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Neuroimaging in alcohol-use disorders: clinical implications and future directions Omar M Alhassoon*,1,2, Scott F Sorg2, Mark J Stern1, Matthew G Hall1 & Scott C Wollman1

ABSTRACT Advances in clinical research have led to significant alterations in diagnostic criteria for alcohol-use disorders (AUD). Neuroimaging techniques are now being called upon to shed light on the validity and clinical utility of diagnostic criteria. For example, craving has recently been added to the diagnostic criteria of AUD based mainly on neurobiological research. In addition to understanding the nuances of the craving process, neuroimaging techniques are helping determine the biological factors that contribute to the onset and maintenance of the disorder and offer insight into the mechanisms underlying treatment. The purpose of this review is to provide a clinically relevant summary of the neuroimaging research that has impacted our understanding of the etiology, treatment and recovery in AUD.

Since the introduction of computed axial tomography in the 1970s, neuroimaging research has been at the forefront of a scientific revolution; transforming how we understand the underlying neurophysiology of alcohol-use disorders (AUD). This revolution has led to a better understanding of some of the most clinically salient characteristics of the disorder. These include the neurocognitive and affective changes observed in patients as well as the process of craving associated with alcohol dependence. This research has also highlighted the reversibility of the neurological impact of AUD and provided hope for partial or full recovery from the devastating impact of long-term and heavy alcohol consumption on the brain. In addition to what has been discovered, there is a tremendous potential in the use of neuroimaging to improve treatment. For example, it is likely that future research focused on both accurately localizing regional damage and identifying the neurotransmitters involved in that damage will distinguish AUD from other substance use disorders and help increase the specificity of treatment. The implications of such research are significant. Since there are so many heterogeneous treatment models for AUD, ranging from drug therapy to psychotherapy, post-treatment neuroimaging might provide a gold standard biological basis for comparing treatment efficacy. The significantly high alcohol relapse rates, reported to be over 40% in both treated and untreated AUD [1] , are another incentive to explore the potential biological correlates for long-term remission in order to inform relapse-reducing interventions. The severity and reversibility of such brain damage can also help determine the best treatment method, environment and intensity of clinical intervention at varying points in the recovery process. Recognizing the neurobiological underpinnings of AUD will help elucidate factors contributing to the maintenance and effects of the disorder; thus providing the basis for developing evidence-based treatment and sobriety-maintenance practices. The purpose of this article is to briefly review some of the clinically relevant neuroimaging research and highlight their implications for understanding symptomology, treatment and recovery in AUD.

KEYWORDS

• alcohol • compulsion • craving • cue-reactivity • neuroimaging • recovery • relapse • response inhibition • treatment • withdrawal

California School of Professional Psychology, Clinical Psychology PhD Program, 10455 Pomerado Road, San Diego, CA 92131, USA Department of Psychiatry, University of California, 9500 Gilman Dr, La Jolla, San Diego, CA 92093, USA *Author for correspondence: Tel.: +1 858 635 4753; [email protected] 1

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Future Neurol. (2015) 10(4), 345–356

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Review Alhassoon, Sorg, Stern, Hall & Wollman Neuroimaging methods widely employed in AUD research The neurobiological dysfunctions in AUDs have been investigated using various neuroimaging techniques, both functional and structural. Functional neuroimaging techniques most widely used with AUD are functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and single photon emission computed tomography, event-related potentials (ERP) and EEG. These techniques reveal neurological activity while participants engage in behavioral, emotional or cognitive tasks. Nowadays, fMRI is the most commonly employed functional neuroimaging tool, measuring the blood oxygen-dependent (BOLD) signals associated with changes due to a particular task [2] . Although also used for functional analysis, both PET and single photon emission computed tomography typically have lower spatial resolution than fMRI. However, when radioactive labeling of ligands is used, they have the advantage of being able to detect specific effects of these ligands (e.g., role of certain proteins in disease process or effect of a certain drug on a specific region of the brain). ERP measures electrical responses associated with neural depolarization, which provide insights into the direct electrical activity of the brain. Although EEG also provides direct measures of electrical depolarization activity with high temporal resolution, its primary use is to differentiate brain states, such as sleeping versus wakefulness, or compare brain waves at rest between AUD and healthy controls. Structural MRI and diffusion tensor imaging (DTI) offer direct measures of brain tissue volume, morphometry and white-matter microstructure associated with differences in AUD compared with healthy controls. Measuring radio signals produced by tissue protons, MRI provides excellent spatial delineation between gray and white matter in the brain, enabling clinical anatomical comparisons. The advent of DTI has facilitated the direct measurement of microstructural changes in white matter. In addition, by capitalizing on fMRI signals that are oscillatory in nature and often show simultaneous fluctuations between different regions in the brain, a new technique called functional connectivity MRI (fcMRI) has been developed to identify brain regions that are functionally connected to each other [3] . In order to cover as wide a variety of neuroimaging techniques as possible

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in the current review, an expert with 6 years of experience as a search analyst for PsycINFO created a broad search to locate as many articles that cover the topic of neuroimaging in AUD. This search was partially based on the algorithm used to create the Neuropsychology PsycSCAN of the American Psychological Association. In addition, the final search was reviewed and modified by the staff of the PsycINFO database to produce the most comprehensive strategy. The final result included terms from both the PsycINFO and PubMed controlled vocabulary (e.g., AUD, alcohol abstinence, alcohol drinking, alcohol-related disorders, alcohol drinking patterns, alcohol abuse) as well as uncontrolled vocabulary truncated appropriately for maximal retrieval (e.g., neuroimaging, fMRI, MRI DTI, diffusion tensor). The abstracts of 1154 articles were retrieved using the search strategy. The results were limited to the English language and to publication date of 1985 up to the present in both the PsycINFO database and PubMed. These were sorted by relevance and approximately eighty articles were selected independently by two of the authors for review because of their clinical implications. The results were integrated in a conceptually coherent review that illuminated the potential future directions of the field. Neuroimaging & the etiology of AUD One method that has been used to study the etiology of AUD using neuroimaging is by examining alcohol-naive populations who are at risk for developing the disorder. Two recent studies by Cservenka et al. [4,5] demonstrate the existence of underlying brain differences associated with neurocognitive vulnerabilities in adolescents with family history of AUD. Specifically, vulnerable participants exhibited lower activation during a risky decision-making task in the right dorsolateral prefrontal cortex and right cerebellar regions. In addition, vulnerable adolescents showed slowing in responses to a verbal memory task. They had lower activation in right anterior PFC, right cingulate gyrus, right inferior frontal gyrus (IFG) and left dorsolateral prefrontal cortex (PFC). Executive dysfunction and slower processing speed were evident during cognitive control tasks. The authors conclude that these functional deficits in vulnerable adolescents may have an effect on poor decision making and response inhibition, which make these participants susceptible to future AUD. Other studies

