Adolescent boys with ASD had extensive neural hypoactivity during risky decision-making, coupled with decreased activity during reward and increased activity during loss. These neural patterns may underlie the dangerous, excessive, sustained risk-taking of such boys. The findings suggest that the dysphoria, reward insensitivity, and suppressed neural activity observed among older addicted persons also characterize youths early in the development of substance use disorders.

We compared 20 abstinent adolescent male patients in treatment for ASD with 20 community controls, examining rapid event-related blood-oxygen-level-dependent (BOLD) responses during functional magnetic resonance imaging. In 90 decision trials participants chose to make either a cautious response that earned one cent, or a risky response that would either gain 5 cents or lose 10 cents; odds of losing increased as the game progressed. We also examined those times when subjects experienced wins, or separately losses, from their risky choices. We contrasted decision trials against very similar comparison trials requiring no decisions, using whole-brain BOLD-response analyses of group differences, corrected for multiple comparisons. During decision-making ASD boys showed hypoactivation in numerous brain regions robustly activated by controls, including orbitofrontal and dorsolateral prefrontal cortices, anterior cingulate, basal ganglia, insula, amygdala, hippocampus, and cerebellum. While experiencing wins, ASD boys had significantly less activity than controls in anterior cingulate, temporal regions, and cerebellum, with more activity nowhere. During losses ASD boys had significantly more activity than controls in orbitofrontal cortex, dorsolateral prefrontal cortex, brain stem, and cerebellum, with less activity nowhere.

Adolescents with conduct and substance problems (“Antisocial Substance Disorder” (ASD)) repeatedly engage in risky antisocial and drug-using behaviors. We hypothesized that, during processing of risky decisions and resulting rewards and punishments, brain activation would differ between abstinent ASD boys and comparison boys.

Competing interests: Dr. Crowley receives travel support from the American Psychiatric Association to participate in revising the Diagnostic and Statistical Manual of Mental Disorders, and from the National Institute on Drug Abuse for serving on its National Advisory Council. His former service on an advisory board of CRS Associates was supported by Reckitt Benckiser. These interests do not alter the authors' adherence to all PLoS ONE policies on data sharing and materials. The other authors report no conflict of interest.

Copyright: © 2010 Crowley et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

With such conflicting suggestions in the literature, we could not make a directional hypothesis for this study. Thus, we simply hypothesized that, as adolescent boys repeatedly decide between doing a risky or a cautious behavior, and as they experience wins or losses from their risky choices, functional magnetic resonance imaging (fMRI) will show that youths with ASD have different brain activation patterns than community-control boys. Unlike some previous adolescent studies, our z-shim procedure [35] allowed good visualization of orbitofrontal regions that are important in processing reward and punishment [18] . Our results strongly supported our hypothesis.

Each intoxication leads to a subjective “high”, with enhanced response to reward due to reduced reward thresholds in medial forebrain bundle. Each acute withdrawal event results in subjective dysphoria with reduced response to reward due to elevated reward thresholds. Frequent cycle repetitions gradually suppress subjective “highs”, deepening dysphoria by further raising reward thresholds. Increasingly, the drug is used to escape dysphoria and achieve normal mood. During abstinence, mood recovers very slowly. (Based on Koob and Volkow [33] ).

Alternatively, reviewing human and animal studies, Koob and Volkow [33] suggest that repeated intoxication-withdrawal cycles from addictive drugs are associated with decreased dopaminergic response to reward, due to increased stimulation thresholds in compromised reward circuits (see also [18] ). These processes would produce “reward insensitivity”, reducing motivation for non-drug stimuli. Koob and Volkow [33] also indicate that chronic drug use disrupts frontal activity in ACC, OFC, and DLPFC, a disruption continuing well into protracted abstinence. Because those areas contribute to decision-making and behavioral inhibition, such disruption would facilitate recurring risk-taking and relapses. These authors further propose that repeated intoxication-withdrawal cycles activate a brain stress system mediated by corticotropin releasing factor (CRF) and other neurotransmitters [33] . They suggest that in human addicts hypodopaminergic reward insensitivity and stress activation present as subjective dysphoria, a “negative emotional state” that continues long into protracted abstinence ( Fig. 1 ). Relapses at least briefly would relieve that dysphoria, negatively reinforcing further drug use ( Fig. 1 and [34] ).

