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. Author manuscript; available in PMC: 2022 Feb 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Dec 31;219:108498. doi: 10.1016/j.drugalcdep.2020.108498

Functional connectivity in frontostriatal networks differentiate offspring of parents with substance use disorders from other high-risk youth

Elizabeth Kwon a, Tom Hummer a, Katharine D Andrews b, Peter Finn c, Matthew Aalsma a, Allen Bailey c, Jocelyne Hanquier d, Ting Wang d, Leslie Hulvershorn a
PMCID: PMC7863979  NIHMSID: NIHMS1661891  PMID: 33440326

Abstract

Background:

Family history (FH) of substance use disorders (SUDs) is known to elevate SUD risk in offspring. However, the influence of FH SUDs has been confounded by the effect of externalizing psychopathologies in the addiction risk neuroimaging literature. Thus, the current study aimed to assess the association between parental SUDs and offspring functional connectivity in samples matched for psychopathology and demographics.

Methods:

Ninety 11–12-year-old participants with externalizing disorders were included in the study (48 FH+, 42 FH−). We conducted independent component analysis (ICA) and seed-based analyses (orbitofrontal cortex; OFC, nucleus accumbens, dorsolateral prefrontal cortex) with resting state data.

Results:

FH+ adolescents showed stronger functional connectivity between the right lateral OFC seed and anterior cingulate cortex compared to FH− adolescents (p < 0.05, corrected). Compared to FH−, FH+ adolescents showed stronger negative functional connectivity between the left lateral OFC seed and right postcentral gyrus and between the left nucleus accumbens seed and right middle occipital gyrus (p < 0.05, corrected). Poorer emotion regulation was associated with more negative connectivity between right occipital/left NAcc among FH+ adolescents based on seed-based analysis. FH− adolescents had stronger negative functional connectivity between ventral attention/salience network and dorsal attention/visuospatial network in the ICA.

Conclusions:

Both analytic methods found group differences in functional connectivity between brain regions associated with executive functioning and regions associated with sensory input (e.g., postcentral gyrus, occipital regions). We speculate that families densely loaded for SUD may confer risk by altered neurocircuitry that is associated with emotion regulation and valuation of external stimuli beyond externalizing psychopathology alone.

Keywords: Addiction Risk, Reward, Attention, Childhood Disorders, Family History, Functional Connectivity

1. Introduction

Substance use is detrimental to adolescent development and predicts increased risk of developing substance use disorders (SUDs) in adulthood (Morin et al., 2019). Youth who have both family members with SUDs and externalizing disorders are at particularly high risk for SUD development (Ivanov et al., 2012). Familial loading of SUDs has been hypothesized to convey risk, in part, via impairment in impulse control and executive functioning (DeVito et al., 2013). Other potential sources of vulnerability, in addition to those conferred childhood externalizing risk phenotypes (Iacono et al., 2008), include aspects of family environment (Biederman et al., 2000) and distinct cognitive abnormalities found in individuals with SUD family histories (Ivanov et al., 2012).

Brain imaging technologies have enabled addiction risk researchers to investigate the neural mechanisms associated with parental SUDs. Several studies investigated the effects of familial loading of alcohol use disorders (AUDs) specifically on offspring neurocircuitry using resting state functional connectivity. In a sample of youth with no reported psychopathology, researchers found that adolescents with family histories of AUDs (FH+; n=47) showed differences in left nucleus accumbens (NAcc) connectivity with bilateral inferior frontal gyri and left postcentral gyrus compared to adolescents without AUDs in their family (FH−; n=50) (Cservenka et al., 2014). Family history positive (FH+) youth also differed from their counterpart (FH−) in functional connectivity between right NAcc seed and the following regions: left orbitofrontal cortex (OFC), left superior temporal gyrus, right cerebellum, left postcentral gyrus, and right occipital cortex. Next, Martz et al. (2019) found in a sample chosen for familial AUDs with little reported offspring psychopathology, that compared to FH+ adolescents who use substances (FH+/high-risk; n=15), FH+ adolescents who have not used substances by age 14 (FH+/resilient; n=21) showed greater integration between the left dorsolateral prefrontal cortex (dlPFC) and left posterior cingulate cortex. In their sample, 4 subjects in FH+/resilient group had ADHD and 1 had ADHD in FH+/high-risk group. Finally, Vaidya et al. (2019) found that connectivity between dorsal premotor cortex and other sensorimotor network regions was lower in AUDs FH+ (n=95) than FH− (n=96) adolescents in a sample that showed significant group differences in externalizing symptom scores (p < .001). Taken together, these findings suggest that familial loading of AUDs is associated with changes in frontostriatal functional circuitry.

Of note, these studies have not disentangled contributions of co-morbid externalizing disorders, which are among the most robust predictors of SUD (Ivanov et al., 2012). Existing studies either excluded subjects with externalizing disorders, misrepresenting high-risk youth in the real world, or did not have even distributions of psychiatric disorders between groups. Additionally, resting state functional connectivity has not been used to examine samples of high-risk youth based on familial use disorders of any drugs other than alcohol (Cservenka et al., 2014; Martz et al., 2019; Vaidya et al., 2019).

Thus, to examine the effects of FH on neural circuitry, we compared functional connectivity between groups of high-risk adolescents, both with externalizing disorders, who differed only in family histories of SUDs. We recruited substance-naïve adolescents to understand the neurobiological mechanisms that may be distinct in families heavily loaded with SUDs, compared to psychiatrically and demographically matched comparisons. To compare these two groups, we compared functional connectivity with specific seeds and examined relationships between brain networks. Research with both resting state and task-based functional connectivity has established that addiction risk is associated with the maturation of cortical executive control and reward responsivity (Volkow et al., 2016). This literature framed our choice of three seed regions. The NAcc has been associated with reward processing and its role in addiction risk has been implicated in hundreds of animal and human studies (Claus et al., 2011). The OFC has been repeatedly associated with addiction risk due to its role in learning stimulus-reinforcement associations (Ersche et al., 2005). Lastly, dlPFC has been implicated in attention orienting and behavioral regulation (Hare et al., 2009). For our network-based analyses, we selected four networks: a core reward network encompassing basal ganglia and thalamus, and an extended reward network consisting of anterior cingulate cortex (ACC) and OFC, along with the networks associated with executive functioning, such as the salience/ventral attention network encompassing frontal opercula and the anterior cingulate gyrus, and visuospatial/dorsal attention network including dlPFC (Laird et al., 2011).

