Análise de conectividade funcional em repouso para medicina de dependência

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CHAPTER
Resting state functional
connectivity analysis for
addiction medicine: From
individual loci to complex
networks
8
Vani Pariyadath*,1, Joshua L. Gowin†,1, Elliot A. Stein*,2
*Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug
Abuse, National Institutes of Health, Baltimore, MD, USA
†Section on Human Psychopharmacology, Intramural Research Program, National Institute on
Alcohol Abuse and Addiction, National Institutes of Health, Bethesda, MD, USA
2Corresponding author: Tel.: +1 (443) 740-2650; Fax: +1 (443) 740-2753,
e-mail address: estein@intra.nida.nih.gov
Abstract
Resting state functional connectivity (rsFC) has provided a new and valuable tool for inves-
tigating network-level dysfunction in addiction. Following the recent development of a frame-
work of large scale network disruptions, we have been able to arrive at unique insights into
craving-related aspects of addiction using rsFC. However, such network-level advancement
has thus far eluded our understanding of mesocorticolimbic involvement in addiction. Given
the importance of this system in vulnerability and resilience to addiction, understanding meso-
corticolimbic dynamics to the same extent could provide critical insights into the disease. To
this end, we review here recent studies on addiction that employ rsfC and suggest a new ap-
proach, one that combines a novel model for addiction with new experimental techniques as
well as participant groups, to accelerate progress in this arena.
Keywords
Resting state functional connectivity, fMRI, Addiction, Mesocorticolimbic system, Dopamine
1These authors contributed equally to the work.
Progress in Brain Research, Volume 224, ISSN 0079-6123, http://dx.doi.org/10.1016/bs.pbr.2015.07.015
2016 Published by Elsevier B.V.
155
http://dx.doi.org/10.1016/bs.pbr.2015.07.015
1 INTRODUCTION
Substance use disorders (SUDs) have a significant impact on public health, affecting
nearly one in three people at some point during their lifetime (Kessler et al., 1994),
and costing the government more than $600 billion annually (Abuse NIoD, 2015).
SUDs are frequently comorbid with other neuropsychiatric disorders (Brown
et al., 1996; Fergusson et al., 1996; Sareen et al., 2006) and involve a wide array
of genes, neurotransmitters, and brain regions (Kreek et al., 2005; Pariyadath
et al., 2013), indicative of a complex underlying impairment. Functional magnetic
resonance imaging (fMRI) has been instrumental in characterizing systems level
malfunction in substance abuse and addiction, including disruption of prefrontal cor-
tical regions involved in self-control and planning (Goldstein and Volkow, 2002,
2011). Traditionally, fMRI has relied on cognitive tasks to probe the function of
brain regions responsible for processing various cognitive constructs. However, be-
cause of the wide range of brain regions implicated in the disease, neuroimaging
tools that enable the monitoring of network function may be especially valuable
to unraveling the neurobiology of addiction.
One approach that allows network-centered analyses of fMRI data relies on rest-
ing state functional connectivity (rsFC) (Biswal et al., 1995). rsFC focuses on cor-
relations between the low-frequency component of time-courses of blood
oxygenation level dependent signaling from different brain regions, or “functional
connectivity,” acquired in the absence of a directed task. rsFC is constrained by
structural connectivity (i.e., anatomical connections between regions), but exhibits
connectivity patterns that go beyond monosynaptic coupling (Barttfeld et al.,
2015; Honey et al., 2009). Critically, rsFC predicts individual performance on cog-
nitive tasks (e.g., Kelly et al., 2008), suggesting that rsFC can illuminate how brain
function affects behavior. Applying independent component analysis (ICA), a com-
putational tool for decomposing complex data into independent subparts, to rsFC
data has revealed a parsing of the brain into consistent resting state networks
(RSNs) that are concordant with networks involved in cognitive function (Smith
et al., 2009). As such, rsFC has emerged as a useful tool that enables researchers
to examine the fidelity of brain networks in the absence of an explicit task
(Bressler and Menon, 2010). Further validating the approach, these RSNs have also
been observed in anesthetized rats (Lu et al., 2012) and awake (Belcher et al., 2013)
and anesthetized (Vincent et al., 2007) monkeys, which suggests that these networks
are conserved across species and, therefore, holds translational potential to under-
standing neuropsychiatric diseases.