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Neuroimaging in alcohol-use disorders have also found similar results. For example, Acheson et al. [6] found that vulnerable individuals evidenced more activation in the left dorsal anterior cingulate gyrus (ACG) and left caudate nucleus during the Iowa Gambling Task, while Bjork et al. [7] found increased activation in the ventral striatum (VS) associated with sensation seeking in children with family history of alcoholism. In addition to fMRI studies, fcMRI has found similar results with this specific population. For example, Wetherill et al. [8] found that adolescents with a family history of AUD evidenced less functional connectivity between posterior parietal and dorsolateral prefrontal regions. Despite that finding, they did not show any frontoparietal structural differences in white-matter tracts. The authors suggest that the frontoparietal connectivity dysfunction could be used as a neurobiological marker for future alcohol-use vulnerability and is evidence for a neurodevelopmental delay in this population. The authors also suggest the use of cognitive rehabilitation might be one way to ameliorate this weakness in brain connectivity. Similarly, Herting et al. [9] used fcMRI to study the frontocerebellar connections in alcohol-naive adolescents with a family history of AUD. They found distinct reductions in connectivity between both left and right prefrontal cortical areas and contralateral cerebellar regions. These reductions were associated with reductions in fractional anisotropy (a measure of white-matter integrity derived from DTI) in the anterior limb of the internal capsule and the superior longitudinal fasciculus. These results all point to the possibility of premorbid brain abnormalities in individuals susceptible to AUD. Neuroimaging & the diagnosis of AUD There has been considerable controversy regarding the shift in diagnostic criteria from the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) to the fifth edition (DSM-5) [10] . Prior to the introduction of the DSM-5 [11] , a distinction was made between alcohol abuse and dependence. The first required at least one form of maladaptive use pattern (e.g., social, interpersonal or physical harm related to use) for the diagnosis to be made. On the other hand, alcohol dependence required three or more of the following criteria: tolerance, withdrawal, use of larger amount or for longer periods of time, multiple attempts to quit, activities given up or significant time


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spent on use or physical/psychological problems related to use [12] . Based on advances in research in the field, DSM-5 has now also added a new criterion: craving. Neuroimaging research is poised to contribute to resolving some of these issues by providing a better biological understanding of origins and manifestation of two of these symptoms: craving and withdrawal. ●●Craving

There is conflict regarding the inclusion of craving in the DSM-5. Its addition did not add significant psychometric information to the diagnostic model of AUD [13]. The addition of craving in DMS-5 was based less on psychometrics and more on “the view that craving may become a biological treatment target” [13]. Conceptualization of craving relies on understanding the combined behavioral and neural correlates associated with pathological levels of alcohol-related urges. Craving, as defined by the DSM-5 is “manifested by an intense desire or urge for the drug that may occur at any time but is more likely when in an environment where the drug previously was obtained or used” [11] . However, this definition fails to highlight the pathologically excessive desire that poses challenge and risk to continued consumption. Analysis of the multiple biological components involved in craving can facilitate a deeper understanding for targeted treatments. Response inhibition

Craving is a motivational and reward-seeking state of mind that requires an actual motoric response in order to be transformed into the behavioral condition of relapse. In AUD patients, these motoric responses are often the result of impulsive choices that reflect and inability to forgo instantaneous gratification [13] or inhibit prepotent responses [14–16] . This motoric process is putatively controlled by two brain systems. The first is the brain pathway that mediates the initiation of movement either in response to internal cues or external ones, namely the frontostriatal pathway [17,18] . The second system is the one that inhibits, self-corrects and modulates these motoric responses composed of such structures as the IFG, subthalamic nucleus, presupplementary motor area, globus pallidus, parietal cortex and insula [15,17] . These pathways are often studied fcMRI. Using this technique Courtney et al. [18] showed that the severity of AUD is associated with less functional

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Review Alhassoon, Sorg, Stern, Hall & Wollman connectivity between the putamen and the anterior insula, IFG, orbitofrontal cortex and ACC. In addition, using traditional fMRI and the Stop Signal Task, Li et al. [19] reported that AUD patients showed less dorsolateral PFC activation during posterior behavioral adjustment compared with healthy controls. In addition to these fundamental motor processes, researchers have studied self-reported trait disinhibition in relation to brain activation and heavy drinking. For example, Bogg et al. [20] used the Balloon Analogue Risk Task to examine reward-seeking behavior in college students and its association with excessive alcohol use. The researchers found that heavier drinking, trait disinhibition and lower IQ were correlated with decreases in medial PFC and ACC activity during reward-seeking choices. This was attributed to lowered cognitive control and less ability to avoid risk in heavy drinking college students when performing a task that presents successively riskier choices. The results point to the involvement of the medial PFC and ACC in risk/reward and outcome surveillance. Reward circuitry

MRI studies have shown gray matter reductions in subcortical and cortical regions associated with reward processing, including the hippocampus, VS, amygdala, right dorsolateralPFC, right anterior insula, right nucleus accumbens (NAc), medial frontal cortex and the parietal–occipital junction [21–23] . Wrase et al. [23] found amygdaloid reductions to be significantly correlated with increased subjective reports of alcohol craving. Though significant gray matter abnormalities are associated with AUD related to reward processing, this does not present a complete picture. Severity of alcohol dependence has also been demonstrated to correlate with weaker functional frontostriatal connectivity [18] . Diffuse white-matter integrity deficits have been further significantly correlated with elevated fMRI BOLD activity in response to alcohol-related cues in the anterior corona radiata, anterior thalamic radiation, external and internal capsule, inferior fronto-occipital fasciculus, fornix, corpus callosum, posterior corona radiata, cingulate gyrus and superior longitudinal fasciculus [24] . Compulsion

Anton [25] provides compelling evidence that the ‘repetitive/uncontrolled’ aspect of craving

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may be better conceptualized by behavioral and neuroanatomically-based obsessions and compulsions, rather than simply a function of desire. Neuroimaging evidence reveals that craving may reflect alterations in brain regions similar to those found in obsessive-compulsive disorder, particularly in corticostriatal networks [26,27] , as well as the precuneus and parietal regions [28] . In fact, the Obsessive Compulsive Drinking Scale [29] , measuring alcohol-related obsessions and compulsions, was able to discriminate outpatient alcoholics from social drinkers with high sensitivity (93%) and specificity (98%) [25] . Analogous to the negative reinforcement that characterizes the development of compulsive behavior in obsessive-compulsive disorder, the compulsion associated with substance use is neurologically correlated with increased sensitization to substance-related cues [30] . Cue-reactivity

