Neuroimaging Markers of Glutamatergic and Gabaergic Systems In

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Neuroscience and Biobehavioral Reviews 61 (2016) 35–52

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Neuroscience and Biobehavioral Reviews

jou rnal homepage: www.elsevier.com/locate/neubiorev

Neuroimaging markers of glutamatergic and GABAergic systems in

drug addiction: Relationships to resting-state functional connectivity

a,∗ a,b c,∗∗

Scott J. Moeller , Edythe D. London , Georg Northoff

Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

Departments of Psychiatry and Biobehavioral Sciences, and Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles,

CA, USA

Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Ottawa, Canada

a r a

t i c l e i n f o b s t r a c t

Article history: Drug addiction is characterized by widespread abnormalities in brain function and neurochemistry,

Received 21 July 2015

including drug-associated effects on concentrations of the excitatory and inhibitory neurotransmitters

Received in revised form 5 November 2015

glutamate and gamma-aminobutyric acid (GABA), respectively. In healthy individuals, these neurotrans-

Accepted 21 November 2015

mitters drive the resting state, a default condition of brain function also disrupted in addiction. Here, our

Available online 1 December 2015

primary goal was to review in vivo magnetic resonance spectroscopy and positron emission tomography

studies that examined markers of glutamate and GABA abnormalities in human drug addiction. Addicted

Keywords:

individuals tended to show decreases in these markers compared with healthy controls, but ﬁnd-

Drug addiction

Glutamate ings also varied by individual characteristics (e.g., abstinence length). Interestingly, select corticolimbic

GABA brain regions showing glutamatergic and/or GABAergic abnormalities have been similarly implicated in

Neurochemistry resting-state functional connectivity deﬁcits in drug addiction. Thus, our secondary goals were to provide

Magnetic resonance spectroscopy a brief review of this resting-state literature, and an initial rationale for the hypothesis that abnormalities

Positron emission tomography in glutamatergic and/or GABAergic neurotransmission may underlie resting-state functional deﬁcits in

Resting-state

drug addiction. In doing so, we suggest future research directions and possible treatment implications.

fMRI

Contents

1. Introduction ...... 36

2. Glutamatergic alterations in drug addiction ...... 38

2.1. Alcohol...... 38

2.1.1. MRS ...... 38

2.1.2. PET/SPECT ...... 38

2.2. Smoking ...... 38

2.2.1. MRS ...... 38

2.2.2. PET/SPECT ...... 38

2.3. Opiates...... 38

2.3.1. MRS ...... 38

2.3.2. PET/SPECT ...... 38

2.4. Cocaine ...... 38

2.4.1. MRS ...... 38

2.4.2. PET/SPECT ...... 41

2.5. Methamphetamine ...... 41

2.5.1. MRS ...... 41

2.5.2. PET/SPECT ...... 41

∗

Corresponding author at: One Gustave L. Levy Place, Box 1230, New York, NY 10029-6574. Tel.: +212-824-8973; fax: +212-803-6743.

∗∗

Corresponding author at: 1145 Carling Avenue, Ottawa; (also: www.georgnorthoff.com)..

E-mail addresses: [email protected] (S.J. Moeller), [email protected] (G. Northoff).

http://dx.doi.org/10.1016/j.neubiorev.2015.11.010

36 S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52

2.6. Cannabis ...... 41

2.6.1. MRS ...... 41

2.6.2. PET/ SPECT ...... 41

2.7. Multiple substances ...... 41

2.7.1. MRS ...... 41

2.7.2. PET/SPECT ...... 41

2.8. Summary ...... 41

3. GABAergic changes in drug addiction ...... 41

3.1. Alcohol...... 41

3.1.1. MRS ...... 41

3.1.2. PET/SPECT ...... 41

3.2. Smoking ...... 42

3.2.1. MRS ...... 42

3.2.2. PET/SPECT ...... 42

3.3. Opiates...... 42

3.4. Cocaine ...... 42

3.4.1. MRS ...... 42

3.4.2. PET/SPECT ...... 42

3.5. Methamphetamine ...... 42

3.6. Cannabis ...... 42

3.7. Multiple substances ...... 42

3.7.1. MRS ...... 42

3.7.2. PET/SPECT ...... 42

3.8. Summary ...... 44

4. Evidence of resting-state functional connectivity deﬁcits in drug addiction ...... 44

4.1. Alcohol...... 45

4.2. Smoking ...... 45

4.3. Opiates...... 45

4.4. Cocaine ...... 46

4.5. Methamphetamine ...... 46

4.6. Cannabis ...... 46

4.7. Summary ...... 46

5. Working hypothesis: Abnormal glutamatergic and/or GABAergic neurotransmission underlies corticolimbic RSFC deﬁcits in addiction ...... 46

5.1. Finer speciﬁcation of the model...... 46

5.2. Integration and translation between human data and animal models...... 46

5.3. More comprehensive methods...... 47

5.4. Testing for substance-speciﬁc effects ...... 47

5.5. Potential applications to treatment ...... 48

6. Conclusion ...... 48

Disclosure/Conﬂict of Interest ...... 48

Acknowledgements...... 48

References ...... 48

1. Introduction neurons (Hyder et al., 2006; Rothman et al., 2011). Several com-

bined fMRI-magnetic resonance spectroscopy (MRS) studies have

Drug addiction is characterized by dysfunction in corticolimbic provided evidence supporting such relationships. For example, the

networks subserving attentional, emotional, and inhibitory pro- higher the glutamate concentrations and the lower the GABA con-

cesses (Goldstein and Volkow, 2011). Insights into these systems- centrations in the posterior cingulate cortex (PCC), the higher was

level deﬁcits have been primarily advanced through in vivo, the RSFC between PCC and pregenual anterior cingulate cortex

non-invasive brain imaging methodologies, such as functional (pACC) (Duncan et al., 2014; Hu et al., 2013; Kapogiannis et al.,

magnetic resonance imaging (fMRI). Increasingly, these methods 2013). GABA concentrations, measured with MRS in the resting

are being used to examine resting-state functional connectivity state, were also negatively correlated with task-evoked fMRI activ-

(RSFC), a measure of intrinsic activity that provides information ity in the pACC, visual cortex, and somatomotor cortex (Duncan

on network-level function and its disruption in neuropsychiatric et al., 2014). The relationship between resting-state glutamate level

disorders (Rosazza and Minati, 2011), including substance use dis- and task-related activity is less clear (Duncan et al., 2014), though

orders (Fedota and Stein, 2015; Lu and Stein, 2014; Sutherland et al., it appears that glutamate mainly exerts transregional effects by

2012) (for an overview of the resting state, see Box 1). acting on the long-range axons of pyramidal cells to enable cortico-

Although the neurochemical bases of RSFC differences between cortical connections (whereas GABA and GABAergic interneurons

addicted individuals and healthy controls are presently unclear, mainly exert local effects by acting on pyramidal-cell dendrites to

evidence from studies of healthy research participants suggests affect the regional processing of inputs). In support of this view are

important contributions of the excitatory and inhibitory neuro- observations that glutamate mediates the transition from resting-

transmitters glutamate and gamma-aminobutyric acid (GABA), state activity in one region (e.g., pACC or PCC) to stimulus-induced

respectively, to the resting state. In particular, glutamate and GABA and resting-state activity in the same or different regions (Duncan

appear to drive the metabolic and neuronal mechanisms underly- et al., 2011, 2013; Hu et al., 2013).

ing the resting state to sustain the excitation-inhibition balance The goals of the current article were: primarily to review evi-

(Duncan et al., 2014). Indeed, resting-state metabolic activity of dence that human drug addiction is marked by abnormalities in

the brain is linearly coupled to its neuronal activity (Hyder et al., brain glutamate and GABA; and secondarily to use ﬁndings of

2013), largely reﬂecting the actions of glutamatergic and GABAergic this literature in combination with select RSFC ﬁndings to build

S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52 37

tomography (SPECT). PET and SPECT are nuclear medicine pro-

Box 1: Overview of the resting state cedures that use tracer kinetic modeling to provide indices of

Resting-state activity is a heterogeneous concept (Cabral et al.,

neurotransmitter receptor binding, including: binding potential

2014; Mantini et al., 2013; Morcom and Fletcher, 2007; Northoff,

(BPND), the product of receptor density and afﬁnity; volume of

2014). The term resting state itself is an operational term that

distribution (VT), the ratio at equilibrium of the sum of the con-

describes the absence of any particular external stimulus, such

centrations of speciﬁcally bound, nonspeciﬁcally bound, and free

as a speciﬁc tactile or visual (or otherwise) stimulus or cogni-

radiotracer to that of parent radioligand in plasma, separated from

tive task. Instead, resting state focuses on internally generated

radiometabolites; and distribution volume ratio (DVR), the vol-

mental activity with internal mental contents (e.g., thoughts

or imagery) as distinguished from externally generated men- ume of distribution normalized to nonspeciﬁc binding. Because

tal contents (e.g., perceptions). Psychologically, resting-state abnormal activity of metabotropic glutamate receptors predis-

activity of the brain can manifest in what is described as mind- poses an individual to multiple disorders including addiction, the

wandering (Smallwood and Schooler, 2015), random thoughts 11

glutamate system has been assessed by PET with [ C]ABP688,

(Doucet et al., 2012), or self-generated thoughts (Smallwood

a radioligand for the metabotropic glutamate receptor subtype 5

and Schooler, 2015).

