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Alcohol Alcohol. 2008 Nov-Dec; 43(6): 683–691.
Published online 2008 September 24. doi:  10.1093/alcalc/agn078
PMCID: PMC2720770

Combined Neuroimaging, Neurocognitive and Psychiatric Factors to Predict Alcohol Consumption Following Treatment for Alcohol Dependence


Aims: Resumption of hazardous drinking after treatment is common in alcohol use disorders (AUD). This study examined the ability of multimodality magnetic resonance, neurocognitive, psychiatric and demographic, to predict alcohol consumption after treatment for AUD. Methods: Seventy treatment-seeking participants completed 1.5T magnetic resonance studies, yielding regional gray matter (GM) and white matter (WM) surrogate markers of neuronal integrity (N-acetylaspartate: NAA) and cell membrane turnover/synthesis (choline: Cho), assessment of major psychiatric disorders and comprehensive neurocognitive assessment after ~1 month of abstinence. Participants were followed up 6–12 months after treatment and classified as Abstainers (no alcohol consumption; n = 26) and Resumers (any alcohol consumption; n = 44). Abstainers and Resumers were contrasted on various outcome measures, and those that significantly differed between groups were entered as factors in a logistical regression model to predict drinking status at follow-up. Results: The following variables were independent predictors of resumption of drinking: temporal GM NAA, frontal WM NAA, frontal GM Cho, processing speed and comorbid unipolar mood disorder. With each standard deviation unit decrease in temporal GM NAA, frontal WM NAA, frontal GM Cho and processing speed, the odds of resumption of drinking were increased 3.1, 3.3, 6.4 and 14.2 times, respectively. Diagnosis of a unipolar mood disorder was associated with 14.5-fold increased odds of resumed drinking. Conclusions: The findings suggest that Resumers, relative to Abstainers, demonstrated greater abnormalities in anterior frontal-subcortical circuits involved in mood and behavioral regulation, and development and maintenance of alcohol use disorders, The magnetic resonance-derived variables used in this study may provide additional information regarding the prediction and neurobiological correlates of resumption of hazardous drinking.


Within ~12 months of completion of treatment for an alcohol use disorder (AUD) (i.e. alcohol dependence or abuse), ~50–80% of individuals will resume consuming alcohol at hazardous levels (Monahan and Finney, 1996; Miller et al., 2001). Resumption of alcohol consumption in those with an AUD appears to be mediated by a combination (and possibly an interaction) of biological, neurocognitive, psychological/ psychiatric and sociodemographic factors (Parsons et al., 1990; Glenn and Parsons, 1991; Jin et al., 1998; Weiss and Porrino, 2002; Heinz et al., 2003; Adinoff et al., 2005; Bottlender and Soyka, 2005; Bradizza et al., 2006). A considerable amount of research investigating the factors associated with resumed alcohol consumption following treatment has focused on psychological/psychiatric variables (e.g. coping styles, comorbid mood, personality or substance abuse disorders), and sociodemographic (e.g. age, education, marital status) and behavioral (e.g. drinking severity and chronicity, previous treatment attempts) factors. In general, poor coping skills, low self-esteem/self-efficacy, social isolation, comorbid mood or personality disorders, greater severity of drinking history and depressive symptomatology have been associated with relapse [see Parsons et al. (1990), Glenn and Parsons (1991), Miller (1991), Miller et al. (1996b), Noone et al. (1999), Boening et al. (2001), Teichner et al. (2001), Bradizza et al. (2006), Krampe et al. (2006), Moos and Moos (2006), Walter et al. (2006) and Kodl et al. (2008)].

Fewer studies have addressed the neurobiological and neurocognitive correlates of resumption of drinking in adult AUD. In treatment seeking AUD, higher brain activation in the putamen, anterior cingulate and medial prefrontal cortex at ~2 months of abstinence was associated with the level of alcohol consumption in those who resumed drinking subsequent to treatment (Grusser et al., 2004). Higher levels of brain activation in the thalamus and striatum in response to affectively positive (versus neutral) cues were inversely related to drinking days and overall alcohol intake in those who relapsed after treatment (Heinz et al., 2007). In a group of treated AUD abstinent for ~18 days, lower frontal cerebral blood flow was observed in individuals who relapsed relative to those who remained abstinent for ~2 months following treatment (Noel et al., 2002). Greater suppression of the hypothalamus–pituitary–adrenal axis during pharmacological or behavioral challenge has been observed in relapsed versus abstinent alcoholics following treatment (Adinoff et al., 2005). Erythrocyte count, hemoglobin concentration and hematocrit in 1-month-abstinent treatment-seeking alcoholics were higher among participants who maintained sobriety compared to those who relapsed over 2–12 months following treatment (Pfefferbaum et al., 2004).

Studies investigating the utility of neurocognitive variables to predict relapse in adult AUD have yielded mixed findings. At admission to treatment, better performance on the Wechsler Adult Intelligence Scale, Block Design was observed in those who abstained versus those who relapsed within 6 months (Donovan et al., 1984). In short-term abstinent AUD, poorer performance on a global measure of learning and memory, problem solving, abstraction and perceptual motor task following detoxification was associated with relapse (Parsons, 1987; Parsons et al., 1990; Glenn and Parsons, 1991). Poorer performance on a composite measure of auditory–verbal and visuospatial learning and memory following detoxification tended to predict lower probability of long-term abstinence (Bartels et al., 2007). In treatment-seeking AUD, the interaction of coping skills and neurocognition was predictive of alcohol consumption following treatment. Specifically, those who concurrently demonstrated higher neurocognitive abilities and poor coping responses had a greater percentage of alcohol-drinking days during relapse (Tapert et al., 2004). Other studies have found neurocognitive functioning to have minimal or no value in predicting relapse in adult AUD [see Glenn and Parsons (1991), Miller (1991), Jin et al. (1998)].

Overall, the neuroimaging and neurocognitive studies of relapse in AUD suggest dysfunction in spatially overlapping brain systems involved in the initiation and maintenance of AUD/substance use disorders, as well as in decision making, impulse control, judgment, planning and reasoning skills (Koob, 2003; Lubman et al., 2004; Bowirrat and Oscar-Berman, 2005; Baler and Volkow, 2006). This is consistent with the vast number of neuroimaging and neurocognitive studies that indicate AUD is associated with abnormalities in the GM and WM of the frontal, temporal and parietal lobes, as well as subcortical nuclei (Oscar-Berman, 2000; Pfefferbaum and Sullivan, 2005; Durazzo and Meyerhoff, 2007).

