Ann. N.Y. Acad. Sci. ISSN 0077-8923

ANNALS OF THE NEW YORK ACADEMY OF SCIENCESIssue: Addiction Reviews 2

Shared brain vulnerabilities open the way fornonsubstance addictions: Carving addictionat a new joint?

Joseph Frascella,1 Marc N. Potenza,2 Lucy L. Brown,3 and Anna Rose Childress4,5

1Division of Clinical Neuroscience and Behavioral Research, National Institute on Drug Abuse, Rockville, Maryland, USA.2Department of Psychiatry and Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA. 3Saul R. KoreyDepartment of Neurology and Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, NewYork, New York, USA. 4Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania,USA. 5VISN 4 MIRECC, Department of Veteran’s Affairs Medical Center, Philadelphia, Pennsylvania, USA

Address for correspondence: Anna Rose Childress, Department of Psychiatry, University of Pennsylvania School of Medicine,3900 Chestnut St., Philadelphia, PA 19104. childress_a@mail.trc.upenn.edu

For more than half a century, since the beginning of formal diagnostics, our psychiatric nosology has compartmental-ized the compulsive pursuit of substance (e.g., alcohol, cocaine, heroin, nicotine) from nonsubstance (e.g., gambling,food, sex) rewards. Emerging brain, behavioral, and genetic findings challenge this diagnostic boundary, pointing toshared vulnerabilities underlying the pathological pursuit of substance and nonsubstance rewards. Working groupsfor the fifth revision of the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V), are thusconsidering whether the nosologic boundaries of addiction should be redrawn to include nonsubstance disorders,such as gambling. This review discusses how neurobiological data from problem gambling, obesity, and “normal”states of attachment (romantic infatuation, sexual attraction, maternal bonds) may help us in the task of carving ad-dictions “at a new joint.” Diagnostic recarving may have a positive effect on addiction research, stimulating discoveryof “crossover” pharmacotherapies with benefit for both substance and nonsubstance addictions.

Keywords: nonsubstance addiction; DSM-V; brain imaging; nosology

“ . . . the . . . principle . . . is that of division . . .

according to the natural formation, where thejoint is, not breaking any part as a bad carvermight. . . . ” —Socrates, in Plato’s Phaedrus1

Overview

Anna Rose Childress, Ph.D.

The nosologic recarving of addictions may soonundergo a significant change, reflecting a shiftin clinical and research thought about the veryessence of these disorders, their critical and neces-sary elements. Charged with Plato’s dictum, work-ing groups for the Diagnostic and Statistical Manualof Mental Disorders, fifth edition (DSM-V2), are ac-tively considering whether nonsubstance disorders,such as gambling, should be classified in the categorypreviously reserved exclusively for substance-related

disorders. Though DSM-V is not scheduled for finalpublication until 2012, the possibility of carving ad-diction at a different joint, somewhere beyond sub-stances, has stimulated spirited exchange and morethan a twinge of nosologic anxiety. If ingesting orinjecting a substance is no longer a necessary featurefor the construct of addiction, how do we find thenew boundaries?

At one level, the recarving of addictions isnot new. Substance-related disorders were initially“carved in” under Sociopathic Personality for thefirst DSM, in 1952,3 and were still considered per-sonality disorders for the next DSM revision, in 1968(DSM-II4). They eventually were “carved out” forindependent status in 1980 (DSM-III5) and have re-mained thus for nearly 30 years. But in each of theseprior nosologic revisions, the substance-relateddisorders (whether “carved in” under broader cate-gories or “carved out” to stand alone) were carved

doi: 10.1111/j.1749-6632.2009.05420.x294 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

together and defined by substance taking. In con-trast to prior revisions, DSM-V is consideringwhether addictions can be defined apart from drugtaking—a fundamental shift in the way these disor-ders have previously been viewed.

This “neither necessary nor sufficient” status ofsubstances for the future nosology obliges us to lookelsewhere for the carving joint, to look for under-lying similarities in the compulsive pursuit of sub-stance and nonsubstance rewards, rather the sin-gle obvious difference. Fortunately, emerging brain,behavioral, and genetic data do point to fundamen-tal, mechanistic ways in which substance and non-substance addictions are similar. On the short listof similarities are preexisting vulnerabilities in themesolimbic dopamine reward system and its failedregulation by frontal regions. For a familiar exam-ple, dopamine agonist treatments can trigger com-pulsive gambling, buying, and sexual behavior ina vulnerable subgroup of Parkinson’s patients, andthese problem behaviors may be intercorrelated.6,7

The brain sciences offer strong hope for discoveringthe new boundaries, the new joint, for the constructof addiction.

The following segments by Drs. Potenza, Fras-cella, and Brown show how brain tools may be usedto parse the new boundaries for nonsubstance ad-dictions, and the sections relate in three differentways to the emerging nosology. We begin with prob-lem gambling, the nonsubstance disorder that ap-pears most likely to gain entry into the addictionscategory for DSM-V. As summarized by Dr. Potenza,phenomenological (compulsive pursuit of gamblingreward, despite severe negative consequences), ge-netic (highly heritable, and often comorbid withother substance addictions), and brain data (e.g.,altered response in reward circuits; poor frontal reg-ulation during exposure to a gambling scenario) ar-gue for including gambling as an addiction.8 Forgambling, biological data encourages carving all in-dividuals with the phenotype into the diagnosticcategory of “addiction.”

We next consider the complex problem of obe-sity. In contrast to gambling, where all those with thephenotype would probably be included in the samediagnostic category, the phenotype of “obesity” orhigh body mass index (BMI) is recognized to beheterogeneous. Several brain and metabolic factorscontrol food intake and weight gain; not all indi-viduals who are overweight are “addicted to food.”

Can we carve a clinically meaningful nosologic dis-tinction among obese individuals? As reviewed byDr. Frascella, rapidly advancing brain and geneticdata may indeed help us move beyond BMI, en-abling us to identify obese individuals who havebrain differences (e.g., low D2 receptor availability)paralleling those in drug addiction.9–11 These indi-viduals may respond to interventions arising fromthe field of drug addiction (e.g., � opioid receptorantagonists block reward from drugs, such as heroinand morphine, and they blunt the reward from thehighly palatable [sweet, high-fat] foods12–14). Ournosologic system may eventually use such brain-and treatment-driven endophenotypes to carve sub-groups of obese individuals into the category ofaddiction.

Our final segment, by Dr. Brown, highlights theutility of brain tools for studying powerful appeti-tive states—such as early romantic infatuation, in-tense sexual attraction, and attachment, that wedefine as normal—but that affect the same brainreward circuitry as, and share some clinical simi-larities with, drug addictions. For example, intenseromantic attachment is “normal,” by definition, be-cause so many humans have experienced it, but itis intensely euphoric, there is strong pursuit of thereward to the exclusion of other activities, and itcan lead to poor decision making (including jealouscrimes of passion). Because the basic reward cir-cuitry for romantic love and attachment is co-optedby drugs of abuse, studying this “normal” alteredstate, in a “normal” circuit, may give us guidanceabout endophenotypes of vulnerability in states thatare pathologic. It is possible, for example, that thosewith greater vulnerability during the “normal” al-tered states (more frequent or prolonged infatua-tions, more difficulty moving on after a rejection)are also at greater risk for other dysregulated patho-logical states, whether substance or nonsubstancerelated.

Taken together, the findings of these authors en-courage us to meet the diagnostic challenges aheadwith our best biological tools, and with an openmind. As we move to carve addiction at a new joint,it will clearly not be meaningful to label as “addic-tion” every pursuit (e.g., food, gambling, sex, shop-ping, internet, exercise) that activates brain rewardcircuits. But it is possible that any of these rewardingpursuits, in the vulnerable individual, may emergeas a clinical problem with brain and behavioral

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 295

Nonsubstance addictions Frascella et al.

features that display striking similarities to thoseseen in drug addiction. We can thus seek parallelsin clinical progression and even in response to simi-lar treatments. The brain and genetic vulnerabilitiesthat allow pursuit of nondrug rewards to becomepathologic are likely to be important in the vulnera-bility to drug addiction. Targeting these shared brainvulnerabilities may accelerate our understanding,and thus our effective treatment, of both substanceand nonsubstance addictions.

Addiction and Pathological Gambling

Marc N. Potenza, M.D., Ph.D.

IntroductionGambling, defined as placing something of value atrisk in the hopes of gaining something of greatervalue, has been observed across cultures for millen-nia.15 Early documents of human behavior show ev-idence of gambling, including problematic forms ofthe behavior. Pathological gambling is the diagnos-tic term used in the current edition of the AmericanPsychiatric Association’s DSM-IV-TR to describeexcessive and interfering patterns of gambling.16

Pathological gambling is currently grouped withkleptomania, pyromania, trichotillomania, and in-termittent explosive disorder in the category of “Im-pulse Control Disorders Not Elsewhere Classified,”although few investigations have studied the extentto which these disorders group together on the ba-sis of biological measures. Inclusionary criteria forpathological gambling share common features withthose for substance dependence. For example, as-pects of tolerance, withdrawal, repeated unsuccess-ful attempts to cut back or quit, and interferencein major areas of life functioning are reflected inthe diagnostic criteria for each disorder. Therefore,pathological gambling has been termed by some as a“behavioral” or non–substance-related addiction.

Clinical and phenomenological similaritiesbetween pathological gambling andsubstance dependenceIn addition to inclusionary criteria common topathological gambling and substance dependence,other clinical features are shared across the disor-ders. For example, craving or appetitive urge statesare seen in both disorders, both are related tem-porally to time of last engagement in gambling or

substance use,17 and the strength of urges has clini-cal implications for treatment.18 Also, similar brainregions (e.g., ventral striatum and orbitofrontal cor-tex) have been found to contribute to gamblingurges in pathological gambling and cocaine cravingsin cocaine dependence.17,19 Pathological gamblingand substance dependence are frequently comor-bid not only with one another but also with similardisorders (e.g., antisocial personality disorder).20,21

Similarities also exist with respect to the courses ofpathological gambling and substance dependence.As with substance dependence, high prevalence es-timates have been reported for pathological gam-bling among adolescents and young adults and lowerestimates among older adults.22,23 A younger ageat gambling onset has been associated with moresevere gambling and other mental health prob-lems, similar to data regarding age at first substanceuse.24,25 A “telescoping” phenomenon appears ap-plicable to both pathological gambling and sub-stance dependence.26,27 This phenomenon, first de-scribed for alcoholism, later for drug addiction andmost recently for gambling, refers to the observationthat although on average women begin engagementin the behavior later in life than do men, the timeframe between initiation and problematic engage-ment is foreshortened (or telescoped) in womencompared with that in men.28 Taken together, thesefindings indicate many common clinical and phe-nomenological features between pathological gam-bling and substance addictions.

Genetic featuresBoth substance dependence and pathological gam-bling have been shown to have heritable com-ponents.29–31 Common genetic contributions topathological gambling and other disorders, includ-ing alcohol dependence and antisocial behaviors,have been reported in men.32,33 However, signifi-cant portions of the genetic contributions to patho-logical gambling were also unique from those un-derlying alcohol dependence and antisocial behav-iors, suggesting specific contributions to each dis-order. For example, allelic variants in genes codingfor enzymes related to alcohol metabolism mightbe anticipated to be unique to potential risk foralcohol dependence, whereas genes related to im-pulsive propensities might be hypothesized to beshared across disorders.34,35 Early investigationsinto specific molecular genetic contributions to

296 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

pathological gambling identified common factorsin substance dependence and pathological gambling(e.g., the TaqI A1 allele of the gene encoding thedopamine D2 receptor).36 However, early studieswere not typically methodologically rigorous (e.g.,did not stratify by racial or ethnic identity and didnot include diagnostic assessments), and more re-cent studies have not replicated some initial find-ings.37 Therefore, more investigation into commonand unique genetic contributions to pathologicalgambling and substance dependence are needed,particularly studies of a genomewide nature.