(mGluR5) (Terbeck et al., 2015). In examining GABA neurotransmis-

Different terms are used to describe the resting state, largely

sion, PET/SPECT studies have concentrated on the GABAA receptor,

reﬂecting different investigative perspectives. In addition to −

a Cl ion channel that produces fast electrical signals and directly

“resting-state activity,” other terms include “spontaneous

controls the efﬁcacy of GABAergic synaptic transmission (Luscher

activity,” “baseline,” or “intrinsic activity” (Deco et al., 2014;

et al., 2011; Xu et al., 2014). These studies have primarily utilized

Fox et al., 2015; Mantini et al., 2013; Northoff, 2014). The term

11 123

“spontaneous activity” highlights the idea that resting-state three radiotracers: [ C]ﬂumazenil and [ I]iomazenil, which bind

activity is not induced by any particular external stimulus or to the benzodiazepine site on the GABAA receptor; and [ C]Ro15

task but instead is generated naturally (Cabral et al., 2014; 4513, which binds to the GABAA receptor alpha-5 subunit (Ravan

Deco et al., 2014; Mantini et al., 2013). Imaging specialists

et al., 2014). Signaling through GABAA receptors, particularly those

often prefer the term “baseline,” indicating that the resting

containing an alpha-5 subunit, contributes to the reinforcing effects

state (which occurs pre-stimulus, pre-task, or between stim-

of alcohol in non-human animal studies (Cook et al., 2005; McKay

uli/tasks) can serve as a reference condition that is subtracted

et al., 2004; Stephens et al., 2005).

from a task condition (Morcom and Fletcher, 2007). The term

We stress from the outset that many interpretative difﬁculties

“intrinsic activity” highlights the idea that the resting-state

emerge in reviewing this MRS and PET/SPECT literature, includ-

has its origin within the brain itself [i.e., as distinguished from

extrinsic activity that originates from stimulus-induced or ing multiple sources of variation between studies that can produce

task-evoked activity (Northoff, 2014)]. inconsistent ﬁndings. One notable difﬁculty is variation in partici-

Both spatial and temporal measures can assess resting-state pant characteristics. Participants often report the use of multiple

activity. Spatially precise modalities such as fMRI use RSFC

drugs of abuse in varying amounts and at different times rela-

approaches to target different neural networks that co-activate

tive to testing, and their self-reports may contain inaccuracies;

spontaneously within and between different networks (Cabral

this difﬁculty is often accentuated in individuals addicted to illicit

et al., 2014; Raichle et al., 2001). This method captures the

drugs, who regularly have more expansive drug use histories.

synchronicity of low-frequency, spontaneous ﬂuctuations in

For example, many drug abusers are also cigarette smokers, and

blood-oxygen-level-dependent (BOLD) signals that reﬂect ﬂuc-

smoking independently affects glutamate and other metabolites

tuations in neuronal activity (Shmuel and Leopold, 2008)

(below). Although most studies employ safeguards against effects

between brain regions in the absence of external stim-

ulation (Fox and Raichle, 2007), but that are linked to of recent use (e.g., exclusionary urine toxicology), ﬁne-grained

task-related functioning of brain regions comprising the same information about participants’ secondary drug use histories are

circuits (Hampson et al., 2006) and to corresponding behavior not routinely provided; understandably, most studies concentrate

(Hampson et al., 2006; Kelly et al., 2008). These synchronous

on the primary substance of abuse. Other sources of partici-

ﬂuctuations are conﬁned to gray matter and can be observed

pant variation could include psychiatric comorbidities and their

for monosynaptic or polysynaptic anatomical connections

treatments. Methodologically, sources of variation include the

(Damoiseaux and Greicius, 2009; Shmuel and Leopold, 2008).

use of small sample sizes in some imaging studies, and differ-

Temporally precise modalities, such as EEG or MEG, can mea-

ing and/or evolving sets of approaches and dependent variables.

sure resting-state activity in electrophysiological or magnetic

For example, MRS studies have measured glutamate signals in

activity (Cabral et al., 2014; Mantini et al., 2013), which tar-

gets neural activity changes in different frequency ranges and multiple ways [e.g., glutamate, glutamine, glutamate/glutamine,

their cross-frequency coupling (Engel et al., 2013). Research glutamine/glutamate, and/or glutamate + glutamine (Glx)]. In

participants, while undergoing these assessments, are often keeping with the glutamate–glutamine cycle [i.e., the conversion

instructed to close their eyes and not think about anything in

of glutamate to glutamine in astrocytes is catalyzed by glutamine

particular (Logothetis et al., 2009); this eyes-closed, though still

synthetase, and, in turn, glutamine is reconverted into gluta-

awake, condition is taken as the operational or methodological

mate in neurons by glutaminase (e.g., Walls et al., 2015)], ratios

gold standard to measure resting-state activity.

reﬂecting increased glutamate and/or decreased glutamine both

putatively indicate increased brain glutamate levels. Glutamate-

related concentrations are sometimes further expressed as a ratio

toward an initial plausible neurochemical framework underly- to creatine, often used as an internal reference metabolite (Licata

ing RSFC deﬁcits in drug addiction, emphasizing important roles and Renshaw, 2010). Given this heterogeneity of reporting, we

for glutamate and GABA. In the primary section, we reviewed attempted throughout to focus on effects from the perspective

in vivo neurochemical imaging studies that tested for glutamater- of glutamate or Glx. Such difﬁculties can also occur for GABA,

gic and GABAergic abnormalities in drug-addicted individuals as although less so. Finally, it is possible that changes in the MRS gluta-

compared with healthy controls. The addictions considered were mate/GABA resonance more immediately reﬂect changes in energy

alcohol, nicotine/tobacco, opiates, cocaine, methamphetamine, and metabolism, and that changes in functional networks measurable

cannabis, each reviewed in turn. Imaging methods included mag- by RSFC may stem more directly from such metabolic changes

netic resonance spectroscopy (MRS), which provides information rather than from speciﬁc neurotransmission per se. This potential

on neurotransmitter or metabolite concentrations; and positron issue provides an important reason for including PET/SPECT studies

emission tomography (PET) and single proton emission computed that measured markers of glutamate and GABA neurotransmission.

38 S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52

Taken together, even if these issues preclude full clarity regarding glutamate levels were also correlated with more alcohol craving

the directionality of effects at this time, our review of this literature (Bauer et al., 2013) and more recent (e.g., 1 month, 3 month) alco-

serves to provide a macroscopic overview of the ﬁeld and brings hol consumption (Hermann et al., 2012b; Lee et al., 2007), and with

to light some inconsistencies that can be speciﬁcally addressed in better memory retention (Lee et al., 2007). However, this view is

future work. complicated by other studies reporting that pACC concentrations of

In the second section of this article, we reviewed select RSFC glutamate, which were initially lower in alcohol-addicted individ-

studies of addiction that followed from, and were informed by, uals (who were abstinent for approximately 1-day or 1-week) than

the primary section (and by neurochemical/RSFC studies in health, healthy controls, returned to control levels over 2–5 weeks of absti-

above). We did not intend for this section to exhaustive, nor did nence (Hermann et al., 2012b; Mon et al., 2012). Higher glutamate

we describe each constituent study in complete depth and report levels were also correlated with more days of current abstinence

every available analysis. Rather, we described representative RSFC (Mon et al., 2012).

ﬁndings in drug addiction, obtained using seed-based and data-

driven methodologies, which examined connectivity differences 2.1.2. PET/SPECT

between addicted individuals and healthy controls in select cor- No studies were found.

ticolimbic brain regions [e.g., ACC, medial prefrontal cortex (PFC),

striatum, and insula]. We marshaled this RSFC evidence, as well as 2.2. Smoking

evidence from the primary MRS/PET section, to support a new neu-

rochemical framework in which we hypothesize that glutamatergic 2.2.1. MRS

and/or GABAergic deﬁcits may underlie RSFC abnormalities in An interesting recent study indicated that smokers had lower

drug addiction. RSFC, then, may serve as an intermediate pheno- glutamate levels in the pACC and DLPFC than nonsmokers, and that

type bridging neurochemical abnormalities and addiction-relevant such differences were accentuated with increasing age (Durazzo

behaviors (e.g., craving, drug-seeking, or engagement with treat- et al., 2015). Moreover, in the DLPFC, higher glutamate levels were

ment). We anticipate that this perspective can spearhead future correlated with better neuropsychological functioning, measured

hypothesis-driven research on this topic. In addition, an under- by a battery of tasks (Durazzo et al., 2015). Other studies did not

standing of the neurochemical bases of the resting-state in drug report differences in glutamate levels between smokers and ex-

addiction can also help advance the development of new therapeu- smokers in either the hippocampus or ACC (Gallinat and Schubert,

tics that target the relevant neurotransmitters and/or RSFC deﬁcits. 2007), or in the thalamus (O’Neill et al., 2014). In the latter study,

however, glutamate levels measured in smokers were negatively

correlated with the frequency and duration of smoking (O’Neill

2. Glutamatergic alterations in drug addiction

et al., 2014), further supporting the idea that lower glutamate levels

are associated with poorer outcomes (i.e., decreased functioning,

Table 1 presents imaging studies examining glutamatergic alter-

increased use).

ations in drug addiction.