To the best of our knowledge, no reports have simultaneously examined the ability of proton magnetic resonance (MR)-derived measures of brain metabolites and morphology, as well as neurocognitive, psychiatric, demographic and behavioral factors to predict resumption of alcohol consumption after treatment for adult AUD. Thus, the primary goal of the current work was to determine the extent to which our quantitative neuroimaging, neurocognitive, clinical laboratory and psychiatric/behavioral outcome measures obtained in treatment-seeking adult alcoholics with ~1 month of abstinence predict drinking status 6–12 months subsequent to outpatient treatment for AUD. We hypothesized that individuals who resumed drinking after treatment demonstrate lower levels of markers of neuronal integrity and cell membrane turnover/synthesis in the frontal, parietal and temporal lobes, poorer executive skills, and greater anxiety and depressive symptomatology relative to those who remained abstinent.


Participants and measures

Seventy outpatient participants (three females) were recruited from the VA Medical Center Substance Abuse Day Hospital (n = 62) and the Kaiser Permanente Chemical Dependence Recovery Program (n = 8) in San Francisco for a longitudinal study on the effects of abstinence from alcohol on neurobiology and neurocognition. Outpatient treatment ranged from 14 to 30 days. All participants were between the ages of 28 and 66 years and met DSM-IV criteria for alcohol dependence at study enrollment. Primary inclusion criteria were fluency in English, DSM-IV diagnosis of alcohol dependence or alcohol abuse at the time of enrollment (all met the criteria for alcohol dependence with physiological dependence), consumption of >150 standard alcoholic drinks (i.e. 13.6 g of ethanol) per month for at least 8 years prior to enrollment for men, or consumption of <80 drinks per month for at least 6 years prior to enrollment for women. See Table Table11 for demographic data.

Table 1
Demographic, alcohol and cigarette consumption, mood and anxiety measures, and clinical laboratory variables

Medical exclusion criteria were history of the following: intrinsic cerebral masses, HIV/AIDS, cerebrovascular accident, brain aneurysm, arteriovenous malformations, peripheral vascular disease, myocardial infarction, uncontrolled chronic hypertension (systolic >180 mmHg and/or diastolic >120 mmHg), type I diabetes, moderate or severe chronic obstructive pulmonary disease, nonalcohol-related seizures, significant exposure to known neurotoxins (e.g. toluene, carbon tetrachloride), demyelinating and neurodegenerative diseases, Wernicke–Korsakoff syndrome, alcohol-induced persisting dementia, penetrating head trauma and closed head injury resulting in loss of consciousness for >10 min. Psychiatric exclusion criteria were history of schizophrenia or other thought disorders, bipolar disorder, dissociative disorders, posttraumatic stress disorder, obsessive compulsive disorder, panic disorder (with or without agoraphobia), major depression with mood-incongruent psychotic symptoms, dependence on any substance other than alcohol or nicotine in the 5 years immediately prior to enrollment, intravenous drug use in the 5 years immediately prior to enrollment in the study and current opioid agonist therapy.

At the time of enrollment (7 ± 3 days of sobriety), participants completed the Clinical Interview for DSM-IV Axis I Disorders, Version 2.0 (SCID-I/P) (First et al., 1998) and semi-structured interviews for lifetime alcohol consumption (Lifetime Drinking History; Sobell et al., 1988; Sobell and Sobell, 1992) and substance use (in-house questionnaire assessing substance type, and quantity and frequency of use). From the Lifetime Drinking History, we derived average number of alcoholic drinks per month over 1, 3 and 8 years prior to enrollment, average number of drinks per month over lifetime, lifetime years of regular drinking (i.e. years in which the participant consumed at least one alcoholic drink per month), age of onset and duration of heavy drinking (defined as drinking more than 100 drinks per month in males and 80 drinks per month in females). After 34 ± 8 days of abstinence and near or following conclusion of outpatient treatment, participants completed proton MR studies at 1.5 Tesla magnetic field strength that yielded regional brain volumes and surrogate markers of neuronal integrity (N-acetylaspartate: NAA) and cell membrane turnover/synthesis (choline-containing compounds: Cho). In the morphological MR studies, regional white matter (WM), gray matter (GM) and cerebral spinal fluid (CSF) volumes were obtained via automated probabilistic segmentation and combined with automated atlas-based labeling of the major lobes, cerebellum and subcortical structures. All volumes were corrected for intracranial volume. Multislice proton magnetic resonance spectroscopic imaging (1H MRSI) yielded regional absolute concentrations for NAA, Cho and other metabolites, which were derived from region-averaged metabolite spectra obtained in three parallel planes through the centrum semiovale, basal ganglia and cerebellar vermis. For details on the acquisition and processing of MR data, see Meyerhoff et al. (2004) and Cardenas et al. (2005).

Within ~1 day of the MR studies, participants completed comprehensive neuropsychological and motor/ataxia assessment (~2.5 h), which evaluated neurocognitive functions known to be adversely affected by alcohol dependence (Rourke and Grant, 1999) and chronic cigarette smoking (Durazzo and Meyerhoff, 2007; Swan and Lessov-Schlaggar, 2007). The domains evaluated and the constituent measures were as follows. Executive skills: Short Categories Test (Wetzel and Boll, 1987), Stroop Color–Word Test (Golden, 1978), Trail Making Test part B (Reitan and Wolfson, 1985), Wechsler Adult Intelligence Scale 3rd Edition (WAIS-III) Similarities (Wechsler, 1997), Wisconsin Card Sorting Test-64: Computer Version 2—Research Edition (Kongs et al., 2000) nonperseverative errors, perseverative errors, and perseverative responses. Fine motor skills: Grooved Peg Board [Lafayette Instrument (1989), Lafayette, IN, USA). General intelligence: Ward-7 Full Scale IQ [(Axelrod et al., 2001) based on WAIS-III Arithmetic, Block Design, Digit Span, Digit Symbol, Information, Picture Completion and Similarities subtests (Wechsler, 1997)]. Learning and memory: Auditory–verbal: California Verbal Learning Test-II (Delis et al., 2000), Immediate Recall trials 1–5 (learning), Short and Long Delay Free Recall (memory); Visuospatial: Brief Visuospatial Memory Test-Revised (Benedict, 1997), Total Recall (learning) and Delayed Recall (memory). Postural stability: Sharpened Romberg, eyes-closed condition (Fregly and Graybiel, 1968). Processing speed: WAIS-III Digit Symbol, Stroop Color & Word (Golden, 1978), WAIS-III Symbol Search (Wechsler, 1997), Trail Making Test-A (Reitan and Wolfson, 1985). Visuospatial skills: WAIS-III Block Design; Luria–Nebraska Item 99 (Golden et al., 1978). Working memory: WAIS-III Arithmetic, WAIS-III Digit Span. Cognitive efficiency: This domain consisted of all tests that were timed, or in which the time to complete the task influenced the score achieved. The Cognitive efficiency domain was calculated by averaging the individual z-scores of those measures (see below). Timed tests included the Luria–Nebraska Item 99 ratio, Stroop word, color and color–word tests, Trails A and B and WAIS-III Arithmetic, Block Design, Digit Symbol, Picture Completion and Symbol Search. Higher scores on these measures reflect better speed and accuracy on principally nonverbal tasks. The cognitive efficiency domain is an approximation of the concept of cognitive efficiency described by Glen and Parsons (1992) and Nixon and colleagues (1995, 1998). Premorbid verbal intelligence was estimated with the American National Adult Reading Test (Grober and Sliwinski, 1991). Participants also completed standardized questionnaires assessing depressive [Beck Depression Inventory: BDI (Beck, 1978)] and anxiety symptomatology [(State-Trait Anxiety Inventory, form Y-2: STAI (Spielberger et al., 1977)], and nicotine dependence via the Fagerstrom Tolerance Test for Nicotine Dependence (FTND) (Fagerstrom et al., 1991). All smoking participants were allowed to smoke ad libitum at the time of all assessments. The total number of cigarettes smoked per day and the number of years of smoking at the current level were recorded and pack years calculated. Follow-up for all participants occurred 6–12 months following the 1-month assessment. Participants were urine-tested for illicit substances immediately before all assessments (i.e. cannabinoids, opiates, phencyclidine, cocaine and amphetamines).