Personality and neurocognitive featuresCommon personality and neurocognitive featureshave been described in pathological gambling andsubstance dependence. As in individuals with sub-stance dependence,34 features of impulsivity andsensation seeking have been found to be elevated inpeople with pathological gambling.35,38–41 Patho-logical gambling, like substance dependence, hasbeen associated with preferential selections of small,immediate rewards over larger delayed ones indelay-discounting paradigms.40 Individuals withpathological gambling like those with drug depen-dence have been found to make disadvantageouschoices on decision-making tasks, such as the IowaGambling Task.42,43 However, unique features be-tween substance dependence and pathological gam-bling have also been reported. For example, onestudy found that subjects with pathological gam-bling and alcohol dependence both showed deficitson tasks of time estimation, inhibition, cognitiveflexibility, and planning.44 In an independent study,individuals with alcohol dependence and patholog-ical gambling showed similar deficits on aspects ofperformance on a gambling task and impulsivitytask, yet they differed with respect to performanceon tasks of executive function on which individualswith alcohol dependence showed greater deficits.45

These findings indicate that specific features of sub-stance dependence (e.g., chronic exposure to sub-stances) may have specific influences on brain struc-ture and function and related behavior that is notseen in pathological gambling.46–48

Neural featuresThe common clinical, phenomenological, genetic,personality, and neurocognitive features betweenpathological gambling and substance dependence

might be hypothesized to be reflected in sharedneural features.35 For example, similar brain re-gions (e.g., ventral striatum and orbitofrontal cor-tex) have been found to contribute to gamblingurges in pathological gambling and cocaine crav-ings in cocaine dependence.19 Diminished ventralstriatal activation has been observed in individu-als with pathological gambling in the processing ofmonetary rewards during a gambling paradigm.49

These findings share similarities with those involv-ing alcohol-dependent or cocaine-dependent sub-jects in which diminished ventral striatal activationhas been reported during the anticipation of mon-etary rewards.50,51

The ventromedial prefrontal cortex, functionallyconnected with the ventral striatum, has been im-plicated in risk–reward decision making and theprocessing of monetary rewards.43,52,53 Diminishedactivation of the ventromedial prefrontal cortex insubjects with pathological gambling was initially re-ported in studies of gambling urges and cognitivecontrol.41,54 A subsequent study found diminishedventromedial prefrontal cortical activation duringsimulated gambling, with degree of activation cor-relating inversely with gambling severity in the sub-jects with pathological gambling.49 More recently,subjects with substance use disorders with or with-out pathological gambling showed diminished ven-tromedial prefrontal cortical activation during per-formance of the Iowa Gambling Task.55 Together,these data suggest dysfunction of ventral fronto-striatal circuitry in pathological gambling and sub-stance dependence that is linked to aspects of rewardprocessing and disadvantageous decision making.

Another recent study examined in healthy sub-jects the neural correlates of the near-miss phe-nomenon.56 A near-miss situation occurs when thefirst two reels of a slot machine stop on the samesymbol and then the third reel locks on a nonmatch-ing symbol. During anticipation of the stoppingof the third reel, activation of reward-processingbrain regions (e.g., striatum) was observed. Dur-ing the outcome phase, several of these brain re-gions (e.g., striatum, midbrain region including theventral tegmental area [VTA]) showed activation,thus appearing to code these events as reinforcing.A region that showed deactivation (thus appear-ing to code these events as nonreinforcing) was theventromedial prefrontal cortex. Because ventrome-dial prefrontal cortical activity has also been linkedto loss chasing in healthy subjects,57 existing data

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 297

Nonsubstance addictions Frascella et al.

suggest that phenomena hypothesized to be asso-ciated with the development of pathological gam-bling are linked to brain regions in which individ-uals with pathological gambling show functionalabnormalities.

TreatmentsBehavioral and pharmacological treatment strate-gies for pathological gambling and substance depen-dence also show similarities. Gamblers Anonymous,based on the 12-step program Alcoholics Anony-mous, is the most widely available form of help forindividuals with pathological gambling, and atten-dance has been associated with positive treatmentoutcome.58,59 Other behavioral therapies, such ascognitive behavioral therapy, have been adoptedfrom the substance dependence field and shown tobe efficacious in the treatment of pathological gam-bling.60 Brief interventions, such as those used inmedical settings for assistance with smoking cessa-tion, have shown promise in the treatment of patho-logical gambling,61 as have motivational interven-tions that have shown success in the treatment ofdrug dependence.62,63

Multiple pharmacotherapies have been investi-gated in the treatment of pathological gambling.19

As with drug dependence, serotonin reuptake in-hibitors have shown mixed results in controlled tri-als.19,64,65 Opioid antagonists, such as naltrexone(a drug with approval for the indications of opi-oid and alcohol dependence), represent the classof drugs that to date has shown the most promisein the treatment of pathological gambling, particu-larly among individuals with strong gambling urgesat treatment onset and those with a family historyof alcoholism.18 More recently and based on workin drug dependence,66 glutamatergic agents, such asN-acetyl cysteine, have been investigated and shownpreliminary efficacy in the treatment of pathologicalgambling.

Summary: addiction and pathologicalgamblingPathological gambling and substance depen-dence show many similarities. Although specificfeatures also probably distinguish pathological gam-bling from drug dependence (much as specific fea-tures distinguish specific forms of substance de-pendence29), existing data suggest a particularlyclose relationship between pathological gambling

and substance dependence that warrant their con-sideration within a category of addictions.

Addiction and obesity

Joseph Frascella, Ph.D.

Neurobiological links between obesityand drug addiction introductionObesity is increasing significantly and representsa public health concern in both the United Statesand now worldwide. Current estimates show thatabout 65% of adults and about 32% of childrenand adolescents in the United States are over-weight or obese.67,68 More than 1 billion adultsand 10% of the world’s children have been classi-fied as overweight or obese, with consequent de-creases in life expectancy as well as increases in ad-verse consequences, such as cardiovascular disease,metabolic syndrome, type 2 diabetes, and some can-cers (e.g., Refs. 69, 70). The etiology of obesity isextremely complex, reflecting varied neurobehav-ioral factors; however, a growing literature pointsto the fact that excessive and compulsive eatingoften can share some of the same processes andbehavioral phenotypes with substance abuse anddependence as described in DSM-IV. For example,DSM-IV substance dependence criteria (tolerance;withdrawal; escalation/using larger amounts; persis-tent desire/unsuccessful effort to reduce use; spend-ing a large amount of time acquiring substance,using it, or recovering from it; sacrificing social,occupational, or recreational activities because ofsubstance use; and continued substance use in theface of persistent or recurrent physical or psycholog-ical problems) can be applied in obesity. For somepeople, food can trigger an addictive process,71–73

and the parallels are so similar that it has been sug-gested that obesity should be recognized in DSM-Vas a mental disorder (Ref. 10; see also Ref. 74 for adiscussion of the complexities surrounding this no-tion). With the abundance and availability of highlypalatable, calorie-dense foods filled with salt, fats,and sugars, these extremely potent reinforcers canbe hard to resist, which can lead to nonhomeostaticeating and to obesity.

This review will discuss some of the relevantneurobiological data that reveal the distinct simi-larities (and differences) between obesity and ad-diction. The goal is to focus on meaningful com-parisons that highlight commonalities and possible

298 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

connections between both fields of study. As a result,obesity research could potentially inform substanceabuse/addiction research and vice versa. Despite agrowing scientific debate as to the existence of “foodaddiction” as an important factor driving the cur-rent obesity epidemic (see Refs. 75–77), this reviewwill not discuss this construct directly but will in-stead focus on the parallels between both obesityand addiction in terms of neurobiological systemsthat underlie motivational processes in both feedingand drug abuse. These neurobiological mechanismscan be affected by potent reinforcers resulting inexcessive behaviors and a loss of control exhibitedin both obesity and addiction. Similarities betweenobesity and substance addiction might highlight theneed to consider a subpopulation of obese individ-uals consistently with other behavioral addictions.

The brain reward system: a common linkbetween obesity and addictionIncreasing evidence, particularly from animal stud-ies, reveals that some of the same brain systemsunderlie compulsive or excessive eating and drugabuse. The neural systems regulating mammalianenergy control and balance are exceedingly complex,with many processes and feedback mechanisms thatinvolve distributed regions of the brain. Regulationof normal feeding is mediated by the monitoring ofenergy needs relative to energy expenditures; whenenergy expenditures exceed energy intake, systemssignal this change and hunger results. Much likesubstances of abuse, highly palatable foods can serveas potent reinforcers that motivate behaviors (i.e.,nonhomeostatic eating). The mechanisms underly-ing excessive food intake leading to obesity, as wellas drug seeking leading to addiction, are extremelycomplex and are influenced by several factors (e.g.,genetic influences, learning and memory, palata-bility/liking, stress, availability, developmental, en-vironmental/social/cultural influences) (for reviewsee Refs. 9, 78).

Central to the motivation and drive in the acqui-sition of certain foods as well as abused substancesis the brain reward system. This highly evolved sys-tem involves an extremely complex neurobiologicalnetwork, particularly the mesolimbic dopamine sys-tem: the VTA in the midbrain and its projections tothe nucleus accumbens, amygdala, ventral striatum,hippocampus, and medial prefrontal cortex (e.g.,

Refs. 79–83). How effective a substance (or food) isin stimulating the brain’s reward system influencesthe likelihood of future intake of that substance (orfood). The brain reward system is linked to feedingcircuits mediating energy balance and control.

Dopamine release in the nucleus accumbens hasbeen shown after administration of most substancesof abuse and is thought to mediate the reward-ing properties of drugs (e.g., Refs. 84–95). Simi-larly, when we ingest foods, dopamine is released,and animal studies have long shown that the re-lease of dopamine occurs in the nucleus accumbensand VTA (e.g., Refs. 96–102). Further studies haveshown that dopamine release in the nucleus accum-bens is a direct function of the rewarding propertiesof food, and dopamine release varies as a functionof food palatability.97,103,104 Such work reveals thelink between palatability, reward, and dopamine, allof which can interact with normal homeostatic ap-petitive states. Pleasantness and palatability of thefood also can be dissociated from hunger (e.g., Refs.13, 105).

The characterization of the neurobiological rela-tionship between taste and reward is critical in theunderstanding of the affective aspects to feeding,motivation, and food preference. The corticolim-bic pathways that mediate motivational factors forfood project to the hypothalamic nuclei, and theconnection of these systems regulates hunger andsatiety.106,107 Other findings suggest that sensoryactivity from a food stimulus is processed by wayof limbic projections to the nucleus accumbens.108

Another brain area that has been shown to be in-volved in the reward or pleasurable aspects of foodand other stimuli is the orbitofrontal cortex (e.g.,Refs. 80, 82, 83, 105, 109–113). Many of these sys-tems involved in food reward overlap with thoseaffected by abused substances. Both palatable foodand drugs are highly rewarding, and both are medi-ated through the dopamine system.

Although the dopamine system plays a key role inreward processing, other systems are also important.A growing literature suggests that the endocannabi-noid system directly modulates reward and drugseeking (e.g., Refs. 114–121). Similarly, the endoge-nous opioid system is involved in reward process-ing,122,123 and both the endogenous cannabinoidand opioid systems interact to mediate brain reward(see Ref. 120). Similar to the effects of these twosystems on reward and drug seeking, studies have

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 299

Nonsubstance addictions Frascella et al.

revealed a link between the endogenous cannabi-noid and opioid systems in feeding and in the reg-ulation of food intake (e.g., Refs. 13, 124–127; forreview see Refs. 128, 129). Recently, opioid systemsmediating palatability and reward value of food wereshown to be neurobiologically distinct.130

Clinical brain imaging findingsMuch of the evidence presented linking both hasbeen from animal studies reporting direct mea-sures of the neurobehavioral aspects of feeding anddrug seeking. Overlapping mechanisms and func-tional processes underlying both obesity and ad-diction are being elucidated in a growing numberof human brain imaging studies. Normal food in-take is regulated by homeostatic processes and isinfluenced by the same rewarding or motivationalprocesses that also control drug seeking. Positronemission tomography and functional magnetic res-onance imaging methods have provided powerfultools to determine brain structures, transmitter sys-tems, and functional circuits involved in food anddrug reward processing.

