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Substantial evidence indicates that parental history of substance abuse (SA) and childhood attention deficit/hyperactivity disorder (ADHD) are associated with risk for later addiction. For instance, the biological children of alcoholics are at greater risk for alcoholism than the general population (Johnson and Leff, 1999), and parental dependence on any substance confers increased risk to offspring not only for life-time drug dependence but also for disruptive behavior disorders (Marmorstein et al., 2009). In addition, childhood ADHD has been associated with a greater prevalence of SA in adolescence and early adulthood (Chilcoat and Breslau, 1999; Barkley et al., 2003; Mannuzza et al., 2008). These observations suggest that vulnerability to addiction may be conferred by an inherited latent factor (Slutske et al., 1998) that increases the incidence of both SA and externalizing disorders including ADHD and conduct disorder (CD) (Young et al., 2000; Kendler et al., 2003). Although childhood ADHD and parental history of SA may independently contribute to the risk for addiction, the neurobiological components of such predisposition, particularly before exposure to drug abuse, are unknown.
It has been hypothesized that the clinical presentation of SA may be linked to relative imbalance in functions of the motivation-reward and behavioral inhibition systems in the brain (Goodman, 2008). Dopamine deficiency in the mesolimbic motivation-reward circuitry, a condition known as the “Reward Deficiency Syndrome” (Blum et al., 2000), is thought to confer vulnerability for SA, with addicts using drugs of abuse to increase dopamine levels transiently in the mesolimbic motivation-reward networks particularly in the ventral striatum. Recent findings indicate that individuals suffering from Reward Deficiency Syndrome, possessing a paucity of dopaminergic and/or serotonergic receptors and a higher rate of synaptic dopamine catabolism as compared with the general population, are predisposed to self-medicating with any substance or behavior that will activate dopamine release (Blum et al., 2011). Relevant also is a more recent discovery of pre-existing differences in dopamine D2/3 receptor expression in the striatum of high-impulsive rats, suggesting a neural endophenotype that may likewise pre-dispose to addiction in humans (Cumming et al., 2010). Beyond this finding in rodents, reduced dopamine transporter and receptor density in the striatum has been documented in adult methamphetamine abusers (Chang et al., 2007) and blunted striatal activity has been reported in detoxified alcoholics (Wrase et al., 2007) and in adults with parental alcoholism compared to control with no such history (Andrews et al., 2011). Further, impulsiveness in human subjects has been linked to D2/3 receptor availability in the striatum and midbrain (Lee et al., 2009; Buckholtz et al., 2010). Impulsivity has also been negatively correlated with striatal activation in fMRI studies, both in individuals with (Scheres et al., 2007; Strohle et al., 2008) and without ADHD (Stark et al., 2011). Moreover, both youths with high levels of externalizing behaviors (Bjork et al., 2010) and adults with childhood ADHD (Stoy et al., 2011) have shown altered activation in the ventral striatum, orbito-frontal cortex and the insula. As with substance abuse, dopamine plays a role in executive functions often affected in ADHD, and medications used in the treatment of ADHD augment catecholamine function (Del Campo et al., 2011).
Given the fact that an action to engage in drug abuse involves a dialectic between a tendency to respond to potentially rewarding environmental stimuli and cortical inhibitory systems, ADHD-related deficits in behavioral inhibition can predispose an individual to SA (Ivanov et al., 2008). Behavioral inhibition is a complex behavior that has been assessed using paradigms that test the ability to suppress emotions (Beauregard et al., 2001; Levesque et al., 2003; Phan et al., 2005), thoughts and memories (Wyland et al., 2003; Anderson et al., 2004), inhibit motor responses (Garavan et al., 1999; Liddle et al., 2001; Rubia et al., 2003; Blasi et al., 2006; Li et al., 2006; Chevrier et al., 2007) and to evaluate and resolve conflict (Walsh et al., 2010). These paradigms generally elicit activation in the anterior cingulate, ventrolateral prefrontal and insular cortices. Accordingly, reduced responsiveness in these brain regions may reflect a predisposition for the later development of disorders characterized by the inability to inhibit undesired behavioral patterns, including obsessive compulsive disorder, Tourette’s syndrome, posttraumatic stress disorder, SA and ADHD (Lerner et al., 2009). More specifically, hypofunction of the anterior cingulate gyrus, which is an integral part of the detecting attentional network (Fan and Posner, 2004) has been observed in patients with ADHD (Bush, 2010). Such deficits may, in turn, compromise an individual’s ability to monitor responses and adjust behavior to meet changing environmental demands.
