seeing eye to eye with threat: atypical threat bias in ...abbie m. popa, joshua r. cruz, ling m....

AMERICAN JOURNAL ON INTELLECTUAL AND DEVELOPMENTAL DISABILITIES �AAIDD

2019, Vol. 124, No. 6, 549–567 DOI: 10.1352/1944-7558-124.6.549

Seeing Eye to Eye With Threat: Atypical Threat Bias inChildren With 22q11.2 Deletion Syndrome

Abbie M. Popa, Joshua R. Cruz, Ling M. Wong, Danielle J. Harvey, Kathleen Angkustsiri, Ingrid N.

Leckliter, Koraly Perez-Edgar, and Tony J. Simon

Abstract

Individuals with 22q11.2 deletion syndrome (22q11DS) show high rates of anxietyassociated with their increased risk of developing schizophrenia. Biased attention isassociated with anxiety and is important to investigate in those with 22q11DS given thisassociation. We analyzed attention bias to emotional faces in 7- to 17-year olds with22q11DS and typically developing controls (TD) using a dot probe threat bias paradigm.We measured response time, eye tracking, and pupilometry. Those with 22q11DS showedno significant changes in early versus late trials, whereas those who were TD showeddiffering patterns in both gaze and pupilometry over time. The patterns in those who are TDmay indicate adaptation that is lacking or slower in individuals with 22q11DS.

Key Words: 22q11.2 deletion syndrome; anxiety; eye tracking; attentional bias

Background

Chromosome 22q11.2 deletion syndrome(22q11DS) results from the most common humanchromosomal microdeletion, affecting as many as1 in every 2,000 live births (Grati et al., 2015;Kobrynski & Sullivan, 2007). Physical manifesta-tions include craniofacial and palatal abnormali-ties, cardiac defects, and endocrine problems(Shprintzen, 2008). Intellectual and behavioralimpairments and psychiatric disorders are alsoprevalent. Cognitive features include borderlineIQ and impaired attentional, executive, andnumerical abilities, delayed speech, social impair-ments, and comprehension impairments (Gur etal., 2014; Woodin et al., 2001).

Behavioral and psychiatric manifestationsinclude anxiety and mood disorders, attentiondisorders, and a 25 to 30-fold increased risk forschizophrenia compared to the general population(Bassett & Chow, 2008; Green et al., 2009;Schneider et al., 2014). In fact, although child-hood anxiety disorder does not guarantee laterpsychosis, it presents a significant risk factor forconversion in 22q11DS (Gothelf et al., 2013; Tang

et al., 2013). Further, anxiety disorders arecommon in 22q11DS, with recent reports sug-gesting around 50% of children and adults with22q11DS experience anxiety disorders (Schneideret al., 2014; Tang et al., 2013). Despite this, manywith 22q11DS and an anxiety disorder are notreceiving treatment for their anxiety (Tang et al.,2013). Because anxiety and stress have negativehealth consequences, both physically and mental-ly (McEwen, 1998; McEwen, Eiland, Hunter, &Miller, 2012), learning more about anxiety in22q11DS may build a literature that will helpclose the gap between anxiety diagnosis andtreatment in those with 22q11DS.

We particularly note, that although IQpredicts adaptive functioning scores in typicallydeveloping (TD) children (Sparrow, Balla, &Cicchetti, 1984) and among individuals with mostneurodevelopmental disorders (Loveland & Kel-ley, 1991), in a previous study it was found thatanxiety, not IQ, predicts adaptive functioning inchildren with 22q11DS (Angkustsiri et al., 2013).In the Angkustsiri study, the authors examined 78children with 22q11DS and 36 TD childrenbetween the ages of 7 and 14 years. The authors

A. M. Popa et al. 549

measured adaptive function using the AdaptiveBehavior and Assessment System, 2nd Edition(ABAS-II; Harrison & Oakland, 2003) and theBehavior Assessment System for Children, 2ndEdition (BASC-2; Reynolds & Kamphaus, 2008).They measured anxiety using the Spence Child-ren’s Anxiety Scale (SCAS; Spence, 1998). In thegroup of children with 22q11DS, higher anxietyscores on the SCAS were related to lower adaptivefunction (r¼�0.27, p¼ 0.015), whereas there wasno relationship between full scale IQ and adaptivefunction. The TD children, unlike those with22q11DS, showed a significant relationship be-tween full scale IQ and adaptive function (r¼ 0.6,p ¼ 0.002; Angkustsiri et al., 2013). Given therelevance of anxiety to adaptive function in thispopulation, we aimed to examine how anxietymight impact a particular cognitive function,attentional deployment, in this population.

In the present study, we examine rates ofanxiety in children with 22q11DS using self andparent reports, which provide a good estimate ofthe anxiety symptoms and severity that partici-pants experience. We predict this analysis willconfirm higher rates of anxiety in our sample ofchildren with 22q11DS. Because anxiety impactsattentional deployment to emotional stimuli intypical adults (MacLeod & Mathews, 1988), it islikely that attentional control in children with22q11DS will also be affected when responding toemotional stimuli.

We measure attentional deployment to emo-tional stimuli. In this study, the emotional stimuliare only mildly threatening. As participantsevaluate mildly threatening stimuli, they mayadapt to the emotional content. They may alsorealize, even within a single trial, that theperceived threat is not real. This adaptation andevaluation may allow the participants to changetheir attentional deployment, even within a singletrial. This is consistent with the observation thattypically developing individuals often show highlevels of emotion in response to salient stimuliwhen they are first presented (Hare, Tottenham,Davidson, Glover, & Casey, 2005; Thomas et al.,2001). However, this initial response will quicklyhabituate with repeated exposures (Hare et al.,2005; Thomas et al., 2001). Individuals with highanxiety, however, may show a potentiated initialresponse, coupled with reduced habituation tothreat. This pattern is consistent with poorextinction shown in anxious adolescent humansand animals (Lee et al., 2014; Pattwell et al., 2012).

The Dot Probe Threat Bias (DPTB) experi-mental paradigm is appropriate for investigatingattention to emotional stimuli in children with22q11DS. In the DPTB, participants are asked torespond to a target that appears in one of twoscreen locations. Targets are preceded by emo-tional or neutral stimuli, which can captureattention to facilitate target identification. Facili-tated response time for targets that subsequentlyappear in the same location as angry, but notneutral, faces, is presumed to indicate a threat bias(Mogg & Bradley, 1998; Perez-Edgar et al., 2011),which is a measure of attention to emotionalstimuli. Many studies (Bar-Haim, Lamy, & Glick-man, 2005; B. Bradley, Mogg, & Millar, 2000;Salum et al., 2013; A. M. Waters, Bradley, &Mogg, 2014) have shown that children and adultswith, or at risk for, anxiety show systematicattention biases to threatening or emotionalstimuli (Roy et al., 2008). Further, because thetask requires multiple trials of each emotionalvalence, habituation to multiple exposures can bemeasured, capturing the dynamic time course ofattention to threat.

