Early adolescent executive functioning, intrauterine exposures and own drug use

Early Adolescent Executive Functioning, Intrauterine Exposuresand Own Drug Use

Ruth Rose-Jacobs, Sc.D.a, Shayna Soenksen, M.S.b, Danielle P. Appugliese, MPHc, HowardJ. Cabral, Ph.D., MPHd, Mark A. Richardson, Ph.D.e, Marjorie Beeghly, Ph.D.f, Timothy C.Heeren, Ph.D.d, and Deborah A. Frank, M.D.aaDepartment of Pediatrics, Boston University School of Medicine and Boston Medical Center,Boston, MA 02118b Department of Pediatrics, Boston Medical Center, Boston, MA 02118 (at time of study)cData Coordinating Center, Boston University School of Public Health, Boston, MA 02118dDepartment of Biostatistics, Boston University School of Public Health, Boston, MA 02118eDepartment of Psychology, Boston University, Boston, MA 02215 and Division of Psychiatry,Boston University School of Medicine and Boston Medical Center, Boston, MA 02118fDepartment of Pediatrics, Harvard University Medical School, Boston, MA 02115 andDepartment of Psychology, Wayne State University, Detroit MI 48202

AbstractIndividual differences in adolescents' executive functioning are often attributed either tointrauterine substance exposure or to adolescents' own substance use, but both predictors typicallyhave not been evaluated simultaneously in the same study. This prospective study evaluatedwhether intrauterine drug exposures, the adolescents' own substance use, and/or their potentialinteractions are related to poorer executive functioning after controlling for important contextualvariables. Analyses were based on data collected on a sample of 137 predominantly African-American/ African Caribbean adolescents from low-income urban backgrounds who werefollowed since their term birth. Intrauterine substance exposures (cocaine, marijuana, alcohol,cigarettes) and adolescents' substance use were documented using a combination of biologicalassays and maternal and adolescent self-report. At 12-14 years of age, examiners masked tointrauterine exposures and current substance use assessed the adolescents using the Delis-KaplanExecutive Function System (D-KEFS), an age-referenced instrument evaluating multipledimensions of executive functioning (EF).

Results of covariate-controlled analyses in this study suggest that when intrauterine substanceexposures and young adolescents' substance use variables were in the same analysis models, subtledifferences in specific EF outcomes were identifiable in this non-referred sample. While furtherstudy with larger samples is indicated, these findings suggest that 1) research on adolescent

© 2011 Elsevier Inc. All rights reserved.Correspondence should be sent to: Ruth Rose-Jacobs, Sc.D, Department of Pediatrics, Boston University School of Medicine, 88 EastNewton Street, Vose Hall 426, Boston, MA 02118; [email protected]; fax: (617) 414-7915.Conflict of Interest Statement: The authors have no financial, personal, or other relationships with people or organizations that mayinappropriately influence the authors' submitted work.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to ourcustomers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review ofthe resulting proof before it is published in its final citable form. Please note that during the production process errors may bediscovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

NIH Public AccessAuthor ManuscriptNeurotoxicol Teratol. Author manuscript; available in PMC 2012 May 1.

Published in final edited form as:Neurotoxicol Teratol. 2011 ; 33(3): 379–392. doi:10.1016/j.ntt.2011.02.013.

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substance use and intrauterine exposure research should evaluate both predictors simultaneously;2) subtle neurocognitive effects associated with specific intrauterine drug exposures can beidentified during early adolescence; and 3) intrauterine substance exposure effects may differ fromthose associated with adolescents' own drug use.

KeywordsExecutive functions; intrauterine substance exposure; adolescent drug use; neurocognition; Delis-Kaplan Executive Function System (D-KEFS); prenatal substance exposure

1. IntroductionConsiderable concern was generated in the 1990's when popular media predicted thatchildren with intrauterine cocaine exposure (IUCE) would experience severe and unusualdevelopmental and educational impairments (Okie, 2009). However, in the interveningyears, well-controlled, multivariate research beyond the neonatal period and into middlechildhood has shown that the effects of IUCE are subtle, rather than global or devastating.

The ascertainment of potential specific neurocognitive effects of IUCE is complicatedbecause IUCE consistently co-occurs with other potentially neurotoxic intrauterineexposures as well as in the context of multiple postnatal bio-psychosocial risk factors (Franket al., 2002; Jacobson et al., 1996) whose effects may be misattributed to IUCE. In somemultivariate studies of school-aged children, specific IUCE effects on neurocognitive skillswere identified (Bandstra et al., 2001; Savage et al., 2005). These subtle effects haveprimarily been detected in specific developmental and behavioral domains, such as aspectsof language (Bandstra et al., 2004; Beeghly et al., 2006), learning disabilities (Morrow et al.,2006), emotional arousal and regulation (Chaplin et al., 2009; Mayes, 2002) and developingexecutive cognitive functions (Rose-Jacobs et al., 2009; Warner et al., 2006). In othermultivariate studies, variables such as children's age and IQ (Hurt et al., 2009) andintrauterine drug exposures to alcohol, marijuana, tobacco, and other drugs (Hurt et al.,2009; Jacobson and Jacobson, 2001; Sood et al., 2005) played an important role, whether ornot IUCE effects were detected. Nearly all studies investigating potential IUCE effects intomiddle childhood have shown that multiple biopsychosocial variables, especially thoseassociated with poverty, influence neurocognition (Hurt et al., 2009; Mayes, 2002; Sood etal., 2005). Further interpretive difficulties have emerged as the participants in longitudinalstudies enter adolescence and may themselves begin using alcohol, marijuana, tobacco, andother drugs, (Fried et al., 2005; Tapert and Brown, 2000) in varying combinations, whichmay or may not be neurotoxic.

As with intrauterine drug exposures, demographic and bio-psychosocial risk factors (e.g.gender, factors associated with poverty, family history of drug use, mental health status)individually or together may be associated with adolescents' drug use (Fried et al., 2005;Tapert and Brown, 2000). The intergenerational nature of substance abuse is wellestablished, but neuropsychological research has not extensively explored the corollary thatadolescent substance users are more likely than non-users to have experienced intrauterineexposures (Frank et al., 2011). With the exception of Fried and colleagues (Smith et al.,2006), few investigators have attempted to ascertain whether neurocognitive effects ascribedto adolescent substance use are in fact predicted by intrauterine exposures or represent thecombined effects of intrauterine exposures and adolescents' own drug use.

The present study addresses two questions related to this complex and understudied issue: 1)Whether the impact of IUCE or other intrauterine exposures on neurocognitive function can

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be detected in adolescence, particularly if the adolescent has initiated substance use; and 2)Whether the impact of adolescents' use of psychoactive substances on concurrentneurocognitive function can be identified if intrauterine exposures are controlled.

1.1 Executive FunctionsAlthough definitions vary, executive functions (EF) generally refer to a set of skillsnecessary for independent, purposeful, goal-directed activity (Lezak, 1995; Stuss et al.,1986), including many higher-level neurocognitive functions (e.g., working memory,inhibitory control, organization, and planning). EF are critical to successful adaptationbecause they facilitate goal-directed behaviors and the capacity to manage multiplecompeting stimuli and/or performance demands (Alvarez and Emory, 2006; Fried, 2001;Noland et al., 2003). Although IQ and EF may share some variance, EF differ from“intelligence” as ascertained by global measures of IQ, which may reflect school experienceand well-learned information (Fried, 2002b; Fried, 2001; Jacobson and Jacobson, 2001;Jurado and Rosselli, 2007). Once thought to be exclusively associated with the frontal lobes,increasing evidence has shown that EF require the participation and coordination of multipleanatomical and functional brain regions (Alvarez and Emory, 2006; Hazy et al., 2006). Thenegative effects of intrauterine exposures on EF may not emerge until middle childhood oradolescence (Rose-Jacobs et al., 2009), given the differential maturation rates of brainregions associated with various aspects of EF and the increasingly complex demands onchildren's cognitive and behavioral performance as they grow older (Blakemore andChoudhury, 2006; Fried, 2002a).

1.2 IUCE and EF during Middle Childhood and AdolescenceAmong preschool and young school-aged children, findings vary as to whether IUCEcorrelates negatively with cognitive functioning (Bandstra et al., 2002; Behnke et al., 2006;Delaney-Black et al., 2000; Frank et al., 2001; Frank et al., 2005; Noland et al., 2005; Singeret al., 2004). However, during pre-adolescence and early adolescence, we (Rose-Jacobs etal., 2009) and others (Mayes et al., 2005; Salvage et al., 2005; Warner et al., 2006) haveidentified subtle negative associations of IUCE with EF measured by neuroimaging and/orfunctional assessments. Warner et al. (2006) evaluated 10-year-old children with andwithout IUCE using the Stroop, a measure of verbal inhibition (Stroop, 1935) and the TrailMaking test (Moses, 2004), a measure of planning and set shifting, and evaluated whetherIUCE was associated with their performance on these tests or to variations inneuroanatomical structure using MRI-based diffusion tensor imaging (DTI). Results showedthat children with IUCE required significantly more time to complete a visual-motor set-shifting task, and exhibited poorer performance on the verbal inhibition task. IUCE also wasassociated with significantly higher average diffusion in the left frontal callosal and rightfrontal projection fibers. Test performance was correlated with fractional anisotropy of thefrontal white matter. In further analyses controlling for gender and intelligence, Warner etal. found that intrauterine exposures to alcohol, marijuana, and the interaction betweencocaine and marijuana were also associated with average diffusion in the left frontal callosalfibers.