2. Material and methods

2.1. Participants

As part of an ongoing longitudinal study, English-speaking, right-handed 11–12-year-old participants were recruited. After guardian consent and youth assent, K-SADS-PL (Kaufman et al., 1997) interviews were conducted to determine psychiatric diagnoses. Individuals who met DSM-5 (Association, 2013) criteria for attention-deficit/hyperactivity disorder (ADHD; any subtype) and a disruptive behavior disorder (DBD; defined as oppositional defiant disorder, conduct disorder or disruptive behavior disorder, unspecified) were eligible. As in utero exposure to substances could directly impact brain development, adolescents with parent-reported prenatal drug exposure were excluded. Among the eligible participants, those with a biological father with a past or present DSM-5 SUD (excluding isolated tobacco or alcohol use disorders in the absence of drug use disorders) and another 1st or 2nd degree family member with a SUD history were considered family history positive (FH+). Mothers with SUD were allowed if no substance use occurred during the pregnancy. Maternal SUD was not targeted given the high probability of use during pregnancy. The family history negative (FH−) group had identical psychopathological criteria but had no history of SUDs in the biological parent and no more than 2 SUD in 2nd degree relatives. Individuals with a history of current or past psychotic symptoms, autism spectrum disorder, current depression or mania, substance use, neurological problems, or debilitating medical conditions were excluded. Also, individuals with estimated Full-Scale IQ < 80 (Wechsler, 1999); routine MRI contraindications; and use of any psychopharmacologic medications within the last two weeks, apart from psychostimulants, were excluded. Any psychostimulant medications were held on the days of participation. All procedures were conducted in accordance with Indiana University Institutional Review Board.

2.2. Measures

2.2.1. Parent and Child Impulsivity

Previous literature has established that impulsivity is associated with substance use (Coskunpinar et al., 2013). Thus, the current study examined whether impulsivity scores differed between groups, in order to account for confounding effects. Parental self-report and child’s self-report of impulsivity was measured using UPPS-P Impulsive Behavior Scale (Whiteside et al., 2001) and the UPPS-P-C (validated for children aged from 7 to 13) (Zapolski et al., 2010), respectively. UPPS-P measures five distinct facets of impulsivity: sensation seeking, lack of premeditation, lack of perseverance, negative urgency, and positive urgency.

2.2.2. Family Functioning

The Family Assessment Measure (FAM-III) (Skinner et al., 2000) was completed by the parent and the child, separately. Consisting of 50 questions, this measure assesses the magnitude of disharmony in the home regarding task accomplishment, role performance, communication, affective expression, involvement, control, and values and norms.

2.2.3. Letter Number Sequencing (LNS)

In order to assess attention span and verbal working memory, participants were asked to complete LNS (Gold et al., 1997). Participants were asked to listen to alternating numbers and letters (e.g., 2-A-3) and then repeat back the given letters and numbers in order of ascending numbers followed by letters in alphabetical order (e.g., 2–3-A).

2.2.4. Flanker

Participants completed the flanker task. They were shown the target arrow surrounded in flanker arrows and instructed to press the red button if the target arrow points to the left and blue button if pointing to the right as fast as they can get the right answer (Eriksen, 1974). Accuracy and reaction time of the performance were computed for final scores (Van ‘t Ent, 2002).

2.2.5. Emotion Regulation Checklist

Parents completed the Emotion Regulation Checklist (ERC) about their children. The ERC consists of 24 questions asking about the child’s emotion regulation and emotional lability using a 4-point Likert scale (Shields and Cicchetti, 1997). After reverse coding negatively weighted items, the mean ERC score was calculated. Higher scores indicated better emotional regulation.

2.3. Imaging data

2.3.1. Functional Neuroimaging Data Acquisition

Resting state fMRI data were collected from 106 adolescents with ADHD and DBDs. Before the actual scan, mock scanning was conducted to help reduce participants’ movement and anxiety. All scans were performed using 3-Tesla Siemens Prisma MRI scanner with a 32-channel head coil. A high-resolution 3D magnetization-prepared rapid gradient echo (MPRAGE; 160 sagittal slices; 1.05×1.05×1.2mm voxel dimension) scan was used for co-registration and normalization of functional image volumes to Talairach space. During the resting state scan (T2*-weighted gradient echo-planar imaging scan; 54 axial slices; voxel size 2.5×2.5×2.5mm; TR/TE 1200/29ms, flip angle 65°; Field-of-view: 220×220mm, Matrix: 88×88), participants were instructed to view a black screen with a fixation cross for 8 minutes.

2.3.2. Preprocessing of fMRI Data

Preprocessing of resting state fMRI data was conducted using AFNI software (Cox, 1996), with slice-time correction, de-spiking of time series outliers (3dDespike algorithm; spikes defined at each time point as >2.5 standard deviations from the median average deviation), motion correction via realignment to a baseline time point, registering the functional image to the structural image, and spatial smoothing with a Gaussian kernel of 6-mm full-width at half-maximum. Mean framewise displacement (motion from one timepoint to the next) was also calculated across the time series and compared between groups. Additional pipeline steps were chosen based on recent evaluations of motion correction strategies to reduce noise (Parkes et al., 2018). These steps included independent component analysis-based automatic removal of nuisance artifacts using ICA-AROMA (part of the FMRIB Software Library (FSL) (Pruim et al., 2015). ICA-AROMA decomposes each subject’s resting-state time series into spatially independent components and associated time series with FSL’s MELODIC (version 3.14). ICA-AROMA identifies components that are likely to be noise based on four features (e.g., high-frequency content, maximum correlation with realignment parameters, edge fraction, and cerebrospinal fluid fraction). These noise components were then regressed from the time series.

The structural T1-weighted image was automatically parcellated into distinct regions via the FreeSurfer software pipeline (version 6.0; https://surfer.nmr.mgh.harvard.edu). This parcellation method enabled the creation of spatial white matter and ventricle maps (eroded from gray matter boundaries). From these spatial maps, eight physiological regressors were created: mean white matter time series and its derivative; mean ventricle time series and its derivative; and the square of each of these time series. In addition, four global signal regressors were used: mean global signal, its derivative, and squares of each. Thus, regressors of no interest removed from each BOLD time series included all components identified as nuisance components with ICA-AROMA, eight physiological regressors, and four global signal regressors. Finally, each time series was filtered with a 0.01 – 0.08 hz bandpass filter. Participants were removed from analysis if their maximum motion exceeded 4mm in any direction at any point during the scan.