Studies in humans have paralleled animal workby characterizing the involvement of the dopaminesystem in substance abuse, specifically through therelationship between brain dopamine levels in thenucleus accumbens and the rewarding properties ofdrugs of abuse. Volkow et al.131 showed that the rein-forcing effects of psychostimulant drugs in humanswere related to increased brain dopamine levels, andthe subjective perception of reward/pleasure waspositively correlated with the amount of dopaminereleased. Also, overall levels of dopamine D2 re-ceptors predicted individual differences in the re-inforcing effects of psychostimulant drugs; that is,low dopamine D2 receptor levels correlated withgreater reinforcing effects of the drug.132 Stud-ies of dopamine release in response to food, orfood-associated stimuli, have similarly shown thatwhen healthy, food-deprived subjects are presentedwith favorite foods, dopamine is released duringthe presentation of food-related cues,133 as wellas after consumption of the meal. The amount ofdopamine released (in dorsal, but not ventral, stria-tum) correlates with meal pleasantness,110 suggest-ing that dorsal striatum may mediate food rewardin healthy individuals.133 This finding of food re-ward/motivation being mediated in the dorsal stria-

tum but not ventral striatum (an area involved indrug reward) reveals a distinction in processing be-tween food and drugs of abuse. The dorsal striatumhas been shown to be important in feeding (e.g.,Refs. 84, 134) and is consistent with findings of in-creased regional cerebral blood flow in the dorsalstriatum during the ingestion of chocolate; bloodflow in this region correlated positively with pleas-antness ratings.111

Craving is a characteristic feature of both obe-sity and addiction. It may underlie overeating anddrug abuse and interferes with maintenance of ab-stinence. Several studies exist attempting to char-acterize the functional correlates of food pleasant-ness or food desirability (e.g., Refs. 11, 110, 111,135, 136); however, relatively few have assessed foodcraving directly. Pelchat et al.137 studied brain acti-vation to food craving and found craving-relatedchanges in the hippocampus, insula, and caudate.In another study, chocolate cravers were comparedto noncravers, and cravers showed greater activationin reward areas, such as the medial prefrontal cortex,anterior cingulate, and ventral striatum.138 Many ofthe areas activated in food craving are somewhatoverlapping with brain areas in drug craving stud-ies, such as the anterior cingulate (e.g., Refs. 139–147), ventral striatum (e.g., Refs. 142, 147), hip-pocampus (e.g., Refs. 141, 147), insula (e.g., Refs.141–144, 146–148), and dorsomedial and dorso-lateral prefrontal cortex (e.g., Refs. 139, 145–147,149). In these brain imaging studies of drug crav-ing, individuals tested were dependent on drugs,whereas in the food craving studies, healthy sub-jects were tested. Therefore, studies assessing crav-ing in obese individuals are needed. Many studies,however, have been conducted to determine brainresponses to food and food cues and have probedthe reward system in obese populations. Dysfunc-tional food reward processing in these individualsis thought to contribute to and represent a neu-robiological substrate to pathological eating andobesity.

For example, brain responses to anticipatory andconsummatory food reward were found to be dif-ferent in obese versus lean individuals. Obese sub-jects showed a significantly greater brain activationduring both anticipated and actual consumption offood in primary gustatory cortex, in somatosensorycortex, and in anterior cingulate.150 A decreased ac-tivation in the caudate was found in obese versus

300 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

lean individuals during consumption, which wasthought to possibly indicate reduced dopamine re-ceptor availability. Also, as a function of BMI, in-creased activation to anticipatory food reward wasfound in the temporal operculum and dorsolateralprefrontal cortex, and increased activation in theinsula and frontoparietal operculum was found inresponse to consummatory food reward. These re-sults show a distinct difference in processing of foodstimuli in obese versus lean individuals. Greater re-sponses to food presentation, coupled with a de-creased striatal response during consumption, wereposited as a potential neurobiological marker of riskfor overeating and obesity.

In another study, the relationship between obe-sity and hypofunctioning of the dorsal striatumwas related to the presence of the A1 allele of theTaqI gene.151 The negative relation between stri-atal response to food intake and BMI was signif-icantly greater in those individuals with the A1allele (see also Ref. 152). It was suggested thatthis difference was possibly related to the reduceddopamine D2 levels in the striatum of obese in-dividuals, thereby compromising dopamine signal-ing, which could lead to overeating to compensatefor a reward deficiency. Also, individuals with thisdopamine D2 receptor gene polymorphism wereshown to have a deficit in learning from errorsin a feedback-based learning task. Dopamine D2receptor reduction has been related to decreasedsensitivity to negative action consequences.153

Studies have also suggested that the dopamine D2receptor TaqI A1 polymorphism is related to sub-stance abuse (e.g., Refs. 154–156). Recently, a sig-nificantly higher prevalence of the dopamine D2receptor TaqI A1 allele polymorphism was foundin methamphetamine-dependent individuals thanin a comparison group.157 Substance-dependent in-dividuals with this polymorphism also had cogni-tive deficits, scoring significantly lower on executivefunction measures.

Despite these results showing a decreased re-sponsiveness in the dorsal striatum, a structureimportant in habit learning (e.g., Refs. 158–160),Rothemund et al.161 found that during food in-gestion, high-calorie food selectively activated thedorsal striatum along with other areas, such as an-terior insula, hippocampus, and parietal lobe, inobese women compared to normal-weight individ-uals, indicating a possible higher reward anticipa-

tion and motivational salience in obesity. Differ-ences in the motivational potency of food cues andthe reactivity of the reward system were also foundin obese individuals. High-calorie foods elicited sig-nificantly greater activation in the brain areas medi-ating motivational and emotional responses to foodand food cues (medial and lateral orbitofrontal cor-tex, amygdala, nucleus accumbens/ventral striatum,medial prefrontal cortex, insula, anterior cingulatecortex, ventral pallidum, caudate, putamen, andhippocampus) for obese versus normal-weight indi-viduals.162 The authors suggest that their results areconsistent with the hypothesis that those brain net-works showing hyperactive responsiveness to foodcues in obesity are also hyperactive to drug cues inaddiction.

A critical question remains as to whether obeseindividuals have a hyperresponsiveness in brain re-ward regions important to food reward or if they, infact, have a hyporesponsive reward circuitry. Sticeet al.163 review behavioral and brain imaging evi-dence for both models. They conclude that much,but not all, of the data suggest that obese comparedto lean individuals report greater pleasure and showgreat activation in gustatory and somatosensory cor-tex in response to food anticipation and consump-tion. This heightened activation in these brain ar-eas could increase vulnerability to overeating. Theyfurther hypothesize that overeating may lead to adownregulation of receptors in the striatum, whichcould further drive individuals to consume highlypalatable/high-calorie foods, all of which could con-tribute to obesity. Some of the discrepant (hyperac-tive versus hypoactive brain regions) results couldbe due to methodological differences. For example,some studies assessed brain activations when sub-jects were in a state of hunger, whereas others studiesdid not. Food preference, history of eating disorders,eating patterns, and present diet are important fac-tors in such studies (see Ref. 162), and control forsuch factors is not consistent across studies. Also, itwas suggested that brain activation results could bedifferent depending on different functional states,that is, resting versus when exposed to food or foodstimuli.150 For example, a study of regional brainmetabolism at rest revealed differences between leanand obese individuals. Obese individuals had a sig-nificantly higher resting metabolic activity than leanindividuals in brain regions underlying sensationsof the lips, tongue, and mouth.164 The authors

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 301

Nonsubstance addictions Frascella et al.

concluded that this enhanced activity in brain re-gions involved with the sensory processing of foodin obese individuals could place them at risk for anincreased motivational drive for food.

In a recent study of functional connectivity withinthe reward network in response to high- and low-calorie food stimuli, Stoeckel et al.165 found abnor-mal connectivity in obese individuals compared tonormal-weight controls. Specifically, a reduced con-nectivity was found in response to food cues fromthe amygdala to the orbitofrontal cortex and nucleusaccumbens, which is thought possibly to producedeficient modulation of the affective/emotional as-pects of food reward value, thereby resulting in alack of devaluation of foods after consumption,leading to an enhanced food drive. An increasedorbitofrontal cortex to nucleus accumbens connec-tivity was found in obese individuals, also thoughtto contribute to an enhanced drive to consumefoods. In a drug study, an enhanced resting stateconnectivity between the nucleus accumbens andorbitofrontal cortex was found in substance addic-tion and was thought to contribute to a strongersalience value of drugs.166

Reward processing is an important factor in obe-sity, but other processes are also involved. Satietysignaling also plays a significant role in the controlof food intake. Brain measures have shown differ-ential signaling to meal satiation; that is, cerebralblood flow changes in response to a meal weredifferent in lean compared to obese individuals.Limbic/paralimbic areas and prefrontal cortex re-sponded differently as a function of low versus highBMI. Obese individuals responded to satiation witha greater activation in the prefrontal cortex and alarger deactivation of limbic and paralimbic areas(frontal operculum, hippocampal formation, in-sula, orbitofrontal cortex, temporal pole), striatum,precuneus, and cerebellum (e.g., Refs. 167–169).

Given the importance of the dopamine systemin substance abuse and addiction, Wang et al.11

assessed brain dopamine D2 receptors in severelyobese (BMI between 42 and 60) individuals.Findings revealed that striatal dopamine receptorswere significantly lower in these individuals, and aninverse relationship was found between D2 receptorlevels and BMI; that is, lower levels of receptors cor-related with higher BMI. The authors suggested thatthis dopamine deficiency in these obese individualsmight contribute to and perpetuate pathological

eating to compensate for the decreased dopaminesignal in these systems, consistent with the notionof “reward deficiency.” Alternatively, with thegenerality of the decrements of dopamine D2receptors, it has been posited that reductions in thedopamine system may be a marker for vulnerabilityor predisposition to excessive or addictive behav-iors.11 As mentioned previously, Stice et al.’s150,151

findings of a reduced caudate activation in obeseversus lean individuals during food consumptionare consistent with reduced dopamine receptoravailability in the dorsal striatum. Similarly,drug-addicted individuals, across a range of addic-tions to different drug classes, have shown cleardisturbances in the dopamine system, particularlyin terms of reduced striatal dopamine recep-tors in cocaine-,170–172 methamphetamine-,173,174

alcohol-,175–177 nicotine-,178 and heroin-addicted179

individuals. Decreases in dopamine trans-porters also were also found in cocaine-,170,180

methamphetamine-,173,181,182 alcohol-,183 andnicotine-addicted184 individuals.

The exact relationship between low dopamine D2receptor levels and the risk for overeating/obesityis not well characterized. Having previously estab-lished that striatal dopamine D2 receptors levels arelower in obese individuals, Volkow et al.185 con-firmed this result and explored the relationshipbetween these decrements and activity in the pre-frontal cortical brain regions that have been impli-cated in inhibitory control in a group of morbidlyobese individuals. In obese individuals, comparedto control individuals, lower dopamine D2 receptoravailability was associated with decreased metabolicactivity during food consumption in prefrontal ar-eas (i.e., dorsolateral prefrontal cortex, orbitofrontalcortex, and anterior cingulate, as well as the so-matosensory cortex). The authors hypothesized thatexcessive eating could result as the consequence oflower striatal dopamine D2 receptors’ influence onthose prefrontal mechanisms involved in inhibitorycontrol. Furthermore, the association between stri-atal dopamine D2 receptors and somatosensory cor-tical metabolism was thought to reflect enhancedfood palatability and food reward. Similar findingsand association between receptor availability andmetabolism had been observed in drug-addicted in-dividuals,170,174,186 and the loss of inhibitory controland the compulsive drug seeking in these individ-uals were suggested to be related to the changes in

302 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

striatal dopamine function and orbitofrontal cortexmetabolism.

These studies reveal that decreases in glucosemetabolism levels in prefrontal regions might po-tentially contribute to obesity because these areasare important in executive function and cogni-tive/inhibitory control. Thus, deficits in these pro-cesses along with increased drive states could leadto the inability to terminate reinforcing behaviors,such as overconsuming palatable foods or abusingaddictive drugs, even in the face of negative healthconsequences. Recent work has further probed pre-frontal metabolic activity to assess its direct rela-tionship with BMI. In healthy adults, a negativecorrelation was found between BMI and baselinebrain glucose metabolism in both prefrontal areasand in the anterior cingulate gyrus,187 and both ofthese areas notably have been suggested to be directlyinvolved in drug addiction. Memory and executivefunction were also assessed, and a similar inverse re-lationship between prefrontal metabolism and per-formance on executive function and verbal learningwas found. This finding of decreased cognitive func-tion in obesity is consistent with a growing literatureshowing that elevated BMI is associated not onlywith adverse health outcomes but also with adverseneurocognitive and neuropsychological outcomesin adults (e.g., Refs. 188–191), including a reduc-tion in mental flexibility and capacity for sustainedattention in obese individuals.192 Interestingly, thesesame findings, however, were not found in childrenand adolescents.193

These functional findings were extended in stud-ies that assessed how obesity might be associatedwith regional brain structure. In a morphometricassessment of brain volumes in obese individualsversus lean individuals, reductions in gray mat-ter density were found in several brain areas (i.e.,postcentral gyrus, frontal operculum, putamen, andmiddle frontal gyrus) that have been implicated intaste regulation, reward, and inhibitory control.194

Similarly, in a large sample of healthy individuals, asignificant negative correlation was found betweenBMI and both global and regional gray matter vol-ume, but in men only.195 This study was supportedby another investigation of brain volume in healthyadults as a function of BMI. Obese individualsshowed overall smaller whole-brain and total graymatter volumes than those of normal or overweightindividuals,196 and the authors suggested that these

morphometric differences in brain might accountfor the inverse relationship between cognitive func-tion and BMI that has been found.