Behavioral inflexibility in combination with high motivational drive may be an important factor in the development of addiction (Hommer et al., 2011), and the strong motivational drive to seek drugs paired with weakened ability to voluntarily control such impulses has been viewed as the mechanism underlying addiction behaviors (Koob and Volkow, 2010; Volkow et al., 2010). Notably both ADHD and SA have been conceptualized as disorders of altered motivation as well as impaired inhibitory control (Volkow et al., 2010). If these putative dysfunctions precede the onset of drug use they may be viewed as constitutional factors that predispose an individual for the development of SA. The identification of a biological underpinning of risk for SA would be studied best in children who have no prior exposure to drugs (Hommer et al., 2011) but exhibit motivational and inhibitory deficits, such as those observed in patients with ADHD (Volkow et al., 2010), a research direction that is currently unexplored. This study, therefore, examined the contribution of parental history of SA to functional activation in the brain motivation-reward and response monitoring neurocircuits in drug-naïve children with purported deficits in motivation and behavioral control. Towards this purpose, we designed a task that includes both reward and executive control components. This task was used in conjunction with functional magnetic resonance imaging (fMRI) to assess brain activation in 20 children who had no history of SA and met diagnostic criteria for ADHD. Based on the available literature related to deficits in reward processing in ADHD and SA, we hypothesized that the participants who had parental history of SA in addition to ADHD would exhibit less activation in regions of the motivation-reward network, specifically the caudate, orbitofrontal cortex and the insula during the reward components of the task. Further, given the existing evidence suggesting that high impulsivity is associated with elevated risk for later SA, we hypothesized that children with ADHD plus parental history of substance abuse would show reduced activation in the anterior cingulate gyrus during conflict resolution.
Twenty drug naïve children aged 8 to 13 years (Mean=10.53, SD= ±1.44) were recruited through fliers posted at the Mount Sinai Child and Adolescent Psychiatry Outpatient Clinic and by word of mouth. Study procedures were approved by the Mount Sinai Institutional Review Board. The legal guardian for each child gave written informed consent and each child provided written assent to an individual unaffiliated with the study. Each family was reimbursed $100 for completion of the study protocol. The initial visit included vital signs measurement and a full medical, developmental and family history as well as assessment of contraindications for MRI. Current and past psychiatric histories were evaluated using the Kiddie-SADS Present and Life Time Version (Kaufman et al., 1997), which was administered to both the parent and the child. All children met DSM IV-TR criteria for ADHD combined or inattentive type. Parents also completed the Conners ADHD Parent Rating Scale (Conners, 2000), which provides measures for ADHD symptom severity, and the Child Behavior Checklist (CBCL), which provides a measure for aggression (Achenbach, 1991). The Matrix Reasoning and Vocabulary subtests of the Wechsler Abbreviated Scale of Intelligence (WASI) (Ryan et al., 2003) were administered to estimate Full Scale IQ (FSIQ). Major psychotic, bipolar and mood disorders, mental retardation (FSIQ<75) as well as prior stimulant treatment, any drug use/experimentation and in-utero exposure to drugs were exclusion criteria. Parental history of substance abuse, which mostly (9 out of 10 in each group) was attributed to a parent who did not participate in the evaluation, was assessed using a semi-structured interview administered to the parent/caregiver. The semi-structured interview asked about past and present substance use for each biological parent. It also queried the type of drug used and the length of abuse, when it occurred. When a positive report was elicited additional questions were asked to determine whether i) the drug use represented a persistent pattern of behavior, ii) if it caused functional impairment and iii) if treatment was deemed necessary. In all cases of reported substance abuse, the reporting parent described the abuse as being a “serious drug problem” and indicated that the affected parent needed “treatment”.
The children were further subdivided into two risk groups based on parental history of substance abuse: i) Low Risk (LR) group (n=10) included youth who met DSM criteria for ADHD and had no parents with substance abuse, and ii) High Risk (HR) group (n=10) included youth who met DSM criteria for ADHD and had at least one biological parent with a history of substance abuse. Demographic characteristics for the two risk groups are presented in Table 1.