2.4. Data Analysis

We conducted both seed-based and independent component analyses (ICA) to compensate for each method’s limitations. Seed-based analyses allow investigation of the correlation of a priori selected regions of the brain to other regions but are not optimal for the exploration of broader network connectivity. ICA, on the other hand, enables researchers to examine relationships within and across common networks that are composed of potentially disparate regions (Lv et al., 2018). Statistical comparisons were done with Stata 16.

2.4.1. Seed based analyses

For seed-based analyses, bilateral OFC and NAcc seed regions were defined based on FreeSurfer parcellation from the Desikan-Killiany atlas (Desikan et al., 2006) and 6mm-radius spherical seeds were used for the dlPFC due to the larger size of prefrontal atlas regions. These seed regions of interest were resampled to the spatial resolution of functional data, and the mean time-series was extracted from each seed. For each seed, whole-brain connectivity maps were created as the correlation of each voxel time series with the seed, followed by a Fisher’s Z transform. These connectivity maps were transformed into standardized Talairach space (Talairach, 1988) and t-test comparisons were made between FH+ and FH− groups. For group comparisons, individual voxels were considered significant at p < .005, with corrections for multiple comparisons via cluster-size thresholds using the estimated spatial auto-correlation of residuals (corrected p < .05), estimated at 266 voxels for this particular sample following Monte Carlo simulation.

2.4.2. Independent components analysis

Network identification.

To identify core reward, extended reward, salience/ventral attention, and visuospatial/dorsal attention networks, independent component analysis (ICA) was conducted with FSL Melodic using multi-session temporal concatenation. Based on prior studies, we constrained component estimation to 20 components (Calhoun and de Lacy, 2017; Laird et al., 2011). The spatial correlation between each estimated component and existing resting state network templates (Laird et al., 2011) was conducted to identify which components aligned with networks of interest. Along with visual inspection, independent components that showed reasonably strong correlations (at least r > 0.3) to templates were selected for further analysis. Finally, subject-specific time courses and spatial maps for each component of interest were estimated from the standard-space map using FSL dual regression.

Network Strength.

On the individual level, there was variability in the degree of spatial correlation with each Laird template. The strength of this spatial connectivity was used to define network strength for each subject. A stronger correlation with the Laird template indicated a stronger network representation for that ICN.

Between-network connectivity.

With the subject-level time series for each ICN, we calculated correlation coefficients between each pair of selected ICNs for each subject (Hobkirk et al., 2019) (e.g., ICN 2-ICN 4). We conducted t-test comparisons between SUDs FH+ and FH− youth on these correlations following Fisher’s transform.

2.4.3. Comparison of imaging findings with behavioral measures

Mean connectivity values were extracted from significant clusters in the seed-based analyses in order to conduct exploratory correlation analyses with ERC and child-report UPPS scores.

3. Results

3.1. Sample Characteristics

Ninety subjects were included in the final analyses (48 FH+, 42 FH−), after excluding 16 subjects for high motion (11 FH+, 5 FH−). Groups did not differ on age, sex, race/ethnicity, IQ, parent education, distribution of disruptive behavior disorder types, lifetime psychiatric diagnoses, and psychotropic medication exposures (all p > 0.05). Likewise, there was no significant difference in mean framewise displacement. Sample characteristics are summarized in Table 1.

Table 1.

Demographic and psychiatric characteristics by group.

Total (n=90) Family history positive (n=48) Family history negative (n=42) Statistics P-values
Age, M (SD) 11.93 (0.55) 11.99 (0.53) 11.86 (0.57) t(88) = 1.1158 0.268
Sex, n (%)
  Male 63 (70.00%) 33 (68.75%) 30 (71.43%) χ2(1) = 0.0765 0.782
  Female 27 (30.00%) 15 (31.25%) 12 (28.57%)
Race/ethnicity, n (%)†
  Caucasian 52 (57.78%) 28 (58.33%) 24 (57.14%) χ2(3) = 2.2768 0.549
  More than 1 race/ethnicity 13 (14.44%) 5 (10.42%) 8 (19.05%)
  African American 24 (26.67%) 14 (29.17%) 10 (23.81%)
  Hispanic 1 (1.11%) 1 (2.08%) 0 (0.00%)
IQ, M (SD) 106.63 (14.59) 105.79 (13.63) 107.57 (15.71) t(86) = −0.5736 0.568
Parent education, n (%)†
 HS or lower 18 (20.22%) 12 (25.00%) 6 (14.63%) χ2(3) = 6.2124 0.105
  Higher than HS, lower than bachelor’s degree 49 (55.06%) 29 (60.42%) 20 (48.78%)
 Bachelor’s degree 8 (8.99%) 3 (6.25%) 5 (12.20%)
  Higher than bachelor’s degree 14 (15.73%) 4 (8.33%) 10 (24.39%)
ADHD, n (%) a 90 (100%) 48 (100%) 42 (100%)
Disruptive behavior disorder type, n (%) a
 Conduct disorder† 8 (8.89%) 4 (8.33%) 4 (9.52%) χ2(1) = 0.0392 0.843
  Oppositional defiant disorder 66 (74.16%) 36 (76.60%) 30 (71.43%) χ2(1) = 0.3090 0.578
  Disruptive mood dysregulation disorder† 2 (2.22%) 0 (0%) 2 (4.76%) χ2(1) = 2.3377 0.126
  Other disruptive behavior disorder 20 (22.47%) 12 (25.53%) 8 (19.05%) χ2(1) = 0.5353 0.464
Lifetime psychiatric diagnoses, n (%) b
 Depressive disorders† 3 (3.33%) 2 (4.17%) 1 (2.38%) χ2(1) = 0.2217 1.000
 Anxiety disorders† 7 (7.78%) 6 (12.50%) 1 (2.38%) χ2(1) = 3.1977 0.116
 Traumatic stress disorders† 4 (4.44%) 3 (6.25%) 1 (2.38%) χ2(1) = 0.7896 0.620
 Adjustment disorders† 5 (5.56%) 2 (4.17%) 3 (7.14%) χ2(1) = 0.3782 0.661
Psychotropic medications, n (%)
 Stimulant ADHD 45 (50%) 20 (41.67%) 25 (59.52%) χ2(1) = 2.8571 0.091
 Non-stimulant ADHD† 7 (7.78%) 4 (8.33%) 3 (7.14%) χ2(1) = 0.0443 1.000
 Antidepressants† 5 (5.56%) 3 (6.25%) 2 (4.76%) χ2(1) = 0.0945 1.000
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Note. M= mean; SD = standard deviation; n = frequency; % = percentage; HS = high school

a

Rates are for current ADHD and disruptive behavior disorder diagnoses

b

Rates are for lifetime history (current and past) of psychiatric diagnoses

†

Fisher‘s exact test was conducted.