Early DPTB experiments exclusively used thefacilitated response time (RT) measure to examinethreat bias to stimuli including affective faces orwords (MacLeod & Mathews, 1988; Wilson &MacLeod, 2003). This early work presentedfindings including the supposition that individu-als experiencing stress or anxiety may attend to,and have difficulty disengaging from threateningstimuli (MacLeod & Mathews, 1988). Later workindicated that the facilitation of RT was depen-dent on experimental as well as individual factors,such as the length of time stimuli were presented(B. P. Bradley, Mogg, Falla, & Hamilton, 1998;Koster, Crombez, Verschuere, & De Houwer,2004), possibly indicating that RT facilitationsubsumes multiple cognitive processes. Work inchildren further showed that younger individualsmay show threat bias (Dawel, Palermo, O’Kearney,Irons, & Mckone, 2014), however the results maybe specific to particular subtypes of anxiety (Salumet al., 2013; Waters & Valvoi, 2009). However,threat bias measured by RT alone has also yieldedinconsistent results (Koster et al., 2004). Further,using the DPTB along with other methodologieshas produced a more nuanced understanding ofattentional deployment to threat. For example, theanalysis of event-related potentials from electro-encephalography showed that although non-anx-ious individuals all show initial capture by threat,


2019, Vol. 124, No. 6, 549–567 DOI: 10.1352/1944-7558-124.6.549

550 Threat Bias in Children With 22q11DS

they do not show longer term perseveration onthreatening stimuli (Kappenman, MacNamara, &Proudfit, 2014). Finally, results from eye trackingstudies indicate that analysis or eye movements,although correlated with RT scores, may produce amore robust measure of attention bias (B. Bradleyet al., 2000).

Based on this literature review, we usedconcurrent eye tracking to measure gaze bias as acomplementary, and possibly more reliable,measure of threat bias than RT alone. The eyetracking apparatus was only available for a subsetof participants, but was added because RTsubsumes motor, cognitive, and perceptualprocesses into a single value. By contrast, eye-tracking directly measures quantitative andqualitative aspects of gaze (overt attention),thereby providing more sensitive indices of thecognitive processing in which participants engagebetween stimulus presentation and motor re-sponse (B. Bradley et al., 2000). Further, manyeye-tracking instruments, such as the one usedhere, generate a rudimentary measure of pupildiameter. To our knowledge, pupil dilation hasnot been measured in previous DPTB paradigms.However, previous work has used pupilometry asa measure of autonomic activation (M. Bradley,Miccoli, Escrig, & Lang, 2008). The Bradleystudy related pupil diameter to other knownindicates of autonomic activity (heart rate andskin conductance during affective picture view-ing) during affective picture viewing. They foundthat pupil diameter increased when viewingaffective pictures, and that this increase wascorrelated with heart rate and skin conductance,indicating that it may be a good measure ofsympathetic nervous system activity. Additionalwork has shown that changes in pupil diameterare also correlated with processing load (Beatty,1982; Laeng, Ørbo, Holmlund, & Miozzo,2011). In the Laeng study, the authors showedthat pupil dilations were correlated with thegreater cognitive load of inhibiting a prepotentresponse during a stroop paradigm. Takentogether, the previous research indicates thatlarger pupil dilation during the DPTB mayindicate the combination of emotional reactivityto affective pictures and the greater processingload of controlling one’s attentional deploymentto these pictures. Disentangling the effects ofemotional reactivity and attentional control willrequire further study, possibly using additionalindicators of emotional reactivity (such as skin

conductance) and cognitive load (such as event-related potential from electroencephalography).

The present study was performed in childrenaged 7 to 17 years. As stated, the DPTB has beenused in younger children, who have shown RTfacilitation and eye movements consistent withthreat bias (B. Bradley et al., 2000; Perez-Edgar etal., 2010). Other studies using affective stimuliindicate that adolescents often perform worsethan adults and children in response to emotion-al stimuli (Casey & Caudle, 2013). This may bebecause during adolescence the ‘‘bottom-up’’limbic system of the brain is more developedthan the ‘‘top-down’’ frontal system of the brain(Lago, Davis, Grillon, & Ernst, 2017). Addition-ally, adolescence is a period where social approvalis particularly important, which may lead todifferent responses to social stimuli used instudies (including this one; Blakemore & Mills,2014). Particularly, emotional regulation to socialstimuli is worse in adolescence (Silvers et al.,2012). These findings in typically developingindividuals emphasize the importance of study-ing attentional regulation to emotional stimuli inchildren with 2211DS in this age group.

Notably, children with 2211DS show a lowerfull-scale IQ than age-matched typically develop-ing children. Although the DPTB has not beenleveraged in children with 22q11DS before, astudy was performed in children with fragile Xsyndrome (Burris, Barry-Anwar, & Rivera, 2017).The study was able to collect meaningful datafrom a similarly cognitively impaired population.Additionally, the Burris study further used agroup of mental age-matched control, whocomprised children with low IQ who did nothave fragile X syndrome. Clean data was alsocollected from this low IQ group. Finally, thestudy found a different pattern between thegroup of children with fragile X syndrome andthe mental age-matched controls, indicated theDPTB is likely sensitive to more subtle effectsthan those from IQ alone.

Hypotheses

Based on previous findings (Angkustsiri et al.,2013) we predicted higher anxiety and loweradaptive functioning in children with 22q11DSthan TD controls. We predicted that the 22q11DSgroup would show bias to negative stimuli (angryfaces) in both RT and gaze. We did not expect the


2019, Vol. 124, No. 6, 549–567 DOI: 10.1352/1944-7558-124.6.549


TD group to show any threat bias. Indeed, datashowing the effect of positive stimuli on youthcognition suggest that the TD group will show abias to happy faces instead (Casey & Caudle,2013), again evident in both RT and gaze. Becausegaze is a more direct index of threat processing wealso predicted that eye-tracking and pupilometrywould produce a stronger and more reliableresponse pattern than RT data.