Because ASD youths combine antisocial conduct problems with SUD, recent publications suggest partially conflicting possibilities for the neural underpinnings of their problems. First, like adults with antisocial or psychopathic traits (but substance-free) [32] , ASD youths' repeated risk-taking might occur because they experience increased dopaminergic response to reward anticipation. Among antisocial adults impaired amygdala and vmPFC function also are thought [23] to reduce responses to punishment or loss. Increased response to reward and decreased response to punishment could cause excessive pursuit of exciting rewards with failure to inhibit behaviors that may be punished.

Structural alterations of brain have been associated with the risk-taking of ASD youngsters, even among those merely vulnerable to ASD through family history. Youngsters with CD reportedly have reduced volume in insula and amygdala [27] , and in temporal lobes, hippocampus, and vmPFC [28] . Compared with controls, alcohol-naïve sons of alcoholic men reportedly have widespread gray-matter volume reductions, the severity of which correlates with the severity of inattention, impulsivity, hyperactivity, and conduct problems [29] . Aggression and defiance negatively correlate with right ACC gray-matter volume among community boys not selected for ASD [30] , while impulsivity negatively correlates with vmPFC volume [31] .

Only a few studies have compared brain activation in ASD youths and controls. ASD youths did show greater activation in amygdala and regions of the default network while performing the Stroop task [24] . In a go/no-go task marijuana-using youths (without CD) had more activation frontally (and elsewhere) than controls [25] . Conversely, youths with familial risk for ASD had less frontal activation than controls during a motor inhibition task [26] , perhaps like substance-involved adults who, when considering risky decisions, showed hypoactivity in brain regions processing potential losses and response conflicts [21] .

The excessive risky behaviors of ASD youths might result, first, from aberrant neural processing of behavior-motivating rewards; e.g., among normal adolescents a risk-taking propensity does correlate with more reward-related activation of nucleus accumbens (NAc) [17] (also see [18] ). Second, aberrant processing of behavior-inhibiting punishments could result in risky behaviors; e.g., after punished responses in reversal learning, children with psychopathic traits show abnormally increased neural activation in ventromedial prefrontal cortex (vmPFC) and caudate [19] (also see [20] ). Third, apart from initial processing of rewards or punishments, impaired integration of reward-punishment information in regions that decide on future behaviors could cause excessive risky behavior; e.g., under risky conditions substance-dependent adults under-recruit specialized conflict-monitoring circuitry in posterior mesofrontal cortex [21] ; also see [22] , [23] . To address these three possibilities, we asked whether ASD youths under conditions of risk process decisions, rewards, or punishments differently from community-comparison youths.

“Risky behaviors” are behaviors that may result unpredictably in rewarding and/or adverse outcomes. Adolescents generally tend to take more risks than adults, but in laboratories and in real life ASD youths, even when abstinent, take more risks than other adolescents [14] , [15] . Indeed, ASD's symptoms of SUD and CD (e.g., fire-setting, break-ins, and continued substance use despite problems [16] ) epitomize extreme risky behaviors. Of note, ASD's risky behaviors are not necessarily “impulsive”, i.e., done quickly without considering possible consequences. Indeed, they often require sustained preparation, such as “casing” a building before breaking in, or obtaining false identification to buy alcohol.

Some 200,000 adolescent admissions annually occur in American substance-treatment programs [1] . Adolescent substance use disorders (SUD) are so strongly comorbid with antisocial conduct disorder (CD) [2] – [4] that the combination may be termed “antisocial substance disorder” (ASD). Both antecedent genetic influences [5] – [8] and toxic effects of drugs [9] – [12] may contribute to these behavioral problems, which often persist for decades [13] . ASD's great costs, both to those with the disorder and to society, make it important to understand this condition's etiology.

