metabolism’’, ‘‘c-fos’’, ‘‘brain’’, and several brain
structures such as ‘‘prefrontal cortex’’, ‘‘perigenual anterior
cingulate cortex’’, ‘‘hippocampus’’ and others (see Tables 1b and
3 for the exactregions). Due tolack of methodological instruments,
absence of precise standardized coordinates systems, the wide
range of experimental procedures, and the diversity of regional
anatomyindifferentspecies,we werenotabletoconductthe same
rigorous meta-analysis in animals as in humans.
SincetherearenorelevantPETorfMRIstudiesinanimals(except
for the study by Jang et al., 2009 noted in Tables 1b and 2b), we
lookedatthefollowingindexesofanimalbrainmetabolism:c-Fosor
Fos-like expression, Fos B/delta Fos B expression, quantitative
cytochrome oxidase, and [14C]-2-deoxyglucose. Each of these
indexes has previously been related to increased neural activity
or metabolism. Considering the broad the spectrum of animal
models of depression (e.g. chronic stress, bulbectomy, genetic
selection, social defeat etc.), we looked at all those data that report
differences in brain metabolism between depressive and normal
animals. We then selected all those areas where differences in
resting state metabolism turned out to be statistically significant
(see Table 1b for the hyperactive structures and Table 2b for
hypoactive structures).
We carried out a descriptive analysis of neurochemical GABA
and glutamate anomalies within those neural structures showing
altered metabolism in animal models of depression. The areas
investigated were those reported in Tables 1b and 2b.
With regard to these neurochemistries, we first considered data
from altered transmission/sensitivity or modified receptor expres-
sion with regard to glutamate/GABA in all those brain regions that
were shown to be abnormal in the resting state condition as
revealed in our first analyses. Secondly, we included data showing
evidence of changes in GABAergic and glutamatergic transmission
as induced by antidepressant treatment in those regions identified
in the resting state analysis. Thirdly, we included data from animal
studies that applied glutamate or GABA modulating drugs into the
resting state regions to induce pro- or anti-depressant effects on
behaviour.
2.2.2. Exclusion criteria
Studies involving adolescent animals and exposure to drugs of
abuse were excluded (although appropriate non-drug-exposed
controls were included), to avoid confounding issues related to
neurodevelopment and drug interactions and/or drug-induced
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changes in brain structure or function unrelated to the depressive-
like phenotype.
In order to compare abnormal resting state activity in human
and animal data, we listed the respective regions for both species
and checked for hyper- and hypoactivity. Any comparison
between human and animal data raises the question of homology
of brain regions. Since they show analogous anatomy and are
described by similar names, analysis of subcortical regions do not
raise the problem of homology (see also Panksepp, 1998). In
contrast, the issue of homology becomes more problematic in the
case of cortical regions that show both anatomical and termino-
logical differences between humans and animals. Nonetheless,
even areas which may be considered largely ‘higher-order’ or
evolutionarily more recent, such as the prefrontal cortex, may
show strong structural and functional homologies between
primates and other mammals, such as rodents (Dalley et al.,
2004; Heidbreder and Groenewegen, 2003). Concerning cortical
regions,wereliedoncriteriaofhomologyasestablishedbyrecent
authors (Ongur and Price, 2000; Shumake and Gonzalez-Lima,
2003; Vertes, 2006).
3. Results
3.1. Regional hyperactivity in the resting state in humans and animals
3.1.1. Humans
Our meta-analysis revealed that MDD patients showed signifi-
cantly higher resting state activity in the following regions when
comparedtohealthysubjects:PACC,ventromedialprefrontalcortex
(VMPFC), thalamic regions including the pulvinar and the dorsome-
dial thalamus (MDT), pallidum/putamen, and midbrain regions
including VTA/SN and PAG/tectum (see Fig. 1 and Table 1a).
3.1.2. Animals
The findings of the various studies are specified in Table 1b;
these are organized by dependent measure (i.e. indices of brain
metabolism), depression models used (e.g. like social defeat,
forced-swimming or uncontrollable shock), and species.
Different indexes of neural activity revealed the presence of a
wide set of hyperactive structures in the resting states of animals
with depressive-like symptoms. Overall, the neural areas showing
Fig. 1. Resting state hyperactivity in humans revealed by ALE analysis [MDD > Co]. See Table 1a for corresponding data. MDD = major depressive disorder; Co = controls.
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hyperactivity in animals with depressive-like symptoms are the
anterior cingulate cortex (ACC), anterior olfactory nucleus (AON),
the central nucleus of the amygdala (CeA) and the basolateral
amygdala (BLA), bed nucleus of the stria terminalis (BNST),
claustrum, dorsal raphe (DR), habenula (Hab), hippocampus
(Hipp), hypothalamus (Hyp), infralimbic cortex (IL Cx), locus
coeruleus (LC), medial preoptic area (mPOA), nucleus accumbens
(Nacc), nucleus of the solitary tract (NST), paraventricular nucleus
of the thalamus (paraV-Th), periaqueductal grey (PAG), piriform
cortex (Pir Cx) and prelimbic cortex (PL Cx).
3.1.3. Comparison between human and animal findings
After having selected all the structures showing significantly
differentmetabolisminanimalmodelsofdepressionandinhuman
depression, we compared the findings between the two species
(see above the discussion of the problem of homology) and
considered possible overlapping regions as well as areas that
showed abnormal metabolism in only humans or animals.