Analyzing responses to early versus late trialswithin our experiment allows us to examine bothinitial threat response and adaptation. Based onevidence from extinction paradigms (Pattwell etal., 2012), we expected that the TD children wouldshow gaze adaptation to the emotional stimuli(Ochsner & Gross, 2005), evident in how theirgaze and pupilometry change over multiplestimulus exposures. We predicted less gaze adap-tation in the children with 22q11DS.

An additional benefit of looking at gazerather than RT alone, is that we can observewhether participants are viewing the faces on thescreen at all, or if they are looking at non-faceareas of the screen. We predict that children whoare TD would initially show gaze to happy andnon-face areas of the screen, perhaps showingmore gaze to angry faces with additional trials asthey learn they are not a threat. We predictedthose with 22q11DS would not show a change innon-face looking over time, consistent with ourprediction of less gaze adaptation. Becausepupilometry can be used as an indicator ofemotional reactivity, we predicted that the TDchildren would show pupil responses consistentwith reducing autonomic activation and cogni-tive effort over time, particularly, more pupildilation to emotional stimuli (Beatty, 1982; M.Bradley et al., 2008; Kahneman & Beatty, 1966;Laeng et al., 2011). We expected to see a moredysregulated pattern in the children with22q11DS, particularly, less change in pupildiameter to emotional stimuli.

Additionally, to examine the effect of age onour findings, we included age as a regressor in ourmodels, however removed it if the effect was notsignificant. We predicted that all measures wouldshow an effect of age, with the more anxiousadolescents showing more RT facilitation, moregaze to emotional stimuli, and more pupil dilationto emotional stimuli, consistent with greateranxiety in adolescence.

Methods

ParticipantsThis study was submitted to and approved by theuniversity’s Institutional Review Board (IRS;#268067) in compliance with the Helsinki Decla-ration. Children in both groups were recruitedthrough flyers at the UC Davis MIND institute,the MIND Institute Subject Tracking System, webpostings and social media, and who had previous-ly participated in Dr. Simon’s studies. Children inboth groups were excluded if they had a history ofhead trauma or were part of a multiple birth.Children who were TD were additionally excludedif they had any known axis 1 disorders. Parentsprovided written informed consent approved bythe IRB for participation and children verballyassented before participation. The total samplecomprised 79 children (41 males) aged between 7and 17 years (M 6 SD ¼ 10.92 years 6 2.33years), there was no significant difference of agebetween groups (p ¼ 0.64). Of these, 32 childrenwere TD and 47 children had a 22q11DSdiagnosis, confirmed via fluorescence in situhybridization or similar genetic test.

MaterialsIQ measures. We measured IQ using either

the Wechsler Intelligence Scale for Children–Fourth Edition (WISC-IV; Wechsler, 2004) or theWechsler Abbreviated Scale of Intelligence (WA-SI; Wechsler, 1999). Trained research personnel orclinicians at the research institute performedassessments. IQ scores for seven participants (322q11DS) were unavailable (Table 1). IQ scoresfor 40 children with 22q11DS came from theWISC-IV and IQ scores for 25 TD children weregenerated from the WASI.

Anxiety measures. Anxiety was measuredusing the Spence Children’s Anxiety Scale (SCAS;Spence, 1998). This scale assesses six domains ofanxiety: generalized anxiety, panic/agoraphobia,social phobia, separation anxiety, obsessive-com-pulsive disorder, and physical injury fears. TheSCAS is typically used in a research setting toexamine the structure of anxiety symptoms in astudy population. We collected both parent andself-reports on the SCAS. Research personnelreceived in-person training by a developmentalneuropsychologist on administering the SCAS tochildren. Parents or guardians completed theSCAS prior to the study visit. Because young,developmentally delayed children are limited in


2019, Vol. 124, No. 6, 549–567 DOI: 10.1352/1944-7558-124.6.549


self-reporting abilities, our primary measure wasthe SCAS total parent score.

Adaptive functioning measures. We mea-sured adaptive functioning, or daily living skills,with the parent report Adaptive Behavior Assess-ment System-Second Edition (ABAS-II; Harrison& Oakland, 2003). This scale assesses ninesubdomains (Home Living, Health and Safety,Leisure, Self-Care, Self-Direction, Functional Ac-ademics, Community Use, Communication, andSocial); three composites (Social, Practical, andConceptual); and one overall measure of adaptivebehavior (general adaptive composite–GAC). Par-ents or guardians completed the ABAS-II formprior to the study visit. Some anxiety scores andadaptive scores were unavailable (see Table 1) dueto skipped questions by the parent or participant.

ProcedureThe DPTB task parameters were the same as inPerez-Edgar et al.’s 2011 study except for theaddition of eye tracking. The task was presentedon a Dell T5500 computer running Windows XPService Pack 3 Professional using E-Prime 2.0software on a 1280 3 1024 pixel resolutionmonitor. The DPTB task consisted of eightpractice trials and 80 experimental trials, presentedin two 40-trial blocks. Each trial began with thepresentation of a central fixation cross (childrenwere required to fixate for 250 ms before stimuliwere presented), followed by the presentation of apair of faces depicting emotions (500 ms, 4.8degrees to the left and right of center, subtending5.3 3 4 degrees of visual angle). Immediately afterthe face pair disappeared, an asterisk probeappeared (2500 ms) in the location previouslyoccupied by one of the faces.

Children were seated 60cm from a 17-inchTobii T120 Eye Tracker Monitor using a chin restto limit head movement. Before beginning thetask, each child completed a 5-point gaze calibra-tion sequence using Tobii Studio EnterpriseEdition version 2.3.2.0. After successful calibra-tion, children proceeded to the task. The softwarerequired the child to maintain fixation within 4.18

of the fixation cross for 250ms to initiate eachtrial, but they were explicitly permitted to lookanywhere during stimulus presentation. Childrenused the index and middle fingers of theirdominant hand on a Psychology Software ToolsSerial Response Box to indicate as quickly andaccurately as possible on which side of the screenthe probe appeared.

There were three types of face pairs presented:angry/neutral (32 trials), happy/neutral (32 trials),and neutral/neutral (16 trials). 30 different actors(50% female) from the NimStim face stimulus setportrayed facial expressions (Tottenham, Borsc-heid, Ellersten, Marcus, & Nelson, 2002). Con-gruent trials were those where the probe appearedin the same location as the emotional face (angryor happy). In incongruent trials, the probeappeared at the same location as the neutral face.Laterality of the emotional face, trial congruency,sex of the face, and probe location were counter-balanced across trials. RTs and response accuracywere recorded for each trial.