While the number and anatomical definitions of these large scale RSNs are still
being determined (Smith et al., 2009), at least three networks have consistently been
reported by virtually every research group: the default mode network (DMN), exec-
utive control network (ECN), and the salience network (SN). The DMN includes the
posterior cingulate, the medial prefrontal cortex, and medial temporal lobe and is
engaged when an individual is not performing a specific task but is awake and restful
156 CHAPTER 8 Resting state functional connectivity analysis
(Raichle et al., 2001). The ECN includes the dorsolateral prefrontal cortex, dorsal
anterior cingulate, and parietal cortex and is engaged when an individual plans, en-
gages workingmemory, or inhibits impulses (Niendam et al., 2012). The SN includes
the dorsal anterior cingulate and anterior insula and is involved in orienting attention
(Seeley et al., 2007).
In the context of SUDs, rsFC has enabled the testing of network-centered hypoth-
eses regarding the underlying mechanisms of the disease. One compelling example
comes from analysis of rsFC in nicotine addiction (Sutherland et al., 2012). Inspired
by the triple network model of cognitive dysfunction in neuropsychiatric disorders
(Menon, 2011), our group previously suggested that nicotine craving might be linked
to altered dynamics within and between these three central RSNs (Fig. 1A). Specif-
ically, they proposed that nicotine addiction may be characterized by reduced coher-
ence of the ECN, enhanced coherence of the DMN, and poor function of the SN in
toggling between the other two networks (Sutherland et al., 2012). A recent rsFC
study supported this hypothesis, showing that increased SN–ECN together with de-
creased SN–DMN coherence among cigarette smokers were associated with lower
levels of craving following 24 h of abstinence from nicotine (Lerman et al., 2014;
Fig. 1B).
Further demonstrating the value of a network-based framework, the ECN–DMN–
SN model has helped interpret findings from studies investigating pharmacological
cessation aids (Sutherland et al., 2013), and cue-induced craving (Moran-Santa
Maria et al., 2015). The ECN–DMN–SNmodel explains certain aspects of addiction,
such as the neural circuitry underlying drug craving and withdrawal (Lerman et al.,
2014), but it does not address why some individuals are more likely to initiate drug
use or transition from repeated to compulsive drug use. Much evidence suggests that
the mesocorticolimbic system is critically involved in developing SUDs (Koob and
Volkow, 2010), and that differences in mesocorticolimbic circuitry may drive some
of the vulnerabilities to addiction (Ersche et al., 2012; George and Koob, 2010).With
regard to rsFC, however, the evidence has not been synthesized to provide a cohe-
sive, network-level picture of how mesocorticolimbic circuit alterations may mod-
ulate risk for or treatment from SUDs. Here, we review rsFC studies that have
focused on mesocorticolimbic circuitry, with an emphasis on more recent research,
in the service of building a network-centered approach, similar to Sutherland et al.
(2012), that could further our understanding of mesocorticolimbic dysfunction in ad-
diction and potentially serve as a heuristic framework for hypothesis testing. Specif-ically, we examine recent rsFC studies investigatingmesocorticolimbic circuits, with
two primary goals in mind: (1) to summarize the extant literature and (2) to use the
evidence as a basis for a model of the dysfunctional mesocorticolimbic circuitry in-
volved in SUDs. We assume here that certain aspects of susceptibility to addiction
are independent of the type of drug (Agrawal and Lynskey, 2008), and the mesocor-
ticolimbic system may play a similar role in the development of SUDs involving all
classes of drugs (Koob and Volkow, 2010). As such, we aggregated rsFC studies ex-
ploring stimulants, alcohol, and opioids in our review.
1571 Introduction
1.1 MESOCORTICOLIMBIC CIRCUITS AND rsFC
The mesolimbic and mesocortical pathways, two of the brain’s major dopaminergic
pathways, have been implicated as key circuits that are disrupted in addictive behav-
iors (Blum et al., 2012). Both pathways originate primarily in the ventral tegmental
area (VTA); the mesolimbic pathway projects to the nucleus accumbens, and is a part
of complex circuits involving the amygdala, hippocampus, and the bed nucleus of the
FIGURE 1
The DMN–SN–ECN model posits that nicotine craving is characterized by reduced
connectivity of the ECN, enhanced connectivity of the DMN, and poor function of the SN in
toggling between the other two networks (A). This model was supported by a recent rsFC
study in which increased SN–ECN together with decreased SN–DMN coherence among
cigarette smokers were associated with lower levels of craving following acute abstinence
from nicotine (B).