These findings in obese individuals are consistentwith a fairly large literature in substance-dependentindividuals, revealing structural and functional ab-normalities in frontal cortical regions. Gray matterreductions have been documented in prefrontal cor-tical regions in polysubstance abusers197; in frontal(cingulate gyrus, orbitofrontal cortex), insular, andtemporal cortical198–201; and in cerebellar202 regionsin cocaine abusers, as well as in prefrontal, insular,and temporal cortical regions in opiate-dependentindividuals.203 These similar and multiple systemsthat are affected in both obesity and addictiondemonstrate both the extent and complexity of cir-cuits involved.

Summary: addiction and obesityThe study of the neurobiological systems underlyingobesity and addiction show some compelling par-allels. A growing body of research, particularly rela-tively recent findings using brain imaging, has doc-umented both structural and functional changes inimportant areas that underlie behavioral regulation,reward and reward processing, executive function,and decision making. Alterations in neurobiologi-cal systems can lead to dysfunctional processing andconsequent highly motivated behaviors (nonhome-ostatic eating/drug seeking) that contribute to obe-sity and addiction. The identification of, and high-lighting of, such commonalities in these processesmight yield new perspectives on obesity and addic-tion with the ultimate possibility that new, intersect-ing clinical approaches and strategies for treatment(and prevention) might be developed. Finally, suchsimilarities might highlight the need for considera-tion of obesity within the new DSM-V.

Addiction and sex, romantic love,and attachment

Lucy L. Brown, Ph.D.

OverviewSex, romantic love, and attachment: each of thesehas addictive qualities; all are part of the human re-productive strategy; all rely on brain reward systemsidentified in animal and human studies. Childresset al.204 suggested that natural reward systems might

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 303

Nonsubstance addictions Frascella et al.

be used when addicts view cues that induce crav-ing, and Kelley205 has reviewed how systems asso-ciated with drug addiction are also associated withreward and motivation. Is the physiology of natu-ral strategies for the survival of the species the basisfor addiction disorders? Is the euphoria of sex andromantic love a normal level of intense pleasure ex-perienced with drugs of abuse? Is the contentmentand safety of attachment the normal action of a sys-tem activated by drugs of abuse, and the reason forrepeated use? Available evidence strongly suggeststhat substance abuse neurophysiology may be basedon survival mechanisms and their mesolimbic re-ward systems associated with sex, romantic love, andattachment.

Medical research places addictions in the contextof disorders, not as a part of natural and produc-tive behaviors. It may be advantageous to considerbehaviors, such as substance abuse, as existing onone end of a continuum. In moderation, these be-haviors are necessary. In the extreme, they can bedangerous and counterproductive. If they are basedon survival systems, then the underlying physiolog-ical systems must be complex and redundant, existat many levels of the brain, and be especially diffi-cult to moderate. It should not be surprising that wewould never “forget” the feeling of sexual arousal,satisfaction, attraction to a specific individual to re-produce with, or the attachment to mother, child,and mate. Evolution would select for that memoryto be stable and long lasting, and for those who seekout sex. It would not be surprising that moderatinga survival system is difficult. Thus, although drugsof abuse may change molecular events to producedestructive addictions (e.g., Refs. 205–207), and al-though there are individual differences in addictionsusceptibility (e.g., Refs. 207–211), the systems maybe difficult to control in most people because theyevolved for survival.

Potenza23 provides a useful definition of ad-diction in his report discussing non–substance-related conditions. It is well described as a “lossof control over a behavior with associated adverseconsequences” (p. 142).23 The behavior is impul-sive and obsessive and includes the feeling of crav-ing. Diagnostic criteria for substance dependenceinclude life interference, tolerance, withdrawal, andrepeated attempts to quit. These descriptions can beapplied to situations in human sexual and attach-ment relationships.

The sex driveSex is necessary for the survival of any species. Thesex act is the final common pathway for repro-duction. Humans almost universally describe sexas pleasurable and it could be considered the pri-mal nondrug reward process. Some people claimthat they are addicted to it.212,213 It occupies theirthoughts and time so much that it has a nega-tive effect on the rest of their lives. It is oftenan impulsive behavior that can not be controlled,in both positive and destructive circumstances. Ev-idence from human brain imaging suggests thatsexual arousal and orgasm affect the mesolimbicreward system. Areas affected are the amyg-dala, ventral striatum (including accumbens),medial prefrontal cortex, and orbitofrontal cor-tex.214–216 These regions are all implicated indrug abuse.217–220 Also, activity in the VTAwas correlated with perceived sexual arousal inwomen,215 an area associated with cocaine high.221

In areas not directly associated with reward,sex-related neural activity was found in the ven-tromedial hypothalamic area/tuberoinfundibulum,paraventricular nucleus insular cortex, and severalneocortical areas.214–216,222 Animal studies suggestthat hypothalamic brain activity during the sexualresponse could depend on opioid receptors223,224

and norepinephrine.225,226 Finally, testosterone andestrogen affect sexual arousal, and testosterone caninduce obsessive thoughts about sex. Testosterone isa controlled substance for its abuse potential. Ani-mals will self-administer it.227 In summary, involve-ment of mesolimbic reward areas in the sex drive inhumans as well as the possible opioid involvementin the sexual response are particularly interesting inthe context of drug abuse. However, there is alsoa strong rationale for more emphasis on the rolesof sex hormones and hypothalamic control in drugabuse.

Romantic loveFisher et al. hypothesized that romantic love isa developed form of a mammalian drive to pur-sue preferred mates,228,229 thus being an essentialaspect of the human reproductive strategy and astrong influence on human behavior. Individuals inthe early stages of romantic love often exhibit ad-dicted characteristics. They are obsessed with theother person so that their lives are oriented aroundthe partners; sufferers can be impulsive and lose

304 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

control over their thoughts and behavior; they mayabandon family to be with the beloved. In extremecases, they commit homicide and/or suicide if loveappears to be withdrawn. The focus on the otherperson can be dangerous to them and others. Wefound in a brain mapping study that early-stageromantic love activates the VTA of the midbrainand the caudate nucleus, suggesting that it does, in-deed, use brain systems that mediate mammalianreward and drives, and is not so much an emo-tion as a survival motivation.230 Participants in lovealso showed deactivation in the amygdala. In addi-tion, the longer the relationship, the more activityin the ventral pallidum and insular cortex.230 Fur-thermore, we looked at young adults who had beenrecently rejected in love,231–232 arguably the groupshowing the greatest “addiction” to another person,experiencing craving, poor self-regulation, painfulaffect, isolation, disordered sense of self-worth, andmost likely to do harm to themselves. In them, wefound activation of the VTA similar to that in theearly-stage romantic love group, suggesting that thesight of the sweetheart is still rewarding, but also inthe accumbens nucleus, and in several regions whereRisinger et al.243 reported activity correlated withcraving in cocaine addicts. These areas include theaccumbens core, an area of the accumbens-ventralpallidum, and an area deep in the middle frontalgyrus.232

Also, we looked at a group of individuals whohad been in long-term marriages (average, 20 years)and claimed to feel the “high” of early-stage love.233

They, too, showed activation in their VTA when theyviewed their beloved, but also their experience in-volved the accumbens, and the ventral pallidum, ar-eas shown to be essential for pair bonding in prairievoles.234,235 In addition, the experience of long-termlove involved the bed nucleus of the stria terminalis,and the area around the paraventricular nuclei ofthe hypothalamus, suggesting that longer-term lovethat includes pair-bond attachment may involve im-portant hormone systems, such as oxytocin and va-sopressin. These two hormones are important forpair bonds in voles.234,235

In summary, feelings of romantic love use rewardand motivation systems consistently, across individ-uals and across circumstances of the love experience.Love includes obsessive behaviors and can ruin lives,just as substance abuse does. Like sex, love may in-

volve hypothalamic hormone control systems. Likesex, it is acting at midbrain, hypothalamic, and ven-tral striatum levels, and it uses subcortical areas as-sociated with reward.

AttachmentThe mother–child relationship reveals attachmentsystems and the importance of attachment behav-iors to our survival.236,237 Strathearn et al.238 usedfunctional magnetic resonance imaging to studymothers looking at images of the faces of theirinfants. They found activation associated with themothers’ own child compared to an unknown childin areas typically associated with reward and drughigh and craving: the VTA, amygdala, accumbens,insula, medial prefrontal cortex, and orbitofrontalcortex. They also found hypothalamic activation,238

but in an area different from sexual arousal214 andlong-term love.233

Flores has suggested that addiction is an attach-ment disorder.239,240 He uses Bowlby’s (1973) as-sertion that attachment is a drive in its own right,thus making it part of a mammalian survival sys-tem. Without normal attachment, emotional regu-lation is compromised and individuals are vulner-able to addictive compulsions. Monkeys raised inisolation have difficulties in a social environmentlater, but they also binge on food and water andconsume more alcohol than normal monkeys (e.g.,Ref. 241). Human individuals who lose a spouse areat greater risk of death, themselves, than the gen-eral population; in the first year one of the great-est causes of death is alcohol-related events.242 Theassociation of isolation in development, or loss ofa spouse, with alcohol use and other addictionshas implications for addiction treatment.240 For ex-ample, successful treatment approaches often usehealthy social relationships to break addictions, suchas the Alcoholics Anonymous program. To breakthe cycle of alienation and isolation that accompa-nies, and may be the cause of, addictions, grouptherapy can be especially therapeutic, and the ex-perience of secure attachment appears to producebetter self-regulation.240 The association of attach-ment with reward and survival systems, and its be-havioral relevance to addiction treatment, make itan especially interesting reward system for futurestudy.

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 305

Nonsubstance addictions Frascella et al.

Drug addiction, lust, love, and attachmentBrain mapping studies have looked at the effectsof acute drug injections and drug cues on neu-ral activity in reward systems (e.g., Refs. 204, 218,221, 243). In one study that scanned cocaine ad-dicts under the two conditions of drug cues anderotic images (sex cues), the amygdala was af-fected in both states.244 The amygdala was af-fected by sexual arousal, orgasm, romantic love, andattachment stimuli.215,216,230,238 Areas consistentlyassociated with the cocaine “high” are the VTA,amygdala, accumbens (positive or negative re-sponse), orbitofrontal cortex, and insular cor-tex.221,243 Areas associated with cocaine craving arethe accumbens, ventral pallidum, and orbitofrontalcortex.221,243 These areas associated with drug highand craving were also affected by sex, love, and at-tachment. Differences between drug cues and repro-ductive system reward systems may be in the ventralpallidum, where mothers’ activation to a picture oftheir child was more anterior and dorsal than forsex, cocaine cues, or romantic love. Also, sex cuesand drug cues were associated with different sides ofthe ventral striatum.244 Thus, the survival systemsmay differ from drug abuse substrates by using dif-ferent regions of, or sides of, reward areas, and morehypothalamic areas.

Summary. Functional brain imaging studies ofsex, romantic love, and attachment provide am-ple evidence for an extended but identifiable sys-tem central to natural, nondrug reward processesand survival functions. The natural reward andsurvival systems are distributed throughout themidbrain, hypothalamus, striatum, insular, andorbitofrontal/prefrontal cortex. Brain areas thatcontrol essential hormones for reproductive capac-ity, childbirth, and water balance as well as brainareas that are rich in dopamine and opioids ap-pear to be involved. The overlap of classic rewardbrain areas involved in sexual arousal, love, and at-tachment is complete (VTA, accumbens, amygdala,ventral pallidum, orbitofrontal cortex). Althoughbrain imaging drug abuse studies have not yet im-plicated hypothalamic and hormonal control areasin addiction, they may be involved and may de-serve more research attention. The main thesis here,however, is that the widely distributed levels of sub-stance abuse–associated systems, because they aresurvival systems, may require several simultaneous

biochemical and behavioral approaches. The side ofthe brain responsive to the different cues can differ,and there are differentially activated subregions inlarge areas, such as the accumbens and orbitofrontalcortex. However, a speculation is justified that asso-ciates survival-level natural rewards with substanceaddictions, expanding the brain systems to be ad-dressed in therapy and increasing our understand-ing of the necessary tenacity of the behaviors.

Summary

As these three authors illustrate, the increased avail-ability of powerful brain and genetic tools hasopened a new era in the diagnostic classification foraddictions. For the first time since the diagnosticmanuals were developed more than half a centuryago, a diagnosis of “addiction” will probably not re-quire substance taking—previously a sine qua nonfor the category. Boundaries for the construct willbe carved somewhere beyond substances. Exactlywhere is not yet clear, but as the authors demon-strate, characterizing shared brain vulnerabilities forthe compulsive pursuit of substance and nonsub-stance rewards may aid not only in carving diag-nostic boundaries but also in the etiologic under-standing and treatment of these difficult disorders.