In other research, Savage et al. (2005) assessed 10-year-old low-income children using theGordon Diagnostic System, a visual continuous performance test measuring impulsivity andsustained attention, as well as Trail Making and Auditory Attention subtests of the Halstead-Reitan Neuropsychological Battery. While the performance of children in both IUCE andunexposed groups was below published norms, on average, children in the IUCE groupmade more errors of commission on the most difficult Gordon tasks, an indicator of mildlycompromised attention and increased impulsivity. In a study of slightly younger children(7-9 year-olds) from another cohort, children with IUCE took longer to process stimuli than



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children without IUCE, and their effort was more likely associated with activity in morediverse cortical brain areas (Mayes et al., 2005). In our recent study of 9.5 and 11 year-oldchildren with and without IUCE (Rose-Jacobs et al., 2009), heavier IUCE compared tolighter/no exposure was associated in covariate-controlled analyses with mild compromiseon children's ability to inhibit prepotent verbal responses on the Stroop (Stroop, 1935), butnot scores on the Rey-Osterrieth Complex Figure task (Rey and Osterrieth, 1993).

1.3 Intrauterine Drug Exposures (IUDE), Marijuana, Alcohol, Tobacco and EF1.3.1 Marijuana—Several researchers following infants prospectively from birth haveevaluated the longitudinal effects of intrauterine marijuana exposure on EF during middlechildhood, adolescence, and young adulthood (Fried, 2001; Fried et al., 1998; Richardson etal., 2002; Smith et al., 2006) controlling for other prenatal substance exposures andcontextual bio-psychosocial variables. However, specific findings vary. Richardson et al(2002) followed a cohort of low-income children whose mothers were recruited duringpregnancy. At the 10-year follow-up visit, marijuana exposure during the second trimesterof pregnancy was associated with lower test scores in learning and memory on the WideRange Assessment of Memory and Learning (WRAML)(Adam and Sheslow, 1990), as wellas with increased impulsivity (i.e., more commission errors on a continuous performancetest). Among a predominantly middle-class European-Canadian cohort of children followedto young adulthood, Fried et al. (1998; 2001) found a somewhat different mix ofneurocognitive deficits associated with intrauterine marijuana exposure. At 9 to 10 years ofage, intrauterine marijuana exposure was negatively associated with performance on tasksrequiring visual analysis, hypothesis testing, and attention, but not global IQ. Furtheranalysis of visual-perceptual abilities in the same cohort between 9 and 12 years-old andagain at 13 to 16 years indicated that intrauterine marijuana exposure was associated withgreater difficulty in problem-solving, analysis, and synthesis associated with visualperception (Fried and Watkinson, 2000; Fried et al., 2003). In a later fMRI study of 31 oftheir participants between 18 and 22 years of age, Fried and colleagues reported thatintrauterine marijuana exposure was related to altered neural activity during responseinhibition (Smith et al., 2004) and visual-spatial working memory processing (Smith et al.,2006), after controlling for other prenatal exposures and the participants' own substance use.

1.3.2 Alcohol—Impairments in EF due to intrauterine exposure to high levels of alcoholare well documented through adulthood, particularly among individuals diagnosed withFetal Alcohol Syndrome (FAS) (Chiriboga, 2003), now identified as the most severemanifestation of Fetal Alcohol Spectrum Disorder (FASD) (Riley and McGee, 2005).Commonly reported EF deficits associated with heavy intrauterine alcohol exposure includedifficulties in planning, cognitive flexibility, selective inhibition, concept formation,reasoning, attention and working memory (Green et al., 2009; Mattson et al., 1999), butfindings vary across studies (Connor et al., 2000; Cottencin et al., 2009; Green et al., 2009;Mattson et al., 1999; Richardson et al., 2002; Riley and McGee, 2005). This variation mayreflect differences in the timing and dosage of exposure, variation in study design (e.g.,cross-sectional/retrospective versus prospective), and/or inconsistency in the measurementand control of potential confounding variables, particularly other prenatal exposures(Connor et al., 2000; Cottencin et al., 2009; Green et al., 2009; Mattson et al., 1999;Richardson et al., 2002; Riley and McGee, 2005). Whether a lower level of intrauterinealcohol exposure below that associated with FAS or depressed birth weight exerts toxiceffects on neurocognitive functioning in later childhood or adolescence (Riley and McGee,2005) has not yet been established.

1.3.3 Tobacco Exposure—Several studies report that intrauterine tobacco exposure isassociated with a variety of neurocognitive deficits in middle childhood and adolescence,



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including deficits in verbal and visual memory, impaired verbal learning, inattention, andimpulsivity (Cornelius and Day, 2000; Milberger et al., 1996). In an MRI study of a subsetof our cohort during middle childhood, intrauterine tobacco exposure was associated withsignificant reductions in cortical gray matter, total parenchyma volumes, and headcircumference after covariate adjustment for demographics, a finding that retained marginalsignificance after additional covariate adjustment for other intrauterine drug exposures(Rivkin et al., 2008). Fried et al. (2002a) reported that intrauterine tobacco exposure wasassociated with poorer impulse control, impaired working memory, decrements in somecomponents of visual-perceptual performance, and lower IQ scores during middle childhoodand adolescence. In contrast, others have reported no significant association betweenintrauterine tobacco exposure and school-aged children's neurocognition or academicachievement (Gilman et al., 2008; Herrmann et al., 2008), or on expressive and overalllanguage functioning (Beeghly et al., 2006), after controlling for biopsychosocial variables.

1.4 Adolescents' Substance Use and Neurocognitive OutcomesCross-sectional studies of adults have examined the relationship between substance use andneuropsychological functioning and have identified a range of neurocognitive deficitsassociated with varying levels of the use and abuse of alcohol and tobacco (Durazzo et al.,2007; Glass et al., 2009; Swan and Lessov-Schlagger, 2007), marijuana (McHale and Hunt,2008; Nestor, 2008; Pope and Yurgelun-Todd, 1996; Ramaekers et al., 2006; Verdejo-Garcia et al., 2006), and cocaine (Browndyke et al. 2004; Hoff et al., 1996; Toomey et al.,2003; Verdejo-Garcia and Perez-Garcia, 2007). However, these results cannot readily begeneralized to adolescents since their brains are still maturing and undergoing dynamicphysiologic changes and synaptic reorganization (Blakemore and Choudhury, 2006; Conklinet al., 2007; Rutter, 2007). Scientists have speculated that, like early childhood, adolescenceand emerging adulthood may be a “sensitive period” when the brain has enhancedvulnerability to environmental influences and toxic substance exposures (Blakemore andChoudhury, 2006; Lubman et al., 2007; Segalowitz and Davies, 2004). Adolescents andyoung adults who initiate drug use may expose the rapidly maturing brain to potentiallyneurotoxic substances and may exhibit longer-term impairments than older individuals whoinitiate drug use in adulthood (Jacobus et al., 2009; Lubman et al., 2007). Thus, adolescentsmay show different patterns of vulnerability or resilience following substance exposure thanadults (Medina et al., 2007; Schweinsburg et al., 2008; Tapert and Schweinsburg, 2005).

1.5 Other Factors that Complicate Measurement of Outcomes in Studies of IntrauterineSubstance Exposure and Substance Use

Methodological differences among studies of intrauterine substance exposure andadolescents' substance use may contribute to the inconsistent results reported in thisliterature. These differences include variations in study design, subject characteristics,demographics, enrollment criteria, and the specific covariates controlled for in the analyses;the unknown purity, route of administration, and timing of illegal drug use; and the specificneurocognitive outcomes tested (Frank et al., 2001). Additional contributing factors mayinclude the chronicity and combinations of drug use, as well as variations across studies inthe definition of abstinence from drug use, including the duration of non- use or amount thatis classified as abstinence (Jacobus et al., 2009; Medina et al., 2009; Schweinsburg et al.,2008). When evaluating potential substance exposure and substance use effects, it istherefore critical to address (either by subject selection or statistical control) factors that areknown to influence neurocognitive outcomes for all individuals (e.g., gender, child IQ, age,and maternal education) (Frank et al., 2001; Messinger et al., 2004; Morrow et al., 2006;Rose-Jacobs et al., 2009; Singer et al., 2008). Moreover, for studies evaluating low-incomeurban samples, it is imperative also to control for adverse environmental factors (e.g.exposure to violence, environmental exposure to lead) that may also contribute to



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neurocognitive deficits. Unless these factors are measured and controlled, there is dangerthat the effect of these factors may be misattributed to the effects of intrauterine substanceexposure or adolescents' own substance use (Chiodo et al., 2004; Singer et al., 2008).

1.6 PurposeBecause few investigators have evaluated the effects of both intrauterine substanceexposures and adolescents' own substance use on neuropsychological outcomes in the samestudy, it is possible that variations in children's neuropsychological functioning could beattributed to the effects of one factor while ignoring the effects of the other. Therefore, thepurpose of this study is to evaluate whether the level of IUCE, other intrauterine drugexposures, adolescents' own substance use, and/or the interactions between intrauterine drugexposures and own substance use are related to poorer neurocognitive functioning asassessed using the Delis-Kaplan Executive Function System (D-KEFS) at early adolescence,after controlling for important contextual variables.

2. Methods2.1 Data Sample

As described elsewhere (Beeghly et al., 2007; Frank et al., 2002; Frank et al., 1999; Tronicket al., 1996), the participants were part of a prospective longitudinal study evaluating theeffects of level of IUCE on children's growth and development from birth to 14 years of age.All study children were born at Boston City Hospital (now Boston Medical Center) andwere from low-income, urban backgrounds. The Human Studies Committees of Boston CityHospital/Boston Medical Center and Boston University School of Medicine approved thestudy at its inception and yearly thereafter. All birth mothers or other primary caregiversgave written informed consent. Beginning at the 8.5-year visit and continuing through theadolescent years, the children themselves also provided written assent. In addition, aCertificate of Confidentiality was obtained from the federal government to protectparticipants from having research data subpoenaed. After each study visit, the caregiver andchild were given store vouchers and/or age appropriate gifts for completion of the interviewand assessment. At the early adolescent protocol (12 to 14 years of age), the caregiverreceived $100 for completing the caregiver interview and for bringing their adolescent to thedevelopmental assessment. In addition, the adolescent received $50 in store vouchers andtwo passes to the local cinema.