Panels (A) and (B) adapted from Sutherland et al. (2012) and Lerman et al. (2014), respectively.
158 CHAPTER 8 Resting state functional connectivity analysis
stria terminalis (Fig. 2A). In contrast, the mesocortical pathway projects primarily to
the prefrontal cortex. Rats rapidly learn to press a lever to self-administer an electric
pulse to regions along this pathway (Olds and Milner, 1954), and activation of do-
paminergic neurons is necessary for this behavior (Garris et al., 1999). Some rats will
even forego food and starve themselves to continue receiving stimuli to the VTA
(Stutz et al., 1971). Together, these findings indicated that mesocorticolimbic dopa-
mine (DA) circuitry contribute to the rewarding aspect of a stimulus. Subsequent
findings showed that dopaminergic transmission in this system not only respond
to rewards, but to the cues that reliably indicate that a reward will or will not arrive
(Schultz et al., 1997).
Many abused drugs also act on this circuitry, either directly as in cocaine
(Hernandez and Hoebel, 1988), or indirectly as in nicotine, heroin, and alcohol
(Nisell et al., 1994), by inducing an increase in DA transmission. One prominent hy-
pothesis of addiction that arose from the findings relating SUDs to mesocorticolim-
bic circuitry is that the circuitry may be sensitized by substance use and this
sensitization biases an individual toward wanting more of a drug (Robinson and
Berridge, 2008). A related model posited that the drug and the cues associated with
the drug, acquire ever-escalating value due to the drug’s “hijacking” of dopaminergic
FIGURE 2
The mesocorticolimbic system (A) is frequently implicated in SUDs. Cocaine-dependent
individuals show reduced functional connectivity between multiple nodes of the
mesocorticolimbic system (indicated by the colored (different gray shades in the print
version) lines; B). Two distinct mesocorticolimbic circuits may be central to the “stop” or
inhibiting and “go” or facilitating responses to drug taking in addicts (C).
Panels (A)–(C) adapted from Haber and Knutson (2010), Gu et al. (2010), and Hu et al. (2015), respectively.
1591 Introduction
neurotransmission (Redish, 2004). Another prominent model incorporating meso-
corticolimbic circuitry postulates that disrupted mesocorticolimbic function results
in impaired inhibitory control and reward processing, which together manifests as
compulsive drug taking (Goldstein and Volkow, 2002, 2011).
1.2 CORTICOLIMBIC CONNECTIVITY IN ADDICTION
At least some of the brain abnormalities observed in addiction appear to predate
drug use, which is why studying at-risk individuals prior to drug consumption is
critical to more fully understanding the disease. One recent study examined adoles-
cents with and without a family history of alcoholism, but who had no personal
history of drinking (Cservenka et al., 2014). The group with a family history of
alcoholism showed greater functional connectivity between the nucleus accumbens
and the ventral lateral prefrontal cortex (Cservenka et al., 2014). The authors inter-
preted this finding as suggesting less segregation between regions involved in re-
ward processing and executive control. Structural brain data also points toward
preexisting deficits in frontostriatal white matter tracts among individuals at risk
for SUDs (Ersche et al., 2012), as does behavioral data indicating at-risk individuals
have deficits in frontal regulation prior to drug use (George and Koob, 2010). To-
gether, these findings suggest that some frontostriatal dysfunction in addiction pre-
dates the drug use.