Methods

Participants and Assessments Ethics Statement: Written informed consent (adults) and assent (minors) was obtained from all research subjects. The Colorado Multiple Institutional Review Board approved all procedures. Patients and controls were males, ages 14–18 years (inclusive) with IQ≥80, without known MRI contraindications (claustrophobia, orthodontic braces, color blindness, ferric metal in the body), and without history of unconsciousness >15 minutes, serious neurological illness, or neurosurgery. They and their parents spoke sufficient English for consenting. After explanation of procedures 18-year-old subjects provided written informed consent for participation; those <18 years old provided written assent and parents provided consent. Subjects were paid $50, won a mean of $6.25 more in the behavioral task, and earned $3 more if head movement was <2mm during the MRI. Patients' inclusion criteria were: in treatment in our programs for youths (most referred by criminal-justice or social-service agencies and on probation); serious antisocial problems including DSM-IV [16] CD symptoms; DSM-IV [16] substance abuse or dependence on a non-nicotine substance; and multisubstance urine and saliva tests drug-free ≥7 days before assessment. Patients' exclusion criteria were: psychosis; current high risk of suicide, violence, or fire setting; or in treatment and abstinent ≥30 days (to minimize treatment effects on risk-taking). We obtained assent/consent on 28 patients, excluding 1 because of past embedded metal, 2 for not meeting substance diagnostic criteria, 4 for motion during imaging, and 1 for brain abnormalities noted during scanning. Twenty others completed all procedures. To maximize similarity with patients we recruited controls in zip-code areas from which previous patients had come. One was referred by a previous control. All others were contacted by a telemarketing company that phoned, described the project, and invited families with possibly-qualifying children to accept a call from the researchers, who then met with the youth and a parent or guardian to explain the project, inviting written parental consent, and youth assent or consent, to participate. Regarding age, gender, English-language skills, and IQ, inclusion criteria were the same as patients'. Exclusion criteria were: court convictions (except minor traffic or curfew offenses); substance-related arrests, treatment, school-expulsions; obvious psychosis; physical illness; urine or breath tests containing non-prescribed substances a few days, or immediately, before scans; meeting criteria for DSM-IV CD in the last year; or non-tobacco substance dependence. As samples accumulated, we skewed control recruitment (e.g., seeking older boys) to maintain patient-control comparability. Twenty-five control candidates provided assent/consent, but we excluded 1 for a substance-positive test, 2 for MRI-incompatible metal, 1 for motion during imaging, and 1 for signal loss from a large sinus; 20 others completed all procedures. Psychosocial assessments were completed several days before fMRI's. Senior staff trained Bachelor-level interviewers and examined all records for accuracy. Typical interview time was 2 hrs for controls and 3 hrs for patients (who reported more symptoms). Assessments were: Child Behavior Checklist (CBCL) and Youth Self Report (YSR) [36], [37] for symptom severity of attention-deficit/hyperactivity disorder (ADHD), anxiety, and depression; Diagnostic Interview Schedule for Children (DISC-IV) [16], [37], [38] for CD symptoms and diagnoses; Composite International Diagnostic Interview-Substance Abuse Module (CIDI-SAM) [37], [39]–[41] for DSM-IV abuse or dependence for 11 substance categories; Peak Aggression Rating Scale [37]; Carroll Self-Rating Scale for depression severity [37], [42], [43]; Synergy Interview [37] for education, legal issues, and medical/psychological history; Modified Hollingshead-Redlich Social Class Rating [44]; Wechsler Abbreviated Scale of Intelligence (WASI) [45] Vocabulary and Matrix Reasoning for IQ estimates; Eysenck Junior Impulsiveness Scale [46]; and handedness preference [47]. Treating therapists tested patients' urine about weekly for substances. Researchers tested patients and controls with urine (AccuTest™) and saliva (AlcoScreen™) dipsticks about 1 week, and immediately, before scanning.

Estimating Abstinence Duration At treatment admission 14 patients, most referred from strictly controlled environments, produced an admission urine sample free of unprescribed drugs; 12 of those also denied any substance use in the previous 30 days and continued producing substance-free urine samples. For those 12 we estimated abstinence duration as: (30 days) + (number of days between admission and imaging). For all other patients abstinence duration was the length in days of a continuous series of during-treatment negative urine samples before imaging. Of the four tobacco-experienced control subjects, one reported using tobacco regularly. All 20 patients reported smoking in the last 6 months, but 14 were now in a residential treatment program that vigorously suppressed smoking. Thus, we estimated that 6 non-residential patients and one control had used tobacco in the few days before imaging. No subjects smoked during the 1 hr pre-MRI training.