At the study's onset, infant-caregiver dyads were recruited on a daily basis from thepostpartum unit of Boston City Hospital (October 1990 to March 1993) if they met thefollowing inclusion criteria: Infant gestational age ≥ 36 weeks; no obvious major congenitalmalformations; no requirement for neonatal intensive (NICU) care; no diagnosis of FetalAlcohol Syndrome in the neonatal record; no indication (either by maternal urine toxicscreen, neonatal urine toxic screen, meconium assay, or medical records) of prenatalexposure to illegal opiates, methadone, amphetamines, phencyclidine, barbiturates, orhallucinogens; and no history of HIV seropositivity noted in the infant's or mother's medicalrecord. In addition, mothers had to be at least 18 years old and fluent in English. Thesecriteria excluded subjects with known major risk factors (e.g., premature birth) that mightconfound any specific effects, if any, of IUCE on child outcomes. English fluency wasrequired because the neuropsychological measures planned for this cohort at older ages werenot standardized for non-English speakers. Further details about recruitment procedures andsample characteristics are reported elsewhere (Frank et al., 1999; Tronick et al., 1996).



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2.2 Intrauterine Drug Exposure ClassificationResearch staff interviewed study mothers at intake during the postpartum period about theirpregnancy and lifetime use of cocaine, alcohol, marijuana, cigarettes, and other illicit drugsusing an adaptation of the fifth edition of the Addiction Severity Index (ASI) (McLellan etal., 1992).

2.2.1 Intrauterine Cocaine Exposure Classification—IUCE was determined using acombination of biological markers and mothers' self-report. At least one biological marker(maternal or infant urine, or infant meconium) was obtained for each recruited dyad toconfirm maternal self-reported infant exposure status. Urine samples were analyzed forbenzoylecgonine, opiates, amphetamines, benzodiazepines and cannabinoids byradioimmunoassay using commercial kits (Abuscreen RIA, Roche Diagnostics Systems,Inc., Montclair, N.J.). We also sought to collect meconium specimens from all enrolledinfants for analysis by radioimmunoassay for the presence of benzoylecgonine, opiates,amphetamines, benzodiazepines and cannabinoids, using a modification of Ostrea's method(Ostrea et al., 1992). Based on composite information derived from maternal self-report and/or the meconium assays, children with IUCE were further classified as having either heavieror lighter IUCE. We a priori defined “heavier” use as the top quartile of days of mother'sself-reported use during the entire pregnancy and/or the top quartile of concentration forcocaine metabolites in the infant's meconium. The mean days of maternal self-reportedcocaine use during pregnancy in this cohort was 20.6 days (range = 0 – 264); mothersreporting 61 or more days of cocaine use during pregnancy fell into the top quartile andwere considered “heavier” users. The mean meconium concentration was 1143 ng ofbenzoylecognine per gram of meconium (range = 0 to 17,950 ng); infants with more than3314 ng of benzoylecognine per gram of meconium were in the top quartile and wereclassified into the “heavier” group. All other IUCE was classified as “lighter”. Aclassification system based on both self-report and meconium assay was used because 18 of141 (13%) of the infants in the present sample (14% in the source sample) had no meconiumassay. Mothers in the top quartile for self-reported use were classified as heavier users, evenif the benzoylecognine level in their infant's meconium was not in the top quartile or wasmissing. This procedure was used because women are more likely to underreport than overreport illicit substance use during pregnancy and because not all infants with IUCE havepositive meconium assays (Lester et al., 2002; Ostrea et al., 1989; van Gorp et al., 1999).This ordinal classification scheme is similar to that used by other investigators of prenatalsubstance exposure (Alessandri et al., 1998; Jacobson et al., 2001; Singer et al., 2004). Priorresearch in the present cohort indicated that level of cocaine exposure defined this way wassignificantly related in a dose-related manner to lower birth weight z-scores covariateadjusted for gestational age and gender (Frank et al., 1999), neonatal ultrasound findings(Frank et al., 1998), and less optimal patterns of newborn neurobehavior (Tronick et al.,1996).

2.2.2 Other Intrauterine Drug Exposure Classifications Determined During theNeonatal Period—Identification of prenatal marijuana exposure was based on results ofpositive urine assay, meconium assay, or maternal self-report. In previous reports, (Frank etal., 2002; Rose-Jacobs et al., 2009) we analyzed marijuana categorically as exposed orunexposed. We used this two-level variable for two reasons. First, ascertainment ofintrauterine marijuana exposure by meconium concentration is not entirely valid due tostorage of marijuana metabolites in mother's body fat (Ostrea et al., 1989) that do not gettransferred to meconium. Second, self-reported use was denied by a third of the marijuanausers in this cohort who were identified solely based on urine assay. In more recent reports,however, we utilized a 3-level index of intrauterine marijuana exposure: no marijuanaexposure (i.e., no evidence for exposure based on meconium and urine assays and self-



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report); heavier use [positive urine assay at delivery or self-reported days of use in the topquartile of the sample, > 8 days during pregnancy); or lighter use (no positive urine assay atdelivery and self-reported days of use below the top quartile). This classification index wasassociated (p<.0001) with level of mothers' alcohol, cigarettes, and cocaine use duringpregnancy and with infants' mean birth weight [marijuana unexposed, M = 3210 ± 477grams; lighter M = 3069 ± 464 grams; and heavier M =2943 ± 511 grams; p= 0.02].

At the time the study was initiated, there was no established biologic marker for gestationalalcohol exposure, and cotinine assays were prohibitively expensive. Therefore, theascertainment of alcohol and tobacco use in pregnancy by self-reports was state of the art atthe time the current sample was recruited. We determined intrauterine alcohol exposureusing mothers' self-reported average daily volume of alcohol in drinks per day during the 30days prior to delivery. This variable was highly correlated (r = 0.81) with mothers' self-reported use through pregnancy. Consistent with the 3-level IUCE and intrauterinemarijuana variables, we categorized prenatal alcohol use in this study as none, lighter (<0.5drinks/day), and heavier (≥ 0.5 drinks/day). During the post partum interview, mothers alsoreported the average number of cigarettes per day that they consumed while pregnant. A 3-level intrauterine tobacco exposure variable was created for analytic purposes (none = nocigarettes during pregnancy, heavier > 10 cigarettes/day, and lighter (<10 cigarettes/day).

2.3 DesignRecruited caregiver/child dyads were evaluated in multiple follow-up assessments betweenchildren's birth and early adolescence (12-14 years of age) (Frank et al., 2002; Frank et al.,2005; Rose-Jacobs et al., 2009; Tronick et al., 1996). The dependent variables analyzed inthis report are children's EF scores from the D-KEFS, which were collected during the earlyadolescent protocol. At each follow-up visit, primary caregivers brought their child to theChild and Adolescent Development Laboratory in the General Clinical Research Center atBoston Medical Center for a developmental and behavioral assessment administered bytrained examiners masked to children's intrauterine exposures, background variables,caregivers' responses on the interviews, and scores on prior developmental assessments.During the child assessment, caregivers were interviewed in a separate room by a trainedresearch interviewer regarding family demographics, their recent substance use,psychosocial adaptation, the caregiving environment, and child's current behavioralfunctioning.

2.3.1 Classification of Adolescents' Own Substance Use: Cocaine, Marijuana,Alcohol, Cigarettes, and Other Drugs—At the early adolescence follow-up visit, theadolescents reported on their own substance use during a computer-assisted self-interview(ACASI). Questions about cocaine, marijuana, alcohol, tobacco, and other substance usewere taken from several validated components of the CDC's 2005 Youth Risk BehaviorSurveillance System (YRBSS), the Wisconsin YRBS Middle School Questionnaire, theState and Local YRBS, and the Wisconsin YRBS High School Questionnaire (Eaton et al.,2006). Adolescents also provided a urine sample at the same visit, which was tested by theUnited States Drug Testing Laboratories, Inc. using the No-Excuse Urine Panel. Thismethod has a limit of detection panel that screens using enzyme multiplied immunoassaytechnique (EMIT) at the lowest validated concentrations that can be achieved with thereagent set, rather than the higher levels required for judicial actions. The GC/MSconfirmations used are either at 1/2 or 1/5 of the SAMHSA screening concentrationsdepending on the drug class for cannabinoids, opiates, amphetamines and cocainemetabolites and ELISA for cotinine. The No-Excuse Urine gives a detection window formost drugs up to approximately a week, longer for marijuana. If adolescents' self-report forsubstance use during the past 30 days or their urine assay was positive, adolescents were



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classified as having used a particular substance(s) during the past 30 days. If they reportedsubstance use prior to the past 30 days, but not more recently, adolescents were classified ashaving used a particular substance(s) “ever”. To maintain adequate cell sizes for statisticalanalyses, adolescent cigarette use and cocaine use each were categorized as two-levelvariables, “never” and “ever”.

2.4 Measures2.4.1 Executive Function—Following Goldberg (2005) and Anderson (2003), multipledimensions of EF were evaluated at early adolescent follow-up using an age-referencedsingle instrument, the Delis-Kaplan Executive Function System (D-KEFS) (Delis et al.,2001). The D-KEFS confronts individuals with multiple tasks that are novel, complex, andrequire integration of information. To minimize participant burden, we selected five of thenine co-normed subtests comprising the D-KEFS: Color-Word Interference, DesignFluency, Trail Making, Word Context, and Tower. We chose these subtests based on theory,a review of literature, and experience with this cohort. Age-standardized scaled scores(population M = 10 ± 3) for each subtest (usually one measure associated with completiontime, and one associated with accuracy/error rates) were used in the statistical analyses.Higher scores reflect more optimal performance. Some scales (e.g., those associated withnumber of errors) were reversed-scored as part of the standard scoring method.