The most commonly used stimuli to study cuereactivity have been visual [31] , although some researchers have also used other sensory modalities or a combination of several of them [32] . Neuroimaging of cue-reactivity reveals regional activity associated with reward processing to be increased during alcohol-related cues compared with neutral cues. Increased craving after verbal cue exposure correlated with increased BOLD response in the subcallosal cortex among alcohol-dependent women [33] . In an fMRI pilot study, alcohol-related pictures induced a significant activation of brain areas associated with visual, emotional, and reward mechanisms; such as the fusiform gyrus, basal ganglia, orbitofrontal gyrus and frontal and parietal cortices [34] . PET analysis is consistent with fMRI findings, showing increased rCBF to reward processing regions during alcohol-related cue-reactivity [35,36] also found that alcohol cue-reactivity was associated with increased BOLD activity in the VTA, NAc and the globus pallidus as well as cingulate, orbitofrontal, superior frontal and insular cortices. Subjective reports of craving, however, were only correlated with activity in NAc, orbitofrontal and anterior cortices. Evidence has continued to build to support alcohol craving to be marked by deficits in alcohol-related reward processing. However, Myrick et al. [35] showed that despite differences in reward processing distinguishing alcohol-related cues from neutral cues, they reported no group differences between AUD and healthy controls.


Neuroimaging in alcohol-use disorders A recent meta-analysis showed that regions involved in the dopaminergic reward circuit are equally active in healthy controls, indicating that this may more related to any positive perception of an anticipated drink, rather than the pathology of craving. In fact, they further showed that only over activation of the precuneus, posterior cingulate and superior temporal lobe differentiated AUD from healthy controls. ERP studies show that the P3 response, known to reflect inhibitory activity [37,38] is particularly smaller in AUDs during alcohol-related cues compared with healthy controls [39] . Though subjective reports of craving was significantly correlated with low P3 amplitude in right frontal, bilateral central, and bilateral parietal regions, the reduced P3 amplitude in response to alcohol cues only differentiated AUDs from healthy controls in the parietal and central channels. These findings may reflect that posterior regions may be more sensitive than frontostriatal activity in distinguishing pathological craving in AUDs from healthy controls in response to alcohol-related cues. Neural responses to alcohol-related cues may be influenced by other factors, such as stress. Evidence suggests that the neural systems involved in stress and craving overlap significantly [40] . Among recently detoxified AUD patients, increased activation in the ventromedial PFC and the ACG, known to be associated with stress even in healthy controls, during neutral fMRI stimuli were associated with both stress and alcohol cue-related craving; as well as, actual relapse to first drink and heavy drinking [31] . Stress may be an important factor in modulating craving and relapse. Future studies should examine stress reduction interventions in combination with traditional treatments. In contrast to previous assumptions, the pathology of craving may not simply be differentiated by heightened subcortical reward salience. The posterior regions differentiating AUD-related craving are known to be involved in attention orientation, set-shifting and decision making [41,42] . Craving may be more associated with heightened attention toward alcohol-related cues coupled with an inability to shift away from alcohol-related urges. These convergent neuroimaging findings help distinguish the pathological symptom of craving from normal desires to drink as well as from withdrawal symptoms in AUD.


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Withdrawal

Alcohol withdrawal symptoms are found to be among the most harmful of criteria and biological correlates of withdrawal are widespread, including autonomic and CNS disruptions, nausea or vomiting and even death [43] . Early PET and SPECT studies have shown that acute cessation of alcohol resulted in reduced frontal, parietal and temporal rCBF [44,45] . However, the frontal hypoperfusion appears to remit postwithdrawal phase, while the temporal and parietal hypoperfusion remains [45] . Neuroimaging evidence suggests that acute phases of withdrawal appear regionally different from later phases of abstinence. Neurochemical evidence from animal studies reveals that withdrawal is marked by reduced mesolimbic dopamine as well as serotonergic activity in the NAc during alcohol withdrawal [46] . Other studies investigating ethanol withdrawal in rats support increases in glutamate in the striatum, NAc and hippocampus [47–49] . However, a PET region-of-interest analysis found that even though low dopamine (D2) receptor availability in the striatum (putamen and caudate) distinguished AUD from controls, it did not distinguish early from late detoxing AUD [50] . This evidence suggests that, contrary to the animal studies, deficits in the dopaminergic reward system may not be related to withdrawal symptoms specifically, but rather a different component of alcohol addiction. These neuroimaging findings offer insight into regional deficits specifically associated with withdrawal as well provide evidence of a time limited course during early detoxification for the effects of withdrawal on CNS functioning. Neuroimaging in treatment of AUD ●●Psychopharmacology

The majority of pharmacotherapy has attempted to address craving as the primary symptom, despite not being listed in DSM until recently. For example, Myrick et al. [51] studied the impact of naltrexone (an opioid antagonist), ondansetron (a 5-HT-3 antagonist) or their combination on alcohol cue-induced craving in nontreatmentseeking AUD patients versus social drinkers. The results indicated that naltrexone significantly resulted in a reduction in cue-induced activation in the VS. However, the combination resulted in significantly greater reductions of cued activation. The authors concluded that naltrexone, due to blocking the opioid modulation of mesolimbic dopamine output [52] , could have impacted


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Review Alhassoon, Sorg, Stern, Hall & Wollman the reward memory associated with craving. Ondansetron may have enhanced that process by secondarily reducing the serotonergic modulation effects on the mesocorticolimbic dopaminergic neurons in relevant brain regions [53] . Both medications might have worked synergistically to disrupt the mesolimbic dopaminergic neurotransmission between the NAc and other sites of the reward mechanism involved in craving (e.g., ventral tegmental area). It is important to note that region-of-interest studies may not reflect reductions in cue-reactivity in other regions relating to craving. For example, wholebrain analysis on extended release naltrexone, found that following 2 weeks postinjection, cueelicited activations were reduced in the superior frontal, supramarginal, postcentral and angular gyri [54] . A similar study [55] examined the impact of acamprosate, an N-methyl-D-aspartate antagonist, versus placebo on cue-reactivity among inpatient AUDs receiving psychiatric treatment. Their results failed to find significant differences in activation between groups. Clinical outcomes reported in a recent meta-analysis support these findings, whereby naltrexone showed larger effect sizes toward reducing craving, while acamprosate showed larger effect sizes on maintaining abstinence [56] . The results from these studies indicate that medications directly impacting the dopaminergic system may have a greater effect on craving-related regions. On the other hand, acamprosate has also been shown to have direct glutamatergic action on improving related withdraw symptoms specifically [57] . Given that the medication showing the most evidence for reductions in craving is not the strongest therapy for overall abstinence, craving may not be the sole moderator of relapse. Further research on pharmacotherapy for AUD should examine the differential effects of medication on neuroanatomical changes to maximize treatment outcomes. ●●Psychotherapy