Although increased connectivity may precede use, the direction of altered con-
nectivity may not stay stable through the course of SUDs; corticolimbic connectivity,
in particular, varies considerably in at-risk individuals as compared to addicted in-
dividuals. Several studies indicate that individuals with SUDs have decreased con-
nectivity to prefrontal cortical regions relative to comparison groups. For example, in
one study, heroin-dependent individuals, relative to controls, showed reduced con-
nectivity between the right caudate and the right dorsolateral prefrontal cortex
(Wang et al., 2013). Further, in a sample of prisoners with and without SUDs, those
with SUDs showed significantly weaker connectivity between the nucleus accum-
bens and dorsal anterior cingulate and dorsolateral prefrontal cortex (Motzkin
et al., 2014). Another study compared resting state connectivity and risk taking be-
havior between methamphetamine-dependent individuals and healthy controls
(Kohno et al., 2014)., The methamphetamine group showed both less modulation
of the right dorsolateral prefrontal cortex during risk taking decisions while also
demonstrating lower rsFC between the dorsolateral prefrontal cortex and the ventral
striatum.
Consistent with the notion that mesocorticolimbic circuitry changes through the
course of SUDs, frontostriatal connectivity patterns in prolonged abstinence resem-
ble those seen in at-risk individuals prior to drug use. In a comparison of short-term
(i.e., several months) and long-term (i.e., several years) abstinent alcoholics, the
long-term abstinent group had greater synchrony between the nucleus accumbens
and the dorsolateral prefrontal cortex than the short-term abstinent group
160 CHAPTER 8 Resting state functional connectivity analysis
(Camchong et al., 2013c). It may be that deficits in corticostriatal connectivity im-
pede recovery from addiction, but more research is needed to ascertain whether res-
toration of connectivity between ventral striatal regions and the prefrontal cortex is
sufficient to sustain sobriety.
It is important to note that some studies also indicate that SUDs are associated
with increased frontostriatal functional connectivity. For example, heroin-dependent
individuals being treated with methadone maintenance show enhanced connectivity
between the nucleus accumbens and both the anterior cingulate cortex and the orbi-
tofrontal cortex relative to controls (Ma et al., 2010). Similarly, pathological gam-
blers, relative to controls, show increased connectivity between the right middle
frontal gyrus and the right putamen (Koehler et al., 2013). In smokers, enhanced
striatocortical connectivity may be exacerbated by craving—in one study,
nicotine-dependent individuals werescanned one-and-a-half hours after smoking,
and then again 1 h later (Janes et al., 2014). Cigarette-craving was assessed at the
beginning of each scan. Unsurprisingly, craving was higher at the beginning of
the second scan relative to the first. Using rsFC analysis, this increase in craving
at the beginning of the second scan was shown to be associated with greater connec-
tivity between the striatum and orbitofrontal cortex. This further supports the sugges-
tion that mesocorticolimbic circuits are dynamic and may change as a function of
SUD status and acute states of craving.
Frontostriatal connectivity has frequently been interpreted in the context of im-
pulsivity, and it has been argued that individual differences in vulnerability to addic-
tion arise from preexisting differences in impulsive behavior (Ersche et al., 2012).
Such an interpretation for rsFC-based findings is made difficult by the variability
in the direction of frontostriatal circuitry differences. For example, data from path-
ological gamblers showed increased connectivity between the right medial ventral
striatum and right dorsolateral prefrontal cortex, where increased connectivity
was associated with greater impulsivity (Koehler et al., 2013). Similarly, a positive
correlation between frontostriatal rsFC and trait impulsivity has been observed in
cocaine-dependent individuals (Hu et al., 2015). On the other hand, prisoners with
SUDs also exhibit greater impulsivity than prisoners without SUDs, where those
with lower connectivity between nucleus accumbens and prefrontal regions showed
the greater impulsivity (Motzkin et al., 2014) although there was no healthy control
group in this study, limiting interpretation.
Based on the studies cited here, we conclude that differences in frontostriatal con-
nectivity may exist prior to drug use and this differential connectivity pattern may
speak to drug addiction risk. Frontostriatal connectivity appears to be dynamic across
the stages of addiction. It may be exacerbated in compulsive drug use, which could
motivate continued drug use through its relationship to craving. However, we need to
be cautious in drawing strong conclusions given the discrepancies in rsFC studies on
frontostriatal circuitry. Lastly, frontostriatal connectivity may be linked to impulsiv-
ity, but the exact relationship between rsFC in this circuit and impulsive behavior in
addiction warrants further investigation.