2.4.1.1 Color-Word Interference: The D-KEFS Color-Word Interference test was adaptedfrom the Stroop Color-Word test (Stroop, 1935) and evaluates an individual's cognitiveflexibility and ability to inhibit a prepotent verbal response while attempting to generate therequired conflicting response (Delis et al., 2001). In each condition, the individual isconfronted with 50 stimuli randomly displayed in five rows of ten stimuli and asked to readthem as quickly as possible without making mistakes. In Condition 1, the adolescent isasked to name the color of patches presented in one of three colors (red, blue, and green). InCondition 2, the adolescent is asked to read the color names printed in black ink. InCondition 3 (the subtest most similar to the most complex outcome of the Stroop Color-Word test), the color names are printed in a contradictory color ink and the adolescent isasked to ignore the word in order to name the ink color. In Condition 4, an even morecomplex interference task than the previous condition, the adolescent is asked to alternatebetween reading the color words and naming the discordant ink colors. In the present study,we used the age-referenced scaled scores for Completion Times in Condition 3 (Inhibition)and for Inhibition/Switching in Condition 4, and Total Errors for both conditions.

2.4.1.2 Design Fluency: The D-KEFS Design Fluency test assesses non-verbal fluency andcognitive flexibility (Delis et al., 2001). This test consists of three conditions in which theadolescent is asked to connect dots using four straight lines to complete as many differentdesigns as possible in 60 seconds. The dots are arranged in response boxes presented in fiverows of seven boxes. In Condition 1, Filled Dots, each box contains five filled dots. InCondition 2, Empty Dots Only, each box contains five filled dots and five unfilled dots, andthe adolescent is asked to connect only the unfilled dots. In Condition 3, Switching, the mostdifficult task, requires the adolescent to alternate between connecting filled and unfilleddots. The scaled scores evaluated in this analysis included the Switching Total Correct scoreand the Percent Design Accuracy score.

2.4.1.3 Trail Making: The Trail Making Test is made up of several timed conditions andassesses flexibility of thinking on a visual-motor sequencing task (Delis et al., 2001).Condition 1, Visual Scanning, requires the adolescent to locate all occurrences of thenumber “3” among other numbers and letters randomly scattered across two pages.Condition 2, Number Sequencing, requires the adolescent to connect in serial order,



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numbers that are scattered across two pages. Condition 3, Letter Sequencing, is similar toCondition 2 but requires connecting letters in alphabetical order. Condition 4, Number-Letter Switching, requires the adolescent to connect alternately numbers and letters in order(e.g. 1-A-2-B-3-C) and serves as the primary measure of executive function for this test.During the test, errors made during the sequencing conditions were pointed out to theadolescents and they were asked to return to the last correct connection and continue fromthere. The scaled scores used in the analyses were the Number-Letter Switching CompletionTime and Error Analysis Score from Condition 4.

2.4.5 Word Context—The D-KEFS Word Context test measures deductive reasoning,ability to integrate multiple pieces of information, hypothesis testing, and flexibility ofthinking (Delis et al., 2001). The adolescent is presented with a fictional word and asked todecipher its meaning from sentences containing clues. Up to five sentences are shown foreach word; the first provides an ambiguous clue and each subsequent sentence suppliesincreasingly more information. The goal of the task is to discover the meaning of thefictional word in as few clue sentences as possible and to report the correct response for allremaining sentences of that item. The scaled scores used in the current analyses were theTotal Consecutively Correct and the Consistently Correct Ratio score.

2.4.1.5 Tower: The D-KEFS Tower Test is made up of nine test items increasing indifficulty. For each item, two to five disks are placed on one or more of three vertical pegsin a predetermined position and a picture of the tower to be built is presented. The goal is tomove the disks across the pegs to build the target tower in as few moves as possible andwithin a set time limit. Only one disk may be moved at a time and a larger disk may not beplaced on a smaller one. The scaled scores used in the current analyses were TotalAchievement and the Rule-Violation-Per-Item Ratio score.

2.4.2 Control Variables—Potential candidate control variables including gender andcurrent IQ were selected a priori based on theoretical considerations, previous literature, andearlier findings in this cohort. IQ was measured by the Wechsler Abbreviated Scale ofIntelligence (WASI) (Weschsler, 1999). If time did not permit the adolescent to completethe WASI, the child's prorated IQ derived from the Wechsler Intelligence Scale for Children,3rd edition (WISC-III) (Weschsler, 1991) administered at the 8.5 or 11 year protocols wassubstituted. Because substance-using individuals often use multiple substances, multivariateanalyses for IUCE and adolescent use were modeled to control for other psychoactivesubstances (e.g., alcohol, marijuana, tobacco) in the analyses. Other candidate covariatesincluded: adolescents' age at the time of testing, birth weight z-score adjusted for gestationalage and gender (National Center for Health Statistics Centers for Disease Control andPrevention, 2000); birth mother's education; birth mother's self-identified African American/Caribbean ethnicity versus other; current category of caregiver (birth mother, kinshipcaregiver, or unrelated foster or adoptive parent), a partial proxy for home environment anda composite score reflecting adolescents' maximum exposure to violence between 8.5 yearsof age and early adolescence, as measured by the Violence Exposure Scale for Children-Revised (VEX-R)(Fox and Leavitt, 1995).

The VEX-R is a 21 item, 4-point Likert self-report scale using cartoon pictures thatexamines children's exposure to violence both as a witness and as a victim. Scores forviolence exposure scores range in intensity from mild (yell, push, and spanking) to severe(threaten with a weapon, shoot, and stab). The standard VEX-R (with cartoons) wasadministered at the 8.5, 9.5, and 11-year visits. At the early adolescence research visit, wemodified the VEX-R to make it more appropriate for adolescent subjects by removing thecartoons and administering only the text of the questions using ACASI. We also removedquestions related to spanking, and made the questions more time-specific to determine if the



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event took place within the past year. The distribution of VEX-R scores was ranked at eachtime point and then subdivided into four groups of approximately equal size. The maximumof these quartile variables was entered as a covariate in the analyses.

Birth mothers' substance use during pregnancy was correlated with caregivers' postpartumand ongoing use of alcohol, cigarettes (by self-report), marijuana, and cocaine (by self-report or assay). Therefore, only variables representing pregnancy use were included in theanalysis model (analyses available from author on request).

Children's maximum blood lead value (mg/dl) obtained as part of clinical care during thepreschool years was evaluated as a potential covariate of EF at early adolescence. Thesevalues were not part of our original protocol and were abstracted from children's medicalrecords. The blood lead levels recorded in the medical records were tested at theMassachusetts State Laboratory Institute, Department of Public Health (Centers for DiseaseControl and Prevention, 2001). Because only 106 of the 147 participants in the currentanalysis had blood lead levels, we evaluated this variable in separate analyses on theavailable sample.

2.6 Statistical AnalysesA multi-step analysis plan was used to evaluate the study's hypotheses. Descriptive statisticswere first generated for each variable of interest, with means, standard deviations, andquantiles (for VEX-R and lead variables only) calculated for continuous variables andcounts and percentages for categorical variables. Second, in bivariate analyses, the effect oflevel of IUCE on D-KEFS scaled scores and the effect of level of adolescents' drug use weretested using one-way analysis-of-variance (ANOVA). Third, multivariate analyses testingthe effect of level of IUCE on continuous variables (sample characteristics, potentialcovariates, and D-KEFS scaled scores) were carried out using one-way analysis-of-variance(ANOVA) followed by Tukey post hoc tests, and on categorical variables using crosstabulations with chi-square tests of significance. For each D-KEFS outcome in separatemodels, interactions between the 3-level IUCE variable and each drug used duringadolescence were tested as well as interactions between these variables and both the child'sIQ and gender. In addition to this formal testing, we also performed our regression analysesin a stratified fashion separately for males and females. Spearman's correlations were used todetermine whether intrauterine drug exposures were correlated substantially (and potentiallycollinear) with adolescent's own use of the same substance.

Following these preliminary analyses, the effects of each intrauterine substance exposureand adolescents' own substance use variables on D-KEFS outcomes were then evaluatedusing multiple linear regressions. Due to the sample size, we evaluated only main effects inall multivariate analyses. Although IUCE has always been the primary exposure of concern,we also always have evaluated the effects of other drug exposures to separate them from thepotential specific effects of cocaine exposure. The interim model for all dependent variablesincluded: level of IUCE, gender, IQ, level of intrauterine exposure to marijuana, alcohol,and cigarettes; adolescents' own use of cocaine, marijuana, alcohol, and cigarettes. Wefound no statistically significant interactions between each prenatal substance use variableand its counterpart among the adolescent use variables. Consistent with methods used inbehavioral teratology research, other candidate covariates (described above) were evaluatedone-by-one in the interim model for potential retention in the final model based on a 10%change-in-estimates criterion applied to the estimate of effect for any one of the substanceexposure variables (Mickey and Greenland, 1989). VEX-R violence exposure did not meetthese criteria for inclusion in the final multiple regression models. There were no significantinteractions in these analyses.



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To evaluate whether preschool blood lead value was a confounding variable, we repeatedour final regression models using the sub sample (n = 106) of participants who hadpreschool blood lead values and compared them to the regression results for the wholesample. We also examined whether or not lead exposure was an independent statisticallysignificant predictor of D-KEFS outcomes.

We used SAS version 9.1.2 for all of our analyses. Results were deemed statisticallysignificant where two tailed p ≤ 0.05.

3. Results3.1. Sample retention

Analyses were based on data from 137 adolescents who completed at least one D-KEFSsubtest at the early adolescent follow-up visit. Potential retention bias was evaluated bycomparing characteristics of the 137 who provided data for the D-KEFS at the earlyadolescent visit to characteristics of the 115 from the original birth cohort who did notprovide data for this assessment. Participants and non-participants did not differsignificantly on level of IUCE, intrauterine exposures to cigarettes, alcohol, or marijuana;infant birth weight, gestational age, gender; birth mothers' education, age, primiparity atdelivery; public/private health insurance payment status; or African-American/Caribbeanrace/ethnicity (p ≥ 0.05).