Two neuroimaging studies have examined postpsychotherapeutic outcomes for AUD. Feldstein et al. [58] investigated changes in taste cue-reactivity related to change talk, one of the key components of Motivational Interviewing. While counterchange talk activated orbitofrontal cortex, nucleus accumbens, anterior insula, posterior insula, caudate and the putamen in response to taste exposure, following the presentation of change talk participants no longer

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showed significant differences in alcohol-related taste activity. Preliminary evidence suggests that change talk may actually inhibit activation of both cortical and subcortical regions associated cue-reactivity. Based on the incentive-sensitization model, cue-exposure-based extinction training was developed to desensitize conditioned responses through repeated exposures [59] and has been found to be one of the most effective psychological interventions for AUD. Compared to supportive therapy, cueexposure-based extinction training has shown reductions in alcohol cue-related BOLD activations in the bilateral anterior cingulate gyrus, the left precentral gyrus, the left insula, the bilateral inferior parietal lobule, the left superior frontal gyrus, the right middle frontal gyrus and the left VS and the left dorsal striatum [59] . Psychotherapy, particularly cognitive-behavioral therapy and interpersonal therapy, may present a top-down mechanism of action by improving activity in frontal regions, as shown in a meta-analysis on depression and anxiety contrasting psychotherapy and pharmacotherapy. Various psychotherapeutic modalities have been meta-analytically shown to be an effective treatment in AUD [60] , and yet there are no imaging studies available to confirm neurobiological correlates of improvement. It is imperative that research begin investigating the efficacy and mechanisms of action of psychotherapy for AUD. ●●Neurotherapies

In addition to traditional pharmacological and psychological treatments, neurotherapies, such as repetitive transcranial magnetic stimulation (rTMS) and neurofeedback have appear to be potential tools for treatment of AUD. Although evidence for the efficacy of rTMS is increasing [61] , only one study examine the impact of rTMS using fMRI. De Ridder et al. [62] described the use of rTMS targeting the dorsal anterior cingulate cortex in a patient with severe alcohol craving. They used fMRI to examine the patient before and after rTMS. Alcoholcue-induced craving was associated with activation of ACC, ventromedial PFC, PCC and lateral frontoparietal areas. The activation in the NAc, ACC, ventromedial PFC and PCC was diminished after successful rTMS. On the other hand, relapse involved an increased activation in NAc, ACC and PCC. Due to the potential for seizure disorders during initial withdrawal [43] ,


Neuroimaging in alcohol-use disorders important considerations must be taken regarding appropriate timing for the initiation of rTMS. Finally, EEG neurofeedback has been studied as a potential treatment of AUDs [63] . Despite initial randomized controlled trials showing long-term abstinence from alcohol and normalization of brainwaves following neurofeedback, there appear to be some methodological issues that warrant further research prior to conclusions of efficacy. Neuroimaging study of recovery & relapse ●●Recovery

Numerous studies have found brain structural alterations associated AUD. Specifically, structural MRI studies have found shrinkage in frontal lobe structures in AUD [64–66] with frontal white matter being especially susceptible to the effects of long-term heavy drinking [67–69] . However, several studies have also shown that the degree of alcohol-related structural brain changes is not permanent and that there can be considerable structural recovery following a period of alcohol abstinence. Improvement in ventricular size and reductions in overall CSF volume after shortterm abstinence (around 5 weeks) were among the first such findings reported [70–72] . Perhaps owing to the white matter’s susceptibility to AUD-associated damage, following abstinence the greatest volumetric improvements appear to be within the white-matter volume [73] . However, not every study investigating recovery of cerebral volume in AUD found increased white matter. Pfefferbaum et al. [74] report increased gray matter volume, and reductions ventricular volume with abstinence, but did not find improvement in white-matter volume. Cardenas et al. [75] found widespread increases in gray matter structure volumes in persons with AUD after an eight-month period of abstinence. The course of structural recovery appears to be nonlinear; with the greatest degree of improvement occurring within the first few weeks of abstinence [76] . Additionally, those regions that demonstrated the most atrophy at baseline, tend to demonstrate the greatest degree of improvement with abstinence [75,77] . The biggest increases in gray matter were found in the cingulate gyrus, temporal gyrus, parietal lobule, cerebellum and precuneus, though all regions except for the hippocampus showed significant changes over time. In a DTI study of white-matter recovery in AUD after 1 year of abstinence, persons with AUD showed improved white-matter integrity


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in the genu and body subregions of the corpus callosum [78] . Those regions had poorer whitematter integrity compared with controls at baseline (i.e., after only 2 weeks of abstinence), and the white-matter recovery coincided with improvement in working memory. As with the volumetric studies, the most affect region at baseline (in this case the genu), demonstrated the greatest degree of recovery. With over 2 years of abstinence, white-matter integrity, as measured by fractional anisotropy, shows no difference between AUD and healthy controls [79] . However, both radial and axonal diffusivity, less commonly examined measures of white-matter integrity, remained significantly higher than controls. ●●Relapse

Duration of abstinence is directly correlated with recovery and relapse rates, with significant reductions in relapse probability past 1 year and even further at 5-year milestones [80] . Neuroimaging has helped identify potential neuroanatomical biomarkers of relapse, which may provide insight for treatment providers in predicting and preventing relapse. Dividing alcohol-dependent patients into subsequent relapsers and abstainers, those who relapsed showed a significantly stronger craving compared with those who abstained [23] . Relapsers, but not abstainers, also showed a significant association between amygdala volume and craving. Smaller medial frontal and parietal–occipital gray matter volumes have each also been shown to predict less time to relapse with any alcohol use and to heavy drinking [22] . Distinct regional gray matter volume may play a significant role in clinical outcomes. Compared to healthy controls and relapsers, abstainers also show increased functional connectivity between the right midbrain and left orbitofrontal cortex during cue-reactivity [81] . Overall BOLD activation during alcohol cues also distinguished relapsers from abstainers; revealing that relapsers show increased activation in the medial prefrontal cortex, while abstainers show increased activation in the right VTA and left VS. Combining gray and white-matter volume as well as affective and neuropsychological deficits, Durazzo, Gazdzinski, Yeh and Meyerhoff [82] were able to accurately classify 83% of abstainers and 90% of relapsers. Specifically, they found that temporal gray matter-N-acetyl-aspartate, frontal white matter-N-acetyl-aspartate, frontal