Impulsivity of parent and child measured by UPPS-P did not differ between groups (Table 2). Mean scores for child- and parent-reported family functioning scores (FAM) were in non-clinical ranges (40–60) for all subcategories. However, the parent ratings suggested significantly higher family dysfunction in task accomplishment, role performance, communication, involvement, and values and norms in the FH+ group. Of note, the social desirability and defensiveness were higher in FH− group, which could lead to artificially depressed clinical ratings on the FAM. The groups did not differ on attentional capacity, as measured by Flanker reaction time and accuracy, nor Letter Number Sequencing scores.

Table 2.

Impulsivity, attentional capacity, and family dysfunction by group.

Total (n=90) Family history positive (n=48) Family history negative (n=42) Statistics P-values
UPPS-P Impulsivity traits (Parent), M (SD)
 Sensation seeking 2.32 (0.59) 2.32 (0.61) 2.31 (0.57) t(87) = 0.0786 0.938
 Lack of planning 1.80 (0.46) 1.82 (0.49) 1.78 (0.44) t(87) = 0.4421 0.660
 Lack of perseverance 1.76 (0.46) 1.82 (0.49) 1.69 (0.43) t(87) = 1.2863 0.202
 Negative urgency 2.16 (0.62) 2.26 (0.58) 2.04 (0.64) t(87) = 1.7492 0.084
 Positive urgency 1.53 (0.49) 1.62 (0.51) 1.43 (0.46) t(87) = 1.8007 0.075
UPPS-P Impulsivity traits (Child), M (SD)
 Sensation seeking 2.72 (0.72) 2.77 (0.64) 2.67 (0.81) t(86) = 0.6344 0.528
 Lack of planning 2.14 (0.62) 2.14 (0.61) 2.14 (0.65) t(86) = 0.0082 0.994
 Lack of perseverance 2.00 (0.44) 2.07 (0.36) 1.92 (0.51) t(86) = 1.5667 0.121
 Negative urgency 2.42 (0.74) 2.43 (0.66) 2.42 (0.83) t(86) = 0.0111 0.991
 Positive urgency 2.50 (0.78) 2.51 (0.76) 2.48 (0.82) t(86) = 0.2062 0.837
Child’s attentional capacity, M (SD)
 Letter Number Sequencing 9.61 (3.01) 9.67 (3.53) 9.54 (2.27) t(80) = 0.1877 0.852
 Eriksen Flanker task, reaction time 83.82 (56.10) 87.32 (64.49) 80.15 (46.23) t(82) = 0.5828 0.562
 Eriksen Flanker task, accuracy 0.06 (0.08) 0.05 (0.08) 0.07 (0.07) t(82) = −0.7559 0.452
FAM (Parent), M (SD) 50.90 (9.35) 53.52 (9.75) 47.83 (7.92) t(87) = 2.9865 0.004
 Task accomplishment 50.42 (13.05) 54.19 (14.20) 46.00 (10.03) t(87) = 3.0908 0.003
 Role performance 56.36 (12.51) 59.29 (13.21) 52.93 (49.51) t(87) = 2.4609 0.016
 Communication 53.34 (11.04) 56.35 (10.55) 49.80 (10.67) t(87) = 2.9051 0.005
 Affective expression 47.80 (11.26) 49.92 (11.69) 45.32 (10.32) t(87) = 1.9517 0.054
 Involvement 47.73 (9.22) 49.83 (9.71) 45.27 (8.03) t(87) = 2.3910 0.019
 Control 50.88 (11.31) 52.79 (12.29) 48.63 (9.70) t(87) = 1.7494 0.084
 Values and norms 49.78 (9.92) 52.25 (9.88) 46.88 (9.25) t(87) = 2.6321 0.010
 Social desirability 47.06 (9.65) 44.13 (9.21) 50.49 (9.10) t(87) = −3.2669 0.002
 Defensiveness 48.65 (13.73) 44.67 (11.84) 53.32 (14.44) t(87) = −3.1054 0.003
FAM (Child), M (SD) 53.17 (9.83) 53.70 (10.31) 52.54 (9.32) t(85) = 0.5432 0.588
 Task accomplishment 51.56 (11.62) 52.34 (11.55) 50.65 (11.78) t(85) = 0.6741 0.502
 Role performance 56.74 (10.95) 57.06 (11.93) 56.35 (9.83) t(85) = 0.3013 0.764
 Communication 54.25 (13.32) 55.87 (12.56) 52.35 (14.08) t(85) = 1.2331 0.221
 Affective expression 52.32 (11.07) 51.49 (12.15) 53.30 (9.70) t(85) = −0.7586 0.450
 Involvement 51.70 (11.78) 52.26 (12.53) 51.05 (10.95) t(85) = 0.4735 0.637
 Control 54.71 (12.45) 55.62 (13.09) 53.65 (11.71) t(85) = 0.7328 0.466
 Values and norms 50.87 (12.19) 51.23 (12.49) 50.45 (11.97) t(85) = 0.2975 0.767
 Social desirability 48.32 (10.23) 47.19 (9.10) 49.61 (11.36) t(86) = −1.1077 0.271
 Defensiveness 48.48 (13.30) 46.30 (11.84) 50.98 (14.55) t(86) = −1.6618 0.100
Emotion Regulation Checklist (ERC), M (SD)
 Emotion regulation 3.00 (0.52) 2.93 (0.61) 3.08 (0.39) t(81) = −1.3519 0.180
 Emotional liability 2.68 (0.43) 2.65 (0.43) 2.71 (0.42) t(81) = −0.6361 0.527
 Total mean score 2.87 (0.43) 2.81 (0.47) 2.93 (0.37) t(81) = −1.2186 0.227
fMRI Mean Framewise Displacement 0.088 (.034) 0.088 (.034) 0.088 (.035) t(88) = −0.041 0.967
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Note. Italics indicate differences between groups. M= mean; SD = standard deviation; UPPS-P = Impulsive Behavior Scale; Eriksen Flanker task reaction time = difference between reaction time from the neutral and incongruent conditions only using correct responses; Eriksen Flanker Task accuracy = difference in accuracy from the neutral and incongruent conditions; FAM = averaged T-scores of the first seven categories of Family Assessment Measure