We observed corresponding resting state hyperactivity in the
PACC, MDT, the VTA/SN, the PAG/Tectum, the premotor cortex and
the pallidum/putamen. Hyperactivityinthe AON,BNST, claustrum,
DR, Pir Cx, Hab, Hipp, Hyp, LC, mPOA, NST, and the VS/Nacc, was
observed onlyin animal models (see Table 1b). This may have been
partly due to the higher anatomical resolution, especially of small
subcortical nuclei, that can be obtained with the direct histological
measures that can be employed in animal models.
3.2. Regional hypoactivity in the resting state in humans and animals
3.2.1. Humans
MDD patients showed significantly lower resting state regions
when compared to healthy subjects in the bilateral anterior insula,
the posterior cingulate cortex (PCC) and adjacent precuneus/
cuneus, the bilateral superior temporal gyrus, the caudate, the left
dorsolateral prefrontal cortex (DLPFC), and the supragenual
anterior cingulate cortex (SACC) (see Fig. 2 and Table 2a).
Table 1b
Resting state hyperactivity in animal models of depression.
RefModel SpeciesMeasureBrain regions
Beck and Fibiger (1995)Chronic stressRatsc-fos ACC, AON, CeA, claustrum, dentate gyr,
Pir Cx, dorsopeduncular Cx, IL Cx, septum,
occipital Cx, Hyp, supramammillary area,
ParaV-Thal, pontine n.
Amyg, Hipp, Hyp, septum, LC, NST
BLA, CeA, DR, IL Cx, LC, MeA, Nacc, PL Cx, VTA
Ventrolateral PAG
Habenula, Hipp, IL Cx, ParaV-Hyp, PL Cx
Matsuda et al. (1996)
Nikulina et al. (1998)
Miczek et al. (1999)
Shumake et al. (2003)
Chronic stress
Social defeat
Social defeat
Genetic
Mice
Mice
Mice
Rats
c-fos
fos-LI
c-fos
Quantitative
cytochrome oxidase
c-fos
c-fos
fos-LI
c-fos
Delta FosB
FosB/Delta FosB
ACh gene express.
c-fos
[18F] FDG PET
Huang et al. (2004)
Greenwood et al. (2005)
Lino-de-Oliveira et al. (2006)
Frank et al. (2006)
Berton et al. (2007)
Frenois et al. (2007)
Kroes et al. (2007)
Stone et al. (2007)
Jang et al. (2009)
Learned helplessness
Learned helplessness
Forced swim test
Social defeat; genetic
Learned helplessness
LPS immune induction
Social defeat
Various
Forced swim test
Mice
Rats
Rats
Rats
Mice
Rats
Rats
Mice
Rats
ParaV-Hyp
BNST, habenula
PAG
CeA, MeA, medial preoptic area, ParaV-Hyp
Ventrolateral PAG
BNST, Hyp, ParaV-Thal, NST
PAG
Anterior Pir Cx, Cg gyr, Nacc, secondary motor Cx
Cerebellum, motor/sensory Cx
ACC, anterior cingulate cortex; ACh, acetylcholine; Amyg, amygdala; AON, anterior olfactory nucleus; BLA, basolateral amygdala; CeA, central nucleus of the amygdala; Cg,
cingulate; Cx, cortex; DR, dorsal raphe; gyr, gyrus; Hipp, hippocampus; Hyp, hypothalamus; IL, infralimbic; MeA, medial nucleus of the amygdala; Nacc, nucleus accumbens
septi; PAG, periaqueductal grey; ParaV-Hyp, paraventricular nucleus of the hypothalamus; ParaV-Thal, paraventricular nucleus of the thalamus; Pir, piriform; PL, prelimbic;
LC, locus coeruleus; NST, nucleus of the solitary tract; VTA, ventral tegmental area.
Table 1a
Resting state hyperactivity in humans revealed by ALE analysis [MDD>Co].
ClusterVolume mm3
Weighted centerExtremaRegion BA
XYZValueXYZ
1 352016.92
?12.957.12 0.0073
0.0071
0.0066
0.0086
0.0063
0.0061
0.0061
0.0063
0.0063
0.0063
0.0066
0.0063
0.0066
0.0065
0.0066
0.0066
0.0066
0.0066
0.0066
0.0066
0.0066
18
20
30
?14
?10
?4
?24
42
58
60
?8
60
60
32
56
32
14 Thalamus
Lateral globus pallidus
Putamen
Thalamus
Inferior frontal gyrus
Superior frontal gyrus
Middle frontal gyrus
Thalamus
Superior frontal gyrus
Middle frontal gyrus
Middle frontal gyrus
Medial frontal gyrus
Anterior cingulate
Caudate
Posterior lobe
Superior temporal gyrus
Superior occipital gyrus
Inferior frontal gyrus
Culmen
Middle frontal gyrus
Lingual gyrus
4
4
2
3
4
976
416
376
?16.96
?47.26
25.16
?24.36
41.18
59.09
11.19
6.04
2.84
?18
?46
24
24
?4
?30
?30
36
?8
12
6
2
4
2
6
8
46
10
10
5
6
336
336
?4.87
?30.91
?7.16
59.77
1.91
6.9210
10
9
10
24
7
8
9
328
312
304
304
288
280
280
272
264
256
216
35.9
?7.15
6.95
?16
16.04
44
34.01
?18.23
3.94
?42.2
7.93
32.03
55.16
31.83
0.67
?61.92
?48.1
?71.83
7.85
?35.76
18.18
?93.82
24.25
6.22
3.8
18.99
?20.14
24.44
28.13
?14.01
?15.97
32.05
?3.93
24
6
46
10
11
12
13
14
15
16
17
?16
16
44
34
?18
0 18
?62
?48
?72
?20
24
28
?14
?16
32
?4
13
19
478
4
?36
18
?94
?429
8 17
See Fig. 1 for corresponding images. MDD=major depressive disorder; Co=controls; BA=Brodmann areas.