1611 Introduction
1.3 STRIATOLIMBIC CONNECTIVITY IN ADDICTION
Examination of studies investigating striatolimbic circuits also reveals inconsis-
tencies in addiction-related dysfunction. A number of studies have shown that indi-
viduals with SUDs have altered connectivity between the striatum and other limbic
regions. For example, sober alcoholics, relative to controls, show lower synchrony
between the nucleus accumbens and other reward network components (i.e., the pu-
tamen and ventromedial prefrontal cortex; Muller-Oehring et al., 2014). The alco-
holics in the study, however, had greater connectivity than controls to regions
outside the reward network (Muller-Oehring et al., 2014). Among the alcoholics,
lower synchrony within the reward network was associated with poorer performance
on a working memory task and worse mood. Similarly, prescription opiate-
dependent individuals, relative to controls, show reduced connectivity between
the nucleus accumbens and right anterior insula and ventromedial prefrontal cortex,
where lower connectivity is associated with longer duration of dependence
(Upadhyay et al., 2010). In another study, cocaine-dependent individuals showed re-
duced synchrony between the amygdala and hippocampus with the medial prefrontal
cortex (Gu et al., 2010; Fig. 2B). The cocaine group also had reduced connectivity
between the VTA and the thalamus and ventral striatum; the individuals who had
used cocaine more during their lifetime had greater reductions in connectivity.
Several studies have reported conflicting findings indicating that individuals with
SUDs have enhanced connectivity in striatolimbic circuits. For example, in a com-
parison of hippocampal connectivity, a heroin-dependent group showed greater syn-
chrony with striatal regions such as the putamen and caudate and cortical regions
including the bilateral insula, the posterior cingulate cortex, and the subgenual an-
terior cingulate cortex (Zhai et al., 2014). The heroin-dependent group also showed
decreased hippocampal synchrony relative to the control group in the amygdala, dor-
sal anterior cingulate, and dorsolateral prefrontal cortex. Among the heroin-
dependent group, stronger hippocampal synchrony with the caudate, and weaker
synchrony with the anterior cingulate, was associated with greater impulsivity. Fur-
ther, a study of methamphetamine-dependent individuals described earlier showed
greater connectivity between a ventral striatal seed and the surrounding striatal
and limbic regions, including the insula, relative to controls (Kohno et al., 2014).
Taken together, these studies suggest that disruption of connectivity between the stri-
atum and brain regions involved in processing reward and motivation may contribute
to SUDs, but given the lack of consensus between rsFC studies, drawing inferences
regarding the underlying mechanisms may be premature at this stage.
1.4 CONCLUSIONS FROM OUR REVIEW OF MESOCORTICOLIMBIC
rsFC STUDIES
Our review of the rsFC literature on the mesocorticolimbic system’s involvement in
addiction revealed somewhat inconsistent patterns in functional connectivity
(Table 1), and poses limitations for arriving at a unifying mesocorticolimbic
162 CHAPTER 8 Resting state functional connectivity analysis
Table 1 Summary of rsFC Studies on Mesocorticolimbic Involvement in Addiction
Phase of
Addiction Study Substance
N (Pts/
Healthy)
Nodes (Seed-Connected
Regions)
Strength (Pts Relative
to Healthy)
At risk Cservenka et al.
(2014)
Alcohol 47/50 NAcc–vlPFC "
NAcc–OFC #
Current
dependence
Janes et al. (2014) Nicotine 17 Caudate–OFC, dlPFC "
Koehler et al. (2013) Gambling 19/19 Putamen–dlPFC "
Ma et al. (2010) Heroin 14/13 NAcc–dmPFC "
NAcc–OFC "
Zhai et al. (2014) Heroin 22/15 Hippocampus–caudate,
putamen, insula, PCC
"
Corticolimbic Kohno et al. (2014) Methamphetamine 25/27 Striato-amygdala, hippocampus,
insula, OFC
"
Caudate–dlPFC #
Muller-Oehring
et al. (2014)
Alcohol 27/26 NAcc–vmPFC #
Upadhyay et al.
(2010)
Opiates 10/10 NAcc–insula, vmPFC #
Wang et al. (2013) Heroin 17/15 Caudate–dlPFC #
McHugh et al.
(2014)
Cocaine 45/22 Amygdala–vmPFC #
Gu et al. (2010) Cocaine 39/39 Amygdala–dmPFC #
Janes et al. (2012) Nicotine 13/16 Striato-hippocampus, amygdala "
Striatolimbic Camchong et al.