3.2. Seed-based Analysis

FH+ participants showed stronger functional connectivity between the right lateral OFC seed and rostral ACC compared to FH− (p < 0.05). Conversely, FH+ group showed stronger negative functional connectivity compared to FH− between the left lateral OFC seed and right postcentral gyrus and between the left NAcc seed and right middle occipital gyrus (p < 0.05; Figure 1; Table 3). If a voxel-level p < .001 threshold is applied (with appropriate multiple comparisons correction), instead of p < 0.005, the reported right OFC/ACC finding was no longer significant. There were no significant group differences in functional connectivity between dlPFC and other regions of the brain. Including framewise displacement in the regression model did not influence the results.

Figure 1.

Figure 1.

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Seed-based connectivity for right lateral orbitofrontal cortex (OFC), left lateral OFC, and left nucleus accumbens (NAcc) (voxel-level p<.005; p<.05 cluster-size corrected for multiple comparisons). Within family history positive (FH+) and family history negative (FH−) group, warm colors indicate areas of positive functional connectivity, while cool colors represent negative functional connectivity. Between groups (FH+ vs. FH−) comparison, higher values indicate higher connectivity in FH+ group.

Table 3.

Seed-based functional connectivity findings

Seed Connected region Brodmann’s area Coordinates
 Peak t Number of voxels (p < .005 / p < .001)
x y z
R lateral OFC (FH+ > FH−) Anterior cingulate cortex 32 −1 31 16 3.49 388 / n.s.
L lateral OFC (FH+ < FH−) Right postcentral gyrus 3 53 −17 42 4.57 443 / 170
L NAcc (FH+ < FH−) Right middle occipital gyrus 19 37 −77 16 4.47 346 / 144
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Note. R = right; L = left, OFC = orbitofrontal cortex; NAcc = nucleus accumbens; FH+ = family history positive; FH− = family history negative

Regarding exploratory correlation analyses with ERC scores, right occipital/left NAcc cluster connectivity was significantly correlated with emotion regulation, such that poorer emotion regulation (lower ERC scores) was associated with more negative connectivity (r(84) = .22, p = .04). However, this finding was driven entirely by a strong correlation in the FH+ group (r(45) = .39, p = .009), with no significant relationship in the FH− group (r(39) = −.05, p = .744). No other significant relationships were found with behavioral measures.

3.3. Independent Component Analysis

We selected four components a priori for group comparisons based on previously developed templates (Hobkirk et al., 2019; Laird et al., 2011): ICN 2, extended reward network; ICN 3, core reward network; ICN 4, salience/ventral attention network; and ICN 7, a visuospatial/dorsal attention network. As no identified components were strongly correlated with ICN 3 (all r < 0.23), we did not include this network for further analysis following previous study (Hobkirk et al., 2019). Pairwise comparisons of the identified networks revealed that FH− adolescents had trend-level stronger negative functional connectivity between the salience/ventral attention network (VAN) and visuospatial/dorsal attention network (DAN; r = −0.29) compared to FH+ adolescents as shown in Figure 2 (controlling for framewise displacement: r = −0.20; F(1,87) = 2.04, p = 0.0524). There were no significant group differences between other networks. Network strength did not differ between groups.

Figure 2.

Figure 2.

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Ventral attention network (upper panel) and dorsal attention network (lower panel) identified with independent component analysis (ICA) and high spatial correlation with templates developed by Laird et al. (2011).

4. Discussion

This investigation compared functional connectivity between ninety 11–12-year-old, substance-naïve adolescents with externalizing disorders who either did or did not have family histories of SUD. Groups did not differ on potential confounds such as age, sex, and race/ethnicity and, importantly, IQ, socioeconomic status, parent and child impulsivity, psychopathology, attentional control, and psychotropic medications exposure. Regarding the seed-based analysis, of the 3 regions of interest tested (OFC, NAcc, dlPFC), functional connectivity differed between the right OFC seed and ACC, between the left OFC seed and right postcentral gyrus, and between the left NAcc seed and right middle occipital gyrus. The ICA results revealed that attention-related brain networks trended toward differences between groups.

Seed-based analyses revealed that functional connectivity between right lateral OFC and rostral ACC was stronger in FH+ than FH− adolescents. The OFC is a higher-order, multifunctional prefrontal region whose role can best be summarized as a hub for value-based decision making (Wallis, 2011). For instance, humans with bilateral OFC lesions obtained in early life demonstrate a lack of awareness of the consequences resulting from a decision (Anderson et al., 1999). The OFC is critical for learning to adapt future behaviors based on past negative outcomes, and plays an important role in the pathology of SUDs (Schoenbaum et al., 2006). OFC development in middle childhood (similar to age of the present participants) predicts future substance use (Luby et al., 2018). Lateral OFC is predominated (more so than medial) by bidirectional connectivity with sensory centers throughout the brain (Kringelbach, 2005). Therefore, lateral OFC may be central to interpreting the sensory component of substance use. Thus, the stronger negative connectivity between OFC and postcentral gyrus in FH+ group in our sample might indicate disconnection in somatosensory interpretation of substances in adolescents with family history. Relatedly, Vaidya (2019) found that compared to FH−, FH+ adolescents showed lower intraconnectivity in sensorimotor network. Taken together, these findings suggest that sensorimotor networks can differentiate youth with and without family loading of SUDs. It has been established that sensory areas show greater activation when persons with SUDs are exposed to substance-related cues compared to comparison group (i.e., healthy control, persons with SUDs with better treatment outcome) (Yalachkov et al., 2010). However, it is unclear why substance naïve adolescents with FH show differences in sensory areas. More studies are warranted to identify mechanisms through which FH of SUDs affect offspring sensory network.