In examining effects of withdrawal, Glx levels were higher in

the left dACC in smokers than in nonsmokers, but this effect did

2.1. Alcohol not emerge when the smokers were in withdrawal (Mennecke

et al., 2014). Unlike Glx, which was lowered during withdrawal, glu-

2.1.1. MRS tamine in the insula was higher during withdrawal (Gutzeit et al.,

Compared with controls, alcohol-addicted individuals had 2013).

lower glutamate levels in the occipital cortex (Bagga et al., 2014)

and ACC (Pennington et al., 2014) [at least among individuals who 2.2.2. PET/SPECT

had achieved remission (Thoma et al., 2011)]. These lower occipital In studies that used [ C]ABP688 as a radioligand for mGluR5,

or ACC (into adjacent white matter) glutamate levels were cor- both DVR and BPND in multiple limbic and PFC brain regions were

related with greater drinking severity [e.g., more alcohol-related lowest in current smokers, followed respectively by short-term

consequences (Thoma et al., 2011), loss of control over drinking ex-smokers, long-term ex-smokers, and controls (highest) (Akkus

(Ende et al., 2013)] or poorer neuropsychological functioning [e.g., et al., 2013; Akkus et al., 2015).

more impaired visual-motor or attentional functioning (Bagga et al.,

2014; Pennington et al., 2014)]. In contrast, other studies reported 2.3. Opiates

no differences between alcohol-addicted individuals and controls

in glutamate or Glx in the cerebellar vermis (Seitz et al., 1999), dor- 2.3.1. MRS

solateral PFC (DLPFC) (Nery et al., 2010), or dACC (Yeo et al., 2013). Compared with controls, opiate-addicted individuals receiv-

For the latter, somewhat surprisingly, higher Glx was correlated ing methadone or buprenorphine maintenance therapy had lower

with more years of drinking (Yeo et al., 2013). Finally, earlier stud- dACC glutamate concentrations than controls (Verdejo-Garcia et al.,

ies reported higher Glx in alcohol-addicted individuals compared 2013; Yücel et al., 2007) [but see (Greenwald et al., 2015)]. It is

with controls (e.g., in basal ganglia) (Jalan et al., 2000; Miese et al., unclear to what extent this ﬁnding reﬂected a pre-existing condi-

2006). However, it is important to note that, in these latter stud- tion or effects of the ongoing treatment. Concentrations of dACC

ies, the samples of alcohol-addicted individuals were older than in glutamate were also positively correlated with the number of pre-

most studies and were not medically healthy (presence of cirrho- vious withdrawals (Hermann et al., 2012a).

sis). Moreover, these latter studies examined Glx, which includes

both glutamate and glutamine, and this difference also might have 2.3.2. PET/SPECT

contributed to inconsistencies between studies. No studies were found.

Other MRS studies have examined the effects of short-term

alcohol abstinence on glutamate concentrations. Multiple studies 2.4. Cocaine

reported that, compared with healthy controls, short-term absti-

nent alcohol-addicted individuals exhibited higher glutamate levels 2.4.1. MRS

in the ACC (Hermann et al., 2012b; Lee et al., 2007) and ventral stri- Compared with controls, chronic cocaine users had lower pACC

atum (Bauer et al., 2013). In these studies, higher ACC and/or striatal glutamate levels (Yang et al., 2009). Other studies did not report

S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52 39 ↑

↑

corr ↑

↑ and & ↑

corr ND

ACC Glu & Glx

Glu corr corr

corr recent & corr

corr craving &

corr

↑

function

severity & Alc ↓

↑

day

attention

Glu-Gln/Cr VS Glu Glu

Glu

visual-motor NP Glu/Cr Glu Glu severity

DLPFC drinking ↓ ↑ ↑ ↑ ↓

severity ↓ ↑ ↑ ↓

per

↑

corr

use

Abst:

breath ACC ACC Glu-Gln

drinking Alc ↑ years Abst functioning memory smoking NS AUD: AUD: AUD: AUD, NA ↑ NA ↑ NS AUD: NA ↑ brainstem NS NA NA SM: Cigarettes/day neuropsych correlates corr Clinical Glx cigs ↑ consequences 3-mo AUD: pack-years corr corr ↓ divided Alc Sh All:

Sh Striat

Temp, (AD

Glx/Cr

PFC,

control) Abst

(SM, outcome glutamate

Gln

Glx

of (modulation ACC,

age)

Amyg, PCC

Glu/Cr Glu ↑

(withdrawal) (acceleration

dACC Glu

(AD

↑

only) Glu

↑ ND controls)

Glu Glu, Glx/Cr Glu-Gln Glu Glu loss

Glu-Gln/Cr VS Glx/Cr ACC Glx/Cr ACC DVR BP Glu Gln left d:

Glu Glu

with (caudate), only), MedOFC imaging (vs. Primary ↓ ↑ NS 1 ↑ ↑ ↑ ↓ NS ↓ NS ↓ NS ↓ NS ↑ ↑ NS only) by Abst Temp, ↓ ↓

method

C]ABP688 C]ABP688 11 11 MRS PET [ PET [ Imaging MRS MRS MRS MRS MRS MRS MRS MRS MRS MRS MRS MRS MRS MRS MRS MRS MRS

Limbic, Limbic, 1

WM)

3 3

Cerebel, VS DLPFC, DLPFC Hipp

into

Ins Hipp Thal, Thal

Occip Cereb, Cereb,

ACC Occip Temp brainstem adjacent Regions 6 6 STR, STR, interest Vermis pACC, R pACC, dACC ACC Cereb, ACC, BG, Occip DLPFC dACC pACC, ACC, Ins dACC, Thal dACC BG Occip

2 or

BP,

liver

had bipolar, ↑

Psy,

OCD,

anxiety

type

phobias

also C,

Bipolar, Depress, PTSD anxiety, Abnormal PTSD, medical Psychiatric NR NR NR NR Cirrhosis NR Cirrhosis Hep AUD Frontal Depres Depress, anorexia Depress, anorexia NR NR NR NR NR cerebellar atrophy diabetes, depress function,

use

Stim,

opioids, Alc, drug

Coc,

Halluc sedatives, Marij

Nic opioids Nic Nic, Marij, NR Nic NR Nic NR NR NR Nic, NR NR Alc Alc NR NR Marij, Mari, Other Nic Coc, anorexia

Center, Center and/or

Benzos Benzos

Detox; Detox; Detox

Benzos Tx

Tx;

97/213)

Medical Medical

Benzos clonidine, VA NR Inpatient NR NR Lithium, AntiDepress, antiPsy, VA Inpatient University Unspec NR NR Tx (n Inpatient NR Medication NR Other NR NR NR Thiamine NR 14

d y

h (SD)

d (7)

study

(4) (min

24 3

abst

(336.1)

d 9 34 (4.3) d (2.9) (19.4) (19.4) h h

(6.5)

10 6 d,

48 24

TP2: (within) duration (within) Mean 473.6 ≥ (Unspec) 17.5 ≤ 1 15.5 During 6.8 NS ≥ ≥ None 25.0 None 25.0 Unspec None 16.5 None, None, Unspec Unspec TP1: d) (within) 3–6

Sex 4M/2F 8M/3F 35M/0F 35M/0F 29M/0F 31M/0F 38M/9F 44M/13F 13M/0F 18M/0F 18M/8F 14M/5F 6M/16F 14M/40F 10M/0F 28M/0F 20M/0F 5M/2F 5M/1F 9M/8F 153M/60F 37M/29F 6M/8F 8M/6F 6M/8F 6M/8F 8M/6F 8M/6F 6M/8F 26M/4F 31M/4F 5M/8F 5M/4F 8M/8F 14M/0F 10M/0F 6M/6F 6M/6F 11M/7F 8M/8F 13M/8F 8M/1F 39M/5F 14M/2F 8M/3F 9M/3F age

(5) (3.7) (10.1) (10.7) (1.5) (1.5) (5.8) (0.9) (12.4) (12.7) (8.8) (11.4) (12.4) (13.9) (10.5) (12.4) (8.2) (5.7) (7.9) (8.3) (10.2) (10.1) (9.6) (10.2) (10.1) (10.1) (9.6) (10.1) (12.0) (10.0) (9.7) (9.6) (9.2) (3.1) (2.7) (2.1) (2.6) (7.5) (4.5) (10.1) (9.0) (12.6)

(10) (12)

(SD) 36.5 40.2 46.3 61 55 33.8 61.1 53.9 39.1 35.5 31.0 36.1 36.1 35.4 25.7 35.4 51.9 44 48.6 50.4 35.2 40.9 45.1 32.9 54.3 49.0 38.7 35.4 36.3 35.4 32.3 37.7 36.8 37.7 37.8 36.8 49.1 41.5 32.8 26.2 33.2 Mean 36.6 29.5 36.6 48.5 38 addiction.