One anticipated clinical benefit of the broadeneddiagnostic boundaries is hypothesis-driven testingof “crossover” medications: agents found helpful forsubstance addictions may be tried in nonsubstancedisorders, and vice versa. Examples include use ofthe opioid antagonist naltrexone, a treatment bene-ficial for opiate addiction245 (and for a genetic sub-group of white male alcoholics246), now being triedas a monotherapy for gambling18 and as a combina-tion therapy (with bupropion) for obesity.247 GABAB agonists, such as baclofen, have shown preclini-cal (cocaine, opiates, alcohol, and nicotine248–251)and clinical252–255 promise in substance addictionsbut may also have crossover promise for overcon-sumption of highly palatable (especially high-fat)foods.75,256,257 Conversely, novel agents, such as theorexin antagonists, though initially studied in foodreward paradigms, may have a much broader effect,including cocaine and heroin reward.258–260

Future carvers of addiction nosology will makeuse of the results from such crossover therapeu-tics to help refine the construct and its boundaries.

306 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

Effective, specific biological treatments often helpto recarve diagnostic boundaries. A case in point isthe historical diagnostic distinction between anxi-ety and depression. As serotonin-specific reuptakeinhibitors often show benefit for both anxiety anddepression, these disorders are increasingly viewedas overlapping “spectra” rather than clearly dichoto-mous disorders. It can be anticipated that the ad-dictions may undergo a similar recarving, if thesame biological interventions are effective againstthe compulsive pursuit of substance and nonsub-stance rewards. Though our nosology has thus farcompartmentalized these problems, we may sooncarve addiction at a new joint that will greatly ben-efit our hypotheses; our clinical research; and mostimportant, our patients.

Acknowledgments

We presented preliminary versions of this materialat a symposium, “Of Vice and Men: Shared BrainVulnerabilities for Drug and Non-Drug (Food, Sex,Gambling) Rewards,” organized and cochaired byDrs. Childress and Potenza at the 70th annual meet-ing of the College on Problems of Drug Dependencein San Juan, Puerto Rico (June 14–19, 2008). Wethank the reviewers for comments that substantiallyimproved the manuscript, and we thank Dr. GeorgeUhl for his guidance and support throughout.

Conflicts of interest

The authors declare no conflicts of interest.

References

1. Jowett, B. 1937. The Dialogues of Plato: Phaedrus. Ran-

dom House. New York.

2. Saunders, J.B. & M.A. Schuckit. 2006. The development

of a research agenda for substance use disorders diag-

nosis in the Diagnostic and Statistical Manual of Mental

Disorders, fifth edition (DSM-V). Addiction 101(Suppl

1): 1–5.

3. American Psychiatric Association Committee on

Nomenclature and Statistics. 1952. Diagnostic and Sta-

tistical Manual of Mental Disorders, 1st ed. American

Psychiatric Association Mental Hospital Service. Wash-

ington, DC.

4. American Psychiatric Association Committee on

Nomenclature and Statistics. 1968. Diagnostic and Sta-

tistical Manual of Mental Disorders, 2nd ed. American

Psychiatric Association. Washington, DC.

5. American Psychiatric Association Committee on

Nomenclature and Statistics. 1980. Diagnostic and Sta-

tistical Manual of Mental Disorders, 3rd ed. American

Psychiatric Association. Washington, DC.

6. Weintraub, D. et al. 2006. Association of dopamine

agonist use with impulse control disorders in Parkinson

disease. Arch. Neurol. 63: 969–973.

7. Weintraub, D. et al. 2009. Validation of the question-

naire for impulsive-compulsive disorders in Parkinson’s

disease. Mov. Disord. 24: 1461–1467.

8. Potenza, M.N. 2006. Should addictive disorders in-

clude non-substance-related conditions? Addiction

101(Suppl 1): 142–151.

9. Volkow, N.D. & R.A. Wise. 2005. How can drug ad-

diction help us understand obesity? Nat. Neurosci. 8:

555–560.

10. Volkow, N.D. & C.P. O’Brien. 2007. Issues for DSM-V:

should obesity be included as a brain disorder? Am. J.

Psychiatry 164: 708–710.

11. Wang, G.J. et al. 2004. Similarity between obesity and

drug addiction as assessed by neurofunctional imaging:

a concept review. J. Addict. Dis. 23: 39–53.

12. Gosnell, B.A. & A.S. Levine. 2009. Reward systems

and food intake: role of opioids. Int. J. Obes. (Lond.)

33(Suppl 2): S54–58.

13. Kelley, A.E. et al. 2002. Opioid modulation of taste

hedonics within the ventral striatum. Physiol. Behav.

76: 365–377.

14. Drewnowski, A. et al. 1992. Taste responses and

preferences for sweet high-fat foods: evidence

for opioid involvement. Physiol. Behav. 51: 371–

379.

15. Potenza, M. 2001. Pathological gambling: a

present problem from the past. Psychiatr. Times

www.psychiatrictimes.com/srpg.html (Sept. 2001

online exclusive) (accessed November 1, 2001).

16. American Psychiatric Association Committee on

Nomenclature and Statistics. 2000. Diagnostic and Sta-

tistical Manual of Mental Disorders (4th ed., text revi-

sion). American Psychiatric Association. Washington,

DC.

17. Tavares, H. et al. 2005. Comparison of craving be-

tween pathological gamblers and alcoholics. Alcohol.

Clin. Exp. Res. 29: 1427–1431.

18. Grant, J.E. et al. 2008. Predicting response to opiate an-

tagonists and placebo in the treatment of pathological

gambling. Psychopharmacology 200: 521–527.

19. Brewer, J.A., J.E. Grant & M.N. Potenza. 2008. The

treatment of pathological gambling. Addict. Disorders

Treatment 7: 1–14.

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 307

Nonsubstance addictions Frascella et al.

20. Cunningham-Williams, R.M. et al. 1998. Tak-

ing chances: problem gamblers and mental health

disorders—results from the St. Louis Epidemiologic

Catchment Area Study. Am. J. Public Health 88: 1093–

1096.

21. Petry, N.M., F.S. Stinson & B.F. Grant. 2005. Co-

morbidity of DSM-IV pathological gambling and other

psychiatric disorders: results from the National Epi-

demiologic Survey on Alcohol and Related Conditions.

J. Clin. Psychiatry 66: 564–574.

22. Wagner, F. & J.C. Anthony. 2002. From first drug use to

drug dependence: developmental periods of risk for de-

pendence upon marijuana, cocaine, and alcohol. Neu-

ropsychopharmacology 26: 479–488.

23. Potenza, M.N. 2006. Should addictive disorders include

non-substance-related conditions? Addiction 101(s1):

142–151.

24. Lynch, W., P.K. Maciejewski & M.N. Potenza. 2004.

Psychiatric correlates of gambling in adolescents and

young adults grouped by age of gambling onset. Arch.

Gen. Psychiatry 61: 1116–1122.

25. Kessler, R.C. et al. 2008. DSM-IV pathological gam-

bling in the National Comorbidity Survey Replication.

Psychol. Med. 38: 1351–1360.

26. Potenza, M.N. et al. 2001. Gender-related differ-

ences in the charactaristics of problem gamblers us-

ing a gambling helpline. Am. J. Psychiatry 158: 1500–

1505.

27. Tavares, H., M.L. Zilberman, F.J. Beites & V. Gentil.

2001. Gender differences in gambling progression. J.

Gambling Stud. 17: 151–160.

28. Piazza, N., J.L. Vrbka & R.D. Yaeger. 1989. Telescoping

of alcoholism in women alcoholics. Int. J. Addict. 24:

19–28.

29. Tsuang, M., M.J. Lyons, J.M. Meyer, et al. 1998. Co-

occurrence of abuse of different drugs in men. Arch.

Gen. Psychiatry 55: 967–972.

30. Eisen, S.A. et al. 1998. Familial influences on gambling

behavior: an analysis of 3359 twin pairs. Addiction 93:

1375–1384.

31. Kendler, K. et al. 2003. The structure of genetic and

environmental risk factors for common psychiatric and

substance use disorders in men and women. Arch. Gen.

Psychiatry 60: 929–937.

32. Slutske, W.S. et al. 2000. Common genetic vulnerability

for pathological gambling and alcohol dependence in

men. Arch. Gen. Psychiatry 57: 666–674.

33. Slutske, W.S. et al. 2001. A twin study of the association

between pathological gambling and antisocial person-

ality disorder. J. Abnorm. Psychology 110: 297–308.

34. Kreek, M.J. et al. 2005. Genetic influences on impulsiv-

ity, risk-taking, stress responsivity and vulnerability to

drug abuse and addiction. Nat. Neurosci. 8: 1450–1457.

35. Brewer, J.A. & M.N. Potenza. 2008. The neurobiology

and genetics of impulse control disorders: relationships

to drug addictions. Biochem. Pharmacol. 75: 63–75.

36. Comings, D.E. 1998. The molecular genetics of patho-

logical gambling. CNS Spectr. 3: 20–37.

37. da Silva Lobo, D.S. et al. 2007. Dopamine genes and

pathological gambling in discordant sib-pairs. J. Gam-

bling Stud. 23: 421–433.

38. Blaszczynski, A., Z. Steel & N. McConaghy. 1997. Im-

pulsivity in pathological gambling: the antisocial im-

pulsivist. Addiction 92: 75–87.

39. Petry, N.M. 2001. Substance abuse, pathological gam-

bling, and impulsiveness. Drug Alcohol Depend. 63: 29–

38.

40. Petry, N. 2001. Pathological gamblers, with and without

substance use disorders, discount delayed rewards at

high rates. J. Abnorm. Psychol. 110: 482–487.

41. Potenza, M.N. et al. 2003. An fMRI Stroop task study

of ventromedial prefrontal cortical function in patho-

logical gamblers. Am. J. Psychiatry 160: 1990–1994.

42. Cavedini, P. et al. 2002. Frontal lobe dysfunction in

pathological gambling. Biol. Psychiatry 51: 334–341.

43. Bechara, A. 2003. Risky business: emotion, decision-

making, and addiction. J. Gambling Stud. 19: 23–51.

44. Goudriaan, A.E. et al. 2006. Neurocognitive functions

in pathological gambling: a comparison with alcohol

dependence, Tourette syndrome and normal controls.

Addiction 101: 534–547.

45. Lawrence, A.J. et al. 2009. Problem gamblers share

deficits in impulsive decision-making with alcohol de-

pendent individuals. Addiction 104: 1006–1015.

46. Crews, F.T. et al. 2005. Alcoholic neurobiology: changes

in dependence and recovery. Alcohol Clin. Exp. Res. 29:

1504–1513.

47. Beveridge, T.J.R. et al. 2008. Parallel studies of cocaine-

related neural and cognitive impairment in humans and

monkeys. Phil. Trans. Royal Soc. B 363: 3257–3266.

48. Nasrallah, N.A., T.W.H. Yang & I.L. Bernstein. 2009.

Risky decision making following adolescent alcohol expo-

sure. In Winter Conference on Brain Research. Copper

Mountain, CO.

49. Reuter, J. et al. 2005. Pathological gambling is linked

to reduced activation of the mesolimbic reward system.

Nat. Neurosci. 8: 147–148.

50. Wrase, J. et al. 2007. Dysfunction of reward processing

correlates with alcohol craving in detoxified alcoholics.

Neuroimage 35: 787–794.

308 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

51. Pearlson, G.D. et al. 2007. Abnormal fMRI Activation

of Reward Circuitry in Current versus Former Cocaine

Abusers. Abstract available from the author, Godfrey

Pearlson: godfrey.pearlson@yale.edu.

52. Knutson, B., G.W. Fong, C.M. Adams, et al. 2001. Disso-

ciation of reward anticipation and outcome with event-

related fMRI. Neuroreport 12: 3683–3687.

53. Knutson, B., G.W. Fong, S.M. Bennett, et al. 2003. A

region of mesial prefrontal cortex tracks monetarily

rewarding outcomes: characterization with rapid event-

related fMRI. Neuroimage 18: 263–272.

54. Potenza, M.N. et al. 2003. Gambling urges in patholog-

ical gamblers: an fMRI study. Arch. Gen. Psychiatry 60:

828–836.

55. Tanabe, J. et al. 2007. Prefrontal cortex activity is re-

duced in gambling and nongambling substance users

during decision-making. Hum. Brain Mapp. 28: 1276–

1286.

56. Rogers, R. 2009. The neural substrates of some of the

behavioral and cognitive biases observed in problem-

gambling . In Winter Conference on Brain Research.

Copper Mountain, CO.