Analysis Plan

Participant CharacteristicsTwo-tailed independent t tests were used tocompare groups on IQ, SCAS Parent Total anxietyscore, and ABAS GAC. We also examined anxietymedications from a health questionnaire parents

Table 1Participant Demographics

Measure 22q11DS TD p

Count 47 32 —

Age (years) 10.83 6 2.08 (n ¼ 47) 11.08 6 2.4 (n ¼ 32) 0.6368

Gender F ¼ 25; M ¼ 22 F ¼ 13; M ¼ 19 0.3854

IQ 73.7 6 14.4 (n ¼ 44) 116.5 6 14.9 (n ¼ 28) ,0.001

Spence Parent Total 65.4 6 11.8 (n ¼ 43) 47.8 6 9.6 (n ¼ 31) ,0.001

Spence Child Total 60.4 6 16.4 (n ¼ 40) 51.5 6 11.7 (n ¼ 28) 0.011

ABAS GAC 69.2 6 16.1 (n ¼ 47) 105.4 6 11.4 (n ¼ 28) ,0.001

Note. ABAS GAC ¼ Adaptive Behavior Assessment System General Adaptive Composite; TD ¼ Typically developing.


2019, Vol. 124, No. 6, 549–567 DOI: 10.1352/1944-7558-124.6.549


completed, though there was insufficient data forformal statistical testing.

Reliability of MeasuresIn order to confirm the reliability of the differentmeasures we used throughout the study, we spliteach of the participant’s trials (in the case of RT)and epochs (in the case of gaze and pupilometry)into even- and odd-numbered trials. We thentested to see if measures correlated between theeven and odd trials using Pearson’s r.

Analysis 1: RT and GazeWe removed outliers using previous criteria (Perez-Edgar et al., 2010; Perez-Edgar et al., 2011). Trialswith errors and trials with responses that wereanticipatory (, 200ms) or unusually slow (. 2.5SD from mean RT of the individual) wereremoved from analyses. In addition, we removedindividual participants with accuracy below 75%on any condition. This resulted in removing sixchildren with 22q11DS. Four TD children werealso removed due to technical difficulties with therecording software, leaving a final sample of 42children with 22q11DS and 28 TD children withRT data. There were no significant effects of age,gender, IQ, anxiety level, or adaptive function onwho was removed from the data (all p . 0.05).The resulting participants had an average of 8.5%of trials removed.

Attention bias was defined as the mean RT oncongruent trials subtracted from the mean RT onincongruent trials for each emotion condition(‘‘angry bias’’ and ‘‘happy bias,’’ respectively) forcorrect trials only. Between-group Student’s two-tailed t tests were performed to assess thedifference in angry bias or happy bias betweenTD and 22q11DS groups. Analyses were per-formed in R version 3.1.3.

Although we acknowledge the poor reliabilityof RT measures in previous DPTB work, we feel itis important to report these analyses given thatmost previous DPTB articles have used RTmeasures. Including these results will increaseresearchers’ ability to compare our findings withthe broader literature.

Eleven children completed the study beforeeye-tracking technology was obtained and thuscould not be included in gaze analyses. Twochildren did not have their gaze recorded due totechnical errors. Before eye tracking analyses wereperformed, time points during which tracking was

lost on one or both eyes were marked ‘‘invalid’’based on Tobii’s validity column. Tracking can belost due to technical problems, the participantblinking, or the participant looking away from thescreen. All other time points were considered validand we used only those for analyses. We excludedparticipants if � 70% of their time points wereinvalid on any condition which resulted in theexclusion of 10 children (6 22q11DS) and in eyetracking data for 46 participants (27 22q11DS).See Figure 1, for information on participantremoval. The resulting participants had an averageof 17.3% invalid eye-tracking data. Tests showedthat the sample with eye-tracking data did notdiffer from the full sample on diagnostic distribu-tion, age, gender distribution, SCAS parent total,ABAS GAC, or IQ.

Tobii Studio software calculated eye positionas the average position tracked from both eyes. Foreach trial, percentage of total time looking at theemotional and neutral face was calculated (time onemotional face or on neutral face divided by totalpresentation time respectively). We use the terms‘‘angry gaze bias’’ and ‘‘happy gaze bias’’ to referto percentage of total time spent looking at each ofthe respective emotional faces.

Analyses were performed as mixed effectslinear models on angry or happy trials (separately)including a random effect of participant andeffects of interest for percentage of time lookingat emotional/neutral faces and diagnosis. Becausean interaction of emotion and diagnosis did notreach significance, separate models were used for22q11DS and TD. We did not combine the twogroups because we wanted to look for differencesbetween these two genetically distinct samples.Age, gender, and IQ were included as factors inthe model only if they contributed significantly.The percentage of time spent on overall emotionalface, overall calm face gaze, and overall non-facegaze would by definition sum to 100%, thereforeto avoid issues of non-independence, gaze to non-face areas was not included in the overall models.

Analysis 2: Change in Gaze Over TimeTo investigate whether children with 22q11DS orTD children adapted differently to emotionalstimuli, we compared gaze patterns on early versuslate trials on the DPTB. Early trials weredesignated as the first 25% of trials for eachstimulus type, whereas late trials were designatedas the last 25% of trials for each stimulus type.Gaze as a function of diagnosis (22q11DS versus


2019, Vol. 124, No. 6, 549–567 DOI: 10.1352/1944-7558-124.6.549


TD) and trial (early versus late) was entered into a2 3 2 mixed effects model. In this case, becausewe are comparing gaze to each respective area onearly trials to gaze to that same area on late thereis no reason to remove non-face areas fromanalysis. For example, it is not the case that non-face gaze early and non-face gaze late necessarilysums to a given value.

Exclusion criteria were the same as previouslydiscussed, except before analyses were conductedwe decided to remove children who made saccadeson fewer than 10% of trials. While in analysis 1, ifa child stared at the same area of the screen (thecenter, for example) this data could meaningfullyindicate attention to that area of the screen, whencomparing early to late this child’s scores wouldsubtract out to zero, because they never movedtheir gaze. This calculation would preclude thecalculation of meaningful eye movement scores(B. Bradley et al., 2000). These criteria resulted inthe removal of one additional child (with22q11DS; Figure 1). Because we ultimately foundno significant difference in eye movementsbetween early and late trials in children with22q11DS, this child’s non-zero change scores donot change our results if included.