Abst Abst

Abst

AUD drug

AUD HC AUD HC AUD AUD HC HC AUD HC AUD HC AUD HC AUD HC PTSD AUD HC HC SM SM HC SM Sh Lg HC SM SM HC SM SM HC SM HC AUD HC HC HC HC

HC AUD AUD AUD SM in

17 14 14 11 6 14 16 66 Sample 35 29 21 47 6 13 26 44 22 10 11 7 213 14 14 35 13 14 12 18 35 31 9 57 18 18 16 54 20 10 14 14 30 9 10 12 16 28

) studies

)

2007

) )

)

) )

2014 ) )

) )

)

2014 ) ) ) )

)

2012b

) ) 2015 al.,

2013 al., 2011

2014

Schubert,

2013 2015 al., 2006 2014 glutamate 2013

2013 al., 2000 1999 2010 2012 et

al.,

2013 et al.,

al., 2007

al., al.,

al., al., et al.,

and

al., al., et al., al.,

al., PET

al., et et et et al., et

et et et et et

et 1 et

and

Bagga Bauer Ende Hermann Jalan Lee Miese Mon Nery Pennington Seitz Thoma Yeo Akkus Akkus Durazzo Gallinat Gutzeit Mennecke O’Neill ( ( ( Nicotine/Smoking ( ( ( ( ( ( ( Reference Alcohol ( ( ( ( ( ( ( ( ( ( MRS Table

40 S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52 L basal ↑

Coc

= Coc

corr ↑ corr

↓

positron ↓

temporal

more Glx corr BG = dose Gln/Cr

resonance

glutamate, =

Glx craving PCC & corr

corr

↑ corr Ins

PET or corr

striatum,

smoking R & PFC Glx Coc

Glu Glx

pACC Glu Glx

yrs

Temp

↓ ↓

↑ ↑ ↑ ↑ ND

↓ ↓

yrs yrs

norm magnetic BP Glu/Cr Abst use V

= cortex,

methadone frequency ↓ recent NS MA: HD: HD: CD: CD: ↓ NS NA NA NA NS NS #withdrawals neuropsych correlates corr abstinence use Amyg, corr use NS Clinical ↓ ↓ ↑ NA MA: NS

matter,

amphetamine,

regions,

MRS =

gray

= mo parietal

Glx

= by Gln/Cr 1

↑ Amph

(no regions)

month, GM Glx ≤ non-SM Precun,

NS or

Glx (

outcome glutamate only)

AD: subcortical

(versus or VS, striatum, (all Pariet Ins O

methadone =

Glx

BG 2 (hemisphere (across PCC, (F mo

ND ND Glu controls) versus

Glu Glu Glx/Cr Glu/Cr Glu Glx;

norm smokers females,

PFC Glx Glx BP BP Glu PFC Glx Glx Glu Glu

medication

Glu/Cr V amygdala,

Subco gyrus, SM putamen; Amyg, x medication) effects) age) imaging (vs. in Primary NS NS ↓ ↓ NS NS ↓ ↓ ↓ NS: ↓ NS ↓ ↓ ↓ ↓ NS effect) either ↓ R ↓ Glx/H Abst) dose) (moderation =

disorder,

Amyg

use

striatum,

program,

method

C]ABP688 C]ABP688 PET STR

11 11

disorder,

[ [

C]ABP688 marijuana parahippocampal

= use 11 =

[ MRS Imaging PET PET MRS MRS MRS MRS MRS MRS; MRS MRS MRS MRS MRS MRS MRS MRS MRS MJ

detoxiﬁcation

stimulants,

alcohol

4 5 ParHipp Bilat =

middle, DLPFC

= Detox

of PFC, STR, DLPFC

Stim

AUD

STR

4 2

Thal MRS R

MedPFC

cortex, ACC, Occip Precun, BG Mid /

Occip

Thal

STR, PFC,

PFC Regions interest Limbic limbic pACC, dACC dACC dACC Mid pACC, STR 3 pACC ACC PFC, Bilat PCC, Med Dorsal BG, ACC 5 Occip values,

smoker,

depression, medial,

= =

orbitofrontal

or Med

short,

Depress

OFC approximated

= Sh

ecstasy,

NR Depress NR medical NR NR NR NR NR NR NR NR NR Depress NR Depress NR NR NR creatine,

right,

disorder, Approx =

R Cr

MDMA

use Psychiatric Alc

Alc Coc Amph,

with,

Marij, Marij NR Marij, Coc Marij, Amph,

MDMA

Alc, benzos, Benzos, Nic, drug disorder,

compulsive medication, Marij,

Alc, Alc, Alc, Marij, Alc, Alc, Marij,

marijuana,

Nic, Marij, Marij, Coc, MDMA MDMA Quaalude Amph, Alc, Nic, Nic, Nic Nic Alc Nic, Alc Marij, NR Other Nic Marij, Nic Nic, Benzos, heroin heroin PCP Nic, stress

correlated MJ

obsessive was

10), 10), = =

= =

antidepressant disorder,

(high (n (n and/or

Psy:

corr

OCD

centers =

Tx MA-Psy: “halfway

14) 14) use

within)

Tx; Tx Rehab, Tx posttraumatic

= = or

lobe, AntiD

(n (n

low,

cocaine,

hospitals

PTSD, = week.

Unspec Rehab vs. house” Antipsychotic Tx Hospital; Methadone Opiate maintenance Methadone NR NR University Inpatient NR Unspec Unspec University Inpatient NR AntiD Unspec Inpatient & Medication Bup Bup Haloperidol =

Coc

alcohol, wk occipital

= =

mo mo NR

Alc

(SD)

psychosis, d dNR mo d

cortex,

(7), (11) h d d =

methamphetamine Occip

abst (median)

d =

striatum, (26.2) (16.7) h h Methadone h

wk d

Psy

(6.8) (4.1) (3.0) (4.0)

(21) (60) (53)

MA 24 24 12

cerebral duration 10–14 44 5.0 Mean Unspec Unspec ≥ ≥ 66.4 7.6 5.7 370 2.1 3–8 ≥ 51.8 Unspec Unspec 60 56 7–10 >20

abstinence,

users, ventral

signiﬁcant, males,

VS Cereb

Abst not

NS long,

cortex, = 14M/1F 13M/1F 10M/4F 7M/7F 12M/0F 8M/0F 6M/1F 3M/2F 11M/6F 13M/9F 13M/11F 13M/11F 13M/11F 13M/11F 26M/0F 26M/0F 18M/0F 18M/0F 7M/2F 6M/3F 18M/11F 23M/6F 36M/9F 11M/14F 14M/14F 11M/7F 12M/10F 16M/11F 10M/16F 20M/4F 24M/6F 15M/2F 8M/9F 18M/0F 18M/0F 13M/3F 10M/1F 16M/3F 25M/19F 13M/11F Sex

polysubstance

unspeciﬁed,

buprenorphine, reported,

cingulate insula,

age

(7.5) (7.2) (9.7) (10.3) (6.9) (6.5) (6.9) (6.5) (9.1) (7.6) (8.3) (8.7) (8.8) (2.6) (3.4) (3.3) (7.4) (8.8) (9/7) (0.6) (3.1) (2.3) (2.2) (1.1) (2.1) (7.6) (8.4) (7.9) (3.7) (1.3) (5.7) (9.4) (7.4) (9.3)

Unspec Bup (3) (4) (4.9) (9.0) (8.2) (9.0) none

Ins

posterior, 45.2 36.4 29.6 29.6 35.8 37.2 21.9 22.4 32.6 19.3 42.2 16.2 36.2 32.6 24.0 25.0 33.0 31.9 36.9 36.3 29.8 29.8 32.0 36.2 43 37 25.6 31.8 24.0 33.0 35.0 19.5 17.8 36.2 Mean 42.4 37.7 39 41 34 39 (SD)

anterior NR

Pos

pressure,

treatment,

dorsal) =

nicotine,

or Tx

hippocampus, cortex,

blood

= =

HD HD HC HD HC CD HC CD HC CD HC CD HC MA Psy HC MA HC MA HC MA HC MA HC MJ HC MJ MJ HC CD HC AD HC MA-Psy HC

Nic HD HC CD HC HC BP

point, 5 9 Sample 17 24 11 15 14 29 25 16 44 18 27 17 18 19 20 24 24 16 18 14 14 29 45 28 10 24 22 26 30 17 18 8

Hipp

) prefrontal (pregenual time

= = =

)7 ) ) 2013

applicable, )

) disorder, PFC

) )

)

) )

)18 )9 ) al., )12 ) )24 )

d)ACC 2015 2008

)24

not ) use

2012a et 2014 2010

= or

2013 2014

benzodiazepines, 2014

al.,

2011

2014

2015 2006

1997 2006

al., 2014a 2014b (p

al.,

2007

et NA

2009

al., al.,

al.,

thalamus, al.,

Chang, et al.,

al., al., et

al., al.,

= al., al.,

et et

al.,

substances et

al.,

et et

Continued tomography, et et

and ( et

Benzos

Thal

hallucinogen

Greenwald Hermann Verdejo-Garcia Yücel Chang Hulka Martinez Milella Yang Crocker Ernst Howells O’Neill Sailasuta Muetzel Chang Prescot Hulka Mason ( Methamphetamine ( ( ( ( ( ( ( Reference Opiates ( Cocaine ( ( ( Cannabis ( ( ( Multiple ( ( ( ( cortex, Hall spectroscopy, emission Table Abbreviations: ganglia,

S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52 41

MRS-measured group differences in glutamate in occipital cor- DLPFC) and subcortical brain regions (e.g., striatum, amygdala, hip-

tex, pACC, DLPFC, or striatum (Chang et al., 1997; Hulka et al., pocampus) (Hulka et al., 2014b).