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Review Alhassoon, Sorg, Stern, Hall & Wollman gray matter-choline compounds, processing and unipolar mood disorder accounted for 72% of the variance in drinking status after 1 month follow-up. To date, only one study has examined whether DTI may predict relapse potential. In a study by Sorg et al. [83] , 45 persons with AUD completed DTI scan within 30 days of abstinence. Significantly lower baseline frontal whitematter integrity was found in a group of 16 AUD participants who resumed heavy drinking after 6 months compared with 29 who maintained abstinence. This finding implicated disruption in corticosubcortical cognitive control networks which modulate behavior following heightened reward saliency [84] . Further, DTI studies of this kind may leverage DTI tractography-derived white-matter structural connectivity indices to investigate whether alterations in structural networks such as those responsible for cognitive control, may promote relapse potential [85] . According to Sorg et al. [83] , the degree of white-matter damage measured at the start of treatment was associated with persisting treatment gains. The results suggest that compromised frontal systems further complete the already difficult undertaking of substance abuse treatment. Findings such as these have led some to consider whether alcohol treatment programs may need to be adapted to better accommodate those patients who may have cognitive limitations [86] . It could be greatly beneficial to treatment providers and to patients to be able to identify biological and behavioral markers, such as DTI and neuropsychological testing, which may inform treatment adherence and lead to more personalized care. Conclusion & future perspective This review synthesizes and highlights the clinical significance of neuroimaging in understanding the symptomatology, treatment, recovery and relapse in AUD. There is significant evidence of differential neurological profiles for varying symptoms. Research also indicates a potentially specific time course for the peak and cessation of neurologically related withdrawal symptoms, beginning as early as 24–48 h, with recovery appearing as early as 2 weeks postabstinence. The current review of the literature reveals that the heterogeneity in imaging studies of craving may reflect a complex interaction between multiple brain networks. Much of these findings also support the obsessive-compulsive [25] and substance-cue sensitization models [30] that may

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partially explain the intensity and lack of control associated with craving. Particularly, accumulated evidence suggests that damage to frontal regions involved in planning and urge suppression may result in an inability to inhibit prepotent responses [14–16] . The reward dependence for alcohol may also be accompanied by marked reduction in reward salience from nonalcoholrelated cues and activities. Furthermore, the posterior brain alterations that distinguish AUD from healthy individuals [41,42] maybe associated with an inability to shift attention from an initial urge, thus disabling the suppression of the compulsion to drink. Given that there is consistent literature reporting neurological deficits in craving, future neuroimaging and behavioral studies should attempt to establish whether craving and related symptoms ought to be considered core criteria for diagnosis. The majority of post-treatment research thus far has focused on the improvements of craving as the target for intervention. Analyses of posttreatment studies indicate that each treatment modality may have different mechanisms of action and subsequently affect different aspects of recovery. The current literature supports the fact that AUD may require multimodal interventions to address the dynamic network disruptions. For example, unimodal treatments targeting reward salience may be necessary to enhance treatment, but not sufficient on its own as a long-term solution. Future studies should look at combined treatments to further explore the link between top-down (e.g., cortical improvements associated with psychotherapy) as well as bottom-up (e.g., subcortical serotonergic improvements with medication). There is ample evidence supporting functional and structural neurological recovery with both short-term and long-term abstinence and that the majority of recovery occurs within the first few weeks of sobriety. However, the extent of the long-term reversibility remains unclear. For example, despite recovery in white-matter volume and fractional anisotropy, there are regional inconsistencies in these findings [78] . Mapping the neurological stages of recovery and the regional brain changes as they correlate with behavior will help improve our understanding of both immediate treatment needs as well as interventions for long-term maintenance. In conclusion, using neuroimaging as one of its major tools, neuroscience is helping develop a more thorough understanding of AUD which


Neuroimaging in alcohol-use disorders will have significant implications for clinicians. In an era of biologically driven diagnoses, more research is warranted to investigate neurobiological correlates of proposed DSM-5 symptom clusters and to develop more biologically driven criteria, treatment approaches and outcome assessment. In addition, findings from regional and connectivity neuroimaging could, in the future, be used as biomarkers of vulnerability used to alert the affected individuals, healthcare providers and caretakers of the need for preventative measures. More research will be underway given the momentum of alcohol-related craving studies already being performed using neuroimaging techniques. This research will influence the process of diagnosis and treatment. Not only will this allow for the discovery of biologically

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targeted treatments but also early detection and prevention, better diagnosis through the incorporation of knowledge of endophenotypes and other biomarkers and, potentially the development of more comprehensive and holistic treatment of AUD.Executive summary Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

EXECUTIVE SUMMARY ●●

Neuroimaging research has led to better understanding of some of the most clinically salient characteristics of alcoholuse disorders (AUD), including a new Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) criterion: craving.

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Individuals with a family history of AUD may have premorbid brain abnormalities making them more susceptible to AUD.

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Neuroimaging correlates of individual symptoms that differentiate AUD from healthy controls may play a role as biomarkers, facilitating greater diagnostic specificity and symptom validity.

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Data generated by neuroimaging studies have highlighted potential reversibility of the neurological impact of AUD, shedding some light on potential treatment, including pharmacotherapy, psychotherapy and neurotherapy.

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Multimodal neuroimaging research suggests potential structural and functional recovery following abstinence from alcohol with some modalities offering potential relapse prediction.

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The study of AUD is being advanced by a more comprehensive understanding of the biological mechanisms

underlying behaviors exhibited by patients. Not only will this allow for the discovery of biologically targeted treatments but also the early detection and prevention, better diagnosis through the incorporation of knowledge of endophenotypes and other biomarkers and potentially the development of more comprehensive and holistic treatment of AUD. References Papers of special note have been highlighted as: • of interest; •• of considerable interest 1

Moos RH, Moos BS. Rates and predictors of relapse after natural and treated remission from alcohol use disorders. Addiction 101(2), 212–222 (2006).

2

Bunge SA, Kahn I. Cognition: an overview of neuroimaging techniques. Encyclopedia Neurosci. 2, 1063–1067 (2009).

3

Buckner RL, Krienen FM, Yeo BTT. Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci.