(2013b)
Alcohol 29/40 NAcc–insula, putamen #
McHugh et al.
(2013)
Cocaine 45/22 Putamen–insula #
Recovery Camchong et al.
(2013c)
Alcohol 59/23 NAcc–insula "
Camchong et al.
(2013a)
Alcohol 23/27 NAcc–dlPFC "
Motzkin et al.
(2014)
Mixed substances 22/18 NAcc–dlPFC, –dmPFC #
addiction model. In our estimation, some of the variance may stem from differences
in the substance use status of participant groups at the time of data collection, such as
acute withdrawal versus ad libitum drug use. Another matter to be considered here is
whether mesocorticolimbic dysfunction can provide a biomarker for predicting re-
lapse or for evaluating treatment efficacy, as there is some evidence to indicate that
impaired mesocorticolimbic connectivity may contribute to relapse among abstinent
substance users. In a group of recently sober (i.e., several months) alcoholics, de-
creased synchrony in resting state connectivity between the nucleus accumbens
and the insula and putamen was associated with greater likelihood of relapse in
the subsequent year (Camchong et al., 2013b). In another study, long-term abstinent
alcoholics both with and without comorbid stimulant dependence showed higher cir-
cuit strength between the nucleusaccumbens and the bilateral anterior insula relative
to a control group (Camchong et al., 2013a). Further, abstinent cocaine-dependent
individuals in a treatment program, relative to controls, showed reduced connectivity
between the bilateral putamen and both the posterior insula and right postcentral gy-
rus (McHugh et al., 2013). The cocaine-dependent group reported greater impulsiv-
ity than controls, and individuals with lower connectivity reported higher
impulsivity. Importantly, the individuals with lower connectivity were more likely
to relapse in the month following treatment. Another analysis in the same sample
revealed that reduced connectivity between the left corticomedial amygdala and
the ventromedial prefrontal cortex was a significant predictor of relapse (McHugh
et al., 2014). These studies suggest disrupted rsFC in mesocorticolimbic circuits
may offer a marker for treatment outcome, but at present, there is little consensus
on the direction of impairments.
rsFC approaches proffer unique characterization tools that allow the observation
of the mesocorticolimbic system as a whole, but their advantages may best be reaped
when paired with a guiding network-centered framework. It is our conclusion that
such a framework, one that combines data from understudied groups and leverages
emerging technologies in rsFC, could substantially advance our understanding of ad-
diction. With this aim in mind, and in light of recent rodent and human research on
individual differences in reinforcement learning, we take a step in this direction by
putting forward a model for the mesocorticolimbic system’s involvement in
addiction.
1.5 ADDICTION—DYSFUNCTIONAL PROCESSING OF NEGATIVE
CONSEQUENCES
Most research on addiction and the mesocorticolimbic system has focused primarily
on reward processing going awry. However, this system is also extensively involved
in the processing of negative feedback. Punishment modulates neural firing in both
the VTA and substantia nigra (Brischoux et al., 2009; Matsumoto and Hikosaka,
2009), and optogenetic inhibition of DA neurons in the VTA induces avoidance
learning (Danjo et al., 2014). Primate electrophysiological and voltammetry data
suggest that DA may encode a negative reward prediction error (Bayer et al.,
164 CHAPTER 8 Resting state functional connectivity analysis
2007; Hart et al., 2014). The striatum has also been linked to punishment processing
(Asaad and Eskandar, 2011; Delgado et al., 2003; Elliott et al., 2000), and midbrain–
dorsal striatal functional connectivity predicts some of the individual differences in
punishment learning (Kahnt et al., 2009). Decision making that incorporates the
evaluation of punishment is likely mediated through interactions of midbrain–striatal
circuits with regions shown to modulate risk, learning rates, uncertainty, and value in
the context of punishment and the absence of rewards, namely the amygdala, medial
prefrontal cortex, and the lateral habenula (Alexander and Brown, 2011; Lammel
et al., 2012; Li et al., 2011; Matsumoto and Hikosaka, 2007; Preuschoff et al.,
2008). Finally, a recent functional connectivity analysis indicated that communica-
tion between these frontal and subcortical regions is central to evaluating negative
feedback (Hennigan et al., 2015).