Analysis 3: Pupil DiameterTo measure dilation and contraction, we com-pared the pupil size recorded by the Tobii eyetracker on the emotional face trials (happy vs.neutral and angry vs. neutral) to our neutral/neutral face control trials (emotional trial sizeminus neutral trial size) for early versus late trials.

Pupil size was calculated as an average of pupildiameter during the first/last 25% of angry/happytrials minus pupil diameter during the first/last25% of neutral trails. Comparing pupil diameterin response to neutral faces also allowed us tocontrol for the effects of viewing faces in general,such as luminance and contrast. This analysischoice allows us to use pupil diameter as apreliminary measure of autonomic reactivity toemotional stimuli. For each family of tests wheremultiple comparisons correction were reported,they were corrected for using the false discoveryrate correction (Benjamini & Hochberg, 1995).

Results

Participant CharacteristicsMean IQ for 22q11DS was 73.7 (M IQ 6 SD¼73.7 6 14.4), and for TD was 116.5 (M IQ 6 SD¼116.5 6 14.9; Table 1). As predicted, initial two-tailed t tests indicated participants with 22q11DShad higher SCAS Parent Total t(70.9)¼ 7.06, p ,

0.0001 (indicating higher anxiety in those with22q11DS) and lower ABAS GAC t(70.6)¼�11.37,p , 0.0001 (indicating lower adaptive function inthose with 22q11DS), scores than TD children(Table 1).

Unfortunately, we did not directly inquire ifchildren were receiving psychotherapy or coun-seling for anxiety. However, parents of fivechildren (all 22q11DS) indicated ‘‘anxiety’’ as amedical problem their child had on a medicalhistory questionnaire. Of these five children,three were receiving some type of anxiety

Figure 1. Participants recruited and included or excluded for each stage of analysis. 22q11DS¼ 22q11.2Deletion Syndrome; TD ¼ Typically Developing; RT ¼ Response Time.


2019, Vol. 124, No. 6, 549–567 DOI: 10.1352/1944-7558-124.6.549


medication (2 zyprexa, 1 prozac). No otherchildren had anxiety medications listed. Addi-tionally, we know that although anxiety isprevalent in those with 22q11DS, it is alsoundertreated (Tang et al., 2013). In summary,we see the expected pattern of lower IQ, loweradaptive function, and higher anxiety in childrenwith 22q11DS than TD children.

Reliability of MeasuresThe RT measures showed low reliability. Specif-ically, although the data in the TD group showedsignificant correlations between even and oddangry face trials (r¼ 0.45, p , 0.05) they did notshow any correlation between even and oddhappy face trials (r ¼�0.17, p ¼ 0.40). The datafrom the 22q11DS group did not show anycorrelation in the angry face trials (r ¼ 0.17, p ¼0.25) and in fact showed a negative correlationbetween even and odd trials for the happy facetrials (r ¼�0.57, p , 0.05).

By contrast, gaze and pupilometry eachshowed very high reliability (between odd andeven trials). Specifically, both groups showedstrong correlations between odd and even trialsacross all conditions (all r . 0.98, all p ,

0.0001).In summary, we see the expected patternof low reliability on RT facilitation and highreliability on gaze and pupilometry during theDPTB experiment.

Analysis 1: RT and GazeWe initially built a model including diagnosticgroup, emotion of faces, age, IQ, and gender toassess RT facilitation. However, age, gender, andIQ were not significant predictors of RT bias ifincluded as regressors, and thus were not includedin final models. Therefore, between-group stu-dent’s two-tailed t tests were performed to assessthe difference in angry bias or happy bias betweenTD and 22q11DS groups. As shown in Figure 2Panel A, there were no significant between groupsdifferences for angry or happy RT bias (angry: p¼0.32, TD M 6 SD¼ 4.8msec 6 56.1, 22q11DS M6 SD¼�8.1msec 6 66.4; happy: p¼ 0.24, TD M6 SD¼�19.6msec 6 34.9, 22q11DS M 6 SD¼�2.6msec 6 65.3). Direct comparisons of happybias to angry bias between groups were notsignificant. In the TD group, RT bias wassignificantly different from zero for happy trials,t(27) ¼�2.75, p , 0.05) but not angry trials (p .

0.05). Bias was not significantly different from

zero in either condition for children with22q11DS (both p . 0.05).

Analyses were performed as mixed effectslinear models on angry or happy trials (separately)including a random effect of participant andeffects of interest for percentage of time lookingat emotional/neutral faces and diagnosis for eachgroup (22q11DS and TD). Age, gender, and IQwere initially included as factors in the model, butfor RT bias gender and IQ were not significant sowere dropped from the model. Both groupsshowed gaze bias toward angry faces on angryversus neutral face trials, TD t(18) ¼�4.15, p ,

0.0001, angry: M 6 SD ¼ 33.73% 6 12.27;neutral M 6 SD¼24.71% 6 8.42; 22q11DS t(26)¼�5.97, p , 0.0001, angry: M 6 SD¼ 34.87% 6

9.54; neutral: M 6 SD¼ 24.90% 6 7.53; Figure 2Panel B. Both groups also exhibited a significantbias towards happy faces on happy versus neutraltrials, TD t(18)¼�2.22 p¼0.01 happy:M 6 SD¼32.28% 6 12.14 neutral: M 6 SD ¼ 25.83% 6

8.03; 22q11DS t(26)¼�3.07, p , 0.01 happy: M6 SD ¼ 32.92% 6 11.09; neutral: M 6 SD ¼24.94% 6 9.39. These results indicate bias towardemotional faces in all children, regardless of groupmembership. (For summary of significant findings,see Table 2).

IQ and gender were not significant predictorsof gaze bias and were not included in final models.Age was a significant predictor in both groups(Figure 3), with older children showing less biastoward emotional faces than younger children,angry trials: t(43) ¼�3.79 p , 0.01; happy trials:t(43) ¼�2.81 p , 0.01.

In summary, there were few significantfindings from RT facilitation; the only findingwas RT facilitation to happy faces in the TDgroup. When examining overall gaze both groupsshowed a bias toward emotional faces. Finally,older children showed less bias toward emotionalfaces than younger children.

Analysis 2: Change in Gaze Over TimeTo investigate whether children with 22q11DS orTD children adapted differently to emotionalstimuli, we compared gaze patterns on early versuslate trials on the DPTB. Models initially includedparameters for age, gender, and IQ. No effectswere seen regarding age or gender. There was aninteraction of diagnosis and IQ, with participantswith 22q11DS showing more gaze to happy facesover time with higher IQ, however this effect didnot survive correction for multiple comparisons.