16(7), 832–837 (2013). 4

5

6

Cservenka A, Herting MM, Nagel BJ. Atypical frontal lobe activity during verbal working memory in youth with a family history of alcoholism. Drug Alcohol Depend. 123(1–3), 98–104 (2012). Cservenka A, Nagel BJ. Risky decisionmaking: an fMRI study of youth at high risk for alcoholism. Alcohol Clin. Exp. Res. 36(4), 604–615 (2012). Acheson A, Robinson JL, Glahn DC, Lovallo WR, Fox PT. Differential activation of the anterior cingulate cortex and caudate nucleus during a gambling simulation in persons with

a family history of alcoholism: studies from the Oklahoma Family Health Patterns project. Drug Alcohol Depend. 100(1–2), 17–23 (2009). 7

Bjork JM, Smith AR, Hommer DW. Striatal sensitivity to reward deliveries and omissions in substance dependent patients. Neuroimage 42(4), 1609–1621 (2008).

8

Wetherill RR, Bava S, Thompson WK et al. Frontoparietal connectivity in substance-naive youth with and without a family history of alcoholism. Brain Res. 1432, 66–73 (2012).

9

Herting MM, Fair D, Nagel BJ. Altered fronto-cerebellar connectivity in alcohol-naive


353

Review Alhassoon, Sorg, Stern, Hall & Wollman youth with a family history of alcoholism. Neuroimage 54(4), 2582–2589 (2011).

dependence: neural measures of stop signal performance. Alcohol Clin. Exp. Res. 33(4), 740–750 (2009).

10 Jones DK, Gill C, Shannon R. Review of the

proposed DSM-5 substance use disorder. J. Addict. Offender Couns. 33, 115–123 (2012).

20 Bogg T, Fukunaga R, Finn PR, Brown JW.

Cognitive control links alcohol use, trait disinhibition, and reduced cognitive capacity: evidence for medial prefrontal cortex dysregulation during reward-seeking behavior. Drug Alcohol Depend. 122(1–2), 112–118 (2012).

11 American Psychiatric Association. Diagnostic

and statistical manual of mental disorders: (DSM-5™) (5th ed.). American Psychiatric Publishing, Inc, Arlington, VA, USA (2013). 12 Hasin DS, O’brien CP, Auriacombe M et al.

DSM-5 criteria for substance use disorders: recommendations and rationale. Am. J. Psychiatry 170(8), 834–851 (2013). •

Provides much needed insight and clarity surrounding the altered criterion in The Diagnostic and Statistical Manual of Mental Disorders diagnoses for substance abuse.

21 Makris N, Oscar-Berman M, Jaffin SK et al.

Decreased volume of the brain reward system in alcoholism. Biol. Psychiatry 64(3), 192–202 (2008). •

13 Claus ED, Ewing SW, Filbey FM, Sabbineni

A, Hutchison KE. Identifying neurobiological phenotypes associated with alcohol use disorder severity. Neuropsychopharmacology 36(10), 2086–2096 (2011). •• Investigating functionally connected brain areas associated with craving and alcohol-use disorder (AUD) severity shows that cue-reactivity in particular brain regions, especially the precuneus, insula and dorsal striatum, can help distinguish not only severity, but potential genetic phenotypes of AUD. 14 Fan J, Hof PR, Guise KG, Fossella JA, Posner

MI. The functional integration of the anterior cingulate cortex during conflict processing. Cereb. Cortex 18(4), 796–805 (2008).

22 Rando K, Hong KI, Bhagwagar Z et al.

Association of frontal and posterior cortical gray matter volume with time to alcohol relapse: a prospective study. Am. J. Psychiatry 168(2), 183–192 (2011). 23 Wrase J, Makris N, Braus DF et al. Amygdala

volume associated with alcohol abuse relapse and craving. Am. J. Psychiatry 165(9), 1179–1184 (2008). •• Reductions in amygdala volumes are associated with increased alcohol craving and future relapse. 24 Monnig MA, Thayer RE, Caprihan A et al.

White matter integrity is associated with alcohol cue reactivity in heavy drinkers. Brain Behav. 4(2), 158–170 (2014).

15 Jahfari S, Waldorp L, Van Den Wildenberg

WP, Scholte HS, Ridderinkhof KR, Forstmann BU. Effective connectivity reveals important roles for both the hyperdirect (fronto–subthalamic) and the indirect (fronto–striatal–pallidal) fronto–basal ganglia pathways during response inhibition. J. Neurosci. 31(18), 6891–6899 (2011). 16 Orr JM, Weissman DH. Anterior cingulate

cortex makes 2 contributions to minimizing distraction. Cereb. Cortex 19(3), 703–711 (2009). 17 Aron AR, Poldrack RA. Cortical and

subcortical contributions to stop signal response inhibition: role of the subthalamic nucleus. J. Neurosci. 26(9), 2424–2433 (2006). 18 Courtney KE, Ghahremani DG, Ray LA.

Fronto-striatal functional connectivity during response inhibition in alcohol dependence. Addict. Biol. 18(3), 593–604 (2013). 19 Li CS, Luo X, Yan P, Bergquist K, Sinha R.

Altered impulse control in alcohol

354

This analysis sought to examine both cortical and subcortical morphometric alterations in the ‘reward-network’ among alcoholics. Results indicated decreased reward-network volume among alcoholic patients.

•

Researchers conducted a large-scale analysis designed to measure the white-matter integrity in heavy drinkers across 18 white-matter tracts. Fractional anisotropy values of nine white-matter tracts were found to be significantly negatively correlated with blood oxygen-dependent activation during an alcohol taste cue.

25 Anton RF. Obsessive-compulsive aspects of

craving: development of the obsessive compulsive drinking scale. Addiction 95(Suppl. 2), S211–217 (2000). 26 Posner J, Marsh R, Maia TV, Peterson BS,

Gruber A, Simpson HB. Reduced functional connectivity within the limbic cortico-striatothalamo-cortical loop in unmedicated adults with obsessive-compulsive disorder. Hum. Brain Mapp. 35(6), 2852–2860 (2014). 27 Yeh PH, Simpson K, Durazzo TC,

Gazdzinski S, Meyerhoff DJ. Tract-based spatial statistics (TBSS) of diffusion tensor


imaging data in alcohol dependence: Abnormalities of the motivational neurocircuitry. Psychiatry Res. 173(1), 22–30 (2009). 28 Millet B, Dondaine T, Reymann JM et al.

Obsessive compulsive disorder networks: positron emission tomography and neuropsychology provide new insights. PLoS One 8(1), e53241 (2013). 29 Anton RF, Moak DH, Latham P. The

obsessive compulsive drinking scale: a self-rated instrument for the quantification of thoughts about alcohol and drinking behavior. Alcohol Clin. Exp. Res. 19(1), 92–99 (1995). 30 Everitt BJ, Robbins TW. Neural systems of

reinforcement for drug addiction: from actions to habits to compulsion. Nat. Neurosci. 8(11), 1481–1489 (2005). 31 Seo D, Lacadie CM, Tuit K, Hong KI,

Constable RT, Sinha R. Disrupted ventromedial prefrontal function, alcohol craving, and subsequent relapse risk. JAMA Psych. 70(7), 727–739 (2013). 32 Yalachkov Y, Kaiser J, Naumer MJ.