Results of ALE analysis [MDD>Co] (resting state).
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3.2.2. Animals
Evidence of hypoactive structures in the resting state of
animal models of depression are sparse (see Table 2b). This may,
in part, be due to the fact brain changes in animal models of
depression are often measured soon after the application of
discrete stressors. Congenitally
decreased metabolism in the caudate in the SACC and in the
DMPFC(Shumake etal.,2003).
presented decreased [14C]-2-deoxyglucose metabolism in the
CP (Skelin et al., 2008). The other findings that have reported
indexes of hypoactivity are not consistent or are contradicted by
other results.
helplessratshave shown
Bulbectomized ratsalso
3.2.3. Comparison between human and animal findings
Hypoactivity in the resting state was observed in both animals
and humans in the SACC, the left lateral prefrontal cortex including
the DLPFC, and the caudate (see Table 3). While hypoactivity in the
bilateral anterior insula and the bilateral superior temporal gyrus
have, to date, been observed only in humans (see Table 3).
3.3. Glutamatergic modulation of resting state activity in humans
and animals
We investigated glutamatergic abnormalities in those regions
that showed abnormal resting state activity thereby focusing
Fig. 2. Resting state hypoactivity in humans revealed by ALE analysis [Co > MDD]. See Table 2a for corresponding data. MDD = major depressive disorder; Co = controls.
A. Alcaro et al./Neuroscience and Biobehavioral Reviews 34 (2010) 592–605
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predominantly on hyperactive resting state regions (see Tables 1a,
1b and 3).
3.3.1. Humans
In summary, human results from MRS, fMRI/PET, postmortem,
and pharmacological studies provide evidence for glutamatergic
abnormalities in anterior ventral midline regions.
3.3.2. Animals
Insummary,animaldatashowincreasedtotalconcentrationand
transmissionofglutamateinseveral regionsthatshowed metabolic
hyperactivity in the resting state. One should however note that
some studies also show decreased total glutamate concentration in
PFC and/or Hipp. In contrast to the glutamate concentration data,
animalresultsareconsistentwithregardtoglutamatergicreceptors
showing increased NMDA receptor and decreased AMPA receptor
sensitivity/expressioninhyperactiverestingstateregionslikePACC/
VMPFC, VS, putamen, and MDT (and DLPFC) (see Table 3). It should
be noted that similar glutamate anomalies were seen also in neural
structures outside of the ventral anterior midline regions, for
example the Amyg, the Hyp, the DR, the VS/NACC and the SN/VTA.
3.3.3. Comparison between humans and animals
Datainhumansindicatelowerglutamatetotal concentrationsin
hyperactive ventral anterior cortical midline regions like the PACC
and VMPFC (see Table 3). Decreased glutamate concentrations in
humans contrast with the findings in animals that show rather
increased glutamate concentrations in cortical (and subcortical)
regions. One should however note that human findings are only
based on the PACC/VMPFC while animal findings highlight
predominantly subcortical (and some cortical) regions. Further-
more,itshouldbenotedthattheanimaldataarenotfullyconsistent
with some studies showing decreased glutamate concentrations
(see Table 3).
In contrast to the data regarding glutamate concentrations, the
animal data on NMDA and AMPA receptors are fully consistent
with what may be pharmacological and biochemically inferred
from thehumandata. Studiesshowincreased NMDAreceptors and
decreased AMPA receptor expression/sensitivity in hyperactive
resting state regions like PACC/VMPFC, VS, putamen, MDT,
hippocampus/amygdala (and DLPFC) (see also Fig. 3). Unfortu-
nately,therearenocomparabledataavailableonNMDAandAMPA
receptors in humans (see Table 3).
3.4. GABAergic modulation of resting state activity in humans and
animals
3.4.1. Humans
In summary, human findings provide some evidence for altered
GABAergic metabolism in cortical regions, though the results
remain controversial and require further investigation.
3.4.2. Animals
In summary, animal data show consistent findings of decreased
total GABA concentration and synthesis and decreased GABAA/B
receptor sensitivities/expression in hyperactive resting state
Table 2b
Resting state hypoactivity in animal models of depression.
Ref Model SpeciesMeasureBrain regions
Caldecott-Hazard et al. (1988)
Persico et al. (1995)
Shumake et al. (2003)
Various
Chronic stress
Genetic
Rats
Rats
Rats
[14C]-2 deoxyglucose
c-fos
Quant cyto oxidase
Secondary motor Cx
PFC
ACC, anterior Pir Cx, BNST, caudate-putamen,
DMPFC, septum, VP, VTA
Dentate gyrus, lateral septal n.