Fifty-two of 70 participants were revaluated 227 ± 71 days after the 1-month assessment with all MR, neurocognitive and psychiatric outcome measures administered at the 1-month assessment. Alcohol consumption during this interval was evaluated with the Timeline Follow-Back Interview (Sobell and Sobell, 1992), and any other substance use was recorded. The disposition of the remaining 18 participants was obtained via brief face-to-face or telephone interview with the participants (n = 5), medical records (confined to entries from mental health professionals providing outpatient substance abuse treatment for the participant; n = 8) or telephone interview of collateral sources (i.e. family or friends; n = 5).

Participants were designated as Abstainers (n = 26) if (a) they self-reported no alcohol consumption between the 1-month assessment and follow-up; (b) there was no report of alcohol consumption between the 1-month assessment and follow-up in medical records; (c) laboratory indicators of recent alcohol consumption (e.g. gamma glutamyltransferase: GGT) were within normal limits at follow-up. Participants were designated as resumers of alcohol consumption (Resumers; n = 44) if (a) they self-reported any alcohol consumption at any time between the 1-month assessment and follow-up via telephone or in-person interview; (b) the use of alcohol was indicated in medical records by mental health professionals providing outpatient substance abuse treatment for the participant or other medical professionals; (c) they report of alcohol use by a relative or close friend of the participant via telephone or in-person interview.

To assist in characterizing the severity of the drinking episode(s) in Resumers, we identified the number of participants who met the Project MATCH criteria for an alcohol relapse (i.e. males: ≥3 consecutive days of consumption of ≥6 drinks per day; females: ≥3 consecutive days of consumption of ≥4 drinks per day). These criteria were applied only to those Resumers who had specific quantity/frequency information regarding their drinking episodes after the 1-month assessment.

The 26 Abstainers were initially reassessed 183 ± 73 days and the 44 Resumers 268 ± 66 days after the 1-month assessment. Of 26 Abstainers, 24 were again successfully interviewed later in person or via telephone, at different intervals, to obtain self-reports on drinking status. At this second follow-up, 24/26 Abstainers self-reported 764 ± 433 days (minimum = 165, maximum = 2004) of continuous sobriety following their 1-month assessment. Two Abstainers were lost to this second follow-up. All participants gave written informed consent, which was approved by review boards of the University of California, San Francisco, and the San Francisco VA Medical Center.

Data analyses

Volumetric and metabolite concentration data were converted to z-scores based on a group of 35 age-equivalent controls. Neurocognitive data were converted to z-scores from age- or age and education-corrected test norms. Outcome measures were grouped according to demographic, alcohol consumption, cigarette consumption, psychiatric variables, clinical laboratories, neurocognitive, regional MRI volumetric and 1H MRSI variables (see Table Table2).2). Abstainers and Resumers were contrasted on these grouped variables using t-tests or nonparametric tests. A modified Bonferroni procedure was employed to adjust alpha levels (P = 0.05, two-tailed) for multiple comparisons. This procedure corrected the alpha level according to the intercorrelations among measures and number of measures in each group of outcome variables (Sankoh et al., 1997). Six individual measures were found to be significantly different after correction for multiple comparisons (see Table Table3).3). These six predictors were simultaneously entered into a binary logistic regression, with future drinking status as the dependent measure. The alpha level for the omnibus binary logistic regression was adjusted for the six significant predictors entered into the model (P = 0.008), and an alpha level of 0.05 was considered statistically significant for each of the individual predictors. All analyses were completed with SPSS v15.

Table 2
Outcome variables and constituent measures
Table 3
Outcome measures significantly different between Abstainers and Resumers at the 1-month assessment


Demographic, alcohol and cigarette consumption variables

Of the 70 participants, 26 (37%) were Abstainers and 44 (63%) were Resumers. Thirty-two of 44 Resumers (73%) met the Project MATCH criteria for an alcohol relapse. Table Table44 provides alcohol use characteristics for Resumers over the interval between the 1-month assessment and follow-up. In the total sample, 76% were Caucasian, 12% African American, 7% Latino and 5% Native American/Aleutian. Abstainers and Resumers were equivalent on age, education, predicted premorbid verbal intelligence and body mass index (see Table Table1).1). Abstainers and Resumers were also equivalent on average number of drinks per month over 1, 3 and 8 years prior to enrollment and number of months of heavy drinking. Resumers tended to consume more drinks per month over lifetime than Abstainers (P = 0.13). Sixty percent of Resumers and 48% of Abstainers were chronic smokers; the frequency of smokers was not significantly different between groups (P = 0.22), and smokers in both groups were equivalent on measures of smoking severity (see Table Table1).1). As shown in Table Table5,5, Abstainers demonstrated a significantly higher level of psychosocial functioning than Resumers at follow-up. There were no significant differences in indexes of psychosocial functioning (e.g. socioeconomic status, percent gainfully employed) between Abstainers and Resumers at study entry.

Table 4
Drinking characteristic of Resumers between the 1-month assessment and follow-up
Table 5
Employment, educational and treatment status for Abstainers and Resumers at the 1-month assessment and follow-up

Comorbid psychiatric disorders, medical and substance use

At enrollment, a significantly greater proportion of Resumers (20 of 44; 46%) met current DSM-IV criteria for a unipolar mood disorder (i.e. major depression, or substance-induced mood disorder with depressive features) compared to Abstainers (4 of 26; 15%) (P = 0.01). Among those who met the criteria for a unipolar mood disorder, 40% were diagnosed with recurrent major depression and 60% with a substance-induced (alcohol) mood disorder with depressive features. At the 1-month assessment, 30% of participants diagnosed with a unipolar mood disorder took an antidepressant medication. There was no difference between Resumers and Abstainers in the frequency of use of antidepressant medication. At the 1-month assessment, 40% of Resumers and 36% of Abstainers had a comorbid medical condition (P = 0.85); for both groups, the most common condition was hypertension, followed by hepatitis C. The frequency of these conditions was equally distributed across groups. In both the Resumer and Abstainer groups, ~70% of participants with hypertension took antihypertensive medications. Seven Resumers (16%) had a previous history of substance abuse or dependence, but in each case, the substance use disorder was in sustained full remission at enrollment. Two Abstainers (8%) met the criteria for current substance abuse at the time of enrollment.