Functional neuroimaging studies in addiction: multisensory drug stimuli and neural cue reactivity. Neurosci. Biobehav. Rev. 36(2), 825–835 (2012). 33 Tapert SF, Brown GG, Baratta MV, Brown

SA. Fmri bold response to alcohol stimuli in alcohol dependent young women. Addict. Behav. 29(1), 33–50 (2004). 34 Wrase J, Grusser SM, Klein S et al.

Development of alcohol-associated cues and cue-induced brain activation in alcoholics. Eur. Psychiatry 17(5), 287–291 (2002). 35 Myrick H, Anton RF, Li X et al. Differential

brain activity in alcoholics and social drinkers to alcohol cues: relationship to craving. Neuropsychopharmacology 29(2), 393–402 (2004). 36 Olbrich Hm VG, Paris C, Hagenbuch F,

Ebert D, Juengling FD. Brain activation during craving for alcohol measured by positron emission tomography. Aust N Z J Psychiatry 40, 171–178 (2006). 37 Smith Me HE, Sokolik M, Baudena AM,

Liegeois-Chauvel C, Chauvel P. The intracranial topography of the P3 eventrelated potential elicited during auditory oddball. Electroencephalogr. Clin. Neurophysiol. 76, 235–248 (1990). 38 Woodward Sh BW, Marsh JT, Dawson ME.

Probing the time‐course of the auditory oddball P3 with secondary reaction. Psychophysiology 28(6), 609–618 (1991). 39 Namkoong K, Lee E, Lee Ch, Lee BO, An


Neuroimaging in alcohol-use disorders SK. Increased P3 amplitudes induced by alcohol-related pictures in patients with alcohol dependence. Alcoholism: Clin. Experimental Res. 28(9), 1317–1323 (2004). 40 Sinha R. How does stress lead to risk of

alcohol relapse? Alcohol Res. 34(4), 432–440 (2012). 41 Cavanna AE, Trimble MR. The precuneus: a

review of its functional anatomy and behavioural correlates. Brain 129(Pt 3), 564–583 (2006). 42 Moll J RO-SD, Moll FT, Bramati IE,

Andreiuolo PA. The cerebral correlates of set-shifting an fMRI study of the trail making test. Arq. Neuropsiquiatr. 60(4), 900–905 (2002). 43 Trevisan LA, Boutros N, Petrakis IL, Krystal

JH. Complications of alcohol withdrawal: pathophysiological insights. Alcohol Health Res. World 22(1), 61–66 (1998). 44 Nicolas JM, Catafau AM, Estruch R et al.

Regional cerebral blood flow-spect in chronic alcoholism: relation to neuropsychological testing. J. Nucl. Med. 34(9), 1452–1459 (1993). 45 Tutus A, Kugu N, Sofuoglu S et al. Transient

frontal hypoperfusion in Tc-99m hexamethylpropyleneamineoxime single photon emission computed tomography imaging during alcohol withdrawal. Biol. Psychiatry 43(12), 923–928 (1998). 46 Weiss F, Parsons LH, Schulteis G et al.

Ethanol self-administration restores withdrawal-associated deficiencies in accumbal dopamine and 5-hydroxytryptamine release in dependent rats. J. Neurosci. 16(10), 3474–3485 (1996). 47 Dahchour A, De Witte P, Bolo N et al.

Central effects of acamprosate: part 1. acamprosate blocks the glutamate increase in the nucleus accumbens microdialysate in ethanol withdrawn rats. Psychiatry Res. 82(2), 107–114 (1998). 48 Dahchour A, Quertemont E, De Witte P.

Taurine increases in the nucleus accumbens microdialysate after acute ethanol administration to naive and chronically alcoholised rats. Brain Res. 735(1), 9–19 (1996). 49 Rossetti Zl CS. Ethanol withdrawal is

associated with increased extracellular glutamate in the rat striatum. Eur. J. Pharmacol. 283, 177–183 (1995). 50 Volkow ND, Wang GJ, Maynard L et al.

Effects of alcohol detoxification on dopamine D2 receptors in alcoholics: a preliminary study. Psychiatry Res 116(3), 163–172 (2002). 51 Myrick H, Anton RF, Li X, Henderson S,


Randall PK, Voronin K. Effect of naltrexone and ondansetron on alcohol cue-induced activation of the ventral striatum in alcohol-dependent people. Arch. Gen. Psychiatry 65(4), 466–475 (2008). 52 Spanagel R HA, Shippenberg TS. Opposing

tonically active endogenous opioid systems modulate the mesolimbic dopaminergic pathway. Proc. Natl Acad. Sci. 89, 2046–2050 (1992). 53 Jung Yc NK. Pharmacotherapy for alcohol

dependence: anticraving medications for relapse prevention. Yonsei Medical J. 47(2), 167–178 (2006). 54 Lukas SE, Lowen SB, Lindsey KP et al.

Extended-release naltrexone (NR-NTX) attenuates brain responses to alcohol cues in alcohol-dependent volunteers: a bold fMRI study. Neuroimage 78, 176–185 (2013). 55 Langosch JM, Spiegelhalder K, Jahnke K et

al. The impact of acamprosate on cue reactivity in alcohol dependent individuals: a functional magnetic resonance imaging study. J. Clin. Psychopharmacol. 32(5), 661–665 (2012). 56 Maisel NC, Blodgett JC, Wilbourne PL,

Humphreys K, Finney JW. Meta-analysis of naltrexone and acamprosate for treating alcohol use disorders: when are these medications most helpful? Addiction 108(2), 275–293 (2013). 57 De Witte P. Imbalance between

neuroexcitatory and neuroinhibitory amino acids causes craving for ethanol. Addict Behav. 29(7), 1325–1339 (2004). 58 Feldstein Ewing SW, Filbey FM, Sabbineni A,

Chandler LD, Hutchison KE. How psychosocial alcohol interventions work: a preliminary look at what fMRI can tell us. Alcohol Clin. Exp. Res. 35(4), 643–651 (2011). 59 Vollstadt-Klein S, Loeber S, Kirsch M et al.