57. Campbell-Meiklejohn, D.K. et al. 2008. Knowing when

to stop: the brain mechanisms of chasing losses. Biol.

Psychiatry 63: 293–300.

58. Petry, N. 2003. Patterns and correlates of gamblers

anonymous attendance in pathological gamblers seek-

ing professional treatment. Addict. Behav. 28: 1049–

1062.

59. Petry, N.M. 2005. Gamblers anonymous and cognitive-

behavioral therapies for pathological gamblers. J. Gam-

bling Stud. 21: 27–33.

60. Petry, N.M. et al. 2006. Cognitive-behavioral therapy

for pathological gamblers. J. Consult. Clin. Psychol. 74:

555–567.

61. Petry, N.M. et al. 2008. A randomized trial of brief

interventions for problem and pathological gamblers.

J. Consult. Clin. Psychol. 76: 318–328.

62. Miller, W. 1995. Motivational enhancement therapy

with drug abusers [cited 2005 January 15]. http://

motivationalinterview.org/clinical/metdrugabuse.

pdf (accessed November 2009).

63. Hodgins, D., S.R. Currie & N. el-Guebaly. 2001. Mo-

tivational enhancement and self-help treatments for

problem gambling. J. Clin. Consult. Psychol. 69: 50–

57.

64. Pettinati, H., D. Oslin & K. Decker. 2000. Role of sero-

tonin and serotonin-selective pharmacotherapy in al-

cohol dependence. CNS Spectr. 5: 33–46.

65. Grant, J., S.W. Kim, M.N. Potenza, et al. 2003. Paroxe-

tine treatment of pathological gambling: a multi-center

randomized controlled trial. Int. Clin. Psychopharma-

col. 18: 243–249.

66. Kalivas, P.W. & N.D. Volkow. 2005. The neural basis of

addiction: a pathology of motivation and choice. Am.

J. Psychiatry 162: 1403–1413.

67. Hedley, A.A. et al. 2004. Prevalence of overweight and

obesity among US children, adolescents, and adults,

1999–2002. JAMA 291: 2847–2850.

68. Ogden, C.L., M.D. Carroll & K.M. Flegal. 2008. High

body mass index for age among US children and ado-

lescents, 2003–2006. JAMA 299: 2401–2405.

69. Haslam, D.W. & W.P.T. James. 2005. Obesity. Lancet

366: 1197–1209.

70. Yach, D., D. Stuckler & K.D. Brownell. 2006. Epidemi-

ologic and economic consequences of the global epi-

demics of obesity and diabetes. Nat. Med. 12: 62–66.

71. Gearhardt, A.N., W.R. Corbin & K.D. Brownell. 2009.

Food addiction: an examination of the diagnostic cri-

teria for dependence. J. Addict. Med. 3: 1–6.

72. Cocores, J.A. & M.S. Gold. 2009. The Salted Food Ad-

diction Hypothesis may explain overeating and the obe-

sity epidemic. Med. Hypotheses 73: 892–899.

73. Ifland, J.R. et al. 2009. Refined food addiction: a classic

substance use disorder. Medical Hypotheses 72: 518–

526.

74. Devlin, M.J. 2007. Is there a place for obesity in DSM-V?

Int. J. Eating Disord. 40(S3): S83–S88.

75. Corwin, R.L. & P.S. Grigson. 2009. Symposium

overview–food addiction: fact or fiction? J. Nutr. 139:

617–619.

76. Dagher, A. 2009. The neurobiology of appetite: hunger

as addiction. Int. J. Obes. 33(S2): S30–S33.

77. Gold, M.S. et al. 2009. Food addiction? J. Addict. Med.

3: 42–44.

78. Rolls, E.T. 2007. Understanding the mechanisms of

food intake and obesity. Obes. Rev. 8(s1): 67–72.

79. Kelley, A.E. & K.C. Berridge. 2002. The neuroscience of

natural rewards: relevance to addictive drugs. J. Neu-

rosci. 22: 3306–3311.

80. O’Doherty, J.P. 2004. Reward representations and

reward-related learning in the human brain: insights

from neuroimaging. Curr. Opin. Neurobiol. 14: 769–

776.

81. Saper, C.B., T.C. Chou & J.K. Elmquist. 2002. The need

to feed: homeostatic and hedonic control of eating.

Neuron 36: 199–211.

82. Valentin, V.V., A. Dickinson & J.P. O’Doherty. 2007. De-

termining the neural substrates of goal-directed learn-

ing in the human brain. J. Neurosci. 27: 4019–4026.

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 309

Nonsubstance addictions Frascella et al.

83. O’Doherty, J. 2007. Lights, camembert, action! The role

of human orbitofrontal cortex in encoding stimuli, re-

wards, and choices. Ann. N. Y. Acad. Sci. 1121: 254–272.

84. Roberts, D.C., M.E. Corcoran & H.C. Fibiger. 1977.

On the role of ascending catecholaminergic systems in

intravenous self-administration of cocaine. Pharmacol.

Biochem. Behav. 6: 615–620.

85. Di Chiara, G. & A. Imperato. 1988. Drugs abused by

humans preferentially increase synaptic dopamine con-

centrations in the mesolimbic system of freely moving

rats. Proc. Natl. Acad. Sci. USA 85: 5274–5278.

86. Bradberry, C.W. 2000. Acute and chronic dopamine

dynamics in a nonhuman primate model of recreational

cocaine use. J. Neurosci. 20: 7109–7115.

87. Carboni, E. et al. 1989. Amphetamine, cocaine,

phencyclidine and nomifensine increase extracellular

dopamine concentrations preferentially in the nucleus

accumbens of freely moving rats. Neuroscience 28: 653–

661.

88. Di Chiara, G. et al. 2004. Dopamine and drug addiction:

the nucleus accumbens shell connection. Neurophar-

macology 47(Suppl 1): 227–241.

89. McCullough, L.D. & J.D. Salamone. 1992. Involvement

of nucleus accumbens dopamine in the motor activity

induced by periodic food presentation: a microdialysis

and behavioral study. Brain Res. 592: 29–36.

90. Pontieri, F.E., G. Tanda & G. Di Chiara. 1995. Intra-

venous cocaine, morphine, and amphetamine prefer-

entially increase extracellular dopamine in the “shell”

as compared with the “core” of the rat nucleus accum-

bens. Proc. Natl. Acad. Sci. USA 92: 12304–12308.

91. Ritz, M.C. et al. 1987. Cocaine receptors on dopamine

transporters are related to self-administration of co-

caine. Science 237: 1219–1223.

92. Ritz, M.C. et al. 1988. Cocaine self-administration ap-

pears to be mediated by dopamine uptake inhibition.

Prog. Neuropsychopharmacol. Biol. Psychiatry 12: 233–

239.

93. Spanagel, R. & F. Weiss. 1999. The dopamine hypothesis

of reward: past and current status. Trends Neurosci. 22:

521–527.

94. Wise, R.A. 2004. Dopamine, learning and motivation.

Nat. Rev. Neurosci. 5: 483–494.

95. Wise, R.A. & M.A. Bozarth. 1987. A psychomotor stim-

ulant theory of addiction. Psychol. Rev. 94: 469–492.

96. Hernandez, L. & B.G. Hoebel. 1988. Feeding and hy-

pothalamic stimulation increase dopamine turnover in

the accumbens. Physiol. Behav. 44: 599–606.

97. Hernandez, L. & B.G. Hoebel. 1988. Food reward and

cocaine increase extracellular dopamine in the nucleus

accumbens as measured by microdialysis. Life Sci. 42:

1705–1712.

98. Hoebel, B.G. et al. 1989. Microdialysis studies of

brain norepinephrine, serotonin, and dopamine re-

lease during ingestive behavior theoretical and clin-

ical implications. Ann. N. Y. Acad. Sci. 575: 171–

193.

99. McCullough, L.D. & J.D. Salamone. 1992. Increases in

extracellular dopamine levels and locomotor activity

after direct infusion of phencyclidine into the nucleus

accumbens. Brain Res. 577: 1–9.

100. Radhakishun, F.S., J.M. van Ree & B.H. Westerlink.

1988. Scheduled eating increases dopamine release in

the nucleus accumbens of food-deprived rats as assessed

with on-line brain dialysis. Neurosci. Lett. 85: 351–

356.

101. Yoshida, M. et al. 1992. Eating and drinking cause in-

creased dopamine release in the nucleus accumbens

and ventral tegmental area in the rat: measurement by

in vivo microdialysis. Neurosci. Lett. 139: 73–76.

102. Westerlink, B.H., A. Teisman & J.B. de Vries. 1994. In-

crease in dopamine release from the nucleus accumbens

in response to feeding: a model to study interactions

between drugs and naturally activated dopaminergic

neurons in the rat brain. Naunyn Schmiedebergs Arch.

Pharmacol. 349: 230–235.

103. Martel, P. & M. Fantino. 1996. Influence of the amount

of food ingested on mesolimbic dopaminergic system

activity: a microdialysis study. Pharmacol. Biochem. Be-

hav. 55: 297–302.

104. Martel, P. & M. Fantino. 1996. Mesolimbic dopamin-

ergic system activity as a function of food reward: a

microdialysis study. Pharmacol. Biochem. Behav. 53:

221–226.

105. Kringelbach, M.L. et al. 2003. Activation of the human

orbitofrontal cortex to a liquid food stimulus is corre-

lated with its subjective pleasantness. Cereb. Cortex 13:

1064–1071.

106. Berridge, K.C. 1996. Food reward: brain substrates of

wanting and liking. Neurosci. Biobehav. Rev. 20: 1–

25.

107. Berthoud, H.-R. 2002. Multiple neural systems control-

ling food intake and body weight. Neurosci. Biobehav.

Rev. 26: 393–428.

108. Norgren, R., A. Hajnal & S.S. Mungarndee. 2006. Gusta-

tory reward and the nucleus accumbens. Physiol. Behav.

89: 531–535.

109. Kringelbach, M.L. 2005. The human orbitofrontal cor-

tex: linking reward to hedonic experience. Nat. Rev.

Neurosci. 6: 691–702.

310 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

110. Small, D.M., M. Jones-Gotman & A. Dagher. 2003.

Feeding-induced dopamine release in dorsal striatum

correlates with meal pleasantness ratings in healthy hu-

man volunteers. Neuroimage 19: 1709–1715.

111. Small, D.M. et al. 2001. Changes in brain activity related

to eating chocolate: from pleasure to aversion. Brain

124: 1720–1733.

112. Berridge, K.C. & M.L. Kringelbach. 2008. Affective neu-

roscience of pleasure: reward in humans and animals.

Psychopharmacology 199: 457–480.

113. Rolls, E.T. 2007. Sensory processing in the brain related

to the control of food intake. Proc. Nutr. Soc. 66: 96–112.

114. De Vries, T.J. & A.N.M. Schoffelmeer. 2005. Cannabi-

noid CB1 receptors control conditioned drug seeking.

Trends Pharmacol. Sci. 26: 420–426.

115. Fattore, L., P. Fadda & W. Fratta. 2007. Endocannabi-

noid regulation of relapse mechanisms. Pharmacol. Res.

56: 418–427.

116. Maldonado, R., O. Valverde & F. Berrendero. 2006. In-

volvement of the endocannabinoid system in drug ad-

diction. Trends Neurosci. 29: 225–232.

117. Onaivi, E.S. 2008. An endocannabinoid hypothesis of

drug reward and drug addiction. Ann. N. Y. Acad. Sci.

1139: 412–421.

118. Parolaro, D., D. Vigano & T. Rubino. 2005. Endo-

cannabinoids and drug dependence. Curr. Drug Targets

CNS Neurol. Disord. 4: 643–655.

119. Solinas, M., S.R. Goldberg & D. Piomelli. 2008. The

endocannabinoid system in brain reward processes. Br.

J. Pharmacol. 154: 369–383.

120. Solinas, M., S. Yasar & S.R. Goldberg. 2007. Endo-

cannabinoid system involvement in brain reward pro-

cesses related to drug abuse. Pharmacol. Res. 56: 393–

405.

121. Yamamoto, T., K. Anggadiredja & T. Hiranita. 2004.

New perspectives in the studies on endocannabi-

noid and cannabis: a role for the endocannabinoid-

arachidonic acid pathway in drug reward and long-

lasting relapse to drug taking. J. Pharmacol. Sci. 96:

382–388.

122. Van Ree, J.M., M.A.F.M. Gerrits & L.J.M.J. Vander-

schuren. 1999. Opioids, reward and addiction: an en-

counter of biology, psychology, and medicine. Pharma-

col. Rev. 51: 341–396.

123. Van Ree, J.M. et al. 2000. Endogenous opioids and re-

ward. Eur. J. Pharmacol. 405: 89–101.