Behavioral Tasks and Analyses In a mock scanner subjects practiced our Colorado Balloon Game (CBG; Fig. 2), which is conceptually different from the Balloon Analogue Risk Task that we previously employed with similar patients [15]. We then conducted rapid event-related fMRI of neural processing (a) as subjects decided between doing a risky or a cautious behavior, and (b) as they experienced wins or losses from risky behaviors. “Decision Balloons” (DecBa) were test trials that forced a choice between doing a risky or a cautious behavior and then provided relatively large monetary wins or losses after risky behaviors. “Directed Balloons” (DirBa) were “baseline comparison” trials that required no decisions and provided only a small monetary reward for following a direction. DecBa and DirBa shared identical motor responses and almost identical visual and auditory stimuli (except for the initial full, vs. half, yellow light (Fig. 2B)), but only DecBa forced decisions and gave larger rewards or losses for risky decisions. Thus, we reasoned that subtracting baseline DirBa brain activation from DecBa activation should remove visual-, auditory-, and motor-related activation, while highlighting decision-related, and win-or-loss-related, activation. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 2. Colorado Balloon Game. A. Decision-Balloon screen, yellow light illuminated. Counter initially $5. B. Events during presentation of 90 paired trials, each Decision Balloon (DecBa) followed by a Directed Balloon (DirBa). Top: timing (seconds). Colored circles represent stoplight lights. DecBa begins (B, upper): yellow light illuminated 4 sec, subject decides to press left (L) or right (R) button. Green light (0.5 sec), subject executes response. Red light, consequence appears (3.5 sec). Risky right press consequence, either: (a) “smiley face”, expanding balloon, puffing sounds, counter adds 5 cents, or (b) “pop” sound, shrinking balloon, “frowney face”, counter loses 10 cents. Cautious left press consequence: +1 cent on counter, dull “thud” sound, unchanged balloon. Then, “jittered” fixation. DirBa's (B, lower) are identical to their paired DecBa's except: only half of initial yellow light illuminates, signaling (i) start of a DirBa and (ii) button to press during green light (e.g., right illumination – press right) – the same button chosen during preceding paired DecBa. Green-light press on directed button: +2 cents on counter. Then balloon repeats the consequence (puff up, pop, or no change) of previous paired DecBa; subject was told that DirBa consequences would not affect earnings. Finally, jittered fixation screen. C. During DecBa, declining proportion of right presses programmed to win as game progresses. Mostly pressing left later in game saves earnings. D. Visual Analog Scales (VAS). After sessions subjects rated their opinions about the stated questions on 100mm lines. Marked positions represent all-subject means; groups did not differ significantly. Upper VAS: subjects' understanding of decision-making source for DecBa vs. DirBa. Lower VAS: Different emotional responses to puff-ups or pops of DecBa, vs. DirBa. E. Mean numbers, DecBa selections. https://doi.org/10.1371/journal.pone.0012835.g002

Imaging Neural Processing Goggles and earphones delivered CBG's stimuli (Fig. 2A,B). The CBG presented 90 pairs of balloons, each pair including one DecBa (“You decide which button to press”) and one DirBa (“The computer will play the game. You don't need to decide anything”). Subjects responded with right and left index fingers on fiber-optic button response pads. Balloons within a pair usually were separated by balloons from other pairs (average 2.9, range 1–5, balloons, programmed with “optseq2” [48]. Each subject's session was divided into 3 runs, each presenting 30 identically-ordered DecBa-and-DirBa balloon pairs. Each DecBa or DirBa trial ended with a fixation screen (Fig. 2B), usually 2 sec, but in each of the subject's 3 runs four trials were “jittered” to 4 sec. Subjects received the amount on the counter at game's end; that amount could not fall below $3.00. Across the 90 DecBa trials the reward schedule changed. To model real-life shifting of reward contingencies, risky right responses were very likely to be rewarded early, and punished later, in the game (Fig. 2C). Moreover, risky-response punishments (10 cents) were larger than rewards (5 cents) to further encourage gradual shifting from risky-right to cautious-left responding. Subjects only were advised, “Try to guess whether [the balloon] will pop from what the last few whole-yellow light balloons did. If the last few popped, maybe this one will pop. If the last few didn't pop, maybe this one won't pop.” Imaging Decision-Making. During DecBa's 4-sec yellow light (Fig. 2B), subjects decided whether they would make a left or a right response when the green light came on. Since the yellow-light preceded responding, these 2 TRs reflected processing of decision, not response. DecBa was the test trial and DirBa was the “baseline comparison” trial; DirBa, unlike DecBa, required only compliance with a simple direction and no risky-vs.-cautious decision-making. Imaging Reward-Punishment Processing. After the 4-sec yellow light (Fig. 2B) subjects responded during the 0.5-sec green light, and then during the 3.5-sec red light they observed the consequences (risky right-response win: smiley face, puff-up sound, balloon enlarges, counter increases 5 cents; risky right-response loss: frowney face, pop sound, balloon shrinks, counter decreases 10 cents; cautious left response: no face, dull thud sound, no change in balloon, counter increases 1 cent). Hence, combining the 2 TR's that spanned the green- and red-light periods (4 sec total; Fig. 2B) permitted us to assess the processing of reward or punishment (across-subject mean: 29 wins, and separately, 23 losses from 52 right presses (Fig. 2E)). In the mock-scanner practice session subjects learned that during DirBa's red-light periods the counter increased 2 cents if the subject responded on the signaled side, regardless of subsequent audio-visual consequences (Fig. 2B); the latter were identical in each DecBa-DirBa pair. DirBa's always-predictable 2-cent reward for compliance was risk-free, certain, and considerably smaller than the 5-cent “win” reward, or the 10-cent “loss” punishment, that followed DecBa's risky choices. Accordingly, to assess win-or-loss related activation we subtracted DirBa activation from DecBa activation during the 4-sec green-and-red light period (Fig. 2B). In these analyses the first 0.5 sec included green-light motor responding, but in each pair of trials the DecBa and DirBa green-light stimuli and responses were identical (Fig. 2B), as were the red-light audio-visual stimuli. The only DecBa-DirBa difference was the meaning of those red-light stimuli (DirBa, 2-cent gain. DecBa risky response: 5 cent win or 10 cent loss; DecBa cautious response: 1 cent gain). Thus, subtracting DirBa activation from DecBa activation was designed to cancel out green-light-related activation, while highlighting activation associated with experiencing a win or a loss. Other Data. We recorded occurrence of left or right responses and reaction times (from green-light onset to response), as well as the resulting consequences (i.e., counter changes and the balloon's puff, pop, or no-change). Post-session debriefings asked about in-magnet experiences, game strategies, etc. On Visual Analogue Scales (VAS; Fig. 2D) subjects rated (a) the extent to which they or the computer made the left-right response decision for DecBa and for DirBa, and (b) their happy-sad reactions to balloon puff-ups or pops. T-tests and chi-square tests compared the groups on demographic and clinical variables, and on debriefing responses regarding DecBa and DirBa. Mixed models examined group and run differences (see below) in CBG's Total risky right presses, and last-session risky right presses.