Caudate-putamen
Hipp, IC, left insula, left amyg
Huang et al. (2004)
Skelin et al. (2008)
Jang et al. (2009)
Learned helplessness
Bulbectomy
Forced swim test
Mice
Rats
Rats
c-fos
[14C]-2 deoxyglucose
[18F] FDG PET
DMPFC, dorsomedial prefrontal cortex; IC, inferior colliculus; PFC, prefrontal cortex; see Table 1b for additional abbreviations.
Table 2a
Resting state hypoactivity in humans revealed by ALE analysis [Co>MDD].
Cluster Volume (mm3)Weighted center Extrema Region BA
XYZValueXYZ
1
2
3
4
5
6
7
8
9
1104
984
928
384
368
352
336
336
336
328
320
312
296
288
288
?34.18
?14.79
?35.56
?39.45
?39.89
?37.78
?36.29
43.81
?27.06
2.77
?46.07
?8.16
23.12
30.64
48.78
?16.9
21.06
12.76
7.05
?5.98
34
0.34
2.15
35.63
31.16
?27.74
24.81
?48.06
?11.11
?25
9.92
38.09
38.3
?10.31
0.22
19.74
?19.52
?4.22
7.36
9.27
24.35
15.96
13.26
?16.98
2.78
0.0101
0.0087
0.0072
0.0066
0.0066
0.0066
0.0067
0.0066
0.0064
0.0062
0.0066
0.0065
0.0063
0.0061
0.0061
0.0061
0.0063
0.0065
0.0065
0.0063
0.0063
0.0066
0.0066
?34
?16
?36
?40
?40
?38
?36
44
?26
?16
20
10
10
38
38
Claustrum
Cingulate gyrus
Precentral gyrus
Sub-lobar
Insula
Middle frontal gyrus
Superior temporal gyrus
Insula
32
9
13
13
46
38
13
8
?10
?6
34
0
20
0
2
?20
?4
36
30
8
10
11
12
13
14
15
2 10
24
16
12
Anterior cingulate
Inferior parietal lobule
Anterior cingulate
Posterior cingulate
Parahippocampal gyrus, hippocampus
Superior temporal gyrus
Superior temporal gyrus
Declive, culmen
Inferior frontal gyrus
Caudate
Superior temporal gyrus
Middle temporal gyrus
Cingulate gyrus
Inferior frontal gyrus
24
40
24
23
36
22
41
?46
?8
22
32
48
48
?12
26
10
?32
?30
12
38
?28
26
?48
?10
?26
?26
?58
18
18
?54
?54
?26
30
?16
2
4
16
17
18
19
280
272
264
264
?11.82
25.27
10.7
?30.97
?57.53
17.47
17.02
?52.92
?16.67
?22.16
1.19
18.77
?18
?2247
0
18
18
40
22
39
31
47
20
21
264
256
12.24
37.91
?25.81
30.1
39.95
?12.15
?12
See Fig. 2 for corresponding images. MDD=major depressive disorder; Co=controls; BA=Brodmann areas.
Results of ALE analysis [Co>MDD] (resting state).
A. Alcaro et al./Neuroscience and Biobehavioral Reviews 34 (2010) 592–605
598

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Table 3
Resting state hyperactivity and glutamate- and GABAergic function in animal and human depression.
Brain regionsHuman
brain
activity
Animal
brain
activity
Human
Glx
levels
Human Glu
receptors
Animal Glu
levels
Animal Glu
receptors
Human
GABA levels
Human
GABA receptors
Animal
GABA
levels
Animal GABA
receptors
Neurochemical-related references
Amyg (right)
AON
BNST
Claustrum
DLPFC/PL
–
–
–
–
Up
Up
Up
Up
Up
–
–
–
–
–
Down (MR) –
–
–
–
–
–
–
–
–
Up/down
High/low NMDA
–
–
–
High NMDA; low AMPA;
low GluR2
–
–
–
–
Normal (MR);
Down (PM)
–
–
–
–
–
Down
–
Down
–
Down
–
–
–
–
High/low GABA-A;
low GABA-B
Ho et al. (2001), Seidel et al. (2008)
–
Bowers et al. (1998)
–
Acosta et al. (1993), Dennis et al. (1993),
Hasler et al. (2007), Li et al. (2008),
Michael-Titus et al. (2008), Sartorius et al. (2007)
Acosta et al. (1993), Dennis et al. (1993),
Hasler et al. (2007), Li et al. (2008),
Michael-Titus et al. (2008), Webster et al. (2000)
DMPFC–Up/down Down (MR) – Up/down High NMDA; low AMPA;
low GluR2
Normal (MR)- DownHigh/low GABA-A;
low GABA-B
DR
Hab
Hipp
–
–
–
Up
Up
Up
–
–
Down (MR) –
–
–
Up/down
–
Up/down
High NMDA
–
High NMDA;
high/low mGlu5
-
–
Down (PM)
–
–
–
–
–
Down
–
–
High/low GABA-A;
low GABA-B
Block et al. (2009), Cullinan and Wolfe (2000),
Drugan et al. (1989), Duncko et al. (2003),
Gronli et al. (2007), Joels et al. (2004), Li et al. (2008),
Sartorius et al. (2007), Wieronska et al. (2001)
Acosta et al. (1993), Cullinan and Wolfe (2000),
Herman et al. (2008)
Hyp– Up–– Up–––Down Low GABA-A
LC
MDT/thalamus
mPOA
NTS
Occ Cx
–
Up
–
Up
Up
Up
UP
–
–
–
–
Normal/up
(MR)
Down (MR;
Gln only)
–
–
–
–
–
–
–
–
–
–
–
–
–
–
High NMDA
–
–
–
–
–
–
Down (MR)
–
–
–
–
–
–
–
–
–
–
–
–
Robichaud et al. (2001); see Table 1a
Bhagwagar et al. (2007), Sanacora et al. (2004)
PACC UpUp––High NMDA Normal
(MR, PM)
–
–
–
–
–
––– Auer et al. (2000), Sartorius et al. (2007),
Walter et al. (2009); see Table 1a
See Table 1a
See Table 1a
PAG/Tectum
Pallidum
Pir Cx
Pulvinar
Putamen
Up
Up
–
Up
Up
Up
Up
Up
–
Up
–
–
–
–
–
–
–
–
–
–
–
–
–
–
High NMDA;
–
–
–
–
–
–
–
–
–
Down
–
–
–
–
Low GABA-A
See Table 1a
Acosta et al. (1993), Drugan et al.