Of the 137 participants in the D-KEFS sample, three adolescents did not complete allsubtests due to scheduling difficulties and the need to shorten the assessment battery. Twodid not complete Trail Making and one did not complete Word Context and Design Fluency.

In separate analyses, we also evaluated sample characteristics of participants with (n=108)and without (n=29) preschool lead values. There were no significant group differences (p<.05) on prenatal exposures to cigarettes, alcohol, or marijuana; infant birth weight, gender,gestational age; or maternal education, age, or primiparity at delivery; public/private healthinsurance payment status; or African-American/Caribbean race/ethnicity. However, thosewith a measured blood lead value had a higher intrauterine exposure level of cigarettes(mean = 1.24, s.d. = 1.26) compared to those without a lead value reported (mean = 0.73,s.d. = 1.07, p = .048).

3.2. Sample characteristicsSample characteristics for the 137 adolescents with D-KEFS data are presented for eachIUCE group in Table 1. Level of IUCE was not significantly associated with birth mother'srace/ethnicity or education; adolescent's gender, age at the early adolescent assessment, IQ,preschool maximum blood lead values, or adolescents' use of marijuana, alcohol, orcigarettes. Level of IUCE was significantly related to intrauterine exposure to cigarettes,alcohol, marijuana, caregiver type, and birth weight z-score.

As we have described elsewhere, (Frank et al., 2011) use of other substances besides thoselisted above (e.g., inhalants, painkillers) during early adolescence was relatively rare andtherefore could not be analyzed in multivariate analyses by those substances. The sixadolescents who used substances other than those listed (inhalants, n = 4; painkillers, n = 2)also had used marijuana, alcohol, or cigarettes and were included in those multivariateanalyses. No participants acknowledged cocaine use by self-report on the ACASI at this age.However, four adolescents had positive urine levels for cocaine metabolites using the No-Excuse panel. The latter values were all low in magnitude and could be explained by passiveexposure. Therefore adolescent cocaine use was not listed in the tables but was considered to



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be adolescent exposure without making judgment whether the exposure were active orpassive and was included as a covariate in all analyses that included adolescent exposures.

Table I describes the adolescent drug use by IUCE. Four of the 75 (5%) adolescents withIUCE versus none of the 62 cocaine-unexposed adolescents used some substance other thanmarijuana or alcohol; 9 of 33 (27%) intrauterine marijuana-exposed adolescents versus 20 of104 (19%) intrauterine marijuana-unexposed adolescents used marijuana; and 15 of 40(38%) adolescents who were intrauterine alcohol-exposed versus 28 of 97 (29%) intrauterinealcohol-unexposed adolescents used alcohol. Of the 29 identified as having used marijuana,21 were indentified by self-reported alone, two were identified by urine alone, and six wereidentified by both urine and self-report. Of the two participants identified by urine alone,one had a carboxy-THC >100 ng/mL, above the confirmatory SAMHSA and the UnitedStates Drug Testing Laboratories, Inc No-Excuse panel. The other adolescent identified byurine alone had a confirmatory carboxy-THC of 9ng/mL that was below the SAMHSA levelof15ng/mL but above the No-Excuse panel level of 2 ng/mL. Ultimately, all adolescentcigarette and alcohol use was by self-report.

3.3 D-KEFS Outcomes by Substance Exposure and Adolescent Substance UseIn Table 2, descriptive statistics are presented for the D-KEFS scaled scores for this sample.Although most scaled scores were within normal limits for age, the sample's average scoresfor most D-KEFS scaled scores fell below the standardization norm (population mean = 10)on most D-KEFS scaled scores, and often standard deviations were narrower than thosereported in the standardization norming tables (population standard deviation = 3).

Tables 3 and 4 present outcomes where at least one outcome correlates with intrauterineexposure or adolescent substance use at p < 0.10. Therefore, results for Color-WordCompletion Time: Inhibition/Switching, and Tower Rule-Violations-Per-Item Ratio werenot included in the tables. Similarly, except for IUCE, only those intrauterine substanceexposures and adolescent substance use variables that were associated (p < 0.10) with atleast one of the outcomes are included in Tables 3 and 4. Therefore, intrauterine alcoholexposure < 0.5 drinks/day and adolescent alcohol use during the past 30 days were notincluded in the tables. All drug comparisons are to the absence of that intrauterine exposureor to non-use in adolescence. For each substance, results for intrauterine substanceexposures as predictors of the outcome are presented first, followed by results foradolescents' own use as predictors.

Tables 3a and 3b present unadjusted means from the ANOVA analyses by intrauterinesubstance exposure or by adolescent use while Tables 4a and 4b present covariate adjustedmeans from the multivariable analyses by intrauterine substance exposure or by adolescentuse.

3.3.1 Multivariate Analyses: Association of Intrauterine Cocaine Exposurewith D-KEFS Scores—There were no significant main effects for the 3-level IUCEvariable or interaction effects of level of IUCE and covariates for any D-KEFS outcome(Table 4a). To explore the possibility of a Type-II error based on our analysis sample size of137, we have computed the smallest effect size detectable for DKEFS scores in contrastsbetween the lighter IUCE and unexposed groups as well as the heavier IUCE and unexposedgroups. For lighter vs. unexposed, we could have detected a standardized effect size(difference in group means/common standard deviation) of 0.54 or greater with 80% powerat a two-sided alpha of 0.05. For the Completion Time: Inhibition variable from the DKEFSthat had a standard deviation in our sample of 2.77, an effect size of 0.54 translates to adifference in means of 1.50. For heavier vs. unexposed, we could have detected astandardized effect size (difference in group means/common standard deviation) of 0.68 or



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greater with 80% power at a two-sided alpha of 0.05. For the Completion Time: Inhibitionvariable, this effect size of 0.68 translates to a difference in means of 1.88. In terms ofobserved differences for this variable in our sample, we found a difference in meanCompletion Time: Inhibition of 0.51 (effect size = 0.18) for lighter vs. unexposed and 0.41(effect size = 0.15) for heavier vs. unexposed.

3.3.2 Multivariate Analyses: Marijuana—Lighter intrauterine marijuana exposurepredicted less optimal Design Fluency Total Correct Switching condition scores comparedwith no intrauterine marijuana exposure (adjusted mean, lighter exposure = 7.89; adjustedmean unexposed = 9.42 difference=1.53, p < 0.05) (Table 4a).

Adolescents' own marijuana use was associated only with Color-Word Interferenceoutcomes (Table 4b). Specifically, adolescents' marijuana use within the past 30 dayspredicted significantly less optimal Inhibition Condition Completion time scores (adjustedmean 30 day use = 7.54; adjusted mean no use = 9.23; difference =1.69, p < 0.05), andsignificantly poorer Total Error Scores (adjusted mean, 30 day use = 6.33; adjusted mean, nouse = 8.69; difference = 2.36, p < 0.05]). Any adolescent marijuana use prior to the past 30days also predicted less optimal Completion Time scores compared with no marijuana use(adjusted mean marijuana use prior to 30 days = 7.18, difference=2.05 p=0.05.

3.3.3 Multivariate Analyses: Alcohol—Intrauterine alcohol exposure ≥0.5 drink/daypredicted significantly poorer Trail Making Test Number-Letter Switching CompletionTime scores compared with no alcohol exposure (adjusted mean heavier exposed = 3.67;adjusted mean unexposed = 7.58; difference = 3.91, p = 001) as well as significantly poorerErrors scores (adjusted mean heavier exposed = 7.53; adjusted mean unexposed = 9.86;difference = 2.36, p < 0.05] (Table 4a).

Adolescents reporting alcohol use prior to the past 30 days versus those reporting no use hadsignificantly poorer Color-Word Interference Total Errors scores for the Inhibition/Switching condition (adjusted mean alcohol use prior to 30 days = 5.95; adjusted. mean non-drinkers = 7.72; difference = 1.77, p < 0.05) (Table 4b).

3.3.4 Multivariate Analyses: Cigarettes—Lighter intrauterine cigarette exposure wasparadoxically associated with significantly more optimal Trail Making Number-LetterSwitching Error Analysis scores compared with no intrauterine cigarette exposure [adjusted.mean, lighter = 10.51, adjusted. mean, unexposed = 8.96, difference = 1.55, p < 0.05] (Table4a).

Similarly, adolescents' own cigarette use was associated with more optimal Design FluencyPercent Design Accuracy Scores compared to non-smokers (adjusted mean smokers = 9.44;adjusted mean non-smokers = 7.33, difference = 2.11, p < 0.05). Cigarette use also wasassociated with more optimal Word Context Consecutively Correct scores (adjusted meansmokers = 10.14; adjusted mean non-smokers = 7.56, difference = 2.58, p < 0.05) (Table4b).

3.4 Analyses on Participants with Lead Exposure ValuesIn the subsample of 106 participants with preschool blood lead values, no evidence forconfounding effects of maximum lead level was found. Maximum preschool blood leadvalues were also not significantly associated with any D-KEFS outcomes (Results of theseanalyses are available upon request.).



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4. DiscussionFor most of the substance predictors and neurocognitive outcomes evaluated in our study,adjusted D-KEFS means were lower than (but within one standard deviation of) thepopulation mean of 10. These somewhat depressed performances are consistent with thelow-average scores described for individuals from relatively economically disadvantagedbackgrounds (Brooks-Gunn and Duncan, 1997). Although the D-KEFS was normed on aUnited States nationally representative sample, the current sample differs from thestandardization sample in that it is relatively homogeneous in terms of race/ethnicity andlow socio-economic status.

Despite such relatively limited demographic variability, findings from covariate-controlledanalyses indicate that even 12 to 14 years after birth, intrauterine marijuana and/or alcoholexposures, though not IUCE, are negatively associated with performance on specific EFtasks, after adolescent use was controlled. Also, results from covariate analyses indicate thatadolescent use of marijuana and/or alcohol are negatively associated with performance onspecific EF tasks, after intrauterine substance exposure was controlled. Notably, intrauterineexposures to marijuana and/or alcohol were associated with tasks that could be categorizedas visual-motor-perceptual, while adolescent use of marijuana and/or alcohol were generallyassociated with tasks that had verbal components. Our results highlight the importance ofevaluating intrauterine exposures to legal and illegal substances and adolescents' personalsubstance use in the same analyses even though our sample was recruited on the basis ofmaternal cocaine use during pregnancy.