Participants underwent task training on a desktop computer and performed one block of the Anticipation-Conflict-Reward (ACR) task (see below). The participants were also trained in a mockscanner, which provided an opportunity for children to familiarize themselves with the scanning environment. The actual MR scan was performed on a separate visit. About 45 minutes before the scan, participants practiced the ACR task again on an office desktop. The length of the scan averaged 35–40 min.
The ACR task uses an event-related design with three temporally distinct probes of reward anticipation, conflict resolution, and reward outcome respectively. A pilot study using the task in 16 healthy adults showed that reward and target components of the task engaged components of the motivation-reward and behavioral inhibition systems accordingly (Ivanov et al., 2007). The ACR was modified to be more developmentally appropriate for the current study, by creating a narrative about the purpose of the task and including animation images for the cues, targets and outcomes (Figure 1, supplemental materials).
The ACR consists of four 6-minute and 20-sec locks including 32-trial with a 30-sec fixation (rest period) at the beginning and the end of each block. All trials begin with a cue presented at fixation for 500 ms, followed by a 2000 ms fixation period. The target is then displayed at fixation for 750 ms, followed by a 1750 ms response window. After this reward outcome is displayed at fixation for 750 ms. The inter-trial interval is jittered from 0 ms to 5000 ms with a mean of 2500 ms in each block and the total length of each trial averages 7750 ms, including the inter-trial interval. There are two cue events, non-reward and reward cue, which are depicted as moneybags that are either blank or contained a “$”, respectively. Targets consist of right- or left-pointing central airplanes that are flanked by double airplanes, which are either congruent or incongruent in direction with the central plane. Children were instructed to respond in the direction of the central airplane as quickly as possible. Reward outcomes were defined in relation to the preceding cues and subject responses as either i) expected reward (reward cues followed by $1 win for correct responses), ii) expected non-reward (nonreward cues followed by $0 for correct responses), iii) unexpected non-reward (reward cue followed by $0 for correct responses) and iv) punishment (either cue followed by $1 loss for errors). Since the ACR task is a performance-dependent task, all errors were linked to punishment outcomes. Trial types were determined by counterbalancing across the two cues (reward vs. non-reward), four targets (left vs. right, congruent vs. incongruent), and three reward outcomes (not including punishment). Participants were told that if they respond correctly to the target that followed a reward cue they can receive a one dollar reward. They were also instructed that if they will not respond or if the response was incorrect or slow a dollar would be taken away. The reward outcome was depicted by an image of a dollar bill; expected and unexpected non-reward outcomes were portrayed by the grayed-out shape of a dollar bill, and punishment was depicted as a hand grabbing a dollar bill. The maximum win for the whole task was $32. The running total was presented at the end of each block of the task. It is noteworthy that the rewards in this task were virtual; children were shown the amount of money they won during the task but did not actually win that much money. However, as children are used to this type of virtual reward in the video games they play, the reward paradigm used here was thought to be developmentally appropriate and likely to be effective.
All participants were scanned on a 3.0 Tesla Siemens Allegra (Siemens Medical Systems) head dedicated MRI scanner using a high-performance head gradient system. Participants were fitted with headphones, and their heads were stabilized with firm foam padding. Stimuli were projected via an SVGA projector system onto a rear-projection screen mounted at the head of the magnet bore. Subjects viewed the stimuli through a mirror on the head coil positioned above their eyes. Scan sessions began with shimming and sagittal localization. A high-resolution T2-weighted anatomical brain scan was acquired with a turbo spin-echo (TSE) pulse sequence with a repetition time (TR) of 4050 ms, echo time (TE) of 99 ms, flip angle of 170°, 210 mm field of view (FOV), and 512 x 336 matrix. Forty axial slices were acquired at a thickness of 4 mm with no gap and an in-plane resolution of 0.47 × 0.47 mm. This sequence was obtained to register and align the functional images with a reference brain. Functional T2*-weighted images depicting the blood oxygenation level-dependent (BOLD) signal were acquired at the same 40 slice locations using gradient-echo echo-planar images with a TR of 2500 ms, TE of 27 ms, flip angle of 82°, FOV of 240 mm, and an acquisition matrix of 64 × 64. Each functional image comprised a brain volume of 40 axial slices, each 3 mm thick with 1mm gaps and an in-plane resolution of 3.75 × 3.75 mm. All images were acquired with slices positioned parallel to the anterior commissure – posterior commissure line. The participants all completed 4 runs of 380 seconds each, yielding data from 152 time points per participant.