(1989); see Table 1a
low AMPA
–
High NMDA; low AMPA
Septum
VMPFC/IL
–
Up
Up/down –
–
––
Up/down
–
–
–
–
Down
Down
High GABA-A
High/low
GABA-A; low
GABA-B
–
–
Kram et al. (2000)
Acosta et al. (1993), Dennis et al. (1993),
Hasler et al. (2007) Li et al. (2008), Sartorius
et al. (2007), Webster et al. (2000); see Table 1a
Borsini et al. (1988), Duncko et al. (2003)
Duncko et al. (2003),
Fitzgerald et al. (1996); see Table 1a
Down (MR) –
VS/NACC
VTA/SN
–
Up
Up
Up/down –
––
–
–
–
High NMDA; low AMPA
High/low NMDA
–
–
–
–
Down
–
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599

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regions such as the PACC/VMPFC, VS, putamen, MDT, hippocam-
pus/amygdala and DLPFC (see Table 3). Other areas outside the
ventral anterior midline regions that also have shown similar
abnormalities are the Hyp, and the VS/NACC.
3.4.3. Comparison between humans and animals
While there is only modest evidence investigating GABA
concentrations in humans, animal data have consistently shown
decreased total GABA concentration in most of the cortical and
subcortical structures that are metabolically hyperactive during
depressive states. Moreover, the animal data support decreased
GABAA/B receptor expression/sensitivity in various cortical and
subcortical regions (VS, putamen, PACC/VMPFC, MDT, hippocam-
pus/amygdala, DLPFC) that show hyperactivity in the resting state
(see Table 3).
4. Discussion
This study investigated the functional anatomy and neuro-
chemicalmodulationofrestingstateactivityusingacross-species
translational approach. A direct comparison was made between
the human data (using a meta-analysis of studies involving
patients with MDD), and the animal data (investigating anatomi-
cal, biochemical, and pharmacological changes and manipula-
tions in models of depression). The main results of our
translational analysis are as follows. First, we observed resting
statehyperactivityinacommonsetofregionsinbothanimalsand
humans that concern predominantly ventral anterior midline
regions like PACC, the VMPFC, the VS, and the MDT. These
hyperactive regions must be distinguished from more dorsal
posterior midline regions that show hypoactivity in the resting
state in both animals and humans. Second, hyperactive resting
state regions tend to show abnormal glutamatergic metabolism
with reduced glutamate levels (humans) as well as increased
NMDA and decreased AMPA receptor density/sensitivity (ani-
mals). Animal findings also suggest decreased GABA concentra-
tions and decreased GABAA/Breceptor expression/sensitivities in
these hyperactive regions.
Taken together, our translational analysis suggests that
abnormal hyperactivity in the resting state may be related to
abnormal glutamatergic and GABAergic metabolism in depression.
This not only sheds light on the abnormal resting state activity in
depression but also on the neurochemical modulation of the
default-mode network as shared among humans and animals. In
addition, it is interesting to note that these neuroanatomical and
biochemical consistencies across both animals and humans
provide evidence for the use of an abnormal resting state as a
possible biological endophenotype of depression—as imaging
techniques such as fMRI, PET, and MRS are increasingly demon-
strating the potential to provide a bridge from the clinical to the
basic mechanism level (Hasler et al., 2004). More specifically, we
suggest that high resting state activity in default-mode network
regions (as mediated by alterations in GABA and glutamate) may
be a potential endophenotype of depression and may also account
for the increased self-focus observed (Grimm et al., 2009b). Such
high resting state activity, including its biochemical and psycho-
pathological manifestations, may distinguish depression from
other psychiatric disorders such as schizophrenia or anxiety
disorders. However, despite its cross-species biological, and
clinical plausibility, the assumption of high resting state activity
as a possible endophenotype needs to be further substantiated,
especially with regard to underlying genetic changes.