2014a; Martinez et al., 2014), although lower pACC glutamate

measures were correlated with higher frequency of cocaine use 2.8. Summary

(Hulka et al., 2014a) but also (again somewhat surprisingly) fewer

years of cocaine use (Chang et al., 1997; Yang et al., 2009). Individuals who abused addictive substances spanning alco-

hol, nicotine/tobacco, opiates, methamphetamine, and cannabis

2.4.2. PET/SPECT showed lower brain glutamate concentrations and/or mGluR5

Compared with controls, cocaine-addicted individuals showed receptor availability than corresponding measurements in controls

lower striatal BPND for [ C]ABP688, consistent with lower mGluR5 (Fig. 1). Differences most consistently emerged in multiple subre-

glutamate receptor availability (Martinez et al., 2014; Milella gions of the ACC and in basal ganglia regions (e.g., striatum). A few

et al., 2014). Other regions showing lower BPND for [ C]ABP688 exceptions to the general pattern of lower glutamate in addiction

in cocaine-addicted individuals compared with controls included deserve mention: (A) studies of cocaine users yielded some equivo-

the amygdala and insula, and lower BPND in these regions was cal ﬁndings, although the ﬁndings were generally more consistent

correlated with more days of abstinence (though still within a with- with the hypothesis of lower glutamate levels than higher gluta-

drawal period) (Milella et al., 2014). mate levels; (B) more research is needed on cannabis, especially

while incorporating PET, before ﬁrm conclusions can be drawn;

2.5. Methamphetamine and (C) when examining the use of multiple substances, MRS and

PET studies yielded results that differed in direction, although each

2.5.1. MRS modality only had one relevant study and included different sub-

Methamphetamine-addicted individuals had lower glutamate stances (alcohol versus cocaine, though both were examined in

concentrations in the pACC/dorsomedial PFC compared with conjunction with cigarette smoking).

healthy controls and even with non-stimulant-using, psychotic Although with multiple exceptions, markers of reduced glu-

patients (Crocker et al., 2014). Similar results were observed for tamatergic neurotransmission were often correlated with greater

Glx levels in the PCC, precuneus, and right inferior frontal cortex drug-related impairment (e.g., higher craving and substance-

(O’Neill et al., 2015), but not glutamate levels in the occipital correlated consequences, reduced neuropsychological function). A

tex (Sailasuta et al., 2010). Moreover, lower PCC Glx levels were notable exception to this pattern was seen in some studies that

correlated with more years of methamphetamine abuse (O’Neill showed positive correlation of glutamate levels with years of use.

et al., 2015). Glx concentrations in the frontal cortex were also These ﬁndings suggest that the extent of dysregulation may vary

reduced in addicted individuals with ≤ 1 month of abstinence (but with length of abuse (O’Neill et al., 2015). Interestingly, acute with-

not after longer abstinence), and reduced Glx levels were correlated drawal (and possibly the number of withdrawals) instead tended

with fewer days of abstinence and higher craving (Ernst and Chang, to correlate with higher brain glutamate associated with the use of

2008). In another study, however, neither glutamate nor Glx levels some substances (especially alcohol and opiates, which perhaps not

in the dACC or DLPFC differed between methamphetamine-using coincidentally produce the most severe withdrawal syndromes).

participants and controls (Howells et al., 2014). More work is needed to corroborate this withdrawal effect, how-

ever, as it was not consistently observed across all studies of early

2.5.2. PET/SPECT abstinent individuals even within the same substance (e.g., alco-

No studies were found. hol). Nevertheless, this pattern of effects squares with ﬁndings

showing that glutamate neurotransmission may be accentuated

2.6. Cannabis during acute withdrawal (Burnett et al., 2015).

2.6.1. MRS 3. GABAergic changes in drug addiction

Compared with controls, chronic marijuana users had lower glu-

tamate levels in the ACC (Prescot et al., 2011) and in basal ganglia Table 2 presents imaging studies examining GABAergic

regions (Chang et al., 2006; Muetzel et al., 2013), although one alterations in drug addiction.

study reported that the basal ganglia effect was speciﬁc to women

(Muetzel et al., 2013). 3.1. Alcohol

2.6.2. PET/ SPECT 3.1.1. MRS

No studies were found. Compared with controls, GABA levels in the occipital cortex

were lower in alcohol-addicted individuals (Behar et al., 1999). In

2.7. Multiple substances examining the dACC, groups comprising alcohol-addicted individ-

uals with PTSD, PTSD only, and controls did not signiﬁcantly differ

2.7.1. MRS on GABA levels (Pennington et al., 2014). However, within this

Although Glx levels in the occipital cortex did not differ between comorbid group, higher GABA levels were correlated with better

alcohol-addicted individuals and healthy controls, Glx levels were verbal learning/memory (Pennington et al., 2014).

higher in alcohol-addicted smokers than in alcohol-addicted non-

smokers (Mason et al., 2006). 3.1.2. PET/SPECT

11 123

In studies using [ C]ﬂumazenil and [ I]iomazenil, alcohol-

2.7.2. PET/SPECT addicted individuals generally exhibited lower ratiotracer uptake

A study compared cocaine-addicted individuals and healthy VT than controls in the cerebellum and medial PFC including

controls using PET with [ C]ABP688. Results revealed no effects the pACC and/or dACC, consistent with reduced GABAA recep-

of cocaine use disorder, but robust effects of smoking status tor availability (Abi-Dargham et al., 1998; Gilman et al., 1996;

were observed. In particular, compared with nonsmokers, smok- Lingford-Hughes et al., 1998), and such decreases have been corre-

ers (especially those who had smoked recently) had lower Vnorm lated with greater severity of alcohol dependence in some studies

(deﬁned as BPND + 1) in multiple cortical (e.g., ACC, medial PFC, (Lingford-Hughes et al., 1998) but not in others (Abi-Dargham

42 S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52

Fig. 1. Overview of the MRS and PET/SPECT studies. Figure displays regions of interest common to many studies (blue: rostral/pregenual anterior cingulate cortex, sometimes

extending into medial prefrontal cortex; red: dorsal anterior cingulate cortex; yellow: basal ganglia/thalamus; green: occipital cortex; pink: cerebellum; brown: insula;

orange: hippocampus; purple: dorsolateral prefrontal cortex) and a table showing the general direction of effects. Arrows in the table reﬂect the preponderance of evidence

while also prioritizing studies with larger and/or more homogeneous samples and not considering acute clinical features such as short-term withdrawal (which can be

associated with opposite effects). ↓ = lower in addiction; ↑ higher in addiction; ↔ = nonsigniﬁcant differences between groups; – = no studies found. (For interpretation of the

references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

et al., 1998). More recently, alcohol-addicted participants exhibited 3.4. Cocaine

lower (than controls) [ C]Ro15 4513 VT in the nucleus accumbens,

parahippocampal gyri, right hippocampus, and amygdala, suggest- 3.4.1. MRS

ing reduced GABAA receptor availability (Lingford-Hughes et al., In cocaine addiction, GABA levels were reduced in the

2012). Within these alcohol-addicted individuals, higher VT in hip- pACC/dorsomedial PFC compared with controls (Ke et al., 2004).

pocampus and parahippocampal gyri was correlated with better

performance on a delayed verbal memory task (Lingford-Hughes 3.4.2. PET/SPECT

et al., 2012). No studies were found.

Other studies disagreed on whether there were differences

in GABAA receptor measures between cases and controls. When 3.5. Methamphetamine

a saturation method or VT was used with [ C]ﬂumazenil or

123

[ I]iomazenil, group differences were either absent (Lingford- No studies were found.

Hughes et al., 2000, 2005; Litton et al., 1993) or reversed (Jalan

et al., 2000). However, recall that this latter study included alcohol- 3.6. Cannabis

addicted individuals of older age and with liver disease, which may

have affected ﬁndings. No studies were found.

3.7. Multiple substances 3.2. Smoking

3.7.1. MRS

3.2.1. MRS

Compared with controls and alcohol-only addicted individuals,

Smokers had lower GABA in the occipital cortex than nonsmok-

polysubstance-addicted individuals had lower pACC GABA levels (a

ers, though this effect was only observed in women (Epperson et al.,

2005). difference that met nominal but not Bonferroni-corrected signiﬁ-

cance), and such lowered pACC GABA was correlated with worse

verbal memory within the polysubstance users (Abe et al., 2013).

3.2.2. PET/SPECT

In the occipital cortex, however, GABA levels were increased in

GABAA receptor availability, indexed by [ C]Ro15 4513 VT, in

nonsmoking (but not smoking) alcohol-addicted individuals; these

the pACC and parahippocampal gyrus was higher in current/past

increased GABA levels declined after 1 month of alcohol abstinence

smokers than nonsmokers (Stokes et al., 2013).

(Mason et al., 2006).

3.3. Opiates 3.7.2. PET/SPECT

Several studies reported increased radiotracer uptake (VT of

123

No studies were found. [ I]iomazenil) in individuals with alcohol use disorder, but

S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52 43 &

↑ corr

Alc years

↑ Hipp

memory memory (Unspec corr

delayed

corr V GABA

↑ corr

↑ ↑ use

T GABA V verbal

NS ↓ NS Clinical Coc recall NS NA NA ↓ NA NA AD: NA AD: GABA ACC, severity neuropsych correlates direction) ↑ delayed

Pariet Hipp

ACC,

outcome

(+homo- (women

PFC, pACC MPFC, Striat, pACC, T T max controls)

V V B GABA T T T T T T

V GABA V V V V V GABA GABA

↓ ↑ NS NS ↓ ↑ ↓ ↓ NS Primary glutamate imaging Cerebel carnosine) only) ↓ NS ↓ ↓ (vs. ParHipp 4513 4513

I]iomazenil I]iomazenil I]iomazenil C]flumazenil C]flumazenil C]flumazenil C]Ro15 C]flumazenil C]Ro15 123 11 11 123 123 11 11 11 11 PET [ MRS PET [ PET [ MRS MRS PET [ PET [ PET [ PET [ SPECT [ MRS PET [ Imaging method

Hipp,

Temp, ACC,

4 of

MedPFC brain brain Cerebel / Cerebel,

Thal, Cerebel

Cerebel, Amyg,

ParHipp /

OFC, ACC, Ins,

OFC

Cereb,

Whole Regions Pariet, Cereb Hipp, BG MedPFC, Cerebel ACC Occip Temp 5 Occip Cereb, Whole ACC, STR, DLPFC STR, Occip pACC pACC, Subco, pACC, interest Occip, Occip, Ins, or

had

also

NR Psychiatric NR NR Cerebel degeneration (n Cirrhosis Depress Depress, anxiety, bulimia Depress Depress NR AUD NR NR medical PTSD

use

Stim,

drug

Amph MJ

MDMA Alc,

Coc, Nic NR NR MJ NR MDMA NR MJ, Nic, Nic MJ, NR Alc Other

Tx)