In our sample, those with lighter intrauterine exposure to marijuana generated significantlyfewer different designs by alternately connecting filled and empty dots within the allottedtime of the Design Fluency test. This task requires, among other skills, sustained attention,visual-perceptual planning and working memory, and inhibition, all areas that Fried (2002a;2003; 2006) identified as problematic following intrauterine marijuana exposure. Like Smithet al. (2004), we did not find that intrauterine marijuana exposure was associated withoverall accuracy differences on an inhibition task. While Smith et al. (2004) did identifysignificantly more commission errors among adolescents with intrauterine marijuanaexposure, the D-KEFS Error Analysis score does not separate errors of omission andcommission and therefore we are unable to make that comparison in the present study.

Interestingly, although adolescents in our sample were not recruited for their own history ofsubstance use, those who reported any history of personal marijuana use had significantlyslower times and more errors on the Color-Word Interference test than those who reportedno marijuana use. Other studies with older adolescents who presumably had used marijuanafor longer periods of time than was likely among our young adolescents, reported a greaterarray of negative EF outcomes associated with adolescents' marijuana use, including:increased effort during an inhibition task (Tapert et al., 2007); more compromised spatialworking memory strategy and increased errors (Harvey et al., 2007); poorer attention andprocessing speed (Jacobus et al., 2009); and increased response perseveration (Lane et al.,2007). Continued longitudinal study of our cohort is needed to older ages when it is assumedthat there will be more drug use among participants. We also expect that some of the cohortwill develop substance use disorders.

Although our sample was not selected for intrauterine alcohol exposure or adolescents' ownuse, participants with heavier intrauterine exposure to alcohol (but not FAS) took longer tocomplete the Trail Making Number-Letter test and made a greater number of errors on thetask. Mattson et al (1999) tested a large age range of clinically referred children (8 to 15years) exposed in utero to daily alcohol with intermittent binges. In analyses uncontrolled



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for other intrauterine exposures, they reported that children with FAS compared to childrenwith lighter or no intrauterine alcohol exposure had significantly poorer scores (notspecifically error scores) on a Number-Letter Switching task as well as on the Color-Word,Tower, and Word Context tests. Unlike many other alcohol exposure studies (Connor et al.,2000; Cottencin et al., 2009; Green et al., 2009; Mattson et al., 1999), our study isprospective, statistically controlled for multiple other substance exposures, and has a samplewith lighter levels of alcohol exposure than FAS, all factors that may account for ourdetection of relatively circumscribed adverse effects of prenatal alcohol exposure.

Adolescents who reported lifetime alcohol use made significantly more errors on the mostdifficult Color-Word Interference test, Inhibition/Switching test (scaled scores greater than astandard deviation below the test mean and 1.77 scaled score points below the studycomparison group) than those who did not report alcohol use. Groups did not differ oncompletion time. These findings are important, as little is known about the neurocognitiveeffects of alcohol use by young adolescents who are not from a clinically referred sample.Continued longitudinal neurocognitive testing of the current non-referred sample is neededto determine whether the adolescents who later heavily abuse alcohol will develop thesignificant neurocognitive deficits, such as poor attention, compromised verbal andnonverbal memory, and structural brain abnormalities that have been reported for olderadolescents who are heavy drinkers (Brown and Tapert, 2004; Medina et al., 2008).

Intrauterine tobacco exposure was associated with seemingly paradoxical findings.Adolescents with intrauterine tobacco exposure had more optimal neurocognitive scoresthan those without intrauterine tobacco exposure. While the explanation for theseassociations are not known, these findings may reflect potential neuroprotective effects oftobacco exposure in the context of other substance exposures or the interaction of geneticand/or environmental factors that some (Gilman et al., 2008; Herrmann et al., 2008), but notothers (Beeghly et al., 2007; Cornelius and Day, 2000; Milberger et al., 1996) haveidentified.

These paradoxical findings were also observed for those young adolescents reportingcigarette smoking. Some investigators have argued that adolescent and adult cigarettesmoking could have diametrically distinct effects on neurocognitive performance, dependingat least in part on the chronicity of such use (Glass et al., 2006). Our findings are consistentwith those in other studies showing that acute nicotine use may enhance attention, vigilance,short-term memory, and cognitive inhibition (Dinn et al., 2004; Ernst et al., 2001; Lawrenceet al., 2002; Potter and Newhouse, 2004). In contrast, chronic smoking by individuals hasbeen found to be associated with poorer neurocognitive abilities including general cognitivefunction, rapid cognitive flexibility and processing, working and verbal memory (Ernst etal., 2001; Glass et al., 2006). The relatively few young adolescents reporting cigarettesmoking versus marijuana use in this study is consistent to the population from which oursample was drawn.

Interestingly, IUCE was not significantly related to any EF outcomes in these earlyadolescent analyses. In our previous covariate-controlled analyses, we reported that between8.5 and 11 years, children with heavier IUCE were more likely to have significantly lowerStroop scores than the lighter/unexposed group (Rose-Jacobs et al., 2009). The difference inour findings during middle childhood and adolescence may be influenced by the differencein the specific versions of the Color-Word test administered, the different methods ofscoring each, or the specific ages tested. Alternatively, the children with heavier IUCE mayhave developed compensatory strategies in this area of EF by early adolescence.



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Our analyses evaluated a non-clinical, early adolescent sample and many of the participantswere in the early stages of drug experimentation and/or drug use. Therefore, our findingsrelated to adolescent drug use are conservative as compared to what might be expected inlater adolescence, when a greater proportion of our sample may develop more problematicpatterns of substance use, which in turn may be associated with more systematic negativeeffects on EF.

4.1 Study Strengths and LimitationsOur covariate-controlled, longitudinal study has many strengths. First, it is one of the few toevaluate simultaneously the associations of a range of intrauterine drug exposures andadolescents' own drug use to several areas of EF in early adolescence (Fried et al., 2006;2005). Second, our study evaluated multiple indicators of EF during early adolescence usingprocess-oriented tests from a recently co-normed neuropsychological battery, which allowfor clearer comparisons across the different domains of EF performance and across differentcategories of intrauterine exposures and adolescent substance use (Homack et al., 2005).Third, the youth who participated in this prospective study were recruited during thenewborn period rather than during middle childhood or adolescence when their schoolperformance, neurocognitive concerns, or own substance use might have prompteddifferential enrollment. Fourth, this study evaluated a host of possible covariates known tobe associated with intrauterine substance exposures/or neurocognitive functioning including:other intrauterine substance exposures; number and type of caregivers; birth mothers'education and race/ethnicity; adolescents' gender; birth weight; age at assessment; IQ;preschool lead exposure; and exposure to violence between the ages of 8.5 years of age andearly adolescence.

Our study also has several limitations. First, because of our recruitment criteria, the presentfindings may not be generalizable to adolescents who were born preterm or those living inmore economically privileged or rural settings. Second, the D-KEFS, a standardizedassessment developed for clinical as well as research settings, was administered on a one-to-one basis in a quiet laboratory setting. The assessment does not evaluate adolescents'neuropsychological functioning in an everyday setting such as school classrooms, whichconfront adolescents with multiple competing challenges and distractions. Third, althoughthe D-KEFS measures multiple domains of executive functioning, the outcomes may not berepresentative of all aspects of EF central to academic and social success in the real world.Fourth, while from a neuropsychological perspective the process orientation and themultiple co-normed tests of the D-KEFS are study strengths, from a statistical perspectivethe multiple outcomes within and across tests on the same participants increases thepossibilities of Type 1 error. In addition, our high-risk sample is also highly mobile. Whilewe statistically compared our tested and non-tested sample and found few differences, it isalways possible that attrition could play a non-obvious role in our findings. Although we didnot identify specific associations of IUCE with D-KEFS scores, it is possible that there wereType II errors as a function of our sample size. However, we did have sufficient power todetect associations of other intrauterine exposures and adolescent substance use on EFoutcomes. Also, although we controlled for gender in the analyses based on theoretical andpreliminary analyses, it is possible that stratified analyses in a larger sample might haveidentified specific gender differences in the neuropsychological outcomes. In multivariateregression models gender was significantly associated with some DKEFS outcomes (maleshaving higher Design Fluency scores and females having higher Word Context scores).However when the models were stratified by gender the relationships between IUSE oradolescent use were similar for both genders. Similarly, while in multivariate analyses IQwas a significant predictor of many of the DKEFS outcomes, IQ was not differentially



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distributed by IUSE or adolescent substance use, even in the four participants with IQsbetween 64 and 70.

Fifth, this study evaluated whether intrauterine exposures and adolescent substance usepredicts EF during early adolescence, a developmental period of substance useexperimentation. Heavier substance use and abuse may be more prevalent at later ages.Stronger associations (and larger cell sizes for different categories of substance use) may befound in later adolescence. Also, the complexity of controlling for intrauterine exposuresand own adolescent substance abuse with our sample size precluded our analyzing possibleadditive effects of multiple combinations of substances across both time periods on the D-KEFS outcomes. In addition, although environmental lead exposure did not appear to be aconfounding variable in this study, these values were not available for all our participants,making it difficult to form firm conclusions in regards to the role of lead exposure andintrauterine substance exposure and/or personal adolescent drug use. Similarly, as in anystudy, there is always the possibility of the influence of additional non-measured factors onthe study outcome. Sixth, although EF was assessed at earlier ages in this cohort, differentinstruments were used at different ages. The D-KEFS was not available when theparticipants were younger. This makes it more difficult to evaluate trajectories of EF as afunction of intrauterine exposures prior to and following initiation of substance use asmeasured by a single instrument. Because the current analyses measured EF at a single timepoint, the direction of causality between EF and adolescent's own substance use may not beclear. For instance, adolescents' own substance use may be a function of less adaptive EF.We also acknowledge that because the present study did not involve a neuroimagingcomponent, any lack of findings on neuropsychological tests does not necessarily rule-outthe possibility of differences in brain structure or function.