Predictors of drinking status at follow-up

The binary logistic regression model was significant [χ2 (6) = 38.0, P < 0.001, r2 (Nagelkerke) = 0.72] with the following predictors: temporal GM NAA [β = −1.13, P = 0.047, Exp(β) = 0.322 (95% CI = 0.170–0.938)], frontal WM NAA [β = −1.81, P = 0.049, Exp(β) = 0.302 (95% CI = 0.080–0.923)], frontal GM Cho [β = −1.85, P = 0.024, Exp(β) = 0.157 (95% CI = 0.032–0.780)], processing speed [β = –4.30, P = 0.003, Exp(β) = 0.070 (95% CI = 0.023–0.232)] and comorbid unipolar mood disorder [β = −4.51, P = 0.004, Exp(β) = 0.069 (95% CI = 0.007–0.158)]. The model accurately classified 83% of Abstainers and 90% of Resumers into their respective groups and accounted for 72% of the variance in drinking status at follow-up. With each standard deviation unit decrease in temporal GM NAA, frontal WM NAA, frontal GM Cho and processing speed, the odds of resumption of drinking were increased 3.1, 3.3, 6.4 and 14.2 times, respectively. Diagnosis of a unipolar mood disorder at enrollment was associated with a 14.5-fold increase of the odds of resumption of drinking. Colinearity diagnostics indicated that each of the five predictors accounted for a unique portion of the variance in drinking status at follow-up. Removal of females from the analysis did not appreciably alter the foregoing findings.

In Resumers, lower frontal WM NAA (r = −0.30), temporal GM NAA (r = −0.37) and processing speed (r = −0.39) at the 1-month assessment were related to a greater number of drinking days at follow-up; however, these associations were not statistically significant after correction for multiple comparisons.

The trend differences for higher BDI score (P = 0.10) and average drinks/month over lifetime (P = 0.13) in Resumers prompted us to conduct a second logistic regression in which these variables were entered together with the five significant predictors described above. Additionally, comorbid medical conditions (e.g. hypertension, hepatitis C), smoking history and substance abuse history (all categorical variables) were also individually entered with the five predictors. None of these additional factors were significant predictors, and each decreased the classification accuracy of the original model.

Neurocognitive and psychiatric evaluations are far more commonplace than MR assessment in treatment settings. Therefore, we examined the added value of using our MR outcome variables in the prediction of resumption of drinking in our cohort by entering processing speed and unipolar depression as the sole predictors into the logistical regression model to predict future drinking status. These two predictors explained only 26% of the variance and accurately classified 62% of Abstainers and 80% of Resumers.


In this sample of predominately male Caucasian veterans, a combination of neurocognitive, psychiatric and MR-derived neurobiological variables were found to predict, with high accuracy, those who resumed alcohol consumption and those who maintained abstinence following completion of outpatient treatment for AUD. At ~1 month of abstinence from alcohol, decreasing levels of temporal GM NAA, frontal WM NAA, frontal GM Cho and processing speed, together with the presence of comorbid unipolar depression (at study entry), were significant predictors of resumed alcohol consumption. Combined, these predictors accurately classified 83% of Abstainers and 90% of Resumers into their respective groups, accounting for 72% of the variance in drinking status at follow-up. Taken together, the data suggest that the combination of these variables are robust predictors of drinking status over 12 or more months after outpatient treatment in this cohort.

The clinical relevance of participant drinking status at follow-up is apparent in the higher psychosocial functioning in Abstainers compared to Resumers, as evidenced by the greater percentages of Abstainers who were gainfully employed, in long-term treatment and/or attending school at follow-up (see Table Table5).5). Abstainers also reported an average of 764 days of continuous sobriety following outpatient treatment at the second follow-up (24 of 26 Abstainers who were successfully contacted). For the vast majority of Resumers, the magnitude and duration of their drinking episode(s) were more than a simple ‘slip’ and commensurate with their overall psychosocial functioning.

Long-term alcohol and substance use disorders promote enduring abnormalities in brain morphology, metabolism and biochemistry, most prominently in the midbrain, basal forebrain, basal ganglia, diencephalon, limbic system, and the frontal and mesial temporal lobes (Pfefferbaum et al., 1998; Sullivan, 2000; Cami and Farre, 2003; Koob, 2003; Crews et al., 2005; Kalivas and Volkow, 2005). These structures/regions are components of the reward circuit implicated in the development and maintenance of alcohol/substance use disorders [see Kalivas and Volkow (2005)]. Baler and Volkow (2006) suggest that chronic alcohol/substance abuse leads to prolonged plastic modifications in the functional connectivity of the reward circuit, which alters the ability of anterior dorsolateral and dorsomedial frontal regions to regulate other frontal and limbic structures/regions involved in motivation, drive and evaluation of stimulus salience. We observed that lower concentrations of temporal GM NAA, frontal WM NAA and frontal GM Cho at 1 month of abstinence were associated with increased risk for resumption of hazardous levels alcohol consumption. The significantly lower temporal GM NAA and frontal WM NAA demonstrated in Resumers relative to Abstainers at 1 month of abstinence may be indicative of morphological abnormalities of axons and neuroglia (Sullivan, 2000; Crews et al., 2005; Harper et al., 2005) and/or derangements of neurometabolism (De Stefano et al., 1995; Hugg et al., 1996; Moffett et al., 2007). The 1H MRSI Cho signal originates mainly from phosphocholine and glycerophosphocholine. Cho concentrations are thought to reflect cellular membrane turnover and density (Barker et al., 1994; Miller et al., 1996a) and/or myelin catabolism (Ross and Bluml, 2001). The lower frontal GM Cho concentration in Resumers at 1 month of abstinence suggests abnormalities in cell membrane synthesis/turnover of neuronal and/or glial tissue in that region. These regional metabolite findings are congruent with Noel and colleagues (2002), who reported lower frontal lobe cerebral blood flow in relapsers relative to individuals who remained abstinent 2 months after their initial assessment. At least 25–30% of our 1H MRSI voxels covering the frontal lobes are spatially located in the anterior mesial (Brodmann areas 9, 10, 24, 32) and dorsolateral (Brodmann areas 9 and 10) frontal cortex, components of the anterior frontal-subcortical circuits implicated in emotional and behavioral regulation (Baler and Volkow, 2006). Virtually, none of our spectroscopic imaging voxels were spatially localized in mesial temporal (e.g. hippocampal complex) or basal forebrain regions/structures that are implicated in the development and maintenance of addiction. However, >30% of our temporal GM spectroscopic imaging voxels were localized in the insula, a temporal GM region implicated in decision making, assignment of emotional valence and cue-induced alcohol/substance craving (Bechara et al., 1999; Paulus, 2007; Sinha and Li, 2007). Studies have reported morphological abnormalities in the insula of cocaine-dependent individuals (Franklin et al., 2002) and chronic cigarette smokers (Gallinat et al., 2006). Taken together, the lower temporal GM NAA, frontal WM NAA and frontal GM Cho seen in Resumers may reflect dysfunction of circuits involved in the modulation of internal drive states, mood and behavior, which may convey increased risk for resumed drinking.