4.1. Resting state hyper- and hypoactivity
To summarize, our first aim was to investigate resting state
activity in both humans and animals. Early PET studies in human
MDD observed increased resting state activity predominantly in
ventral anterior cortical midline regions like the PACC and the
VMPFC (see Mayberg, 2002, 2003; Phillips et al., 2003 for reviews).
The assumption of abnormalities in resting state regions was
further corroborated by recent findings of abnormalities in the
ventral regions of the default-mode network in human MDD
(Greicius et al., 2007; Grimm et al., 2009a). Our meta-analysis of
resting state studies in human MDD confirms abnormal resting
state hyperactivityin ventral cortical midline regions like the PACC
and the VMPFC (see also Fitzgerald et al., 2007 who observed
similar regions). It is important to note that while there are
limitations to the ALE method compared to other approaches, as
discussed elsewhere (Wager et al., 2009), the current meta-
analysis results are in accord with the results described. The ALE
technique has been employed in several meta-analyses (see Brown
et al., 2005; Owen et al., 2005, for more see www.brainmap.org/
pubs) including in MDD (Fitzgerald et al., 2006). Though ALE has
some weakness in methodological terms because it is coordinate-
based, a recent study comparing different meta-analytic programs
Fig. 3. Glutamat- and GABAergic modulation of resting state hyperactivity in a ventral anterior cortico-subcortical–cortical reentrant circuit (thick arrows).
DLPFC = dorsolateral prefrontal cortex; MDT = mediodorsal thalamus; PACC = pregenual anterior cingulate cortex; SN = substantia nigra; VTA = ventral tegmental area.
A. Alcaro et al./Neuroscience and Biobehavioral Reviews 34 (2010) 592–605
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yielded similar results for ALE and other coordinate-based and
image-based programs (Salimi-Khorshidi et al., 2009).
Interestingly, we observed that many regions, similar to those
found in the human studies, showed hyperactivity in the resting
state in animal models of depression. Concordant findings were
evident in cortical regions like the PACC and the VMPFC as well as
subcortical regions like the MDT, pallidum, putamen, VTA/SN,
and the midbrain. Animal studies also revealed hyperactivity in
more discrete subcortical regions like the locus coerulus, raphe
nucleus, Hab, BNST, solitary tract, and the septum, all of which,
due to low spatial resolution, cannot be readily imaged in
humans. This fact raised the question of whether such hyperac-
tive subcortical loci may also be considered part of the ventral
anterior midline regions. Alternatively, it is likely that these
subcortical areas both modulate and are modulated by ventral
anterior midline regions. Regardless, strong reciprocal connec-
tivity between hyperactive subcortical areas and those belonging
to ventral anterior midline regions has been strongly supported
in animal studies (Gabbott et al., 2005; Hoover and Vertes, 2007;
but see also Stone et al.,2008).
translational data suggest resting state hyperactivity across
species in ventral anterior midline regions like PACC, VMPFC, VS,
putamen and MDT in depression.
Whatremainsunclearthoughishowrestingstatehyperactivity
in these ventral anterior midline regions translate into behaviour,
e.g., depressive symptoms. Stone et al. (2007, 2008) associate these
ventral regions with a neural circuit involved in the organization of
the stress response. Many of these regions have also been
associated with the Behavioral Inhibition System (BIS) by Gray
(1994) and various other negative affective systems (Panksepp,
1998). The BIS is proposed to regulate avoidant behaviour,
inhibition of behaviour, anxiety, negative affective states and
neuroticism. In more refined studies of emotional systems, it is
clear that the higher reaches of various emotional systems,
especially those related to social processes such as separation
distress and pro-social behaviours such as maternal nurturance
and play, are also concentrated in these regions of the brain
(Panksepp, 1998). Our translational findings suggest that various
negative/emotional stress systems, however they are conceptual-
ized, are hyperactive in depression; this is in accord with clinical
symptoms, behaviour, and personality-related characteristics in
MDD patients. However, the specific types of emotional/stress
systems that are most affected in depression needs to be addressed
in future translational studies. Furthermore, one might consider
that ventral anterior midline regions have also been associated
with self-relatedness in healthy humans (Northoff and Bermpohl,
2004; Northoff et al., 2006; Northoff and Panksepp, 2008). This
may lead one to speculate that abnormal resting state hyperactivi-
ty may be related to the ruminations, with an increased, affectively
negative self-focus, often observed in clinically depressed patients
(Northoff, 2007). Though there is currently little direct support for
this conjecture, there is at least one study that relates the MDD
patients’ increased self-focus to abnormal activity in anterior
midline regions (Grimm et al., 2009b).
In contrast to hyperactive regions, regions that were hypoactive
were located more dorsally and posterior—such as the SACC and
the PCC (and might be deemed to be more cognitive regions of the
forebrain). However, it should be emphasized that at the present
time the regional overlap between findings in animals and humans
is not as consistent and clear-cut as it is for the more ventral
hyperactive regions, including various subcortical regions long
implicated in emotionality in animal studies (Panksepp, 1998). In
sum, the translational findings show a stronger and more
consistent convergence between animal and human data with
respectto the hyperactiveresting state regionsthanthose showing
hypoactivity. Because of this, we have concentrated on the
Taken together, these
pathophysiological mechanisms of the hyperactive resting state
regions.