4.3 ConclusionsResults of these covariate-controlled analyses indicate that both intrauterine substanceexposures and young adolescents' own substance use in a prospective non-clinicalcommunity sample are associated with different EF test outcomes, when each is controlledfor the other. These results, although evaluated using a relatively small sample, suggest theneed for evaluating both predictors simultaneously in future analyses. Our findings arenotable in that the participants in the study were early adolescents between the ages of 12and 14 years and heterogeneous in their drug use. Some of these adolescents may have beenusing drugs temporarily on an experimental basis, while others may be on a trajectoryleading to heavier and more chronic substance use with progression to substance usedisorders. Findings of this study do suggest that subtle neurocognitive effects associatedwith some intrauterine drug exposures can be identified as late as 12-14 years postnatal.Because EF related performance has been shown to continue to develop through youngadulthood (Adleman et al., 2002), only longitudinal assessment of our sample into lateradolescence and adulthood will allow us to evaluate the effect of the combination ofmaturation, intrauterine drug exposure, and participants' own chronic drug use or abstention,on EF.

Our study has several important public health implications. The effects of intrauterinealcohol and marijuana exposure on some neurocognitive functions even into earlyadolescence on EF were detected but IUCE effects were not. Therefore, prenatal counselingshould emphasize abstinence from legal as well as illegal substances. Moreover, the use ofdrugs during early adolescence, even if below clinical levels of abuse, can have importantnegative effects on neurocognitive functioning, although the associations hold only for somesubstances and some aspects of EF. Notably, these associations differ from those observedfor intrauterine exposures. Most of what is known about adolescent drug use and its effectson EF derives from studies of clinical populations and samples identified by heavy use, even



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though early experimental substance use is more prevalent than substance use disordersduring early adolescence (O.o.A. Studies, 2007). Future longitudinal studies with largersample sizes should continue to evaluate prospectively the neurocognitive effects ofintrauterine drug exposures, early substance experimentation, as well as the longer-termeffects of evolving substance use to abuse. Comprehensive assessments over a range ofdevelopmental epochs should include not only structured laboratory measures but alsoobservational reports by parents or teachers of day-to-day function in areas associated withEF.

AcknowledgmentsThis study was supported by grant DA06532 from the National Institute of Drug Abuse (to Dr. Frank) and by grantMO1 RR00533 and RR025771 from the National Institutes of Health/National Center for Research Resources, acomponent of the National Institutes of Health (NIH). We are continually grateful for the guidance and support ofDr. Vincent Smeriglio throughout this longitudinal study and for his important role in the advancement of the studyof intrauterine substance exposure.

Thanks to the families and children for their gracious participation in this work and to Heather Baldwin, Ph.D. andMattia Chason for research assistance in testing the children.

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Table 1Birth/Early Adolescent Characteristics by Three-Level Intrauterine Cocaine Exposure(n=137)

Participant characteristics Means (standarddeviations) or %

Intrauterine cocaine exposure

Unexposed (n = 62) Lighter Exposed (n =50)

Heavier Exposed (n =25)

P-value

Birth mother characteristics

Ethnicity: African American/ African Caribbean 58 (94%) 42 (84%) 22 (88%) 0.24*

Maternal education at child's delivery (years) ** 11.63 (1.31) 11.64 (1.32) 11.36 (1.19) 0.63†

Average daily cigarettes during pregnancy <0.0001*

Non-smoker 48 (77%) 15 (30%) 3 (12%)

> 0, < ½ pack per day 5 (8) 17 (34) 11 (44)

≥ ½ pack per day 9 (15) 18 (36) 11 (44)

Average daily alcohol (last 30 days beforedelivery) <0.0001*

None 60 (97%) 30 (60%) 7 (28%)

> 0, < 0.5 drinks per day 2 (3) 17 (34) 12 (48)

≥ 0.5 drinks per day 0 (0) 3 (6) 6 (24)

Marijuana use (dosage) 0.002*

Unexposed 56 (90%) 32 (64%) 16 (64%)

Lighter 5 (8) 9 (18) 4 (16)

Heavier 1 (2) 9 (18) 5 (20)

Caregiver category at adolescent protocol < 0.0001*

Birth mother 56 (90%) 29 (58%) 11 (44%)

Kinship caregiver 5 (8) 15 (30) 10 (40)

Non-kinship caregiver 1 (2) 6 (12) 4 (16)

Adolescent characteristics

Birth weight (g) ** 3350.18 (519.41) 3020.40 (307.31) 2854.52 (354.12) <0.0001†

Gender – male 32 (52%) 25 (50%) 12 (48%) 0.95*

Age (years) ** 14.12 (0.69) 14.19 (0.68) 14.36 (0.56) 0.30†

WASI IQ at EA ** 92.15 (12.48) 91.58 (13.28) 95.32 (10.94) 0.45†

Maximum lead exposure (n=108) 8.85 (4.62) 8.88 (4.67) 11.60 (8.57) 0.15†

Age at Maximum Preschool Lead (years) 2.17 (1.36) 2.43 (1.37) 2.25 (1.28) 0.68†


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Participant characteristics Means (standarddeviations) or %

Intrauterine cocaine exposure

Unexposed (n = 62) Lighter Exposed (n =50)

Heavier Exposed (n =25)

P-value

Maximum sample quartile of VEX-R up toadolescence 0.02*

1st 0 (0%) 9 (18%) 1 (4%)

2nd 11 (18) 8 (16) 3 (12)

3rd 25 (40) 14 (28) 8 (32)

4th 26 (42) 19 (38) 13 (52)

Alcohol use 0.70

Never 45 (73%) 34 (68%) 15 (60%)

During lifetime but none during past 30 days 12 (19) 2 (24) 6 (24)

Within past 30 days 5 (8) 4 (8) 4 (16)

Marijuana use 0.28*

None 53 (85%) 36 (72%) 19 (76%)

During lifetime but none during past 30 days 3 (5) 8 (16) 2 (8)

Within past 30 days 6 (10) 6 (12) 4 (16)

Cigarette use 0.65*

Never 58 (94%) 46 (92%) 22 (88%)

Ever 4 (6) 4 (8) 3 (12)

Other drug use <0.0001*

Never 62 (100%) 50 (100%) 21 (84%)

Any past 30 days 0 (0) 0 (0) 4 (16)

*P-value via chi-square test or Fisher's exact test, where appropriate.

**Means (S.D.) P-value via proc means.

†P-value via one-way ANOVA (proc glm).


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Table 2D-KEFS scaled scores for 137 adolescents

D-KEFS Outcome Variables Mean Std Dev

Color-Word Interference

Completion Time: Inhibition 8.84 2.77

Completion Time: Inhibition/Switching 7.96 2.67

Total Errors: Inhibition 8.26 3.22

Total Errors: Inhibition/Switching 7.28 3.28

Design Fluency

Total Correct: Switching 9.20 2.68

Percent Design Accuracy 7.51 3.20

Trail Making Number-Letter Switching

Completion Time 7.16 3.55

Error Analysis 9.59 2.69

Word Context

Total Consecutively Correct 7.76 3.57

Consistently Correct Ratio 8.18 3.94

Tower

Total Achievement 8.91 2.49

Rule-Violations-Per-Item Ratio 9.12 1.79


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aret

tes

Une

xpos

ed

N25

5062

1518

104

997

3833

66

Col

or-W

ord

Inte

rfer

ence

Com

plet

ion

Tim

e: In

hibi

tion

9.36

8.44

8.95

9.00

8.22

8.92

10.1

18.

798.

978.

558.

94

Tota

l Err

ors:

Inhi

bitio

n8.

888.

268.

008.

337.

678.

358.

898.

158.

797.

638.

35

Tota

l Err

ors:

Inhi

bitio

n/Sw

itchi

ng7.

447.

087.

396.

807.

117.

386.

677.

187.

456.

847.

45

Des

ign

Flue

ncy

Tota

l Cor

rect

: Sw

itchi

ng (n

=136

)10

.13

8.86

9.11

9.33

8.28

9.34

9.33

9.14

9.55

9.14

9.06

% D

esig

n A

ccur

acy

(n=1

35)

7.74

7.08

7.77

7.67

6.53

7.65

6.44

7.48

7.73

6.97

7.71

Trai

l Mak

ing

Num

ber-

Lette

r Sw

itchi

ng

Com

plet

ion

Tim

e (n

=135

)7.

806.

607.

377.

276.

447.

274.

00*

7.39

7.24

7.13

7.14

Erro

r Ana

lysi

s (n=

135)

10.0

49.

769.

2610

.00

10.0

69.

448.

259.

6010

.21

9.55

9.28

Wor

d C

onte

xt

Tota

l Con

secu

tivel

y C

orre

ct (n

=136

)8.

607.

807.

397.

938.

117.

688.

117.

548.

487.

427.

60

Con

sist

ently

Cor

rect

Rat

io (n

=136

)8.

168.

408.

008.

007.

448.

338.

007.

999.

337.

527.

97

Tow

er

Tota

l Ach

ieve

men

t9.

368.

948.

698.

478.

898.

979.

898.

938.

649.

008.

98

Tab

le 3

b: U

nadj

uste

d m

eans

, exp

osur

e by

out

com

e va

riab

les i

dent

ified

in T

able

2. S

ampl

e si

ze is

137

exc

ept w

here

not

ed in

the

tabl

e. A

lcoh

ol u

se o

nly

duri

ng th

e pa

st 3

0 da

ys (n

= 13

) not

show

n be

caus

e no

t ass

ocia

ted

with

any

of t

he o

utco

mes

. Ref

eren

cegr

oup

is a

lway

s the

une

xpos

ed g

roup

in e

ach

cate

gory

. *p

< 0.