In this study, participants who met DSM-IV criteria for a unipolar mood disorder at study enrollment (34%) had 14.5 times greater odds for relapse. Participants were either diagnosed with recurrent major depression or substance-induced (alcohol) mood disorder with depressive features. For each subtype, the depressive symptomatology was generally characterized by multiple episodes over several months to years during the participant's lifetime. There was a trend (P = 0.10) for a higher mean BDI in Resumers (9.9 ± 8.5) relative to Abstainers (6.5 ± 5.7) at 1 month of abstinence. However, the mean score of Resumers was just above the clinical cutoff of 9, indicating only a mild level of depressive symptomatology, and the BDI was not a significant predictor of relapse in this cohort. Approximately 30% of participants diagnosed with a unipolar mood disorder in both groups took an antidepressant at the time of the 1-month assessment. Outpatient substance abuse treatment, antidepressants, and any neurobiological and neurocognitive recovery during early recovery may have contributed to the low BDI scores at 1 month of abstinence in both the groups. Previous studies with treatment-seeking alcoholics indicated that higher BDI scores (Parsons et al., 1990; Glenn and Parsons, 1991; Miller et al., 1996b; Bottlender and Soyka, 2005; Kodl et al., 2008), negative affect (Zywiak et al., 2006) and endorsement of depressed mood (Strowig, 2000) were associated with greater risk for relapse. In this cohort, a diagnosis of a unipolar mood disorder at study entry predicted relapse, whereas the self-reported level of depressive symptomatology (via the BDI) at 1 month of abstinence was not a significant predictor. This finding may be related to a diagnosis of unipolar depression reflecting a chronic and recurrent neuropsychiatric condition in our participants, while the BDI represents the magnitude of depressive symptomatology over a 7-day period, which can be strongly influenced by recent life circumstances (Richter et al., 1998). Both unipolar mood disorders and addiction evidence morphological, metabolic and neurotransmitter/neuromodulator abnormalities in similar brain regions (e.g. orbitofrontal, dorsolateral and ventromedial frontal lobes, anterior cingulate gyrus), as well as their WM connectivity with other cortical and subcortical regions/structures (e.g. basal ganglia, thalamus) (Drevets, 1999; Benes and Berretta, 2001; Deicken et al., 2001; Wang et al., 2001; Sheline, 2003; Kanner, 2004; Seminowicz et al., 2004; Kalivas and Volkow, 2005; Baler and Volkow, 2006; Campbell and MacQueen, 2006; Yildiz-Yesiloglu and Ankerst, 2006; Hasler et al., 2007). This is particularly apparent for circuits involved in affective expression/regulation and behavioral regulation. Enduring disturbances in these frontal GM regions, their WM connectivity and/or corresponding neurotransmitters may decrease the ability to respond adaptively to common or significant psychosocial stressors, suppress alcohol cravings and/or attain/maintain a euthymic mood, thereby increasing the risk for relapse. Distinctions have been made between alcohol-induced unipolar depressive disorders and independent major depressive disorders with regard to onset, course, and persistence of symptoms following detoxification (Raimo and Schuckit, 1998; Verheul et al., 2000; Kahler et al., 2002). The small size of the subgroups with substance-induced and recurrent major depression, however, precluded their use as factors in our study models.

Lower processing speed at 1 month of abstinence was associated with a 14 times greater risk for relapse in this sample. Measures comprising the processing speed domain were Symbol Search and Digit Symbol from the WAIS-III, Color & Word trials of the Stroop Test and Trail Making Test-A. These measures involve visuomotor scanning, cognitive flexibility and incidental learning, where rapid and accurate responses lead to better performance. While frontal-subcortical circuitry likely contributes to the execution of these tasks (Cummings, 1998), multiple primary and heteromodal neocortical regions, subcortical nuclei, the cerebellum, and the WM interconnecting these regions/structures all contribute to the processing speed domain. Tapert and colleagues (2004) observed that the interactions of Trails A and Digit Symbol (measures of processing speed), with coping styles, predicted number of days of alcohol consumption following treatment. Previous studies found that worse performance on a composite measure of learning and memory, problem solving, abstraction and perceptual motor function was associated with relapse (Parsons et al., 1990; Glenn and Parsons, 1991); however, in these studies depressive symptomatology, as measured by the BDI, was a stronger predictor of relapse than neurocognitive measures. In the present report, both lower processing speed and comorbid unipolar mood disorder were independent predictors of drinking status and both were associated with nearly the same increase of odds for relapse at follow-up. There were trends for Abstainers to have better executive skills (P = 0.07) and cognitive efficiency (P = 0.10) than Resumers at the 1-month assessment. Subtle deficiencies in these domains, in addition to processing speed, may interfere with the integration and/or practical application of skills presented in the participant's early recovery outpatient programs (Bates et al., 2002; Tapert et al., 2004).

Limitations of this study include the reliance on self-report and/or medical records for the determination of drinking status in some participants and the inability to examine sex effects due to the small number of female participants. We did not obtain measures of coping skills, self-esteem/self-efficacy, social support and personality disorders, which have been shown to predict drinking behavior after treatment. We were also unable to assess if the relapse risk associated with substance-induced depressive disorders is different from that imposed by recurrent major depression. It is also possible that the drinking behavior subsequent to treatment in this cohort was influenced by genetic or environmental factors not assessed in this report.