Three-way Group (HR, LR) x Cue (reward, non-reward) x Flanker (congruent, incongruent) ANCOVAs were performed with RT and accuracy as dependent variables. The alpha level for these analyses was set at p < 0.05. Post hoc pairwise t-tests were performed to compare between-groups RT for reward, unexpected non-reward and punishment trials, as well as RT for the trials that followed each of these trial types in order to assess the patterns of RT change following different reward outcomes.
Image processing was conducted using statistical parametric mapping (SPM5; Wellcome Department of Imaging Neuroscience, London, U.K.). Standard SPM pre-processing of the four functional time series was performed individually for each subject. The functional scans were slice scan time-corrected, realigned to the first volume to correct for inter-scan motion, co-registered to the T2 image, normalized to a standard template (Montreal Neurological Institute), and spatially smoothed with an 8 × 8 × 8 mm3 full-width at half-maximum (FWHM) Gaussian kernel. First level (within-subject) analyses were conducted individually for each participant with a general linear model (GLM) to quantify the relationship between the observed event-related BOLD signals and regressors encoding expected trial-specific responses. Regressors were created by convolving a train of time-locked delta functions, encoding the occurrence of each trial type, with the canonical hemodynamic response (Friston et al., 1998). The six movement estimates created during motion correction were entered as covariates of no interest (Johnstone et al., 2006). The design matrix comprised 10 regressors: 6 for cue (reward vs. non-reward) and flanker type (congruent or incongruent) effects and 4 for outcome-related effects. The first 6 regressors comprised 2 regressors modeling the main effect of reward vs. non-reward cue over all trials (i.e. anticipation), while the effects of reward and congruence (and their interaction) were modeled for correct (and non-punishment) trials in an additional 4 regressors. The four outcome related effects were: reward following reward cue, non-reward following reward cue, non-reward following non-reward cue or punishment for incorrect or missing response. The main effect of reward anticipation was tested with appropriate linear contrasts of the parameter estimates for the reward cue minus non-reward cue. The neural substrate of cognitive inhibition was tested by contrasting incongruent vs. congruent flankers (the main effect of congruency in correct trials). The outcome-related effects were tested with three contrasts: the effect of reward per se was summarized by subtracting the expected non-reward from the expected reward. The effect of surprising outcomes was assessed by subtracting the expected non-reward outcome from the unexpected non-reward. Finally, we quantified punishment-related responses (i.e. money loss due to incorrect responses minus expected non-reward). Based on the piloting of the task we determined that minimum of 15 event of each contrast will be sufficient in order to complete the analyses.
To test our hypotheses regarding activation due to reward and behavioral inhibition, we used the usual summary statistic approach for second level (between-subject) inference: First level (subject-specific) contrast images of the above effects, for high-risk and low-risk participants, were entered into separate second-level (random-effects) analyses of covariance (ANCOVA). In addition to group effects (High Risk vs. Low Risk), the omnibus ANCOVA model was supplemented with the following covariates: FSIQ, ADHD severity score, age and gender to remove potential effects of these variables from those of group differences. Group matching for ADHD severity score, age and gender assured that these variables were orthogonal to the Group (High Risk vs. Low Risk) variable. The resulting Statistical parametric t-maps (SPMs) were used to test for activation on the groups separately and group differences. The height (intensity) threshold of each activated voxel was considered significant at a nominal alpha level of p < 0.01 and a cluster extent of 85 contiguous resampled voxels (2 × 2 × 2 mm3) was established via Monte-Carlo simulations to correct for multiple voxel comparisons at p < 0.05.
There were no significant differences between the groups on RT and accuracy (Table 2). There were significant main effects of flanker on RT (F=9.09, df=2/18, p=0.007) and on accuracy (F=8.58, df=2/18, p=0.009), but there were no main effects of cue. Post-hoc analyses showed that RT decreased for both groups following reward; conversely, RT increased following a surprising outcome and punishment. RT increase (i.e. slower response) was significant only for the LR group for trials following a surprising non-reward outcome (F=1.83, df=1/9, p=0.04), and was highly significant for both groups following punishment. While subjects from both groups exhibited the same response adjustment following punishment (i.e. slower RT), RT for HR subjects was significantly shorter following a punishment trial (F=6.37, df=1/9, p=0.001; supplemental materials), suggesting a tendency for more impulsive responding and poor response monitoring.