05

Ado

lesc

ent U

se

Mar

ijuan

a w

ithin

pas

t 30

days

Mar

ijuan

a du

ring

life

time

but

none

pas

t 30

days

No

Mar

ijuan

aA

lcoh

ol d

urin

g lif

etim

e bu

tno

ne p

ast 3

0 da

ysN

o A

lcoh

olE

ver

Cig

aret

tes

No

Cig

aret

te

N16

1310

830

9411

126

Col

or-W

ord

Inte

rfer

ence

Com

plet

ion

Tim

e: In

hibi

tion

7.81

7.62

9.14

9.03

8.86

9.00

8.23


NIH


NIH


NIH


Rose-Jacobs et al. Page 29T

able

3b:

Una

djus

ted

mea

ns, e

xpos

ure

by o

utco

me

vari

able

s ide

ntifi

ed in

Tab

le 2

. Sam

ple

size

is 1

37 e

xcep

t whe

re n

oted

in th

e ta

ble.

Alc

ohol

use

onl

y du

ring

the

past

30

days

(n=

13) n

ot sh

own

beca

use

not a

ssoc

iate

d w

ith a

ny o

f the

out

com

es. R

efer

ence

grou

p is

alw

ays t

he u

nexp

osed

gro

up in

eac

h ca

tego

ry. *

p <

0.05

Ado

lesc

ent U

se

Mar

ijuan

a w

ithin

pas

t 30

days

Mar

ijuan

a du

ring

life

time

but

none

pas

t 30

days

No

Mar

ijuan

aA

lcoh

ol d

urin

g lif

etim

e bu

tno

ne p

ast 3

0 da

ysN

o A

lcoh

olE

ver

Cig

aret

tes

No

Cig

aret

te

Tota

l Err

ors:

Inhi

bitio

n6.

44*

7.31

8.64

8.20

8.46

9.00

8.19

Tota

l Err

ors:

Inhi

bitio

n/Sw

itchi

ng6.

447.

927.

336.

33*

7.70

8.18

7.21

Des

ign

Flue

ncy

Tota

l Cor

rect

: Sw

itchi

ng (n

=136

)9.

818.

389.

219.

279.

259.

099.

21

% D

esig

n A

ccur

acy

(n=1

35)

7.31

6.46

7.67

6.93

7.78

9.00

7.38

Trai

l Mak

ing

Num

ber-

Lette

r Sw

itchi

ng

Com

plet

ion

Tim

e (n

=135

)7.

007.

697.

127.

707.

106.

827.

19

Erro

r Ana

lysi

s (n=

135)

10.3

19.

549.

489.

739.

5610

.18

9.53

Wor

d C

onte

xt

Tota

l Con

secu

tivel

y C

orre

ct (n

=136

)7.

508.

317.

748.

037.

859.

277.

63

Con

sist

ently

Cor

rect

Rat

io (n

=136

)8.

888.

318.

069.

307.

908.

188.

12

Tow

er

Tota

l Ach

ieve

men

t8.

199.

088.

998.

838.

957.

819.

00


NIH


NIH


NIH



Tabl

e 4a

Adj

uste

d m

eans

, exp

osur

e by

out

com

e va

riabl

e fo

r out

com

es id

entif

ied

in T

able

2 (A

djus

ted

for I

Q; g

ende

r; pr

enat

al c

ocai

ne, m

ariju

ana,

alc

ohol

and

cig

aret

tes;

ow

n us

e of

mar

ijuan

a, a

lcoh

ol, c

igar

ette

s, an

dco

cain

e). S

ampl

e si

ze is

137

exc

ept w

here

not

ed in

the

tabl

e. R

efer

ence

gro

up is

alw

ays t

he u

nexp

osed

gro

up in

eac

h ca

tego

ry. *

p <

0.05

Intr

aute

rine

Exp

osur

es

Coc

aine

Hea

vier

Coc

aine

Lig

hter

Coc

aine

Une

xpos

edM

ariju

ana

Hea

vier

Mar

ijuan

aL

ight

erM

ariju

ana

Une

xpos

edA

lcoh

ol≥0.

5dr

inks

/day

Alc

ohol

Une

xpos

edC

igar

ette

s≥½

pack

/day

Cig

aret

tes

< ½

pack

/day

Cig

aret

tes

Une

xpos

ed

N25

5062

1518

104

997

3833

66

Col

or-W

ord

Inte

rfer

ence

Com

plet

ion

Tim

e: In

hibi

tion

8.99

8.53

9.02

9.13

7.99

8.95

10.5

78.

768.

979.

178.

60

Tota

l Err

ors:

Inhi

bitio

n8.

718.

487.

898.

067.

488.

429.

008.

277.

958.

758.

18

Tota

l Err

ors:

Inhi

bitio

n/Sw

itchi

ng7.

087.

087.

536.

167.

227.

467.

207.

137.

237.

647.

14

Des

ign

Flue

ncy

Tota

l Cor

rect

: Sw

itchi

ng (n

=136

)9.

648.

969.

229.

247.

89*

9.42

9.18

9.28

9.40

9.88

8.75

% D

esig

n A

ccur

acy

(n=1

36)

7.03

7.19

7.94

7.42

6.64

7.66

7.05

7.43

7.53

8.07

7.20

Trai

l Mak

ing

Num

ber-

Lette

r Sw

itchi

ng

Com

plet

ion

Tim

e (n

=135

)7.

916.

527.

367.

586.

307.

243.

67*

7.58

7.76

7.96

6.40

Erro

r Ana

lysi

s (n=

135)

9.58

9.72

9.42

9.87

9.81

9.47

7.53

*9.

869.

7810

.51*

8.96

Wor

d C

onte

xt

Tota

l Con

secu

tivel

y C

orre

ct (n

=136

)7.

567.

877.

767.

097.

747.

878.

617.

688.

337.

947.

38

Con

sist

ently

Cor

rect

Rat

io (n

=136

)7.

128.

388.

457.

966.

908.

438.

958.

027.

989.

507.

63

Tow

er

Tota

l Ach

ieve

men

t9.

059.

248.

577.

908.

499.

1210

.11

8.96

9.28

8.56

8.86

Tab

le 4

b: A

djus

ted

mea

ns, e

xpos

ure

by o

utco

me

vari

able

s ide

ntifi

ed in

Tab

le 2

(Adj

uste

d fo

r IQ

; gen

der;

pre

nata

l coc

aine

, mar

ijuan

a, a

lcoh

ol a

nd c

igar

ette

s; o

wn

mar

ijuan

a, a

lcoh

ol, c

igar

ette

s, an

d co

cain

e). S

ampl

e si

ze is

137

exc

ept w

here

not

ed in

the

tabl

e. R

efer

ence

gro

up is

alw

ays t

he u

nexp

osed

gro

up in

eac

h ca

tego

ry. *

p <

0.05

Ado

lesc

ent U

se

Mar

ijuan

a w

ithin

pas

t 30

days

Mar

ijuan

a du

ring

life

time

but n

one

past

30

days

No

Mar

ijuan

aA

lcoh

ol d

urin

g lif

etim

ebu

t non

e pa

st 3

0 da

ysN

o A

lcoh

olE

ver

Cig

aret

tes

No

Cig

aret

te

N16

1310

830

9411

126

Col

or-W

ord

Inte

rfer

ence

Com

plet

ion

Tim

e: In

hibi

tion

7.54

*7.

18*

9.23

9.55

8.54

9.36

8.79


NIH


NIH


NIH


Rose-Jacobs et al. Page 31T

able

4b:

Adj

uste

d m

eans

, exp

osur

e by

out

com

e va

riab

les i

dent

ified

in T

able

2 (A

djus

ted

for

IQ; g

ende

r; p

rena

tal c

ocai

ne, m

ariju

ana,

alc

ohol

and

cig

aret

tes;

ow

n m

ariju

ana,

alc

ohol

, cig

aret

tes,

and

coca

ine)

. Sam

ple

size

is 1

37 e

xcep

t whe

re n

oted

in th

eta

ble.

Ref

eren

ce g

roup

is a

lway

s the

une

xpos

ed g

roup

in e

ach

cate

gory

. *p

< 0.

05

Ado

lesc

ent U

se

Mar

ijuan

a w

ithin

pas

t 30

days

Mar

ijuan

a du

ring

life

time

but n

one

past

30

days

No

Mar

ijuan

aA

lcoh

ol d

urin

g lif

etim

ebu

t non

e pa

st 3

0 da

ysN

o A

lcoh

olE

ver

Cig

aret

tes

No

Cig

aret

te

Tota

l Err

ors:

Inhi

bitio

n6.

33*

6.99

8.69

8.46

8.22

9.94

8.11

Tota

l Err

ors:

Inhi

bitio

n/Sw

itchi

ng6.

788.

357.

235.

95*

7.72

8.86

7.15

Des

ign

Flue

ncy

Tota

l Cor

rect

: Sw

itchi

ng (n

=136

)10

.25

8.08

9.18

9.28

9.22

8.67

9.25

% D

esig

n A

ccur

acy

(n=1

36)

7.30

6.42

7.66

6.95

7.66

9.44

*7.

33

Trai

l Mak

ing

Num

ber-

Lette

r Sw

itchi

ng

Com

plet

ion

Tim

e (n

=135

)7.

367.

297.

117.

647.

026.

697.

20

Erro

r Ana

lysi

s (n=

135)

10.5

98.

989.

489.

529.

5910

.00

9.52

Wor

d C

onte

xt

Tota

l Con

secu

tivel

y C

orre

ct (n

=136

)7.

517.

727.

817.

867.

7310

.14*

7.56

Con

sist

ently

Cor

rect

Rat

io (n

=136

)9.

157.

078.

179.

247.

818.

188.

18

Tow

er

Tota

l Ach

ieve

men

t8.

318.

958.

998.

888.

818.

508.

94


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Early adolescent executive functioning, intrauterine exposures and own drug use

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