In summary, at the 1-month assessment, Resumers relative to Abstainers demonstrated lower metabolite concentrations in the frontal lobe and temporal GM, as well as lower processing speed, and a higher frequency of comorbid unipolar depression. Overall, this suggests that Resumers experienced abnormalities in anterior frontal-subcortical circuits that are involved in the development and maintenance of AUD, emotional processing, mood and behavioral regulation, and fast and flexible cognitive processes. Altered integrity of these circuits may have increased the risk of resumed alcohol consumption through a combination of factors, such as diminished coping skills, increased sensitivity to cue-induced cravings, deficient cognitive flexibility, decreased impulse control and chronically dysphoric mood. It is noteworthy that our Abstainers demonstrated a markedly higher level of psychosocial functioning than Resumers (which were comparable at enrollment), as evidenced by the considerably higher percentages of Abstainers who were gainfully employed, in long-term treatment and/or school at follow-up (see Table Table5).5). Additionally, Abstainers reported >2 years of continuous sobriety following outpatient treatment at the second follow-up. This may be related to overall better neurobiological, neurocognitive and psychiatric functioning in our Abstainers relative to Resumers, as suggested by the pattern of group differences on these measures observed at 1 month of abstinence from alcohol. Our findings highlight the benefit of including MR-derived neurobiological measures in addition to the more conventional neurocognitive and psychiatric factors in the prediction of relapse following treatment. Given the indications from this and other studies that relapse in AUD may preferentially involve abnormalities in frontal- and temporal-subcortical circuitry implicated in the development and maintenance of substance use disorders [see Baler and Volkow (2006)], future neuroimaging research may wish to investigate separately those regions of the anterior frontal and mesial temporal regions, and their WM connectivity.


This project was supported by NIH AA10788 (D.J.M.). We thank Mary Rebecca Young and Bill Clift of the VA Substance Abuse Day Hospital and Dr David Pating, Karen Moise and their colleagues at the Kaiser Permanente Chemical Dependency Recovery Program for their valuable assistance in recruiting participants. We also wish to extend our gratitude to the study participants, who made this research possible.