Rodent studies, together with computational modeling, suggest that vulnerability
to addiction stems, at least in part, from insensitivity to negative consequences
(Dalley et al., 2007; Deroche-Gamonet et al., 2004; Piray et al., 2010; Redish,
2004). According to this school of thought, addiction arises from an uncontrolled
escalation of value for drugs, which is driven by individual differences in learning
from negative feedback (Piray et al., 2010). We extend this idea to include vulner-
abilities stemming from impaired incorporation of negative consequences at the
decision-making stage, even if learning from negative feedback is intact. The latter
scenario could arise due to frontal dysfunction or as a result of impaired communi-
cation between frontal and subcortical regions involved in punishment processing. In
support of our hypothesis, recent optogenetic work in rats indicated that excitation of
medial frontal regions, with known projections to the striatum and amygdala, pre-
vents compulsive drug seeking in vulnerable rats, while inhibition ignites this behav-
ior in resilient animals (Chen et al., 2013). Critically, the optogenetic manipulation of
this circuit was only effective in modulating drug seeking in the presence of negative
consequences.
Viewed in the context of punishment, mesocorticolimbic system-centered
models stand to offer new and valuable heuristic insights into vulnerability to addic-
tion, and developing intervention strategies targeted at this phase of the addiction
cycle. A recent rsFC study from our group attempted a similar approach in identify-
ing frontostriatal dysruptions in cocaine dependence (Hu et al., 2015; Fig. 2C). The
authors suggested that two distinct frontostriatal circuits may be central to the “stop”
or inhibiting and “go” or facilitating responses to drug taking, and that the interaction
between these two circuits explained the shift to compulsive drug taking in addicts.
We argue here that the frontostriatal imbalance further exemplifies mesocorticolim-
bic dysfunction resulting specifically in impaired evaluation of negative feedback,
driving decision making in favor of the drug reward.
Our model puts forward specific testable predictions:
1. Addicted individuals are more likely to present with impairments in learning
from negative feedback, or in their ability to act on evaluation of negative
feedback.
1651 Introduction
2. These behavioral differences would be mediated by impaired function of
mesocorticolimbic circuits involved in aversive processing—circuits involving
the midbrain, striatum, amygdala, and medial prefrontal cortex.
3. Importantly, the above differences in circuit function and behavior would predate
drug use, but may be exacerbated by initial drug use.
1.6 THE MISSING PARTICIPANT GROUPS IN ADDICTION STUDIES
In addition to employing an alternative perspective when interpreting mesocortico-
limbic system involvement in addiction, understanding network-level dynamics of
the mesocorticolimbic will also require data from groups that have hitherto been
understudied. For example, since there is compelling preclinical data supporting a
role for the mesocorticolimbic in susceptibility to addiction (Belin et al., 2008;
Chen et al., 2013; Dalley et al., 2007; Ersche et al., 2012), mesocorticolimbic func-
tional connectivity architecture in dependent substance users versus occasional users
or “chippers” could provide valuable clues as to why some individuals are conferred
with resilience. Two other groups that merit investigation are adolescent users who
have not yet progressed to compulsive use, to better characterize their increased risk
for SUDs (Odgers et al., 2008; Whelan et al., 2014), and former substance users, to
understand the neural circuitry underlying their successful recovery from the disease.
In all three cases, examining network-level differences in function of the DMN–
ECN–SN system versus the mesocorticolimbic system might help ascertain their re-
spective roles in the addictive process (Fig. 3).
FIGURE 3
Mesocorticolimbic and DMN–SN–ECN dynamics might mediate different stages of the
addiction trajectory; mesocorticolimbic dysfunction might be critical at earlier stages, i.e.,
initiation and the transition from repeated to compulsive use, while DMN–SN–ECN
impairments may better explain recovery and response to treatment.
166 CHAPTER 8 Resting state functional connectivity analysis
1.7 EMERGING TOOLS FOR rsFC RESEARCH
Another hurdle to the development of a unifying model to the mesocorticolimbic sys-
tem’s role in addiction has been the absence of appropriate tools to identify networks
withinor encompassing the system. Researchers seeking to investigate DMN–ECN–
SN dynamics typically parse resting data into these three RSNs generally through
ICA. However, ICA parcellation generally does not identify mesocorticolimbic sub-
components (see Smith et al., 2009 for an example of typical ICA-based RSNs).