124. Shinohara, Y. et al. 2009. Cannabinoid in the

nucleus accumbens enhances the intake of palat-

able solution. Neuroreport 20: 1382–1385. DOI:

10.1097/WNR.0b013e3283318010.

125. Cota, D. et al. 2003. Endogenous cannabinoid system

as a modulator of food intake. Int. J. Obes. Relat. Metab.

Disord. 27: 289–301.

126. Tanda, G. & S.R. Goldberg. 2003. Cannabinoids:

reward, dependence, and underlying neurochemical

mechanisms—a review of recent preclinical data. Psy-

chopharmacology 169: 115–134.

127. Levine, A.S. & C.J. Billington. 2004. Opioids as agents of

reward-related feeding: a consideration of the evidence.

Physiol. Behav. 82: 57–61.

128. Cota, D. et al. 2006. Cannabinoids, opioids and eating

behavior: the molecular face of hedonism? Brain Res.

Rev. 51: 85–107.

129. Jesudason, D. & G. Wittert. 2008. Endocannabinoid

system in food intake and metabolic regulation. Curr.

Opin. Lipidol. 19: 344–348.

130. Wassum, K.M. et al. 2009. Distinct opioid circuits deter-

mine the palatability and the desirability of rewarding

events. Proc. Natl. Acad. Sci. USA 106: 12512–12517.

131. Volkow, N.D. et al. 1999. Reinforcing effects of psy-

chostimulants in humans are associated with increases

in brain dopamine and occupancy of D2 receptors. J.

Pharmacol. Exp. Ther. 291: 409–415.

132. Volkow, N.D. et al. 1999. Prediction of reinforcing

responses to psychostimulants in humans by brain

dopamine D2 receptor levels. Am. J. Psychiatry 156:

1440–1443.

133. Volkow, N.D. et al. 2002. “Nonhedonic” food motiva-

tion in humans involves dopamine in the dorsal stria-

tum and methylphenidate amplifies this effect. Synapse

44: 175–180.

134. Salamone, J.D., K. Mahan & S. Rogers. 1993. Ventro-

lateral striatal dopamine depletions impair feeding and

food handling in rats. Pharmacol. Biochem. Behav. 44:

605–610.

135. O’Doherty, J. et al. 2000. Sensory-specific satiety-

related olfactory activation of the human orbitofrontal

cortex. Neuroreport 11: 893–897.

136. Rolls, E.T. 2006. Brain mechanisms underlying flavor

and appetite. Philos. Trans. R. Soc. Lond. B Biol. Sci. 361:

1123–1136.

137. Pelchat, M.L. et al. 2004. Images of desire: food-craving

activation during fMRI. Neuroimage 23: 1486–1493.

138. Rolls, E.T. & C. McCabe. 2007. Enhanced affective brain

representations of chocolate in cravers vs. non-cravers.

Eur. J. Neurosci. 26: 1067–1076.

139. McClernon, F.J. et al. 2009. 24-h smoking abstinence

potentiates fMRI-BOLD activation to smoking cues in

cerebral cortex and dorsal striatum. Psychopharmaco-

logy 204: 25–35.

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 311

Nonsubstance addictions Frascella et al.

140. Childress, A.R. et al. 1999. Limbic activation during

cue-induced cocaine craving. Am. J. Psychiatry 156: 11–

18.

141. Kilts, C.D. et al. 2001. Neural activity related to drug

craving in cocaine addiction. Arch. Gen. Psychiatry 58:

334–341.

142. Franklin, T.R. et al. 2007. Limbic activation to cigarette

smoking cues independent of nicotine withdrawal: a

perfusion fMRI study. Neuropsychopharmacology 32:

2301–2309.

143. Filbey, F.M. et al. 2009. Marijuana craving in the brain.

Proc. Natl. Acad. Sci. USA 106: 13016–13021.

144. Garavan, H. et al. 2000. Cue-induced cocaine craving:

neuroanatomical specificity for drug users and drug

stimuli. Am. J. Psychiatry 157: 1789–1798.

145. Maas, L.C. et al. 1998. Functional magnetic resonance

imaging of human brain activation during cue-induced

cocaine craving. Am. J. Psychiatry 155: 124–126.

146. McBride, D. et al. 2006. Effects of expectancy and ab-

stinence on the neural response to smoking cues in

cigarette smokers: an fMRI study. Neuropsychopharma-

cology 31: 2728–2738.

147. Wang, Z.F. et al. 2007. Neural substrates of abstinence-

induced cigarette cravings in chronic smokers. J. Neu-

rosci. 27: 14035–14040.

148. Bonson, K.R. et al. 2002. Neural systems and cue-

induced cocaine craving. Neuropsychopharmacology 26:

376–386.

149. Grant, S. et al. 1996. Activation of memory circuits

during cue-elicited cocaine craving. Proc. Natl. Acad.

Sci. USA 93: 12040–12045.

150. Stice, E. et al. 2008. Relation of reward from food intake

and anticipated food intake to obesity: a functional

magnetic resonance imaging study. J. Abnorm. Psychol.

117: 924–935.

151. Stice, E. et al. 2008. Relation between obesity and

blunted striatal response to food is moderated by TaqIA

A1 allele. Science 322: 449–452.

152. Epstein, L.H. et al. 2007. Food reinforcement, the

dopamine D2 receptor genotype, and energy intake

in obese and nonobese humans. Behav. Neurosci. 121:

877–886.

153. Klein, T.A. et al. 2007. Genetically determined differ-

ences in learning from errors. Science 318: 1642–1645.

154. Blum, K. et al. 2000. Reward deficiency syndrome: a

biogenetic model for the diagnosis and treatment of

impulsive, addictive, and compulsive behaviors. J. Psy-

choactive Drugs 32(Suppl i–iv): 1–112.

155. Young, R.M. et al. 2004. Advances in molecular genetics

and the prevention and treatment of substance misuse:

implications of association studies of the A1 allele of the

D2 dopamine receptor gene. Addict. Behav. 29: 1275–

1294.

156. Najafabadi, M.S. et al. 2005. Association between the

DRD2 A1 allele and opium addiction in the Iranian

population. Am. J. Med. Genet. B Neuropsychiatr. Genet.

134B: 39–41.

157. Han, D.H. et al. A preliminary study: novelty seeking,

frontal executive function, and dopamine receptor (D2)

TaqI A gene polymorphism in patients with metham-

phetamine dependence. Compr. Psychiatry 49: 387–

392.

158. Ito, R. et al. 2002. Dopamine release in the dorsal stria-

tum during cocaine-seeking behavior under the control

of a drug-associated cue. J. Neurosci. 22: 6247–6253.

159. Everitt, B.J. & T.W. Robbins. 2005. Neural systems of re-

inforcement for drug addiction: from actions to habits

to compulsion. Nat. Neurosci. 8: 1481–1489.

160. Koob, G.F. & N.D. Volkow. 2010. Neurocircuitry of

addiction. Neuropsychopharmacology 35: 217–238.

161. Rothemund, Y. et al. 2007. Differential activation of the

dorsal striatum by high-calorie visual food stimuli in

obese individuals. Neuroimage 37: 410–421.

162. Stoeckel, L.E. et al. 2008. Widespread reward-system

activation in obese women in response to pictures of

high-calorie foods. Neuroimage 41: 636–647.

163. Stice, E. et al. 2009. Relation of obesity to consumma-

tory and anticipatory food reward. Physiol. Behav. 97:

551–560.

164. Wang, G.-J. et al. 2002. Enhanced resting activity of the

oral somatosensory cortex in obese subjects. Neurore-

port 13: 1151–1155.

165. Stoeckel, L.E. et al. 2009. Effective connectivity of a

reward network in obese women. Brain Res. Bull. 79:

388–395.

166. Ma, N. et al. 2010. Addiction related alteration in

resting-state brain connectivity. Neuroimage 49: 738–

744.

167. Del Parigi, A. et al. 2002. Neuroimaging and obesity:

mapping the brain responses to hunger and satiation in

humans using positron emission tomography. Ann. N.

Y. Acad. Sci. 967: 389–397.

168. Gautier, J.F. et al. 2000. Differential brain responses to

satiation in obese and lean men. Diabetes 49: 838–846.

169. Gautier, J.-F. et al. 2001. Effect of satiation on brain

activity in obese and lean women. Obesity 9: 676–684.

170. Volkow, N.D. et al. 1993. Decreased dopamine D2 re-

ceptor availability is associated with reduced frontal

metabolism in cocaine abusers. Synapse 14: 169–

177.

312 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

171. Volkow, N.D. et al. 1990. Effects of chronic cocaine

abuse on postsynaptic dopamine receptors. Am. J. Psy-

chiatry 147: 719–724.

172. Volkow, N.D. et al. 1997. Decreased striatal dopamin-

ergic responsiveness in detoxified cocaine-dependent

subjects. Nature 386: 830–836.

173. Chang, L. et al. 2007. Structural and metabolic brain

changes in the striatum associated with metham-

phetamine abuse. Addiction 102(Suppl 1): 16–32.

174. Volkow, N.D. et al. 2001. Low level of brain dopamine

D2 receptors in methamphetamine abusers: associa-

tion with metabolism in the orbitofrontal cortex. Am.

J. Psychiatry 158: 2015–2021.

175. Hietala, J. et al. 1994. Striatal D2 dopamine receptor

binding characteristics in vivo in patients with alcohol

dependence. Psychopharmacology 116: 285–290.

176. Volkow, N.D. et al. 1996. Decreases in dopamine re-

ceptors but not in dopamine transporters in alcoholics.

Alcohol Clin. Exp. Res. 20: 1594–1598.

177. Volkow, N.D. et al. 2002. Effects of alcohol detoxifica-

tion on dopamine D2 receptors in alcoholics: a prelim-

inary study. Psychiatry Res. 116: 163–172.

178. Fehr, C. et al. 2008. Association of low striatal dopamine

d2 receptor availability with nicotine dependence sim-

ilar to that seen with other drugs of abuse. Am. J. Psy-

chiatry 165: 507–514.

179. Wang, G.-J. et al. 1997. Dopamine D2 receptor avail-

ability in opiate-dependent subjects before and after

naloxone-precipitated withdrawal. Neuropsychophar-

macology 16: 174–182.

180. Malison, R.T. et al. 1998. Elevated striatal dopamine

transporters during acute cocaine abstinence as mea-

sured by [123I]ß-CIT SPECT. Am. J. Psychiatry 155:

832–834.

181. McCann, U.D. et al. 1998. Reduced striatal dopamine

transporter density in abstinent methamphetamine and

methcathinone users: evidence from positron emission

tomography studies with [11C]WIN-35,428. J. Neu-

rosci. 18: 8417–8422.

182. Sekine, Y. et al. 2001. Methamphetamine-related psy-

chiatric symptoms and reduced brain dopamine trans-

porters studied with PET. Am. J. Psychiatry 158: 1206–

1214.

183. Laine, T.P. et al. 1999. Dopamine transporters increase

in human brain after alcohol withdrawal. Mol. Psychi-

atry 4: 189–191.

184. Yang, Y.K. et al. 2008. Decreased dopamine transporter

availability in male smokers—A dual isotope SPECT

study. Prog. Neuropsychopharmacol. Biol. Psychiatry 32:

274–279.

185. Volkow, N.D. et al. 2008. Low dopamine striatal D2

receptors are associated with prefrontal metabolism in

obese subjects: possible contributing factors. Neuroim-

age 42: 1537–1543.

186. Volkow, N.D. et al. 2007. Profound decreases in

dopamine release in striatum in detoxified alcoholics:

possible orbitofrontal involvement. J. Neurosci. 27:

12700–12706.

187. Volkow, N.D. et al. 2008. Inverse association between

BMI and prefrontal metabolic activity in healthy adults.

Obesity 17: 60–65.

188. Elias, M.F. et al. 2003. Lower cognitive function in the

presence of obesity and hypertension: the Framingham

Heart Study. Int. J. Obes. Relat. Metab. Disord. 27: 260–

268.

189. Elias, M.F. et al. 2005. Obesity, diabetes and cognitive

deficit: the Framingham Heart Study. Neurobiol. Aging

26(1, Suppl 1): 11–16.

190. Gunstad, J. et al. 2006. Obesity is associated with

memory deficits in young and middle-aged adults. Eat

Weight Disord. 11: e15–19.

191. Gunstad, J. et al. 2007. Elevated body mass index is asso-

ciated with executive dysfunction in otherwise healthy

adults. Compr. Psychiatry 48: 57–61.

192. Cserjesi, R. et al. 2009. Altered executive function in

obesity. Exploration of the role of affective states on

cognitive abilities. Appetite 52: 535–539.