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Because the effect was not robust with multiplecomparisons corrections and there was no consis-tent pattern across tests showing an influence ofIQ, IQ was not included in the final model.

An omnibus 2 (early vs. late trials) by 2(22q11DS vs. TD) test did not find a significantinteraction, t(43) ¼ 0.24, p . 0.05 for angry trials(Figure 4, Panel A). Given the novel nature of thisanalysis in this population, we performed explor-atory pairwise follow-up tests. On angry trials, theTD children showed a significantly higher per-centage of gaze to angry faces in the late versusearly trials, t(18) ¼ 3.83, p , 0.05, M 6 SD ¼

þ10.2% 6 11.7%). Additionally, though TDchildren did not increase gaze to neutral facesover time (p . 0.05) they looked less at non-faceareas and towards faces over time, t(18)¼�3.45, p, 0.05, M 6 SD ¼ �10.5% 6 19.0%. Thechildren with 22q11DS showed no significantchanges in gaze to neutral faces, angry faces, ornon-face areas (all p . 0.05) over time. There was atrend towards a significant group difference for thenon-face change between children with 22q11DSand TD analysis, t(43) ¼ 1.73, p ¼ 0.09, M 6 SDfor TD¼�10.5% 6 19.0% M 6 SD for 22q11DS¼�1.5% 6 2.9%. There were no significant group

Figure 2. Panel A shows Mean RT bias (incongruent RT – congruent RT) scores by emotion conditionand genetic diagnosis. Error bars indicate SEM. TD n¼ 28; 22q11DS n¼ 41. Panel B shows percentage oflooking time for each face type by emotion condition and diagnosis. TD n¼ 19; 22q11DS n¼ 27. Errorbars indicate SEM. * indicates p , 0.05; † indicates p ¼ 0.05. RT ¼ Response Time; TD ¼ TypicallyDeveloping; 22q11DS ¼ 22q11.2 Deletion Syndrome.


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differences in gaze change to neutral or angry faces(both p . 0.05). (For summary of significantfindings, see Table 3.)

An omnibus 2 (early vs. late trials) by 2(22q11DS vs. TD) test for happy trials found asignificant interaction, t(43) ¼ 2.21, p , 0.05,supporting follow-up tests (Figure 4 Panel B). Onhappy trials, TD children increased gaze to neutralfaces over time, t(18)¼ 3.46 p , 0.05, M 6 SD¼þ6.3% 6 7.7%), increased gaze to happy facesover time, t(18)¼ 2.19 p , 0.05 M 6 SD¼ 7.4%6 7.8%, and again showed less non-face gaze astime went on, t(18)¼�3.45 p , 0.05, M 6 SD¼�13.7% 6 15.9%. (See Table 3 for a summary ofsignificant findings.) Again, children with22q11DS showed no significant change in gazeto neutral, happy, or gaze to non-face regions withtime (all p , 0.05). The difference between groupsfor change in gaze to happy faces over timereached significance, t(43) ¼ 2.21 p , 0.05, M 6

SD for TD¼ 7.4% 6 7.8% M 6 SD for 22q11DS¼�1.6% 6 2.6%. The difference between groupsfor change in gaze to non-face areas over time alsoreached significance, t(43)¼�3.22, p , 0.05 M 6

SD for TD ¼ �13.7% 6 15.9%, M 6 SD for22q11DS¼ 1.2% 6 3.0%. There was no apparentdifference between groups for change in neutralgaze (p . 0.05).In summary, children showedmore gaze to all types of faces on late trials thanearly trials, children with 22q11DS showed nosignificant changes in gaze over time.

Analysis 3: Pupil DiameterWe initially built a model including diagnosticgroup, time in experiment (early vs. late), age,gender, and IQ. Age, IQ, and gender were notsignificant predictors of pupil contraction ordilation and so were removed from the model.

The 2 (early vs. late) by 2 (22q11DS vs. TD)omnibus tests for angry faces and happy faces bothfailed to reach significance (both p . 0.05; Figure5). Given the novel nature of this analysis in thispopulation, we performed exploratory pairwisefollow-up tests. The only significant finding wasgreater pupil contraction to happy trials comparedto neutral trials in the TD population, t(18) ¼�2.14, p , 0.05, for later trials only. There wasalso a trend for more contraction in late trials thanearly trials for happy stimuli in the TD children,t(18) ¼ 1.93, p ¼ 0.062. (All other p . 0.05.)

Discussion

This study explored attention to threat in aDPTB paradigm performed by children with22q11DS. We examined the effect of positiveand negative emotional stimuli compared toneutral stimuli on attentional control as afunction of genetic diagnosis. Scores on theSCAS and ABAS confirmed that the group ofchildren with 22q11DS was more anxious, andhad lower adaptive functioning than their TDpeers, as predicted.

The RT facilitation for threat stimuli reportedin numerous other studies was not observed in oursample. We concluded that this was likelyattributable to the fragility of the RT facilitationmeasure and the more variable response patternsof our young sample. In fact, previous research hascriticized the use of RT bias on the DPTB as anunreliable measure (Schmukle, 2005). This fragil-ity was confirmed in our analyses showing verylow consistency in either group’s RT bias respons-es to odd and even trials, consistent with recentwork showing the reliability of eye-tracking in thisparadigm (Burris et al., 2017).

Table 2Summary of Significant Findings, Overall Gaze

Measure Diagnosis M – SD t p

gaze bias to angry faces on angry vs.

neutral face trials

TD angry: 33.73% 6 12.27

neutral: 24.71% 6 8.42

�4.15 ,0.0001

gaze bias to angry faces on angry vs.

neutral face trials

22q11DS angry: 34.87% 6 9.54

neutral: 24.90% 6 7.53

�5.97 ,0.0001

gaze bias to happy faces on happy vs

neutral face trials

TD happy: 32.28% 6 12.14

neutral: 25.83% 6 8.03

�2.22 ,0.01

gaze bias to happy faces on happy vs

neutral face trials

22q11DS happy: 32.92% 6 11.09

neutral: 24.94% 6 9.39

�3.07 ,0.01

Note. TD ¼ Typically developing.