  • Adinoff B, Junghanns K, Kiefer F, et al. Suppression of the HPA axis stress-response: implications for relapse. Alcohol Clin Exp Res. 2005;29:1351–5. [PMC free article] [PubMed]
  • Axelrod BN, Ryan JJ, Ward CL. Evaluation of seven-subtest short forms of the Wechsler Adult Intelligence Scale-III in a referred sample. Arch Clin Neuropsychol. 2001;16:1–8. [PubMed]
  • Baler RD, Volkow ND. Drug addiction: the neurobiology of disrupted self-control. Trends Mol Med. 2006;12:559–66. [PubMed]
  • Barker PB, Breiter SN, Soher BJ, et al. Quantitative proton spectroscopy of canine brain: in vivo and in vitro correlations. Magn Reson Med. 1994;32:157–63. [PubMed]
  • Bartels C, Kunert HJ, Stawicki S, et al. Recovery of hippocampus-related functions in chronic alcoholics during monitored long-term abstinence. Alcohol Alcohol. 2007;42:92–102. [PubMed]
  • Bates ME, Bowden SC, Barry D. Neurocognitive impairment associated with alcohol use disorders: implications for treatment. Exp Clin Psychopharmacol. 2002;10:193–212. [PubMed]
  • Bechara A, Damasio H, Damasio AR, et al. Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. J Neurosci. 1999;19:5473–81. [PubMed]
  • Beck AT. Depression Inventory. Philadelphia, PA: Center for Cognitive Therapy; 1978.
  • Benedict R. Brief Visuospatial Memory Test—Revised: Professional Manual. Odessa, FL: Psychological Assessment Resources; 1997.
  • Benes FM, Berretta S. GABAergic interneurons: implications for understanding schizophrenia and bipolar disorder. Neuropsychopharmacology. 2001;25:1–27. [PubMed]
  • Boening JA, Lesch OM, Spanagel R, et al. Pharmacological relapse prevention in alcohol dependence: from animal models to clinical trials. Alcohol Clin Exp Res. 2001;25:127S–31S. [PubMed]
  • Bottlender M, Soyka M. Efficacy of an intensive outpatient rehabilitation program in alcoholism: predictors of outcome 6 months after treatment. Eur Addict Res. 2005;11:132–7. [PubMed]
  • Bowirrat A, Oscar-Berman M. Relationship between dopaminergic neurotransmission, alcoholism, and reward deficiency syndrome. Am J Med Genet (Part B: Neuropsychiatr Genet) 2005;132:29–37. [PubMed]
  • Bradizza CM, Stasiewicz PR, Paas ND. Relapse to alcohol and drug use among individuals diagnosed with co-occurring mental health and substance use disorders: a review. Clin Psychol Rev. 2006;26:162–78. [PubMed]
  • Cami J, Farre M. Mechanisms of disease: drug addiction. N Engl J Med. 2003;349:975–86. [PubMed]
  • Campbell S, MacQueen G. An update on regional brain volume differences associated with mood disorders. Curr Opin Psychiatry. 2006;19:25–33. [PubMed]
  • Cardenas VA, Studholme C, Meyerhoff DJ, et al. Chronic active heavy drinking and family history of problem drinking modulate regional brain tissue volumes. Psychiatry Res. 2005;138:115–30. [PubMed]
  • Crews FT, Buckley T, Dodd PR, et al. Alcoholic neurobiology: changes in dependence and recovery. Alcohol Clin Exp Res. 2005;29:1504–13. [PubMed]
  • Cummings JL. Frontal-subcortical circuits and human behavior. J Psychosom Res. 1998;44:627–8. [PubMed]
  • De Stefano N, Matthews PM, Arnold DL. Reversible decreases in N-acetylaspartate after acute brain injury. Magn Reson Med. 1995;34:721–7. [PubMed]
  • Deicken RF, Eliaz Y, Chosiad L, et al. Proton MRSI of prefrontal–thalamic–cerebellar circuits in bipolar I disorder. Biol Psychiatry. 2001;49:27S.
  • Delis DC, Kramer JH, Kaplan E, et al. California Verbal Learning Test, 2nd Edition. San Antonio, TX: The Psychological Corporation; 2000.
  • Donovan DM, Kivlahan DR, Walker RD. Clinical limitations of neuropsychological testing in predicting treatment outcome among alcoholics. Alcohol Clin Exp Res. 1984;8:470–5. [PubMed]
  • Drevets WC. Prefrontal cortical-amygdalar metabolism in major depression. Ann N Y Acad Sci. 1999;877:614–37. [PubMed]
  • Durazzo TC, Meyerhoff DJ. Neurobiological and neurocognitive effects of chronic cigarette smoking and alcoholism. Front Biosci. 2007;12:4079–100. [PubMed]
  • Fagerstrom KO, Heatherton TF, Kozlowski LT. Nicotine addiction and its assessment. Ear Nose Throat J. 1991;69:763–5. [PubMed]
  • First MB, Spitzer RL, Gibbon M, et al. Structured Clinical Interview for DSM-IV Axis I Disorders—Patient Edition (SCID-I/P, Version 2.0, 8/98 Revision) New York: Biometrics Research Department; 1998.
  • Franklin TR, Acton PD, Maldjian JA, et al. Decreased gray matter concentration in the insular, orbitofrontal, cingulate, and temporal cortices of cocaine patients. Biol Psychiatry. 2002;51:134–42. [PubMed]
  • Fregly AR, Graybiel A. An ataxia test battery not requiring rails. Aerosp Med. 1968;39:277–82. [PubMed]
  • Gallinat J, Meisenzahl E, Jacobsen LK, et al. Smoking and structural brain deficits: a volumetric MR investigation. Eur J Neurosci. 2006;24:1744–50. [PubMed]
  • Glenn SW, Parsons OA. Prediction of resumption of drinking in posttreatment alcoholics. Int J Addict. 1991;26:237–54. [PubMed]
  • Glenn SW, Parsons OA. Neuropsychological efficiency measures in male and female alcoholics. J Stud Alcohol. 1992;53:546–52. [PubMed]
  • Golden CJ. Stroop Color and Word Test. Chicago, IL: Stoelting Company; 1978.
  • Golden CJ, Hammeke TA, Purisch AD. Diagnostic validity of a standardized neuropsychological battery derived from Luria's neuropsychological tests. J Consult Clin Psychol. 1978;46:1258–65. [PubMed]
  • Grober E, Sliwinski M. Development and validation of a model for estimating premorbid verbal intelligence in the elderly. J Clin Exp Neuropsychol. 1991;13:933–49. [PubMed]
  • Grusser SM, Wrase J, Klein S, et al. Cue-induced activation of the striatum and medial prefrontal cortex is associated with subsequent relapse in abstinent alcoholics. Psychopharmacology (Berl) 2004;175:296–302. [PubMed]
  • Harper C, Matsumoto I, Pfefferbaum A, et al. The pathophysiology of ‘brain shrinkage’ in alcoholics—structural and molecular changes and clinical implications. Alcohol Clin Exp Res. 2005;29:1106–15.
  • Hasler G, Van Der Veen JW, Tumonis T, et al. Reduced prefrontal glutamate/glutamine and gamma-aminobutyric acid levels in major depression determined using proton magnetic resonance spectroscopy. Arch Gen Psychiatry. 2007;64:193–200. [PubMed]
  • Heinz A, Schafer M, Higley JD, et al. Neurobiological correlates of the disposition and maintenance of alcoholism. Pharmacopsychiatry. 2003;36(Suppl 3):S255–8. [PubMed]
  • Heinz A, Wrase J, Kahnt T, et al. Brain activation elicited by affectively positive stimuli is associated with a lower risk of relapse in detoxified alcoholic subjects. Alcohol Clin Exp Res. 2007;31:1138–47. [PubMed]
  • Hugg JW, Kuzniecky RI, Gilliam FG, et al. Normalization of contralateral metabolic function following temporal lobectomy demonstrated by h-1 magnetic resonance spectroscopic imaging. Ann Neurololgy. 1996;40:236–9. [PubMed]
  • Jin H, Rourke SB, Patterson TL, et al. Predictors of relapse in long-term abstinent alcoholics. J Stud Alcohol. 1998;59:640–6. [PubMed]
  • Kahler CW, Ramsey SE, Read JP, et al. Substance-induced and independent major depressive disorder in treatment-seeking alcoholics: associations with dysfunctional attitudes and coping. J Stud Alcohol. 2002;63:363–71. [PubMed]
  • Kalivas PW, Volkow ND. The neural basis of addiction: a pathology of motivation and choice. Am J Psychiatry. 2005;162:1403–13. [PubMed]
  • Kanner AM. Is major depression a neurologic disorder with psychiatric symptoms? Epilepsy Behav. 2004;5:636–44. [PubMed]
  • Kodl MM, Fu SS, Willenbring ML, et al. The impact of depressive symptoms on alcohol and cigarette consumption following treatment for alcohol and nicotine dependence. Alcohol Clin Exp Res. 2008;32:92–9. [PubMed]
  • Kongs SK, Thompson LL, Iverson GL, et al. Wisconsin Card Sorting Test-64 Card Version Professional Manual. Lutz, FL: PAR Inc; 2000.
  • Koob GF. Alcoholism: allostasis and beyond. Alcohol Clin Exp Res. 2003;27:232–43. [PubMed]
  • Krampe H, Wagner T, Stawicki S, et al. Personality disorder and chronicity of addiction as independent outcome predictors in alcoholism treatment. Psychiatr Serv. 2006;57:708–12. [PubMed]