SSRI

Tx (Unspec (Unspec oxazepam

Medical Medical Medical Medical

NR ﬂuoxetine, venlafaxine, amitriptyline Medication and/or Fluoxetine Center (Unspec Center Center Center Tx) Tx) VA VA VA NR Unspec midazolam challenge; paroxetine Detox, VA NR NR clomipramine

(SD)

d d h

abst

d (50.2) (44.7) (53.2)

48 mo

(5)

(46) (20) d

17 6 h,

17 ≥ 22.5 30.4 7.3 NS NS 0 NS 98 ≥ 33.4 (within) Mean 34 mo mo duration

Sex 5M/0F 5M/0F 4M/2F 8M/3F 12M/0F 14M/0F 0M/9F 0M/13F 11M/0F 10M/0F 10M/0F 28M/0F 20M/0F 8M/0F 12M/0F 10M/6F 7M/13F 26M/9F 7M/13F 11M/0F 11M/0F Unspec 17M/0F 14M/0F 8M/0F 11M/0F age

(10.9) (8.5) (7.6) (9.0) (1.8) (2.6) (13.9) (10.5) (12.4) (6.8) (7.9) (5.9) (7.4) (8.0) (16.0)

(8) (10) (11) (7) (13) (7) (7)

(SD) 43 35 43 37 25 Mean 44 52 (Approx) 46 61 55 43.2 42.9 44 48.4 46 44.5 51.9 36.0 43.2 39.9 38.1 46.2 35.4 36.3 46.4 (Approx) 39.2 32.3

AUD HC AUD HC HC AUD HC HC AUD HC PTSD HC SM CD HC HC AUD HC HC HC

AUD AUD AUD AUD AUD HC SM

addiction.

11 14 14 10 20 20 20 28 Sample 11 5 17 6 12 9 11 8 5 10 8 16 35 10 11 13 11 5 12

) ) ) ) drug

) in

1998 2000 2005 2012 )

) al., al., al., al., 1998

)

) 2014

et et et et ) )

studies

al., )

2005

al.,

et ) 1996

2013 al.,

1993 1999 et

2000 GABA

al.,

al., 2004 al.,

substances al., et

et PET

et et

al., et

2 et

and

Abi-Dargham Behar Gilman Jalan Lingford-Hughes Lingford-Hughes Lingford-Hughes Lingford-Hughes Litton Pennington Stokes Epperson Ke Reference Alcohol ( ( Opiates None Cocaine ( ( ( ( ( ( ( ( ( Methamphetamine None Cannabis None Multiple Nicotine/Smoking ( ( MRS Table

44 S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52

that these effects were blunted by active smoking. In particular,

gyrus, alcohol-addicted individuals in withdrawal (approximately 5-day

middle,

↑

corr temporal

GABA abstinence) had higher GABAA receptor availability, indexed by

crave amygdala, pACC

= T

↑ depression,

or =

cig

↑ 123

↓ V

memory

= [ I]iomazenil V , in the medial PFC, ACC, hippocampus-amygdala,

corr T Mid MPFC A

↑

Abst

and

and cerebellum, but these effects were less pronounced either corr Temp

Amyg T

AD/SM: verbal Clinical AD/PS: V NA AD: neuropsych correlates GABA GABA Alc alc if they were active smokers or after they achieved 4 weeks of Depress medial,

alcohol abstinence (Staley et al., 2005). In the alcohol-addicted parahippocampal

regions, =

smokers, higher GABAA receptor availability was correlated with disorder, Med

T T

Abst): longer initial abstinence from alcohol (1–7 days) (Staley et al., by

pACC V V

outcome creatine,

use

(non-SM) A A ↓

2005). In a subsequent corroborative study, alcohol-addicted par- ParHipp

Cr ecstasy, controls)

subcortical (short

ticipants, further stratiﬁed by smoking status, were evaluated at 3,

= GABA GABA GABA

lobe,

alcohol Primary glutamate imaging smoking) AD ↑ AD/PS: (buffered ↑ GABA (uncorrected) (vs. ↑

10, and 30 days into withdrawal. Both alcohol-addicted smokers =

with,

and nonsmokers had higher GABA receptor availability, indexed A Subco

MDMA 123

AUD

by [ I]iomazenil V , compared with smoking-matched controls

T occipital

(Cosgrove et al., 2014). However, smoking status modulated the

striatum, neuroadaptations seen during withdrawal. Alcohol-addicted non- values, correlated

later)

I]iomazenil I]iomazenil

= Occip

marijuana,

smokers showed the highest and most widespread differences from = 123 123

PET [ Imaging method mo (baseline PET [ MRS MRS STR was

controls at the 10-day assessment versus the 3-day and 4-week =

Marij

assessments, whereas the alcohol-addicted smokers had a more

corr

signiﬁcant,

consistent pattern of differences from controls across all assess- ACC,

of approximated

brain Hipp, males,

DLPFC,

not

stimulants, ment time points. In the alcohol-addicted smokers, higher GABA

= =

M receptor availability was correlated with more craving for alco-

cocaine, NS

pACC, Cerebel Regions Whole MedPFC, interest Occip Amyg,

Occip

Stim hol (at 10-day withdrawal) and cigarettes (at 3-day withdrawal)

Approx

Coc

(Cosgrove et al., 2014).

disorder, or

BP, reported, liver liver

smoker,

↑ (3x (3x

= use

type-2

cortex,

3.8. Summary none medication,

NR depress diabetes, function function Hep Psychiatric Abnormal NR Abnormal medical higher allowed) higher allowed)

Overall, GABA was less studied than glutamate. MRS studies sug- right,

cerebral

R gested lower GABA concentrations in abusers of alcohol, nicotine, =

use

and cocaine (Fig. 1), which was also the typical direction of MRS MJ, Coc

users,

antidepressant Cereb

effects for glutamate. Perhaps due to availability of more radio- methamphetamine

= drug

Benzos tracers, and/or because of their availability for a longer period of

Nic,

time, there were more PET/SPECT studies related to GABA than AntiD Other Opiates, NR MJ, Nic, MDMA

neuropsychological,

for glutamate, particularly for alcohol (which is unsurprising given =

insula,

alcohol’s known effects on the GABA receptor). These studies gen- NP A polysubstance

= alcohol,

buprenorphine,

erally showed decreased GABA receptor availability/distribution

= A

Ins Tx Tx = PS

volume in the addicted individuals compared with controls. Nico- Alc Tx

VA Tx Bup

nicotine,

tine, however, showed an opposite pattern of effects. History of

smoking was not only associated with higher GABAA receptor avail- Inpatient 1 Local Inpatient Medication and/or Nic posterior,

ability on its own, but smoking also modulated the early abstinence hippocampus, abstinence,

pressure,

Pos

= course of individuals with alcohol dependence. Interestingly, the

(SD) d d d

effects of smoking on alcohol dependence showed an opposite pat-

abst

Abst applicable, Hipp

Unspec

Abst;

blood

(1.8) (4.0) (2.9) tern of effects to that of glutamate. Examining the joint effects of mo cortex,

1 not

smoking and alcohol abuse, while incorporating markers of both Alc: 5.0 ∼ 5.0 Mean 4.6 Sm: – – duration = BP

cortex,

glutamate and GABA neurotransmission, will be an interesting and NA

glutamate,

important direction for future research. week. prefrontal

= =

It is also important to note that, similarly to glutamate, GABA

Glu

wk cingulate

PFC

effects appeared to be sensitive to study participant characteristics, Sex 15M/2F 7M/3F 13M/2F 8M/2F 12M/0F 8M/0F

26M/2F 37M/3F 15M/1F 15M/0F 8M/0F 5M/0F 10M/0F

such as the length of abstinence and/or drug-related medical dis- spectroscopy,

benzodiazepines, matter,

eases (less evidence for the latter). We did not locate any PET/SPECT =

age

studies labeling the GABAB receptor, which unlike the fast ligand- gray unspeciﬁed, (9.6) (9.1) (8.6) (9.2) (7.5) (10.1) (8.1)

= tomography, =

(11) (9) (10) (8.2) (9.0) (9)

gated action of the GABAA receptor, is instead associated with Benzos resonance

dorsal) 42 40.9 45.3 (SD) 49 40 39 48 Mean 39 39.9 52.1 49.0 41.2 35.9 GM

long-term modulation through G protein-regulated gene transcrip- or

Unspec tion and protein synthesis (Xu et al., 2014). More research, both

emission

ganglia, MRS and PET, is also needed in opiates, methamphetamine, and magnetic females

= =

cannabis. F

AUD/PS HC AUD/SM HC/SM AUD AUD/SM AD AUD HC HC

(pregenual

HC AUD HC/SM positron basal MRS

treatment,

= = 16 15 40 10 10 8 8 Sample 28 17 15 5 10

Tx 4. Evidence of resting-state functional connectivity deﬁcits

) PET

program,

d)ACC

month, )12

) in drug addiction =

) 2014 or )

cortex, mo 2006

2005 al.,

thalamus,

A large literature has examined RSFC deﬁcits in drug addiction 2013 al., et

al.,

et (Fedota and Stein, 2015; Lu and Stein, 2014; Sutherland et al., 2012), al.,

Continued amphetamine, detoxiﬁcation parietal (

Thal

= and we did not reprise all of this important work here. Rather, our = =

marijuana,

= current goal was to provide evidence that some of the same regions Abe Cosgrove Mason Staley

Reference ( ( ( (

Table Abbreviations.