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Figure 3. Effect of age in months on overall gaze bias to Angry and Happy. Panel A: 22q11DS n¼ 27, r¼�0.53, p¼0.0019; TD n¼19, r¼�0.44, p¼0.07; combined r¼0.48, p¼0.0005. Panel B: 22q11DS n¼27, r ¼�0.33, p ¼ 0.028; TD n ¼ 19, r ¼�0.34, p ¼ 0.16; combined r ¼�0.33, p ¼ 0.008. 22q11DS ¼22q11.2 Deletion Syndrome; TD ¼ Typically developing.


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In contrast to the RT bias responses, both ourgaze and pupilometry measures were consistentacross trials within the same experiment. Thisnovel analysis of the consistency and reliability ofthe different measures deployed in the studystrongly increases our confidence in the followinginterpretations of each group’s behavior in re-sponse to differently valenced affective stimuli.Gaze measures showed a bias towards bothpositive and negative emotional faces in boththe TD and 22q11DS groups. This indicates thatwithin our sample, regardless of valence ordiagnostic group, participants oriented towardthe emotional faces. This may be due to thegreater social relevance of emotional, as opposedto neutral, faces. Alternately, emotional faces maybe more interesting than neutral faces.

Assessing response to perceived threat aver-aged over multiple exposures ignores differences inadaptation that might produce divergent patterns(Hare et al., 2005). As such, we also comparedresponses to early versus late trials. This analysisproduced different patterns in our two groups.Although TD children had markedly differentgaze patterns on early versus late trials, childrenwith 22q11DS showed no significant changes ingaze between initial and later exposures to eitherpositive or negative affective stimuli. We foundthat TD children gradually attended more to alltypes of faces over time whereas children with22q11DS made no significant changes to gazewith repeated exposures. The gaze pattern in TDchildren indicates controlled gaze, primarilystaying on the center of the screen, early in theexperiment. Then with repeated exposures thechildren who were TD evaluated the faces moreregardless of valence. The children with 22q11DS,however, maintained gaze to non-face areasthroughout. It could be that children with22q11DS do not shift in response over time asthey do not habituate to the faces enough to want

Figure 4. Panel A shows mean change in gazewith standard error bars for angry (*p , 0.05, †p ,

0.1). TD n¼ 19; 22q11DS n¼ 26. Panel B showsmean change in gaze with standard error bars forhappy (*p ,0.05, †p , 0.1). TD n¼ 19; 22q11DSn ¼ 26. Note, bracketed comparisons are betweengroup comparisons of change scores while un-bracketed symbols indicate significant early versuslate gaze. TD¼ Typically developing; 22q11DS¼22q11.2 Deletion Syndrome.

Table 3Summary of Significant Findings, Change in Gaze Over Time

Measure Diagnosis M – SD t p

change in angry face gaze, early vs late trials TD þ10.2% 6 11.7 3.83 ,0.05

change in non-face gaze, early vs late trials with angry faces TD �10.5% 6 19.0 �3.45 ,0.05

change in neutral gaze, early vs late trials with happy faces TD þ6.3% 6 7.7 3.46 ,0.05

change in happy face gaze, early vs late trials with happy faces TD 7.4% 6 7.8 2.19 ,0.05

change in non-face gaze, early vs late trials with happy faces TD �13.7% 6 15.9% �3.45 ,0.05

Note. TD ¼ Typically developing.


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to explore their content, or that they would haveneeded many more trials to reach the same patternof exploration as the TD children. Alternately, thelack of shift in attention may be due to lowerflexibility in those with a 22q11DS, an effect of

the lower executive function in this population(Shapiro, Tassone, Choudhary, & Simon, 2014).

In addition, we made exploratory use ofpupilometry data to provide an autonomic indexof arousal. We chose to examine pupil dilation asit is an objective measure of arousal and processingload providing continuous values for analysis. Inaddition, pupil dilation, unlike the other availablemeasures, is not under conscious control. Here,TD children showed increased pupil contractionto happy faces with repeated exposure, perhapsindicating that the happy faces produced lessarousal, or required less effortful processing, asthese children became accustomed to the stimuli.In contrast, children with 22q11DS showed noreal changes over time, possibly suggesting thattheir arousal levels did not change or that theyexerted the same amount of cognitive effort inscrutinizing and interpreting the facial emotionsthat were presented. This pattern parallels theirstatic gaze patterns.

Taken together, our findings suggest that TDchildren increased viewing of positive, negative,and neutral emotion faces over the course of theDPTB experiment. Pupilometry data suggest, inturn, that the increase in processing time waslinked to less arousal and cognitive load. Onepossible interpretation of this pattern is thateffective emotional regulation mechanisms enabletypically developing children to continually viewand evaluate the emotional content of the faces,without accompanying affective reactivity. Thismay be in contrast to previous work that hasfound decreasing RT bias to threat with repeatedexposures (Staugaard, 2009). Those studies weredifferent from ours in that the participants wereyoung adults rather than children. Thus, it may bethat developmental differences result in differenthabituation processes (i.e., increasing emotionregulation abilities in older children). In fact,research has shown that although children withoutanxiety diagnoses can be trained to attend more tothreat faces, it is difficult to train them to attendmore toward neutral ones (Eldar, Ricon, & Bar-Haim, 2008). On the other hand, our differingresults may be due to our use of gaze andpupilometry rather than RT bias. It may be thatwith repeated exposures and decreasing saliencethough participants explore the faces more theyare less ‘‘stuck’’ on this area of the screen. They areable to disengage when the target asterisk appearsand show less RT bias in spite of the increased

Figure 5. Mean pupil dilation/contraction withstandard error bars for early (panel A) and late(panel B) trials by group. 22q11DS n¼ 27; TD n¼19. (*p , 0.05, †p , 0.1) TD ¼ Typicallydeveloping; 22q ¼ 22q11.2 Deletion Syndrome.


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exploration, and therefore processing, of theemotional faces.

By contrast, the children with 22q11DS, whowere as a whole highly anxious, showed nosignificant changes in viewing patterns or in pupilresponses across the task. A possible interpretationof this pattern is that their reduced emotionregulation abilities did not allow them to modu-late their response to the faces.