Image Acquisition In a 3T General Electric MRI scanner, with stimuli synchronized to trigger pulses, subjects first observed a video during a 3D T1 anatomical scan (IR-SPGR, TR = 9 ms, TE = 1.9 ms, TI = 500 ms flip angle = 10°, matrix = 256×256, FOV = 220 mm2, 124 1.7 mm thick coronal slices; 9 min 12 sec). Sessions then presented 90 paired DecBa-and-DirBa trials, divided into 3 runs. Each echo-planar (EPI) run (TR = 2000ms, TE = 26 ms, flip angle = 70°, FOV = 220 mm2, 642 matrix, 36 slices, 4 mm thick, no gap, angled parallel to the planum sphenoidale) lasted 10 min, 23 sec, and had 30 paired DecBa-and-DirBa presentations. One-minute rest images (abstract nature drawing) separated the 3 runs. Individual trials were discounted if the subject failed to respond behaviorally during the 0.5 sec green light. Data from individual trials with spike-like movement of the head >2 mm were replaced with dummy fixation data. The subject was excluded from analyses if 10 or more trials in a 30-trial run failed those criteria. The 50-min session ended with T1 FLAIR images (T1-weighted spin-echo data set: 31 slices of part head, matrix = 256×192, NEX = 2, TE/TR/TI = 7.3ms/2000ms/860ms; imaging time = 4 min, 25 sec). Additionally, we acquired one IR-EPI (TR = 2000 ms, TE = 26 ms) volume (with excellent contrast between gray and white matter) to improve coregistration between EPIs and the IR-SPGR. Our fast z-shimmed image acquisition was designed to reduce inferior frontal susceptibility artifact [35]. Compensation was applied only to a few slices covering the inferior frontal region to improve temporal resolution in a whole brain scan. Slice-acquisition order assured effective, constant repetition time in both the z-shim slices and other slices. Moreover, we applied z-shim compensation to 5 of the 31 slice locations in the OFC region. To optimize the amplitude of z-shim compensation gradient, G c , we ran on each subject a trial scan with 3 different G c values (i.e., 0.55, 0.70, and 0.85G null ), where G null is an amplitude that nulls the MRI signal in regions without susceptibility effect. We determined that a G c of 0.70 G null gave optimal signal recovery in the ventral-medial OFC. This G c value produced robust OFC activation.