In addition, we speculate that OFC functioning may drive substance use pathology, perhaps to a greater degree in youth with familial SUDs, via impaired associative learning processes that guide future behavioral outcomes. Indeed, a recent study (Ersche et al., 2020) reported that SUD was associated with hypoconnectivity between OFC and ventromedial prefrontal cortex, a network central to goal-directed decision making. Mediated by atypical OFC connectivity, FH+ adolescents may fail to associate substance use with its negative physical and social consequences (Brown et al., 1999). Likewise, FH+ adolescents may be less capable of perceiving or interpreting the effects of problematic substance use in close social relationships (i.e., their father in the present study), and employ these observations to inform their own decisions. Thus, our OFC findings suggest that preventive interventions should consider incorporating education programs that focus on detrimental consequences of substance use in ways that are clear and objective.

Broadly, the ACC is thought to integrate social, emotional, and cognitive information as well as regulate empathy (Lavin et al., 2013). The ACC has been specifically implicated in balancing reward seeking and loss avoidance (Wang et al., 2019). One recent study identified that increased connectivity between right OFC and right ACC was correlated with increased risk preference, and this was largely mediated by measures of self-control, which is impaired in adolescents who use substances (Wills et al., 2006). Based on findings of the present study, the presence of SUDs in families may influence communication between brain centers of valuation and social/emotional regulation, resulting in impaired self-control. Thus, improving self-control, which is dampened by genetic and environmental risk factors in FH+ youth, should be addressed in prevention efforts.

Regarding the NAcc seed, we found that the left NAcc and right middle occipital gyrus (MOG) showed stronger negative connectivity in FH+ compared to FH− adolescents. The NAcc integrates signals from cortical and limbic structures to mediate goal-directed behaviors (Scofield et al., 2016). The occipital cortex has been implicated in visuospatial (Renier et al., 2010) and emotionally salient visual stimuli (Gabbay et al., 2013) processing. Interestingly, connectivity between NAcc and occipital regions has been reported in studies involving offspring of parents with AUDs. Cservenka et al. (2014) found right NAcc and right occipital cortex showed weaker negative connectivity in FH+ compared to FH− youth and Weiland et al. (2013) found that FH+ youth showed negative connectivity between left NAcc and occipital regions during incentive anticipation. Our finding that associated emotion regulation with NAcc/Occipital connectivity in FH+ subjects suggests that one mechanism for SUD transmission within families is conferred by altered neurocircuitry that is associated with emotion regulation. This finding implies that treatment or prevention efforts should address emotional regulation, although this hypothesis clearly warrants more evidence.

As for the ICA, we selected four independent networks (Laird et al., 2011) based on their association with reward systems (ICNs 2, 3) and executive functioning (ICNs 4, 7). As a review, we found trend-level stronger negative connectivity in FH− compared to FH+ adolescents between 1) a network partially overlapping with both VAN and salience network (ICN 4) and 2) a network partially overlapping with DAN and a visuospatial network (ICN 7). VAN is involved in detecting unexpected external stimuli and breaking attention (i.e., bottom-up) while DAN is associated with maintaining attention and top-down processing (Dosenbach et al., 2007; Farrant and Uddin, 2015). In healthy individuals, when DAN is involved in attention maintenance, the response to the VAN is suppressed to filter irrelevant information (Farrant and Uddin, 2015). On the other hand, when VAN is activated to direct attention to external stimuli, the sustained activation of the DAN for attention maintenance is interrupted (Farrant and Uddin, 2015). Thus, the decreased segregation in these networks may cause interruption in attention maintenance, where the VAN breaks the attention maintenance with external stimuli (Sidlauskaite et al., 2016). This would be expected in a sample of youth with ADHD, but our finding suggests particularly poor dissociation of these networks in the FH+ group, despite similar levels of attentional deficits. Weaker segregation between these may increase one’s risk for cognitive control deficits when exposed to rewards such as drugs of abuse and suggest that prevention and treatment models must assume attentional control deficits in youth participants.

Collectively, the findings from seed-based and ICA analyses complemented each other as both highlight the importance of brain networks associated with self-regulation, decision making, and sensory processing. Finding similar implications from the two different methodologies is not surprising given that we selected both the ROIs and networks a priori based on the hypothesis that FH of SUDs would confer risk by impaired executive functioning. Particularly, across our seed-based and ICA approaches, we discovered that groups differed in functional connectivity between brain regions related to integration of sensory information into the decision-making process. These findings point to amplified decision making and self-control deficits in FH+ youth with externalizing disorders that are not apparent on traditional psychological tests or self-report measures and suggest that this group of youth should be the target of unique selective prevention approaches.

We note that with this design, we are not able to identify the origin of the additional risk conferred to offspring in families with SUDs. We assume a genetic as well as environmental contribution, likely with epigenetic influences given the multi-generational SUDs in the FH+ group. Although we matched our sample on major psychological and environmental factors, the current sample differed in family dysfunction (parent report only). Further investigation is needed to understand how family dynamics independently affect offspring functional connectivity.

The limitations of the current study include a lack of available youth-specific templates for ICA analyses. This may have influenced the fact that neither the VAN nor SN precisely matched with ICN 4 anatomically, as children show reduced segregation between the VAN and SN compared to adults (Farrant and Uddin, 2015). In addition, because group ICA assigns identical components to each subject, individual variability in network location is not fully captured. Also, these data reflect brain connectivity differences in youth who have not been followed longitudinally. Thus, while we refer to certain participants as being at elevated risk for SUD development, this study does not confirm that our findings predict substance use or SUDs. Rather these findings should assist in the generation of hypotheses to be tested with longitudinal designs. Lastly, we elected not to apply alpha correction for multiple comparisons with the seed-based analysis as the three regions being compared were preplanned (Armstrong 2014).

At present, this is the first study which investigated group differences in functional connectivity between youth whose families have or do not have parental SUDs, while accounting for child psychopathology. Both seed-based and ICA analyses suggest that the mechanisms that increase risk for SUDs in youth born into families affected by SUDs may occur via aberrant functions in sensory, valuation and social/emotional regulation, and attention-related cognitive control areas. Interestingly, groups did not differ in attentional capacity when assessed via psychological testing and self-report measures, yet functional connectivity did differ in attention-relevant regions. Aberrant functional connectivity in these networks, may ultimately be determined to be biomarkers in larger samples (e.g., ABCD) which could identify individuals longitudinally followed into the period of SUD emergence. Our findings suggest that frontostriatal circuits involving reward processing and executive functioning are possible neural biomarkers for adolescents with FH of SUDs, even after controlling for increased impulsivity associated with externalizing disorders. Ultimately, strategies to prevent SUDs before the onset of problematic substance use behaviors, particularly in high-risk youth, should address biologically driven deficits outlined here. This work suggests that potential biomarkers in familial high-risk youth may improve predictive validity of SUDs beyond demographic and behavioral variables.