It was somewhat unexpected that we did notsee major differences dependent on the valence ofthe trial type (happy or angry), a finding that hasalso been shown using event related potentialsfrom EEG (Thai, Taber-Thomas, & Perez-Edgar,2016). Recent research has suggested differentresponse patterns for ‘‘fearful’’ versus ‘‘distress’’anxiety subtypes in pediatric populations (Salumet al., 2013; A. M. Waters et al., 2014) it is possiblethat these subgroups were present within oursample. Fearful children (mainly those withphobias and separation anxiety) display avoidanceof mildly threatening stimuli, whereas distressedchildren (mainly those with generalized anxietydisorder [GAD], major depressive disorder, andposttraumatic stress disorder) display vigilancetoward mildly threatening stimuli. However, thesize and variability of our sample of children with22q11DS, did not produce sufficiently largegroups for the different anxiety subtypes to allowus to examine our data in this way. With a muchlarger sample, we might have observed differencesbetween anxiety-type groups as well as betweenstimulus type.

These findings contribute to the attentionbias literature in several ways. We supported theconsensus that RT measures may be too variableto accurately index the noisy patterns of threatbias in certain populations, especially phenotyp-ically variable children with neurodevelopmentaldisorders like those in our study. We also foundthat the relationship of attention bias to anxietyis not as simple, as those with anxiety show morethreat bias. Although many studies have shownthreat bias in anxious populations and not innon-anxious populations, our results differedbecause we observed a gaze bias to emotionalfaces, regardless of valence, in both of our groups.It is possible that different subtypes of anxietyproduce different response patterns, or thatresponse patterns change during childhood andthat our age group showed a propensity toward abroad emotional bias. The correlation betweenage and reduced gaze to emotional faces in the

older participants in our sample supports thisassertion. We also supported the importance ofexamining responses to threat stimuli over thecourse of an experiment rather than collapsing alltrials together. Finally, our exclusion of inaccu-rate trials and requirement that participants fixatebefore stimuli are presented allowed us to look atthe effect of emotional stimuli beyond justinattention to the task.

Our findings also have important implicationsfor clinical practice with children with 22q11DSand others suffering from anxiety. Interventionsaddressing attention bias in anxiety have tried toreduce threat bias or increase positivity bias(Hakamata et al., 2010; A. M. Waters et al.,2013) by training participants to pay increasingattention to positively valenced stimuli. Attentionbias modification has shown positive results inanxious children and teenagers without 22q11DS(Sylvester, Petersen, Luby, & Barch, 2016; A. M.Waters et al., 2013). Our findings indicate thosewith 22q11DS are already avoiding the negativestimuli, and so training them to continue avoidingnegative stimuli may not be effective. Indeed, thiscould have the unintended effect of increasing,rather than reducing, their anxiety. Additionally,we encourage clinicians to discuss and addressanxiety concerns patients with 22q11DS areexperiencing, and incorporate anxiety treatmentinto the patient’s treatment plan.

Work in fragile X syndrome showed that thesechildren showed RT facilitation to angry, but nothappy faces (Burris et al., 2017). It is difficult todirectly compare these results to our own becauseour RT results were noisy and unreliable. Howev-er, the results we see from gaze show more gaze toboth angry and happy faces in those with22q11DS, perhaps indicating that although theangry face bias is common across these twodevelopmentally delayed groups, the bias towardhappy is unique to 22q11DS. Future research maywish to directly compare different geneticallydistinct groups on the same paradigm to producea more reliable comparison. Exploring patterns inmultiple groups with different genetic back-grounds could additionally connect our resultsto genetics, particularly if the genetic profile ofparticipants were known. It may be interesting, forexample, for future studies to correlate these typeof results with COMT genotype, which isimplicated in 22q11DS and known to affectexecutive function tasks (Shapiro, Takarae, Har-vey, Cabaral, & Simon, 2012; Shapiro et al., 2014).


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LimitationsFirst, the lack of an interaction affect in our originalmodels is concerning. Performing separate modelsfor each group means we are not statisticallycomparing the two groups for group differences,but rather discussing the results of two separatemodels. However, we note that the sample sizeneeded to detect an interaction effect may be 7to 9times as large as that needed to detect a simple effect(Wahlsten, 1991). As such, in a rare population suchas 22q11DS we argue that examining main effects intwo models between groups is necessary toperforming preliminary research. We hope thatsimilar studies will be conducted in larger samples toconfirm our findings.

Second, although we initially attempted toperform correlations between task responses andcontinuous anxiety scores, we did not find signif-icant results. Upon performing a power analysis, wefound to reach an 80% chance of identifying suchan effect in a sample of our size, the correlationwould have to be at least r¼0.41. This is a very largeeffect size for this type of data so we did not furtherpursue this route because we did not feel we hadsufficient power to address this question.

Finally, 22q11DS is a complicated disorder,and although we find that the anxiety andadaptive functioning explains a substantialamount of the variance in our outcomes, it islikely that other aspects of this disorder are alsoimportant. The common spatiotemporal attentionimpairments in 22q11DS (Simon, Bearden, Mc-Ginn, & Zackai, 2005) may contribute to perfor-mance in the DPTB. Second, social stimuli mayhave induced children of both groups to attend tothe more socially relevant and salient emotionalrather than neutral faces. Third, it is possible thatangry faces, although mildly threatening, were notthreatening enough to elicit a threat bias behavior.It is also possible that we did not have enoughtrials in our experiment to induce differentialpatterns of adaptation. Our study is also limitedby the relatively small sample size, an unfortunateconsequence of working with a special population.These results nevertheless provide valuable infor-mation about attention bias as measured by gazein this population.

ConclusionsGaze and pupilometry were more reliable mea-sures in a DPTB paradigm than RT alone.Additionally, when examining performance over

time, children with 22q11DS showed less adap-tation than those who were TD. This mayindicate fewer emotion regulation skills in thispopulation, making emotion regulation a poten-tial treatment target.

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Received 12/20/2018, accepted 6/4/2019.

This work was supported by NIH R01 HD042974and U54 HD079125. AMP was supported in part byNIH training grant T32 MH82174 funded by theJointly Sponsored Ruth L. Kirschstein NRSA Institu-

tional Predoctoral Training Program in the Neurosci-ences.

Authors:Abbie M. Popa, Joshua R. Cruz, Ling M. Wong,Danielle J. Harvey, Kathleen Angkustsiri, andIngrid N. Leckliter, UC Davis, MIND Institute,Sacramento, CA; Koraly Perez-Edgar, Penn StateUniversity, University Park; and Tony J. Simon,UC Davis, MIND Institute, Sacramento, CA.

Correspondence concerning this article should beaddressed to Abbie M. Popa, UC Davis, MINDInstitute, 2825 50th Street, Rm. 1361, Sacramento,CA 95817 (e-mail: [email protected]).


2019, Vol. 124, No. 6, 549–567 DOI: 10.1352/1944-7558-124.6.549


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