Amph MJ Detox cortex, Pariet implicated in glutamate and GABA MRS and PET studies in addiction

S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52 45

the resting state is a condition replete with mind-wandering and

Box 2: Methodological advantages and disadvantages self-generated thinking (Smallwood and Schooler, 2015), and these

of resting-state functional connectivity (RSFC)

self-referential functions have been linked with activation of corti-

Assessment of RSFC is a sensitive and highly generalizable

cal midline regions, including the pACC and medial PFC, in healthy

fMRI methodology. Using RSFC, group differences between

individuals (Abraham, 2013; D’Argembeau, 2013; de Greck et al.,

cases and controls have emerged across numerous psychi-

2008; van der Meer et al., 2010) and addicted individuals (de Greck

atric and neurological conditions even in the absence of gross

et al., 2009; Moeller and Goldstein, 2014). Thus, although larger

morphological abnormalities (Barkhof et al., 2014). Further-

regions, such as the ACC and medial PFC, have sometimes been

more, because this approach does not depend on a particular

experimental context (task), it becomes possible to identify selected as regions of interest in MRS studies for practical reasons

commonalities and differences between individuals in clinical (i.e., large area to position the sequence), effects in these regions

groups, who otherwise probably would not undergo similar are nonetheless highly anticipated for both MRS studies and RSFC

experimental paradigms [e.g., addicted individuals are rou-

studies; recent combined fMRI-MRS studies in healthy participants

tinely exposed to drug cues (Jasinska et al., 2014), whereas

further speak to this point (see Introduction). The insula has a crit-

depressed individuals are routinely exposed to emotional

ical role in mediating interoception (Craig, 2009) and the detection

faces (Stuhrmann et al., 2011)]. It even becomes possible to

of behaviorally relevant stimuli (Uddin, 2015). In drug addiction,

scan individuals unable to perform task-based fMRI at all,

these functions subserved by the insula appear vital for the experi-

including individuals with altered or diminished states of con-

ence of drug craving (Naqvi et al., 2007; Verdejo-Garcia et al., 2012).

sciousness, or severe cognitive decline (e.g., coma, psychosis,

The striatum forms a key part of the mesocorticolimbic dopamine

Alzheimer’s disease, etc.) (Brier et al., 2014; Demertzi et al.,

2014; Satterthwaite and Baker, 2015). Because many discrete projections that mediate the reinforcing effects of addictive drugs;

psychopathologies share deﬁcits in network-level functional chronic perturbation of this system ultimately leads to enduring

connectivity, it is possible that this transdiagnostic tool can changes in striatal-PFC glutamatergic projections (Kalivas, 2007,

suggest previously unrecognized overlap among disorders

2009). Although MRS measurement of glutamate and GABA is more

that may be targeted for previously unrecognized therapeutic

difﬁcult in the striatum than in the insula (Wiebking et al., 2014),

interventions [e.g., consistent with the Research Domain Crite-

some studies included in this review indeed have reported striatal

ria (RDoC) approach]. RSFC, then, may indeed be regarded an

effects. Importantly, prior resting-state studies of healthy individ-

intermediate phenotype that may be compared across different

uals have revealed functional connections between these three

diagnostic groups.

regions (Margulies et al., 2007; Uddin et al., 2009).

Nevertheless, it is also important to note some of the inter-

pretative issues of RSFC, which have been well-articulated

elsewhere (Weinberger and Radulescu, 2015). In brief, such 4.1. Alcohol

issues include systematic differences in the use of (potentially

multiple) substances; inability to discern what participants are

Alcohol-addicted individuals had weaker connectivity between

thinking and feeling during the resting-state scan, with pos-

subregions of the ACC with the insula (Sullivan et al., 2013)

sible group differences in these psychological states; and the

and subthalamic nucleus (Morris et al., 2015). Using ICA, it was

possibility of prominent artifacts resulting from head move-

shown that alcohol-addicted individuals had stronger connectiv-

ment and other motion, which may also differ at the group

ity than controls within and between various networks, including

level. However, even with these potential sources of variabil-

an orbitofrontal cortex (OFC) network, an amygdala-striatum net-

ity, effect sizes of RSFC studies generally have been large in

work, and a DMN network (Zhu et al., 2015).

magnitude. In particular, the select studies/ﬁndings included

in the current review were estimated to have following M ± SD

effect sizes [Cohen’s d, calculated based on sample sizes 4.2. Smoking

and means ± standard deviations (or t-values), where avail-

able]: alcohol (d = 0.84 ± 0.0), nicotine (d = 1.32 ± 0.22), opiates

Smokers in withdrawal showed stronger RSFC between the ACC

(d = 2.39 ± 1.66), cocaine (d = 1.01 ± 0.22), methamphetamine

and dorsal striatum compared with controls; these same smokers

(d = 1.04 ± 0.0), and cannabis (d = 0.98 ± 0.0). (Note that because

showed stronger RSFC between the ACC and bilateral insula in a

this manuscript is not a meta-analysis and includes only a

withdrawal study condition compared with a satiated study con-

portion of possible resting-state studies and a portion of pos-

dition (Huang et al., 2014). Similarly, 12-hour abstinent smokers

sible effects within those studies, extensive discussion about

showed stronger global connections to the insula compared with

these effect size estimates or their interpretations is outside

the scope of this review; rather, the goal here was to provide controls, and this difference was not observed when the smokers

macroscopic view of the magnitude of effects in these kinds of were satiated (Wang et al., 2014). Interestingly, strengthened con-

studies.) nections with the insula (e.g., to regions of the DMN, such as the

ventromedial and dorsomedial PFC) were abolished by a nicotine

challenge (Sutherland et al., 2013). A different pattern of effects

was observed with ICA, however. Relative to nonsmokers, satiated

are also functionally disrupted as revealed by RSFC. We focused on smokers exhibited stronger connectivity between the medial PFC

studies that examined RSFC differences between addicted individ- and a left fronto-parietal network (including ACC, DLPFC, and insula

uals and healthy controls [using approaches that were seed-based extending into putamen) (Janes et al., 2012).

and/or whole-brain (e.g., independent components analysis (ICA)

or the graph theory-based metric degree (i.e., number of con- 4.3. Opiates

nections exceeding a speciﬁed correlation threshold)] in the (A)

ACC extending into the dorsomedial and/or ventromedial PFC, (B) The most RSFC studies have been conducted with individ-

insula, and (C) striatum (for more discussion of the advantages and uals addicted to opiates. Opiate-addicted individuals had weaker

disadvantages of using RSFC in psychopathology, see Box 2). RSFC between the pACC with the dACC, DLPFC, medial PFC, and

The rationales for focusing on these regions are as follows. The PCC/precuneus (Ma et al., 2010, 2015; Wang et al., 2013; Yuan

ACC (especially, pACC) and adjacent medial PFC (encompassing et al., 2010); and between the caudate and DLPFC (middle frontal

dorsomedial and ventromedial subsections) form part of the default gyrus) (Wang et al., 2013). In ICA or other whole-brain approaches,

mode network (DMN), which is activated during the resting state compared with controls, opiate-addicted individuals had weaker

(Gusnard et al., 2001; Molnar-Szakacs and Uddin, 2013). Moreover, resting-state functional connectivity of the ACC and basal ganglia

46 S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52

regions (including the striatum) (Liu et al., 2011a; Ma et al., 2011; medial PFC, insula, and striatum), and these group differences have

Schmidt et al., 2015). been relatively large in magnitude (see Box 2).

Other studies, however, have shown stronger connectivity in

opiate-addicted individuals: higher RSFC between the pACC, dACC, 5. Working hypothesis: Abnormal glutamatergic and/or

or precuneus with the striatum (Ma et al., 2010; Zhang et al., 2015) GABAergic neurotransmission underlies corticolimbic RSFC

and insula (Zhang et al., 2015) [but see (Upadhyay et al., 2010)]. deﬁcits in addiction

Stronger connectivity between the IFG with the dACC and ventro-

medial PFC also has been reported (Ma et al., 2010; Wang et al., Taken together, the literature indicates that drug-addicted indi-

2013). Connectivity between the insula and the amygdala was also viduals exhibit abnormal neurotransmission involving glutamate

stronger in opiate-addicted individuals compared with controls and GABA in corticolimbic brain regions of core relevance to their

(Xie et al., 2011). In whole-brain RSFC approaches, heroin-addicted disease (e.g., ACC, medial PFC insula, and striatum), and that these

individuals had stronger overall connectivity of the ACC, midcin- same regions also show disruptions in RSFC. Because glutamater-

gulate, insula, OFC, and putamen (Liu et al., 2009, 2011a). gic and GABAergic neurotransmission in such regions also drive the

resting state in health, we raise the hypothesis that corticolimbic

RSFC can provide an intermediate phenotype to explain asso-

4.4. Cocaine

ciations between addiction-relevant glutamatergic and/or GABA

dysregulation and addiction symptomatology (e.g., craving, drug-

Compared with controls, cocaine-addicted individuals generally

seeking, engagement with treatment) (Fig. 2). Future work can

had weaker RSFC of the pACC and dACC with subcortical regions,

center on the following areas.

including the striatum, amygdala, thalamus, hippocampus, and

parahippocampal gyrus (Gu et al., 2010; Hu et al., 2015; Verdejo-

5.1. Finer speciﬁcation of the model

Garcia et al., 2014) [but see (Wilcox et al., 2011)]. In one study,

pACC-amygdala connectivity was also associated with clinical out-

It is crucial to incorporate the modulating inﬂuences of clini-

come (30-day relapse after treatment) (McHugh et al., 2014).