Highlights.

  • Family history (FH) of substance use disorder (SUD) affects offspring neurocircuitry

  • FH may alter offspring brain centers of valuation and social/emotional regulation

  • FH positive group showed aberrant visual stimuli processing compared to FH negative

  • FH affects integration of sensory information into decision-making

  • Attention-related brain networks trended toward differences between groups

Acknowledgments

We would like to thank Tiffany Hatfield, Jackson Richey, Jose Chimelis Santiago, Dr. Ally Dir, Laura Redelman, Eric Bryan, Kourtnee Boose, Alexa Manley, Violet Davies, and Lauren Adams for their contributions to data collection and study management.

Role of Funding Source

The funding for the study is from National Institute on Drug Abuse (1R01DA039764 PI: Dr. Leslie Hulvershorn). Sponsors had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

Footnotes

Conflict of interest

No conflict declared

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References

  1. Anderson SW, Bechara A, Damasio H, Tranel D, Damasio AR, 1999. Impairment of social and moral behavior related to early damage in human prefrontal cortex. Nat Neurosci 2(11), 1032–1037. [DOI] [PubMed] [Google Scholar]
  2. Armstrong RA (2014). When to use the B onferroni correction. Ophthalmic Physiol Opt, 34(5), 502–508. [DOI] [PubMed] [Google Scholar]
  3. Association AP, 2013. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub. [DOI] [PubMed] [Google Scholar]
  4. Biederman J, Faraone SV, Monuteaux MC, Feighner JA, 2000. Patterns of alcohol and drug use in adolescents can be predicted by parental substance use disorders. Pediatrics 106(4), 792–797. [DOI] [PubMed] [Google Scholar]
  5. Brown SA, Tate SR, Vik PW, Haas AL, Aarons GA, 1999. Modeling of alcohol use mediates the effect of family history of alcoholism on adolescent alcohol expectancies. Exp Clin Psychopharmacol 7(1), 20–27. [DOI] [PubMed] [Google Scholar]
  6. Calhoun VD, de Lacy N, 2017. Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis. Neuroimaging Clin N Am 27(4), 561–579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Claus ED, Ewing SW, Filbey FM, Sabbineni A, Hutchison KE, 2011. Identifying neurobiological phenotypes associated with alcohol use disorder severity. Neuropsychopharmacology 36(10), 2086–2096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Coskunpinar A, Dir AL, Cyders MA, 2013. Multidimensionality in impulsivity and alcohol use: a meta-analysis using the UPPS model of impulsivity. Alcohol Clin Exp Res 37(9), 1441–1450. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cox RW, 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29(3), 162–173. [DOI] [PubMed] [Google Scholar]
  10. Cservenka A, Casimo K, Fair DA, Nagel BJ, 2014. Resting state functional connectivity of the nucleus accumbens in youth with a family history of alcoholism. Psychiatry Res 221(3), 210–219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ, 2006. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980. [DOI] [PubMed] [Google Scholar]
  12. DeVito EE, Meda SA, Jiantonio R, Potenza MN, Krystal JH, Pearlson GD, 2013. Neural correlates of impulsivity in healthy males and females with family histories of alcoholism. Neuropsychopharmacology 38(10), 1854–1863. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dosenbach NU, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RA, Fox MD, Snyder AZ, Vincent JL, Raichle ME, Schlaggar BL, Petersen SE, 2007. Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci U S A 104(26), 11073–11078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Eriksen BA, Eriksen CW, 1974. Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics 16, 143–149. [Google Scholar]
  15. Ersche KD, Fletcher PC, Lewis SJ, Clark L, Stocks-Gee G, London M, Deakin JB, Robbins TW, Sahakian BJ, 2005. Abnormal frontal activations related to decision-making in current and former amphetamine and opiate dependent individuals. Psychopharmacology (Berl) 180(4), 612–623. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Ersche KD, Meng C, Ziauddeen H, Stochl J, Williams GB, Bullmore ET, & Robbins TW, 2020. Brain networks underlying vulnerability and resilience to drug addiction. PNAS, 117(26), 15253–15261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Farrant K, Uddin LQ, 2015. Asymmetric development of dorsal and ventral attention networks in the human brain. Dev Cogn Neurosci 12, 165–174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gabbay V, Ely BA, Li Q, Bangaru SD, Panzer AM, Alonso CM, Castellanos FX, Milham MP, 2013. Striatum-based circuitry of adolescent depression and anhedonia. J Am Acad Child Adolesc Psychiatry 52(6), 628–641 e613. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gold JM, Carpenter C, Randolph C, Goldberg TE, Weinberger DR, 1997. Auditory working memory and Wisconsin Card Sorting Test performance in schizophrenia. Arch Gen Psychiatry 54(2), 159–165. [DOI] [PubMed] [Google Scholar]
  20. Hare TA, Camerer CF, Rangel A, 2009. Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324(5927), 646–648. [DOI] [PubMed] [Google Scholar]
  21. Hobkirk AL, Bell RP, Utevsky AV, Huettel S, Meade CS, 2019. Reward and executive control network resting-state functional connectivity is associated with impulsivity during reward-based decision making for cocaine users. Drug Alcohol Depend 194, 32–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Iacono WG, Malone SM, McGue M, 2008. Behavioral disinhibition and the development of early-onset addiction: common and specific influences. Annu Rev Clin Psychol 4, 325–348. [DOI] [PubMed] [Google Scholar]
  23. Ivanov I, Liu X, Shulz K, Fan J, London E, Friston K, Halperin JM, Newcorn JH, 2012. Parental substance abuse and function of the motivation and behavioral inhibition systems in drug-naive youth. Psychiatry Res 201(2), 128–135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, Williamson D, Ryan N, 1997. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry 36(7), 980–988. [DOI] [PubMed] [Google Scholar]