Second-level analysis of reward minus non-reward cue contrasts showed that both groups activated the right visual cortex and fusiform gyrus (supplemental materials). The HR group showed significantly higher activation than the LR group in the left insula cortex, extending to the head of the left caudate and also in the left inferior prefrontal cortex that extended into the left orbito-frontal cortex (OFC) (Table 3 & Figure 2). In contrast, LR children exhibited significantly higher activation in the premotor/supplementary motor cortex bilaterally compared to the HR group (results not shown).
SPMs of the incongruent minus congruent flanker contrast showed robust activation in a distributed cortico-thalamic network including the right fusiform gyrus, right somatosensory association and temporal cortexes, right caudate, right dorso-medial prefrontal cortex as well as supplemental motor area and thalamus bilaterally over both groups (Table 4). Between group comparison showed significantly higher activation in the dorso-medial prefrontal and anterior cingulated cortex (ACC) in the LR than the HR group (Figure 3), whereas the HR group had higher activation in the somatosensory association and insula cortices bilaterally (results not shown).
SPMs of the expected reward (i.e. reward outcome that followed a reward cue and correct flanker response) minus expected non-reward (i.e. neutral outcome that followed a nonreward cue and correct flanker response) contrast showed increased activation in somatosensory and fusiform cortices bilaterally in the whole sample (supplemental materials). The HR group exhibited higher activation than the LR subjects in the anterior insula cortex bilaterally and in the left OFC (Table 3 & Figure 4).
The unexpected non-reward (i.e. non-reward outcome that followed a reward cue and correct flanker response) minus expected non-reward elicited deactivation in the right posterior insula cortex for the whole sample (results not shown). The HR group showed higher activation in the right hippocampus and the right middle occipital cortex but not in any motivation-reward system related regions (results not shown).
Contrasts of punishment (i.e. money loss due to incorrect flanker response minus expected non-reward) for both groups elicited activation in a widely distributed cortical network including the left ACC, supplemental motor area, middle frontal gyrus, temporal, parietal and occipital cortices (supplemental materials). In addition, the punishment contrast also elicited deactivation in the head of the left caudate for all subjects. In contrast, the HR subjects showed higher activation in the right insula cortex (Table 3 & Figure 5).
The additional regressors introduced in the analyses showed that the IQ differences did not influence the activation in regions of interest (supplemental materials) whereas no appreciable effects of gender were detected.
The findings of this study indicate that reward components of the ACR task (e.g. reward cues and reward outcomes including expected rewards and punishments) elicit more activity related to both reward anticipation and reward notification in brain regions of the motivational-reward system in children at high-risk for substance abuse; conversely, cognitive conflict elicit higher activation in brain regions associated with the behavioral inhibition system in low-risk than in high-risk subjects. These differences are not accounted for by the severity of ADHD symptoms as high- and low-risk subjects did not differ on their ADHD-RS scores. Also there were no significant differences in performance between the groups; therefore performance on the ACR task did not account for the differences in brain activation between the two groups. Lastly, the groups did differ in FSIQ (Table 1) but the patterns of brain activity linked to this variable could not explain the differences seen in reward/motivation nor the behavioral inhibition systems because FSIQ was used as a separate regressor in the second level analyses (supplemental materials).
Hypo-activity of the striatum and other regions within the motivational-reward system has been hypothesized to represent a constitutional predisposition for later SA. In addition, adolescents with ADHD exhibit lower striatal activation than control subjects during reward cue/outcome trials (Scheres et al., 2007), which suggests that striatal hypoactivity in childhood may be accounted for by the presence of ADHD. Therefore the differences in brain motivational-reward system response between the two groups may be explained by i) hypoactivity in the brain reward circuitry in the LR subjects linked to ADHD, and ii) hyperactivation of these same brain regions in the HR relative to the LR subjects, which may be linked to the presence of parental history of SA. One study that examined adolescents without SA, with and without alcoholic parents (Bjork et al., 2008) found no differences in brain reward activation between these groups; however, as shown here, they also demonstrated reward-related activation in OFC and insula. It is, therefore, possible that the influence of parental history of addiction on the brain sensitivity to reward may be different in childhood than adolescence. Alternatively, the study by Bjork et al. (2008) focused on the effects of family history of alcoholism in youths with no psychiatric conditions, as illustrated by compatible CBCL scores in the two groups. This suggests that in this sample the adolescents with parental alcoholism may have been resilient to the development of drug related disorders and in that respect no much different than the controls. In our study we examined the contribution of parental SA in children who were all diagnosed with ADHD and had elevated scores on ADHD symptom severity and CBCL aggression scales. Therefore, the reported differences in our sample are attributable to the effects of familial SA in the context of already developed childhood psychopathology.