4.2. Resting hyperactivity and glutamatergic and GABAergic
modulation
The second main aim of our translational analysis was to
investigate the relationship between abnormal resting state
activity and neurochemical parameters, with a focus on glutamate
and GABA. Recent studies in human MDD have demonstrated a
potential glutamatergic mechanism as indicated by the antide-
pressant effects of the NMDA receptor antagonist ketamine and
some AMPA agonists (Bleakman et al., 2007; Chourbaji et al., 2008;
Maeng and Zarate, 2007; Maeng et al., 2008). Spectroscopic
findings showed reduced glutamate in the PACC in human MDD
(Auer et al., 2000; Berman et al., 2000; Hasler et al., 2007; Northoff
et al., 1997, 1999; Walter et al., 2009; Zarate et al., 2006).
Importantly, the spectroscopic findings were obtained in the
resting state raising the question of whether this abnormal resting
state hyperactivity may be due to abnormal glutamatergic
metabolism in depression.
If resting state hyperactivity in human PACC is indeed due to
abnormal glutamatergic metabolism, one would also expect
abnormal expression/sensitivity of glutamatergic receptors (e.g.
NMDA and AMPA receptors). Consistent with this possibility,
animal studies report predominant increases in NMDA, and
decreases in AMPA, receptor sensitivity/expression as well as
reduction of NMDA receptors by antidepressant treatment in
certain resting state regions (see Fig. 3). These observations are in
accord withthe effects ofketamineonfunctional PACCactivityand
therapeutic effects in human depression (Northoff et al., 1997,
1999; Salvadore et al., 2009; Zarate et al., 2006). Ketamine
antagonizes the NMDA receptor hyperfunction (as observed in
animals) and may thereby reduce the abnormally increased neural
excitation that contributes substantially to resting state hyperac-
tivity in regions like PACC, VMPFC, VS, putamen, and MDT (see
Fig. 3). Hence, while there seems to be convergence between
human and animal data, future investigation of NMDA receptors in
human depression are needed to further corroborate our
assumption of the linkage between NMDA receptor hyperfunction
and increased resting state activity.
One may question how the reduced PACC glutamate concen-
tration in human depression may be related to resting state
hyperactivity and NMDA receptor hyperfunction in the same
region (and others), as observed in animals. A recent study (Walter
et al., 2009) demonstrated that an fMRI marker of possible resting
state hyperactivity in the PACC (i.e. decreased negative BOLD
response (NBR)), correlated abnormally with the concentration of
glutamate in the same region in depressed patients. In contrast, we
did not observe this correlation in healthy subjects suggesting that
their resting state activity was not directly regulated by glutamate
(but may perhaps be regulated by GABA)—although, as discussed
in Northoff (2007) , it is important to note the possibility that the
excitation-inhibition balance producing NBR may be related to
glutamatergic input. These results support our proposal of
glutamatergic mediation of resting state hyperactivity in the
PACC. Future studies in human depression are needed to
investigate the relationship between glutamate and NBR during
challenge with an NMDA antagonist, such as ketamine, which may
reduce resting state hyperactivity (which may reflect attenuation
of negative affective arousal) by decoupling it from overactive
glutamatergic influences that promote negative affect. Parallel
animal studies may provide the opportunity to test the impact of
locally applied NMDA receptor agonists and antagonists on PACC
(and other regions’) resting state activity in both normal and
depressive-like states.
A. Alcaro et al./Neuroscience and Biobehavioral Reviews 34 (2010) 592–605
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In addition to glutamatergic abnormalities, GABAergic metab-
olism may also be involved in resting state hyperactivity. Though
some human studies (e.g. MRS; postmortem) show reduced GABA
concentration, one has to take into account the low number of
studies and the variability of existing results. In contrast to human
findings, a more coherent picture emerges from the animal data.
These data show reductions in both GABA concentrations and
GABAA/B receptor sensitivity/expression in various hyperactive
resting state regions, including the PACC/VMPFC,VS, putamen, and
MDT (see Table 3 and Fig. 3). This is in line with the observation of
the therapeutic efficacy of GABAergic agonists like lorazepam in
acute depression. However, future studies are necessary to
demonstrate that GABAergic drugs directly impact resting state
activity in PACC and other ventral midline regions in both healthy
(see Northoff et al., 2002 for one study in this direction) and
depressed subjects.
These GABAergic abnormalities may be related to resting state
hyperactivity in ventral anterior midline regions. In human MDD,
resting state hyperactivity is indicated by reduced NBR during
emotion processing(Grimmetal.,2009a). NBRhavebeenshownin
healthy humans to be related to neural inhibition and GABA (see
Shmuel et al., 2002, 2006; Northoff et al., 2002; Northoff 2007).
One would consequently expect that reduced NBR in human MDD
may no longer be modulated by GABA which is exactly what a
recent fMRI-MRS study demonstrated (Walter et al., 2009). Instead
of being modulated by GABA, reduced NBRs in human MDD were
related to glutamate. Hence, resting state activity in depression
appears to shift from a primarily GABA-mediated modulation to a
glutamate-mediated modulation.
The findings by Grimm et al. (2009a,b) and Walter et al. (2009)
are very much in line with those reported recently by Sheline et al.
(2009). In line with the study of Grimm et al. (2009a), Sheline et al.