  25. Kringelbach ML, 2005. The human orbitofrontal cortex: linking reward to hedonic experience. Nat Rev Neurosci 6(9), 691–702. [DOI] [PubMed] [Google Scholar]
  26. Laird AR, Fox PM, Eickhoff SB, Turner JA, Ray KL, McKay DR, Glahn DC, Beckmann CF, Smith SM, Fox PT, 2011. Behavioral interpretations of intrinsic connectivity networks. J Cogn Neurosci 23(12), 4022–4037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lavin C, Melis C, Mikulan E, Gelormini C, Huepe D, Ibanez A, 2013. The anterior cingulate cortex: an integrative hub for human socially-driven interactions. Front Neurosci 7, 64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Luby JL, Agrawal A, Belden A, Whalen D, Tillman R, Barch DM, 2018. Developmental Trajectories of the Orbitofrontal Cortex and Anhedonia in Middle Childhood and Risk for Substance Use in Adolescence in a Longitudinal Sample of Depressed and Healthy Preschoolers. Am J Psychiatry 175(10), 1010–1021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lv H, Wang Z, Tong E, Williams LM, Zaharchuk G, Zeineh M, Goldstein-Piekarski AN, Ball TM, Liao C, Wintermark M, 2018. Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know. AJNR Am J Neuroradiol 39(8), 1390–1399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Martz ME, Cope LM, Hardee JE, Brislin SJ, Weigard A, Zucker RA, Heitzeg MM, 2019. Frontostriatal Resting State Functional Connectivity in Resilient and Non-Resilient Adolescents with a Family History of Alcohol Use Disorder. J Child Adolesc Psychopharmacol 29(7), 508–515. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Morin JG, Afzali MH, Bourque J, Stewart SH, Seguin JR, O’Leary-Barrett M, Conrod PJ, 2019. A Population-Based Analysis of the Relationship Between Substance Use and Adolescent Cognitive Development. Am J Psychiatry 176(2), 98–106. [DOI] [PubMed] [Google Scholar]
  32. Parkes L, Fulcher B, Yucel M, Fornito A, 2018. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage 171, 415–436. [DOI] [PubMed] [Google Scholar]
  33. Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF, 2015. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage 112, 267–277. [DOI] [PubMed] [Google Scholar]
  34. Renier LA, Anurova I, De Volder AG, Carlson S, VanMeter J, Rauschecker JP, 2010. Preserved functional specialization for spatial processing in the middle occipital gyrus of the early blind. Neuron 68(1), 138–148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Schoenbaum G, Roesch MR, Stalnaker TA, 2006. Orbitofrontal cortex, decision-making and drug addiction. Trends Neurosci 29(2), 116–124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Scofield MD, Heinsbroek JA, Gipson CD, Kupchik YM, Spencer S, Smith AC, Roberts-Wolfe D, Kalivas PW, 2016. The Nucleus Accumbens: Mechanisms of Addiction across Drug Classes Reflect the Importance of Glutamate Homeostasis. Pharmacol Rev 68(3), 816–871. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Shields A, Cicchetti D, 1997. Emotion regulation among school-age children: The development and validation of a new criterion Q-sort scale. Dev Psychol, 33(6), 906–916. [DOI] [PubMed] [Google Scholar]
  38. Sidlauskaite J, Sonuga-Barke E, Roeyers H, Wiersema JR, 2016. Altered intrinsic organisation of brain networks implicated in attentional processes in adult attention-deficit/hyperactivity disorder: a resting-state study of attention, default mode and salience network connectivity. Eur Arch Psychiatry Clin Neurosci 266(4), 349–357. [DOI] [PubMed] [Google Scholar]
  39. Skinner H, Steinhauer P, Sitarenios G, 2000. Family Assessment Measure (FAM) and Process Model of Family Functioning. Journal of Family Therapy 22(2), 190–210. [Google Scholar]
  40. Talairach J, 1988. Co-Planar Stereotaxic Atlas of the Human Brain. Thieme. [Google Scholar]
  41. Vaidya JG, Elmore AL, Wallace AL, Langbehn DR, Kramer JR, Kuperman S, O’Leary DS, 2019. Association Between Age and Familial Risk for Alcoholism on Functional Connectivity in Adolescence. J Am Acad Child Adolesc Psychiatry 58(7), 692–701. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Van ‘t Ent D, 2002. Perceptual and motor contributions to performance and ERP components after incorrect motor activation in a flanker reaction task. Clin Neurophysiol 113(2), 270–283. [DOI] [PubMed] [Google Scholar]
  43. Volkow ND, Koob GF, McLellan AT, 2016. Neurobiologic Advances from the Brain Disease Model of Addiction. N Engl J Med 374(4), 363–371. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Wallis JD, 2011. Cross-species studies of orbitofrontal cortex and value-based decision-making. Nat Neurosci 15(1), 13–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Wang M, Chen Z, Zhang S, Xu T, Zhang R, Suo T, Feng T, 2019. High Self-Control Reduces Risk Preference: The Role of Connectivity Between Right Orbitofrontal Cortex and Right Anterior Cingulate Cortex. Front Neurosci 13, 194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Wechsler D, 1999. Manual for the Wechsler abbreviated intelligence scale (WASI). The Psychological Corporation, San Antonio, TX. [Google Scholar]
  47. Weiland BJ, Welsh RC, Yau WY, Zucker RA, Zubieta JK, Heitzeg MM, 2013. Accumbens functional connectivity during reward mediates sensation-seeking and alcohol use in high-risk youth. Drug Alcohol Depend 128(1–2), 130–139. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Whiteside SP, Lynam DRJP, differences, i., 2001. The five factor model and impulsivity: Using a structural model of personality to understand impulsivity. 30(4), 669–689. [Google Scholar]
  49. Wills TA, Walker C, Mendoza D, Ainette MG, 2006. Behavioral and emotional self-control: relations to substance use in samples of middle and high school students. Psychol Addict Behav 20(3), 265–278. [DOI] [PubMed] [Google Scholar]
  50. Yalachkov Y, Kaiser J, Naumer MJ, 2010. Sensory and motor aspects of addiction. Behav Brain Res 207(2), 215–222. [DOI] [PubMed] [Google Scholar]
  51. Zapolski TC, Stairs AM, Settles RF, Combs JL, Smith GT, 2010. The measurement of dispositions to rash action in children. Assessment 17(1), 116–125. [DOI] [PMC free article] [PubMed] [Google Scholar]

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