Numerous reports have linked activity in the ACC and dorso-medial prefrontal cortex to the ability to inhibit behavioral impulses and to monitor behavioral errors (Modirrousta and Fellows, 2008). It has also been suggested that functional interactions between dorso-medial prefrontal and motor cortexes may produce a top-down control signal that inhibits inappropriate responding (Nandakumar and Laubach, 2006). Conversely, abnormal ACC activation is conceptualized as underlying the deficits in behavioral self-control and response monitoring observed in ADHD; accordingly, altered ACC activation is one of the most consistently replicated finding in ADHD research (Bush et al., 2005). Further, deficient behavioral self-control, a prominent characteristic of addiction related problems, may also facilitate the transition from childhood ADHD to adolescent SA (Ivanov et al., 2008). As all subjects in this study shared a common clinical risk factor of childhood ADHD, the observed between – group differences during the congruent/incongruent targets of the ACR task could be attributed to the risk status of the subjects. Therefore, we suggest that parental SA may further compromise the functions of the behavioral inhibition system and that HR status in this sample will be associated with greater deficits in conflict/response monitoring as evident by lower activation in ACC and dorso-medial prefrontal cortex. Such biological correlates may set the stage for increased risk for SA- a hypothesis supported by reports suggesting that deficits in behavioral control are associated with elevated risk for the development of substance abuse related problems in humans (Kirisci et al., 2005).
The observed activation differences in the caudate nucleus, OFC and insula between the HR and LR groups in relation to reward incentives are consistent with findings of preexisting abnormalities in the fronto-striatal motivational circuit in children of alcoholics (Heitzeg et al., 2010). These findings may reflect heightened sensitivity in brain regions involved in reward seeking, reward reinforcement and reward related learning in HR individuals. If true, the presence of constitutional high sensitivity to reward-related stimuli may be linked to a propensity to seek, identify and direct behavior towards potentially rewarding stimuli, which may include the use of drugs of abuse. Such a behavioral propensity may be less pronounced in individuals who are more efficient in suppressing and modulating behavioral impulses (as was the case in the LR group). This notion is further supported by our behavioral results, showing that LR subjects significantly decreased their RT following negative outcomes. Conversely, individuals with deficits in their ability to modulate behaviors may be more likely to perseverate in seeking reward, following initial drug exposure and, therefore, to further develop impulsive behaviors consistent with drug abuse. The observation that HR subjects decreased their RT following negative outcomes to a lesser degree and tended to respond significantly faster than the LR group further supports the above hypotheses. These results are consistent with the notion that increased sensitivity to expectations for reward may overpower the brain control circuits (Volkow et al., 2010). Our study provides preliminary evidence that a possible disassociation between the responsiveness of the motivational-reward (higher in HR vs. LR subjects) and the behavioral inhibition system (lower in HR vs. LR subjects) may be a constitutional abnormality linked to parental SA. Therefore, vulnerability for later addiction in children who have parents with SA may be associated with a functional mismatch between an overactive motivational-reward network and a deficient inhibitory control system.
This study examined a relatively small sample of drug naive ADHD youth with and without parental history of SA and therefore we consider the reported results preliminary. Having said this, the significant results we obtained (with small groups) suggest that the effect sizes are very large, however, they should be replicated in future studies. One methodological limitation is that we used a narrative interview to assess parental SA instead of any kind of validated or commonly-used instrument. Further, the current structure of the ACR task does not include a condition for “surprising reward following a non-reward cue,” and therefore the data presented here do not constitute a fully factorial design. We hope to include this additional task component in future studies. The lack of a typically-developing control group represents a challenge for interpreting the study results, since it is not possible to know whether the directions of the observed differences in activation are either “low” or “high” when compared to children without risk for SA.
This study examined fMRI activation of the motivational-reward and inhibitory control systems in drug-naïve youth who may be at risk for later SA. The results suggest heightened activation in brain regions of the motivational-reward system and possibly deficiency in activation of the inhibitory control system in children with ADHD and parental history of SA when compared to children with ADHD alone. We posit that a possible functional mismatch between these two systems may present one biological underpinning of SA risk, which is conferred by a parental history of addiction.
This work is supported by NIDA/AACAP K 23 (PA-00-003)
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