(2009) conducted an emotional appraisal task and observed a
failure to properly deactivate in several regions of the default-
mode network (including the ventromedial prefrontal cortex, the
anterior cingulate, and lateral parietal cortex). This is very much in
line with the findings by Grimm et al. (2009a) who also observed
significantly reduced NBR in depressed patients during both
emotionperceptionandappraisal.WhilebothGrimmetal.(2009a)
and Sheline et al. (2009) investigate emotional appraisal, another
paper by Grimm et al. (2009b) directly targeted self-relatedness by
investigating appraisal or judgment regarding the degree of self-
relatedness. Interestingly, they observed altered neural activity in
predominantly midline regions that overlapped with those
showing reduced NBR. These results suggest that the reduced
NBR observed in the default-mode network may be traced back
psychologically to altered self-relatedness; more specifically, to an
increased self-focus—as was behaviourally observed by Grimm
et al. (2009b) (see also Northoff, 2007).
Overall, animal findings suggest increased expression/sensitiv-
ity in NMDA receptors while AMPA receptors and GABAA/B
receptors seem to be decreased. This may result in a net effect
of increased neural excitation and decreased neural inhibition.
Such increases in neural excitation may in turn reduce the amount
of NBR that, as studies in healthy subjects show (see above),
strongly relies on neural inhibition. One may consequently
hypothesize that resting state hyperactivity in depression may
be related to the lack of GABA-mediated neural inhibition and an
excess in glutamate-mediated neural excitation. This however
needs to be tested in future studies focusing on electrophysiology
measurements in these regions during neurochemical modulation,
along with causal pharmacological studies in animal models.
Finally, while not a main focus of this review, it is important to
note recent work regarding the role of various neuromodulators in
the regulation of GABA and glutamate, with particular relevance to
depression. Neuromodulators, in this context, include both
substances other than GABA and glutamate that may modulate
their activity as well as agonistic and antagonistic substances that
directly modulate GABA- and glutamatergic receptors.
For instance, some neurosteroids (e.g. allopregnanolone) act as
positive allosteric modulators of the GABAA receptor. There is
evidence that the effects of serotonin selective reuptake inhibitors
on depression may be in part related to their ability to increase
neurosteroid concentrations and, thus, increase the inhibitory
effects of GABA (MacKenzie et al., 2007; Pinna et al., 2006). There is
also strong evidence that some neurotrophins (especially brain-
derived neurotrophic factor) and neuropeptides (e.g. corticotro-
pin-releasing factor and substance P) play a key role in the
pathophysiology of depression (Nemeroff, 1996; Rakofsky et al.,
2009; Thakker-Varia and Alder, 2009); their mechanism of action
mayincludearebalancingofaminoacidneurotransmitterfunction
(Skorzewska et al., 2009; Stacey et al., 2002a,b; Ungless et al.,
2003), though much more work is needed to clarify the precise
mechanisms and brain areas involved.
In addition, as the GABA and glutamate systems appear to be
key in depression – although mimetic drugs tend to have many
unwanted side effects – allosteric modulators of their receptors
have been developed and found to be effective against some
depressive symptoms. Interestingly, GABABreceptor antagonists,
and both GABAA and GABAB receptor positive allosteric mod-
ulators may have antidepressant properties; although the results
are largely limited to animal studies (Frankowska et al., 2007;
Kalueff and Nutt, 2007). Positive allosteric modulators of AMPA
and mGlu receptors also show promise in reducing depressive
symptoms though, once again, there is limited data regarding
humans (Black, 2005; Gasparini and Spooren, 2007). Consistent
with the role of multiple neuromodulators, a promising line of
antidepressant drug development and current treatment has
focused on compounds that selectively target multiple systems
(Millan, 2009).
4.3. Conclusion
To our knowledge, this is the first report utilizing a systematic
translational approach to animal and human data with regard to
depression. Regarding the pathophysiology of depression, our
focus was on the anatomy and neurochemistry of resting state
activity. We demonstrated that studies of animal and human
depression show resting state hyperactivity primarily in midline
subcortical and cortical regions. Neurochemical findings suggest
that resting state hyperactivity in this loop may be related to
glutamatergic abnormalities with up-regulation of NMDA recep-
tors and down-modulation of AMPA receptors across these
regions. This may lead to increased neural excitation, accompanied
by negative affective/emotional arousals, which may be further
enhanced by decreased neural inhibition in these regions as
mediated by reduced GABA activity (and perhaps concentrations)
and/or GABAA/Breceptor expression/sensitivity. Taken together,
these translational results demonstrate resting state hyperactivity
in ventral anterior midline regions in depression and its modula-
tion by abnormal glutamate- and GABAergic metabolism. This also
contributes to a better understanding of the biochemical under-
pinnings of the resting state in the default-mode network of both
animals and humans.
Acknowledgements
We wish to acknowledge the generous support of Audrey Gruss
and the Hope of Depression Research Foundation (HDRF) to G.N.
and J.P. and the German Research Foundation (SFB77686). G.N.
holds a Canada Research Chair for Mind, Brain imaging and
Neuroethics as well as an EJLB-CIHR Michael Smith Chair in
A. Alcaro et al./Neuroscience and Biobehavioral Reviews 34 (2010) 592–605
602

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Neurosciences and Mental Health. Jaak Panksepp is Bailey
Endowed Professor of Animal Well-Being Science at WSU.
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