Effects of cue-exposure treatment on neural cue reactivity in alcohol dependence: a randomized trial. Biol. Psychiatry 69(11), 1060–1066 (2011). 60 Miller Wr WP. Mesa grande: a

methodological analysis of clinical trials of treatments for alcohol use disorders. Addiction 97, 265–277 (2002). 61 Bellamoli E MP, Schwartz RP, Rimondo C,

Gomma M, Serpelloni G. rTMS in the treatment of drug addiction: an update about human studies. Behav. Neurol. Article ID 815215, 1-11 (2014). 62 De Ridder D, Vanneste S, Kovacs S, Sunaert

S, Dom G. Transient alcohol craving suppression by rtms of dorsal anterior

Review

cingulate: an fMRI and loreta EEG study. Neurosci. Lett. 496(1), 5–10 (2011). 63 Sokhadze TM CR, Trudeau DL. EEG

biofeedback as a treatment for substance use disorders: review, reating of efficacy, and eecommendations for further research. Appl. Psychophysiol. Biofeedback 33(1), 1–28 (2008). 64 Fein G, Di Sclafani V, Cardenas VA,

Goldmann H, Tolou-Shams M, Meyerhoff DJ. Cortical gray matter loss in treatmentnaive alcohol dependent individuals. Alcohol Clin. Exp. Res. 26(4), 558–564 (2002). 65 Jernigan TL, Butters N, Ditraglia G et al.

Reduced cerebral grey matter observed in alcoholics using magnetic resonance imaging. Alcohol Clin. Exp. Res. 15(3), 418–427 (1991). 66 Pfefferbaum A, Lim KO, Zipursky RB et al.

Brain gray and white matter volume loss accelerates with aging in chronic alcoholics: a quantitative mri study. Alcohol Clin. Exp. Res. 16(6), 1078–1089 (1992). 67 Dirksen CL, Howard JA, Cronin-Golomb A,

Oscar-Berman M. Patterns of prefrontal dysfunction in alcoholics with and without Korsakoff ’s syndrome, patients with Parkinson’s disease, and patients with rupture and repair of the anterior communicating artery. Neuropsychiatry Dis. Treat. 2(3), 327–339 (2006). 68 Oscar-Berman M, Marinkovic K. Alcohol:

effects on neurobehavioral functions and the brain. Neuropsychol. Rev. 17(3), 239–257 (2007). 69 Rosenbloom M, Sullivan EV, Pfefferbaum A.

Using magnetic resonance imaging and diffusion tensor imaging to assess brain damage in alcoholics. Alcohol Res. Health 27(2), 146–152 (2003). 70 Carlen PL, Wortzman G, Holgate RC,

Wilkinson DA, Rankin JC. Reversible cerebral atrophy in recently abstinent chronic alcoholics measured by computed tomography scans. Science 200(4345), 1076–1078 (1978). 71 Schroth G, Naegele T, Klose U, Mann K,

Petersen D. Reversible brain shrinkage in abstinent alcoholics, measured by MRI. Neuroradiology 30(5), 385–389 (1988). 72 Zipursky RB, Lim KC, Pfefferbaum A. MRI

study of brain changes with short-term abstinence from alcohol. Alcohol Clin. Exp. Res. 13(5), 664–666 (1989). 73 Shear PK, Jernigan TL, Butters N.

Volumetric magnetic resonance imaging quantification of longitudinal brain changes in abstinent alcoholics. Alcohol Clin. Exp. Res. 18(1), 172–176 (1994). 74 Pfefferbaum A, Sullivan EV, Mathalon DH,

Shear PK, Rosenbloom MJ, Lim KO.


355

Review Alhassoon, Sorg, Stern, Hall & Wollman Longitudinal changes in magnetic resonance imaging brain volumes in abstinent and relapsed alcoholics. Alcohol Clin. Exp. Res. 19(5), 1177–1191 (1995). 75 Cardenas VA, Studholme C, Gazdzinski S,

Durazzo TC, Meyerhoff DJ. Deformationbased morphometry of brain changes in alcohol dependence and abstinence. Neuroimage 34(3), 879–887 (2007). 76 Gazdzinski S, Durazzo TC, Meyerhoff DJ.

Temporal dynamics and determinants of whole brain tissue volume changes during recovery from alcohol dependence. Drug Alcohol Depend. 78(3), 263–273 (2005). 77 Gazdzinski S, Durazzo TC, Studholme C,

Song E, Banys P, Meyerhoff DJ. Quantitative brain MRI in alcohol dependence: preliminary evidence for effects of concurrent chronic cigarette smoking on regional brain volumes. Alcohol Clin. Exp. Res. 29(8), 1484–1495 (2005). 78 Alhassoon OM, Sorg SF, Taylor MJ et al.

Callosal white matter microstructural recovery in abstinent alcoholics: a longitudinal diffusion tensor imaging study. Alcohol Clin. Exp. Res. 36(11), 1922–1931 (2012).

356

79 Zorlu N, Gelal F, Kuserli A et al. Abnormal

white matter integrity and decision-making deficits in alcohol dependence. Psychiatry Res. 214(3), 382–388 (2013). 80 Dennis ML, Foss MA, Scott CK. An

eight-year perspective on the relationship between the duration of abstinence and other aspects of recovery. Eval. Rev. 31(6), 585–612 (2007). 81 Beck A WT, Genauck A, Wrase J et al. Effect

of brain structure, brain function, and brain connectivity on relapse in alcohol-dependent patients. Arch. Gen. Psych. 69(8), 842–853 (2012). 82 Durazzo TC, Gazdzinski S, Yeh PH,

Meyerhoff DJ. Combined neuroimaging, neurocognitive and psychiatric factors to predict alcohol consumption following treatment for alcohol dependence. Alcohol Alcohol 43(6), 683–691 (2008). 83 Sorg SF, Taylor MJ, Alhassoon OM et al.

Frontal white matter integrity predictors of adult alcohol treatment outcome. Biol. Psychiatry 71(3), 262–268 (2012).


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Compromised frontal white-matter integrity is associated with resumed heavy drinking following treatment. This finding highlights a biological marker that may guide treatment options for individuals with AUD.

84 Baler RD, Volkow ND. Drug addiction: the

neurobiology of disrupted self-control. Trends Mol. Med. 12(12), 559–566 (2006). 85 Kuceyeski A, Meyerhoff DJ, Durazzo TC, Raj

A. Loss in connectivity among regions of the brain reward system in alcohol dependence. Hum. Brain Mapp. 34(12), 3129–3142 (2013). 86 Bates ME, Buckman JF, Nguyen TT. A role

for cognitive rehabilitation in increasing the effectiveness of treatment for alcohol use disorders. Neuropsychol. Rev. 23(1), 27–47 (2013). •• Highlights important future developments of alcohol treatment programs and provides evidence that including a cognitive rehabilitation approach may play an important role in treatment outcomes for individuals with AUD.