cal characteristics, especially withdrawal/abstinence and smoking

Other studies have reported stronger connectivity between the

(Fig. 2). Withdrawal carries a high vulnerability to relapse, which

ACC subregions and other cortical regions (e.g., middle frontal

may partially stem from associated perturbations in brain gluta-

gyrus, inferior parietal lobe, or supramarginal gyrus) in cocaine-

mate or GABA (Mashhoon et al., 2011). Smoking history, as shown

addicted individuals than controls (Camchong et al., 2011; Konova

above, exerts important independent effects on brain glutamate

et al., 2013), with the dACC and ventromedial PFC in particular

and GABA metabolites. Current smoking also modulates the effects

receiving an abnormally high number of short- and long-range

of other substances, such as alcohol (especially during withdrawal),

functional connections (Konova et al., 2015).

and the resulting effects on brain glutamate and GABA may differ

The directionality of limbic–limbic connectivity was less clear.

depending on which neurotransmitter is examined. Future studies

Cocaine-addicted individuals had weaker connectivity between the

might also investigate whether neurochemical deﬁcits in one cor-

bilateral putamen and the left posterior insula compared with con-

ticolimbic brain region have reverberations across the brain. This

trols, and this effect was driven by data from individuals who

may be especially true for deﬁcits in glutamate, which has more

relapsed 30 days after treatment discharge (McHugh et al., 2013).

global (transregional) effects (Duncan et al., 2013). RSFC meth-

However, cocaine-addicted individuals had stronger connectivity

ods, especially using whole-brain graph theory approaches, are

between the ventral striatum and dorsal striatum than controls

ideally suited to test such hypotheses. Finally, future studies can

(Konova et al., 2013).

incorporate direct measures of brain metabolism, such as PET with

[ F]ﬂuorodeoxyglucose. Indeed, energy metabolism may repre-

4.5. Methamphetamine

sent an intermediary process between fast neurotransmission and

the slow RSFC blood-oxygen-level dependent (BOLD) response, and

Compared with healthy controls, methamphetamine-addicted

this kind of precision would increase mechanistic understanding.

individuals exhibited greater RSFC between a midbrain seed and

a number of subcortical (e.g., putamen and insula) and cortical

5.2. Integration and translation between human data and animal

regions (e.g., OFC) (Kohno et al., 2014). models

4.6. Cannabis

Another important future direction for enhancing mechanistic

understanding is to conduct studies with tighter experimental con-

Chronic marijuana users showed weaker RSFC between an insu-

trol, as can be achieved in animal models. Animal models offer

lar seed and the ACC (Pujol et al., 2014).

the advantages of more controlled drug histories and more inva-

sive assessments, which could clarify how addiction may causally

4.7. Summary change glutamate/GABA neurotransmission and metabolite levels

in select brain regions, as well as their consequent associations with

As also articulated elsewhere (Lu and Stein, 2014), RSFC in addic- RSFC.

tion remains an emerging field, and conflicting findings have been In such animal studies, lower Glx levels in the dorsal striatum

quite common. One potentially interesting pattern of results for of rhesus monkeys due to chronic methamphetamine exposure

opiates and cocaine, perhaps the most widely studied addictions in showed a linear pattern of recovery with abstinence over one year

this ﬁeld, appears to be that cortical-cortical connections generally (i.e., returning to control levels) (Yang et al., 2015) (but see Liu

appear to be weakened, whereas corticolimbic connections gener- et al., 2011b, where cocaine administration over the course of 9

ally appear to be strengthened; more research is clearly required, months increased levels of glutamate and glutamine in squirrel

however, before ﬁrm conclusions can be drawn, especially for cer- monkeys). In another study, rats received subcutaneous twice-

tain substances (e.g., methamphetamine, marijuana). Despite these daily injections of 2.5 mg/kg methamphetamine for one week. This

inconsistencies of directionality, these studies have revealed reli- drug exposure resulted in decreased MRS-measured glutamate,

able RSFC differences between cases and controls in regions that glutamine, and GABA in hippocampus, nucleus accumbens, and PFC

have also been investigated using MRS or PET/SPECT (e.g., ACC, (Bu et al., 2013). Interestingly, a different study revealed decreased

S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52 47

Figure 2. Schematic of the hypothesized model. Deﬁcits in glutamate and/or GABA, which are further modulated by clinical characteristics including withdrawal/abstinence,

are associated with deﬁcits in brain resting-state functional connectivity (e.g., anterior cingulate cortex with the dorsal and ventral striatum), which in turn are associated

with drug-related symptoms. [The metabolite image (left) is adapted from Abe et al. (2013), with permission from Elsevier; the brain image (top) is adapted from Garland

et al. (2014), under the Creative Commons Attribution License; and the drug image (right) is adapted from Moeller et al. (2009), with permission from Elsevier].

RSFC in cocaine-exposed rats between the nucleus accumbens and basal ganglia network (including striatum) (Schmidt et al., 2015).

the dorsomedial PFC as a function of the degree of cocaine self- Similarly, opiate-addicted individuals receiving high methadone

administration escalation (Lu et al., 2014). These combined studies doses showed higher ACC glutamate levels (Greenwald et al.,

generally support our hypothesized model. 2015; Verdejo-Garcia et al., 2013). However, these studies did not

Alternatively, drug-administration schedules not intended to incorporate both neurochemical measurements and RSFC. Finally,

produce addiction have largely produced opposite results. For perhaps the most methodologically rich study to date evaluated

example, following short-term administrations of cocaine (Li et al., the effects of 12-week varenicline administration on dACC Glx

2012) or alcohol (Zahr et al., 2015), rats showed transient striatal (Li levels and fMRI BOLD response (during a color-word Stroop task)

et al., 2012) or whole-brain (Zahr et al., 2015) increases in glutamate (Wheelock et al., 2014). The varenicline regimen decreased dACC

and/or GABA [but see (Lee et al., 2014)]. Such results are consis- Glx levels, modulated DMN regions (including pACC and PCC)

tent with the idea that addiction-related decreases in glutamate during task performance, and changed dACC-DMN connectivity

or GABA could reﬂect neuroadaptations to chronic drug exposure. as revealed by psychophysiological interaction (PPI) analysis

Such conclusions are difﬁcult, if not impossible, to achieve in stud- (Wheelock et al., 2014). Future iterations of this study type would

ies of already-addicted humans. need to include a control group and could beneﬁt from using a

pharmacological probe that modulates the neurotransmitter sys-

tem of interest more directly (i.e., because varenicline is a nicotinic

5.3. More comprehensive methods

receptor partial agonist, the Glx results could represent secondary

effects). A future study that integrates these various components

Because human studies cannot achieve the level of precision

within a single design promises to be highly informative.

attained in animal studies, mechanistic clarity needs to rely on

more comprehensive and innovative experimental methods. A drug

challenge model, if employed in combination with fMRI and with 5.4. Testing for substance-speciﬁc effects

MRS or PET, can address causality by modulating underlying glu-

tamate/GABA neurotransmission that can then be correlated with It would be interesting to test whether addiction-related effects

resting-state fMRI and then other clinical variables. on brain glutamate and GABA are speciﬁc to addiction related to

We are aware of no previous studies in this ﬁeld that have substances rather than behaviors. One could compare and con-

attempted this kind of ambitious design, though some have trast effects in individuals with substance use disorders with those

incorporated various components. For example, one study showed in individuals who have behavioral addictions, such as gambling

that acute alcohol administration reduced occipital GABA levels (Clark and Limbrick-Oldﬁeld, 2013). We are aware of no MRS or PET

(Gomez et al., 2012). However, because this experiment was studies that contrasted substance addiction and gambling addic-

conducted in social drinkers (not in alcohol-addicted individuals), tion, but several studies on this front have been conducted using

the potential relevance to addiction is unclear. Another study RSFC. For example, whereas increased intrinsic local connectivity

found that a heroin challenge (versus placebo) in opiate-addicted of the PCC was observed for both behavioral (gambling) and sub-

individuals strengthened connectivity within an ICA-deﬁned stance (alcohol) addictions, decreased connectivity of the ACC was

48 S.J. Moeller et al. / Neuroscience and Biobehavioral Reviews 61 (2016) 35–52

speciﬁc to alcohol addiction (Kim et al., 2015). Moreover, cocaine Disclosure/Conﬂict of Interest

addiction was uniquely associated with enhanced connectivity

between the subgenual ACC with OFC or striatum (in further cor- None declared.

relation with measures of impulsivity) (Contreras-Rodriguez et al.,

2015a, 2015b). In contrast, connectivity in cocaine addiction over- Acknowledgements

lapped with that in gambling addiction in the OFC and dorsomedial

PFC, and in the amygdala and insula (Contreras-Rodriguez et al., This work was supported by grants from the National Insti-

2015b) [note that this latter connection was also reported in opiate tute on Drug Abuse (K01DA037452; R21DA040046) (to SJM). The

dependence (Xie et al., 2011)]. content is solely the responsibility of the authors and does not nec-

essarily represent the ofﬁcial views of the National Institutes of

Health. Additional support came from: Brain Imaging Center (BIC)

5.5. Potential applications to treatment

pilot funds from the Icahn School of Medicine at Mount Sinai (to

SJM); endowments from the Thomas P. and Katherine K. Pike Chair

A relatively small but growing literature suggests that gluta-

of Addiction Studies and the Marjorie Greene Trust (to EDL); grants

matergic and/or GABAergic medications modulate neural activity

from the Canadian Institutes of Health (CIHR 465 and CIHR-EJLB)

in brain regions spotlighted in this review. In smokers, the GABAB

(to GN); and the Michael Smith Chair for Neuroscience and Mental

receptor agonist baclofen, given both acutely and after 3 weeks of

Health (to GN).

treatment, decreased cerebral blood ﬂow during perfusion fMRI in

several regions including the dACC (Franklin et al., 2011, 2012). In

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