However, emerging approaches to rsFC analysis may offer alternative approaches
to probe this system. For example, most functional connectivity studies have focused
on understanding addiction-related circuits in the context of “true” rest. However,
examining rsFC following various tasks have reported that functional connectivity
changes based on recent experiences (Albert et al., 2009; Hartzell et al., 2015;
Hasson et al., 2009; Tambini et al., 2010). This experience dependence of rsFC poses
a serious confound when carrying out meta-analyses or pooling together datasets col-
lected following vastly differing task contexts, especially when experimental context
may not be readily available (Carp, 2012). However, if rsFC is experience dependent,
then it opens up the possibility of manipulating mesocorticolimbic circuits through a
task context prior to investigation at rest. Another variant of this approach is to assess
functional connectivity while viewing naturalistic stimuli as opposed to at rest
(Bartels and Zeki, 2005). Finally, while RSNs were initially presumed to be station-
ary, recent work exploring the temporal dynamics of these RSNs have determined
remarkable instability in their structure: membership of any given region in an
RSN fluctuates on the scale of seconds to minutes (Chang and Glover, 2010;
Hutchison et al., 2013). Since then, researchers have begun to probe the spatiotem-
poral profile of functional connectivity in various populations, notably in schizo-
phrenia (Damaraju et al., 2014; Sakoğlu et al., 2010), bipolar disorder (Rashid
et al., 2014), and Alzheimer’s disease (Jones et al., 2011). Approaches examining
“dynamic functional connectivity” hold unique advantages in the context of addic-
tion, especially in order to draw comparisons between craving and sated states.
Finally, much like with other domains of addiction research, rsFC analysis of the
mesocorticolimbic system might benefit from evolving views on the root cause of
the disease. Recently, the habenula has received attention for its putative role in reward
and punishment processing (Matsumoto and Hikosaka, 2007; Salas et al., 2010;
Stamatakis and Stuber 2012) and the influence it exerts on dopaminergic activity
(Jhou et al., 2009; Ji and Shepard, 2007).Owing to its small size, until recently, research
on the habenula was largely confined to invasive rodent and primate experiments. New
high-resolution fMRI techniques now allow the study of habenula connectivity in
humans (Lawson et al., 2013, 2014). Understanding the precise role of the habenula
in processing negative feedback could potentially help address one the longstanding
puzzles of addiction—why do some individuals continue drug consumption in the
face of severe negative consequences (Deroche-Gamonet et al., 2004; Piray et al.,
2010)? rsFC analysis is well situated to make inroads in this area by enabling better
characterization of habenular connectivity to midbrain and striatal structures.
1671 Introduction
2 CONCLUSIONS AND FUTURE DIRECTIONS
Our understanding of addiction-related disruptions in rsFC has been advanced sig-
nificantly following the development of a guiding model conceptualized through
network-level visualization of the brain (Sutherland et al., 2012). However, such
an approach has eluded the incorporation of the mesocorticolimbic system. Our re-
view of the extant rsFC literature incorporating mesocorticolimbic components sug-
gests that development and testing a network-level model may be beneficial. We lay
the groundwork for developing such a model with three recommendations: (1) shift-
ing focus from reward to punishment processing in evaluating vulnerability to addic-
tion, (2) acquiring data from understudied groups in addiction, and (3) harnessing
new and emerging rsFC technologies to better characterize addiction-related circuit
dysfunction.
ACKNOWLEDGMENTS
This work was supported by the Intramural Research Programs of the NIDA and NIAAA.
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	Resting state functional connectivity analysis for addiction medicine: From individual loci to complex networks
	Abstract
	Keywords
	Introduction
	Mesocorticolimbic Circuits and rsFC
	Corticolimbic Connectivity in Addiction
	Striatolimbic Connectivity in Addiction
	Conclusions from Our Review of Mesocorticolimbic rsFC Studies
	Addiction-Dysfunctional Processing of Negative Consequences
	The Missing Participant Groups in Addiction Studies
	Emerging Tools for rsFC Research
	Conclusions and Future Directions
	Acknowledgments
	References