193. Gunstad, J. et al. 2008. Body mass index and neuropsy-

chological function in healthy children and adolescents.

Appetite 50: 246–251.

194. Pannacciulli, N. et al. 2006. Brain abnormalities in hu-

man obesity: a voxel-based morphometric study. Neu-

roimage 31: 1419–1425.

195. Taki, Y. et al. 2008. Relationship between body mass

index and gray matter volume in 1,428 healthy individ-

uals. Obesity 16: 119–124.

196. Gunstad, J. et al. 2008. Relationship between body mass

index and brain volume in healthy adults. Int. J. Neu-

rosci. 118: 1582–1593.

197. Liu, X. et al. 1998. Smaller volume of prefrontal

lobe in polysubstance abusers: a magnetic resonance

imaging study. Neuropsychopharmacology 18: 243–

252.

198. Franklin, T.R. et al. 2002. Decreased gray matter con-

centration in the insular, orbitofrontal, cingulate, and

temporal cortices of cocaine patients. Biol. Psychiatry

51: 134–142.

199. Fein, G., V. Di Sclafani & D.J. Meyerhoff. 2002.

Prefrontal cortical volume reduction associated with

frontal cortex function deficit in 6-week abstinent

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 313

Nonsubstance addictions Frascella et al.

crack-cocaine dependent men. Drug Alcohol Depen-

dence 68: 87–93.

200. Matochik, J.A. et al. 2003. Frontal cortical tissue com-

position in abstinent cocaine abusers: a magnetic reso-

nance imaging study. Neuroimage 19: 1095–1102.

201. Tanabe, J. et al. 2009. Medial orbitofrontal cortex gray

matter is reduced in abstinent substance-dependent in-

dividuals. Biol. Psychiatry 65: 160–164.

202. Sim, M.E. et al. 2007. Cerebellar gray matter vol-

ume correlates with duration of cocaine use in

cocaine-dependent subjects. Neuropsychopharmacology

32: 2229–2237.

203. Lyoo, I. et al. 2006. Prefrontal and temporal gray matter

density decreases in opiate dependence. Psychopharma-

cology 184: 139–144.

204. Childress, A.R. et al. 1999. Limbic activation during

cue-induced cocaine craving. Am. J. Psychiatry 156: 11–

18.

205. Kelley, A.E. 2004. Memory and addiction: shared neural

circuitry and molecular mechanisms. Neuron 44: 161–

179.

206. Argilli, E. et al. 2008. Mechanism and time course of

cocaine-induced long-term potentiation in the ventral

tegmental area. J. Neurosci. 28: 9092–9100.

207. Wanat, M.J. et al. 2008. Strain specific synaptic modi-

fications on ventral tegmental area dopamine neurons

after ethanol exposure. Biol. Psychiatry 65: 646–653.

208. Mayfield, R.D., R.A. Harris & M.A. Schuckit. 2008.

Genetic factors influencing alcohol dependence. Br. J.

Pharmacol. 154: 275–287.

209. Schuckit, M.A. 2009. An overview of genetic influences

in alcoholism. J. Subst. Abuse Treat. 36: S5–14.

210. Sinha, R. 2008. Chronic stress, drug use, and vulnera-

bility to addiction. Ann. N. Y. Acad. Sci. 1141: 105–130.

211. Archer, J.S. et al. 2006. Effect of estradiol versus estra-

diol and testosterone on brain-activation patterns in

postmenopausal women. Menopause 13: 528–537.

212. Carnes, P.J. 2000. Sexual addiction and compulsion:

recognition, treatment, and recovery. CNS Spectr. 5:

63–72.

213. Delmonico, D.L. & P.J. Carnes. 1999. Virtual sex ad-

diction: when cybersex becomes the drug of choice.

Cyberpsychol. Behav. 2: 457–463.

214. Arnow, B.A. et al. 2002. Brain activation and sexual

arousal in healthy, heterosexual males. Brain 125(Pt 5):

1014–1023.

215. Georgiadis, J.R. et al. 2006. Regional cerebral blood

flow changes associated with clitorally induced orgasm

in healthy women. Eur. J. Neurosci. 24: 3305–3316.

216. Hamann, S. et al. 2004. Men and women differ in amyg-

dala response to visual sexual stimuli. Nat. Neurosci. 7:

411–416.

217. Volkow, N.D. et al. 2009. Imaging dopamine’s role

in drug abuse and addiction. Neuropharmacology

56(Suppl 1): 3–8.

218. Volkow, N.D. et al. 2007. Dopamine in drug abuse and

addiction: results of imaging studies and treatment im-

plications. Arch. Neurol. 64: 1575–1579.

219. Knutson, B. et al. 2005. Distributed neural representa-

tion of expected value. J. Neurosci. 25: 4806–4812.

220. Kufahl, P.R. et al. 2005. Neural responses to acute co-

caine administration in the human brain detected by

fMRI. Neuroimage 28: 904–914.

221. Breiter, H.C. et al. 1997. Acute effects of cocaine on

human brain activity and emotion. Neuron 19: 591–

611.

222. Ortigue, S. et al. 2007. The neural basis of love as a

subliminal prime: an event-related functional magnetic

resonance imaging study. J. Cogn. Neurosci. 19: 1218–

1230.

223. Agmo, A. & M. Gomez. 1993. Sexual reinforcement is

blocked by infusion of naloxone into the medial preop-

tic area. Behav. Neurosci. 107: 812–818.

224. Paredes, R.G. 2008. Evaluating the neurobiology of sex-

ual reward. Ilar. J. 50: 15–27.

225. Etgen, A.M., M.A. Ansonoff & A. Quesada. 2001. Mech-

anisms of ovarian steroid regulation of norepinephrine

receptor-mediated signal transduction in the hypotha-

lamus: implications for female reproductive physiology.

Horm. Behav. 40: 169–177.

226. Gonzalez-Flores, O. et al. 2007. Facilitation of es-

trous behavior by vaginal cervical stimulation in female

rats involves alpha1-adrenergic receptor activation of

the nitric oxide pathway. Behav. Brain Res. 176: 237–

243.

227. Wood, R.I. 2004. Reinforcing aspects of androgens.

Physiol. Behav. 83: 279–289.

228. Fisher, H. 1998. Lust, attraction and attachment in

mammalian reproduction. Hum. Nat. 9: 23–52.

229. Fisher, H.E., A. Aron & L.L. Brown. 2006. Romantic

love: a mammalian brain system for mate choice. Philos.

Trans. R. Soc. Lond. B Biol. Sci. 361: 2173–2186.

230. Aron, A. et al. 2005. Reward, motivation, and emotion

systems associated with early-stage intense romantic

love. J. Neurophysiol. 94: 327–337.

231. Fisher, H., A. Aron & L.L. Brown. 2005. Romantic love:

an fMRI study of a neural mechanism for mate choice.

J. Comp. Neurol. 493: 58–62.

232. Fisher, H. et al. 2005. Motivation and emotion systems

associated with romantic love following rejection: an

314 Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences.

Frascella et al. Nonsubstance addictions

FMRI study. In Society for Neuroscience. Society for

Neuroscience San Diego.

233. Acevedo, B. et al. 2008. Neural correlates of long-

term pair-bonding in a sample of intensely in-love

humans. Program No. 297.10; Abstract No. TT28.

2008 Neuroscience Meeting Planner. Washington, DC:

Society for Neuroscience, 2008. http://www.sfn.org/

index.aspx?pagename=abstracts ampublications.

234. Lim, M.M., A.Z. Murphy & L.J. Young. 2004. Ven-

tral striatopallidal oxytocin and vasopressin V1a recep-

tors in the monogamous prairie vole (Microtus ochro-

gaster). J. Comp. Neurol. 468: 555–570.

235. Liu, Y. & Z.X. Wang. 2003. Nucleus accumbens oxytocin

and dopamine interact to regulate pair bond formation

in female prairie voles. Neuroscience 121: 537–544.

236. Bowlby, J. 1973. Attachment and Loss: Separation: Anx-

iety and Anger, Vol. 2. Basic Books. New York.

237. Harlow, H.F. & R.R. Zimmermann. 1959. Affectional

responses in the infant monkey; orphaned baby mon-

keys develop a strong and persistent attachment to inan-

imate surrogate mothers. Science 130: 421–432.

238. Strathearn, L. et al. 2008. What’s in a smile? Maternal

brain responses to infant facial cues. Pediatrics 122: 40–

51.

239. Flores, P.J. 2001. Addiction as an attachment disorder:

implications for group therapy. Int. J. Group Psychother.

51: 63–81.

240. Flores, P.J. 2004. Addiction as an Attachment Disorder.

Jason Aronson. New York, p. 345.

241. Kraemer, G.W. 1985. Effects of differences in early so-

cial experience on primate neurobiological-behavioral

development. In The Psychobiology of Attachment and

Separation. M. Reite & T. Fields, Eds: 135–157. Aca-

demic Press. New York.

242. Martikainen, P. & T. Valkonen. 1996. Mortality after the

death of a spouse: rates and causes of death in a large

Finnish cohort. Am. J. Public Health 86: 1087–1093.

243. Risinger, R.C. et al. 2005. Neural correlates of high

and craving during cocaine self-administration using

BOLD fMRI. Neuroimage 26: 1097–1108.

244. Childress, A.R. et al. 2008. Prelude to passion: limbic

activation by “unseen” drug and sexual cues. PLoS ONE

3: e1506.

245. O’Brien, C. & J.W. Cornish. 2006. Naltrexone for pro-

bationers and parolees. J. Subst. Abuse Treat. 31: 107–

111.

246. Oslin, D.W., W.H. Berrettini & C.P. O’Brien. 2006. Tar-

geting treatments for alcohol dependence: the pharma-

cogenetics of naltrexone. Addict. Biol. 11: 397–403.

247. Greenway, F.L. et al. 2009. Rational design of a combi-

nation medication for the treatment of obesity. Obesity

(Silver Spring) 17: 30–39.

248. Roberts, D.C. 2005. Preclinical evidence for GABAB

agonists as a pharmacotherapy for cocaine addiction.

Physiol. Behav. 86: 18–20.

249. Di Ciano, P. & B.J. Everitt. 2004. Contribution of the

ventral tegmental area to cocaine-seeking maintained

by a drug-paired conditioned stimulus in rats. Eur. J.

Neurosci. 19: 1661–1667.

250. Paterson, N.E., W. Froestl & A. Markou. 2004. The

GABAB receptor agonists baclofen and CGP44532 de-

creased nicotine self-administration in the rat. Psy-

chopharmacology (Berl) 172: 179–186.

251. Colombo, G. et al. 2003. Baclofen suppresses motiva-

tion to consume alcohol in rats. Psychopharmacology

(Berl) 167: 221–224.

252. Brebner, K., A.R. Childress & D.C. Roberts. 2002. A

potential role for GABA(B) agonists in the treatment

of psychostimulant addiction. Alcohol Alcohol. 37: 478–

484.

253. Ameisen, O. 2005. Complete and prolonged sup-

pression of symptoms and consequences of alcohol-

dependence using high-dose baclofen: a self-case report

of a physician. Alcohol Alcohol. 40: 147–150.

254. Ameisen, O. 2008. The End of My Addiction. Farrar,

Straus and Giroux. New York.

255. Addolorato, G. et al. 2006. Baclofen: a new drug for the

treatment of alcohol dependence. Int. J. Clin. Pract. 60:

1003–1008.

256. Corwin, R.L. & F.H. Wojnicki. 2009. Baclofen, raclo-

pride, and naltrexone differentially affect intake of fat

and sucrose under limited access conditions. Behav.

Pharmacol. 20: 537–548.

257. Wojnicki, F.H., D.C. Roberts & R.L. Corwin. 2006. Ef-

fects of baclofen on operant performance for food pel-

lets and vegetable shortening after a history of binge-

type behavior in non-food deprived rats. Pharmacol.

Biochem. Behav. 84: 197–206.

258. Aston-Jones, G. et al. 2009 Lateral hypothala-

mic orexin/hypocretin neurons: a role in reward-

seeking and addiction. Brain Res. in press. S0006-

8993(09)02096-4 doi: 10.1016/j.brainres.2009.09.106.

259. Harris, G.C., M. Wimmer & G. Aston-Jones. 2005. A

role for lateral hypothalamic orexin neurons in reward

seeking. Nature 437: 556–559.

260. Borgland, S.L. et al. 2009. Orexin A/hypocretin-1 se-

lectively promotes motivation for positive reinforcers.

J. Neurosci. 29: 11215–11225.

Ann. N.Y. Acad. Sci. 1187 (2010) 294–315 c© 2010 New York Academy of Sciences. 315