  • Lafayette Instrument 1989. Grooved Pegboard Test, Model 32025. Lafayette, IN.
  • Lubman DI, Yucel M, Pantelis C. Addiction, a condition of compulsive behaviour? Neuroimaging and neuropsychological evidence of inhibitory dysregulation. Addiction. 2004;99:1491–502. [PubMed]
  • Meyerhoff D, Blumenfeld R, Truran D, et al. Effects of heavy drinking, binge drinking, and family history of alcoholism on regional brain metabolites. Alcohol Clin Exp Res. 2004;28:650–61. [PMC free article] [PubMed]
  • Miller L. Predicting relapse and recovery in alcoholism and addiction: neuropsychology, personality, and cognitive style. J Subst Abuse Treat. 1991;8:277–91. [PubMed]
  • Miller BL, Chang L, Booth R, et al. In vivo 1H MRS choline: correlation with in vitro chemistry/histology. Life Sci. 1996;58:1929–35. [PubMed]
  • Miller WR, Walters ST, Bennett ME. How effective is alcoholism treatment in the United States? J Stud Alcohol. 2001;62:211–20. [PubMed]
  • Miller WR, Westerberg VS, Harris RJ, et al. What predicts relapse? Prospective testing of antecedent models. Addiction. 1996;91(Suppl):S155–72. [PubMed]
  • Moffett JR, Ross B, Arun P, et al. N-acetylaspartate in the CNS: from neurodiagnostics to neurobiology. Prog Neurobiol. 2007;81:89–131. [PMC free article] [PubMed]
  • Monahan SC, Finney JW. Explaining abstinence rates following treatment for alcohol abuse: a quantitative synthesis of patient, research design and treatment effects. Addiction. 1996;91:787–805. [PubMed]
  • Moos RH, Moos BS. Rates and predictors of relapse after natural and treated remission from alcohol use disorders. Addiction. 2006;101:212–22. [PMC free article] [PubMed]
  • Nixon SJ, Paul R, Phillips M. Cognitive efficiency in alcoholics and polysubstance abusers. Alcohol Clin Exp Res. 1998;22:1414–20. [PubMed]
  • Nixon SJ, Tivis R, Parsons OA. Behavioral dysfunction and cognitive efficiency in male and female alcoholics. Alcohol Clin Exp Res. 1995;19:577–81. [PubMed]
  • Noel X, Sferrazza R, Van Der Linden M, et al. Contribution of frontal cerebral blood flow measured by (99m)Tc-Bicisate spect and executive function deficits to predicting treatment outcome in alcohol-dependent patients. Alcohol Alcohol. 2002;37:347–54. [PubMed]
  • Noone M, Dua J, Markham R. Stress, cognitive factors, and coping resources as predictors of relapse in alcoholics. Addict Behav. 1999;24:687–93. [PubMed]
  • Oscar-Berman M. NIAAA Research Monograph No. 34. Human brain vulnerability to alcoholism: evidence from neuroimaging studies. In: Noronha A, Eckardt M, Warren K, editors. Review of NIAAA's Neuroscience and Behavioral Research Portfolio. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism; 2000. pp. 437–72.
  • Parsons O. Neuropsychological consequences of alcohol abuse: many questions–some answers. In: Parsons O, Butters N, Nathan P, editors. Neuropsychology of Alcoholism: Implications for Diagnosis and Treatment. New York: Guilford Press; 1987. pp. 153–75.
  • Parsons OA, Schaeffer KW, Glenn SW. Does neuropsychological test performance predict resumption of drinking in posttreatment alcoholics? Addict Behav. 1990;15:297–307. [PubMed]
  • Paulus MP. Neural basis of reward and craving—a homeostatic point of view. Dialogues Clin Neurosci. 2007;9:379–87. [PMC free article] [PubMed]
  • Pfefferbaum A, Rosenbloom MJ, Serventi KL, et al. Brain volumes, RBC status, and hepatic function in alcoholics after 1 and 4 weeks of sobriety: predictors of outcome. Am J Psychiatry. 2004;161:1190–6. [PubMed]
  • Pfefferbaum A, Sullivan EV, Rosenbloom MJ, et al. A controlled study of cortical gray matter and ventricular changes in alcoholic men over a 5-year interval. Arch Gen Psychiatry. 1998;55:905–12. [PubMed]
  • Raimo EB, Schuckit MA. Alcohol dependence and mood disorders. Addict Behav. 1998;23:933–46. [PubMed]
  • Reitan RM, Wolfson D. The Halstead-Reitan Neuropsychological Test Battery: Theory and Interpretation. Tucson, AZ: Neuropsychological Press; 1985.
  • Richter P, Werner J, Heerlein A, et al. On the validity of the Beck Depression Inventory. A review. Psychopathology. 1998;31:160–8. [PubMed]
  • Ross B, Bluml S. Magnetic resonance spectroscopy of the human brain. Anat Rec. 2001;265:54–84. [PubMed]
  • Rourke SB, Grant I. The interactive effects of age and length of abstinence on the recovery of neuropsychological functioning in chronic male alcoholics: a 2-year follow-up study. J Int Neuropsychol Soc. 1999;5:234–46. [PubMed]
  • Sankoh AJ, Huque MF, Dubey SD. Some comments on frequently used multiple endpoint adjustment methods in clinical trials. Stat Med. 1997;16:2529–42. [PubMed]
  • Seminowicz DA, Mayberg HS, McIntosh AR, et al. Limbic-frontal circuitry in major depression: a path modeling metanalysis. Neuroimage. 2004;22:409–18. [PubMed]
  • Sheline YI. Neuroimaging studies of mood disorder effects on the brain. Biol Psychiatry. 2003;54:338–52. [PubMed]
  • Sinha R, Li CS. Imaging stress- and cue-induced drug and alcohol craving: association with relapse and clinical implications. Drug Alcohol Rev. 2007;26:25–31. [PubMed]
  • Sobell LC, Sobell MB. Timeline follow-back: a technique for assessing self-reported alcohol consumption. In: Litten R, Allen J, editors. Measuring Alcohol Consumption. Totowa, NJ: Humana Press; 1992. pp. 41–72.
  • Sobell LC, Sobell MB, Riley DM, et al. The reliability of alcohol abusers’ self-reports of drinking and life events that occurred in the distant past. J Stud Alcohol. 1988;49:225–32. [published erratum appears in J Stud Alcohol (1989) 50: 92] [PubMed]
  • Spielberger CD, Gorsuch RL, Lushene R, et al. Self-Evaluation Questionaire. Palo Alto, CA: Consulting Psychologists Press; 1977.
  • Strowig AB. Relapse determinants reported by men treated for alcohol addiction: the prominence of depressed mood. J Subst Abuse Treat. 2000;19:469–74. [PubMed]
  • Sullivan EV. NIAAA Research Monograph No. 34. Human brain vulnerability to alcoholism: evidence from neuroimaging studies. In: Noronha A, Eckardt M, Warren K, editors. Review of NIAAA's Neuroscience and Behavioral Research Portfolio. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism; 2000. pp. 473–508.
  • Sullivan EV, Pfefferbaum A. Neurocircuitry in alcoholism: a substrate of disruption and repair. Psychopharmacology (Berl) 2005;180:583–94. [PubMed]
  • Swan GE, Lessov-Schlaggar CN. The effects of tobacco smoke and nicotine on cognition and the brain. Neuropsychol Rev. 2007;17:259–73. [PubMed]
  • Tapert SF, Ozyurt SS, Myers MG, et al. Neurocognitive ability in adults coping with alcohol and drug relapse temptations. Am J Drug Alcohol Abuse. 2004;30:445–60. [PubMed]
  • Teichner G, Horner MD, Harvey RT. Neuropsychological predictors of the attainment of treatment objectives in substance abuse patients. Int J Neurosci. 2001;106:253–63. [PubMed]
  • Verheul R, Kranzler HR, Poling J, et al. Axis I and Axis II disorders in alcoholics and drug addicts: fact or artifact? J Stud Alcohol. 2000;61:101–10. [PubMed]
  • Walter M, Gerhard U, Duersteler-MacFarland KM, et al. Social factors but not stress-coping styles predict relapse in detoxified alcoholics. Neuropsychobiology. 2006;54:100–6. [PubMed]
  • Wang PW, Sachs N, Sailasuta N, et al. 3Tesla 1H-magnetic resonance spectroscopic (MRS) detection of cerebral gamma-aminobutyric acid (GABA) in bipolar disorder patients and healthy volunteers. Biol Psychiatry. 2001;49:27S.
  • Wechsler D. The Wechsler Adult Intelligence Scale—Third Edition. San Antonio, TX: The Psychological Corporation; 1997.
  • Weiss F, Porrino LJ. Behavioral neurobiology of alcohol addiction: recent advances and challenges. J Neurosci. 2002;22:3332–7. [PubMed]
  • Wetzel L, Boll TJ. Short Category Test, Booklet Format. Los Angeles: Western Psychological Services; 1987.
  • Yildiz-Yesiloglu A, Ankerst DP. Review of 1H magnetic resonance spectroscopy findings in major depressive disorder: a meta-analysis. Psychiatry Res. 2006;147:1–25. [PubMed]
  • Zywiak WH, Stout RL, Trefry WB, et al. Alcohol relapse repetition, gender, and predictive validity. J Subst Abuse Treat. 2006;30:349–53. [PubMed]

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