RESEARCH ARTICLE The Association Between Social Cognition and Executive Functioning and Symptoms of Anxiety and Depression in Adolescents With Autism Spectrum Disorders Matthew J. Hollocks, Catherine R.G. Jones, Andrew Pickles, Gillian Baird, Francesca Happé, Tony Charman, and Emily Simonoff While high levels of anxiety and depression are now recognized as major co-occurring problems in children and young people with an autism spectrum disorder (ASD), research examining possible associations with individual differences in neurocognitive functioning has been limited. This study included 90 adolescents with an ASD aged 14–16 years with a full-scale IQ > 50. Using structural equation modeling, we examined the independent relationships between multiple measures of executive functioning and social cognition on severity of anxiety or depressive symptoms. Results indicated a significant association between poorer executive functioning and higher levels of anxiety, but not depression. In contrast, social cognition ability was not associated with either anxiety or depression. This study is the first to report significant associations between executive functions and anxiety in ASD. This may suggest that poor executive functioning is one factor associated with the high prevalence of anxiety disorder in children and adolescents with ASD. Autism Res 2014, 7: 216–228. © 2014 International Society for Autism Research, Wiley Periodicals, Inc. Keywords: anxiety; ASD; depression; executive functions; social cognition; neuropsychology

Autism spectrum disorders (ASD) are characterized by a pervasive impairment in reciprocal social interaction and communication skills, and the presence of stereotyped behavior, interests, and activities. In addition to these core impairments, those with ASD often have poor regulation of emotion [Samson, Huber, & Gross, 2012] and difficulties with temperament [Konstantareas & Stewart, 2006]. People with ASD also report more anxiety symptoms and have higher rates of anxiety disorders than their typically developing peers, with a meta-analysis suggesting a prevalence rate of ∼40% [van Steensel, Bogels, & Perrin, 2011]. Depression also appears to be common in adolescents with ASD [Mazefsky, Conner, & Oswald, 2010; Strang et al., 2012]. There has been no metaanalysis of depression in ASD, and studies vary widely in the reported rates, ranging from 1.4% in a populationderived sample [Simonoff et al., 2008] to 70% based on clinical case reports [Lugnegard, Hallerback, & Gillberg, 2011]. There are a number of difficulties in the assessment of emotional problems in ASD. First, people with ASD often have difficulty identifying and reporting their feelings

[Losh & Capps, 2006]. Second, there is considerable behavioral overlap between symptoms of ASD and symptoms of anxiety. For instance, social disinterest in ASD may be difficult to distinguish from social avoidance in social phobia. It has been suggested that these symptom overlaps may lead to difficulty in differential diagnosis and inflate estimates of the prevalence of anxiety in ASD [Wood & Gadow, 2010]. Given that anxiety and depression are highly prevalent in ASD, and that there may be diagnostic challenges based on mental state assessment alone, it is important to consider which factors place people with an ASD at a greater risk for developing affective disorders. These factors could be those that are core to the diagnosis of ASD, such as difficulties in communication and restricted and repetitive behaviors, or associated cognitive difficulties such as deficits in social cognition. This includes the theory of mind (ToM) or “mentalizing,” which is the ability to represent mental states of others and to understand and predict behavior based on these representations [Happé & Frith, 1996]. Alternatively, anxiety in ASD may be driven by the same processes commonly reported

From the Department of Child & Adolescent Psychiatry, Institute of Psychiatry, King’s College London, London, United Kingdom (M.J.H.); School of Psychology, Cardiff University, Cardiff, United Kingdom (C.R.G.J); Biostatistics Department and Biomedical Research Centre for Mental Health, Institute of Psychiatry, King’s College London, London, United Kingdom (A.P.); Guy’s & St Thomas’ NHS Foundation Trust, London, United Kingdom (G.B.); MRC SGDP Research Centre, Institute of Psychiatry, King’s College London, London, United Kingdom (F.H.); Department of Psychology, Institute of Psychiatry, King’s College London, London, United Kingdom (T.C.); Department of Child & Adolescent Psychiatry and NIHR Biomedical Research Centre for Mental Health, Institute of Psychiatry, King’s College London, London, United Kingdom (E.S.) Received June 27, 2013; accepted for publication January 12, 2014 Address for correspondence and reprints: Matthew J. Hollocks, Department of Child & Adolescent Psychiatry, Institute of Psychiatry, King’s College London, PO85, De Crespigny Park, Denmark Hill, London, SE5 8AF. E-mail: [email protected] Published online 12 March 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/aur.1361 © 2014 International Society for Autism Research, Wiley Periodicals, Inc.

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Autism Research 7: 216–228, 2014

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in pediatric anxiety disorders, such as difficulties in executive, top-down control of attention [Bishop, 2007; Derakshan, Smyth, & Eysenck, 2009b]. An important component of anxiety disorders is a difference in cognitive processing. Theories of anxiety highlight the importance of competition between goaldirected attention and salient threat-related stimuli, suggesting that allocation toward threat can be inhibited by top-down control [Eysenck, Derakshan, Santos, & Calvo, 2007]. When someone is anxious, the threshold to detect negative cues in the environment is lowered; this is thought to occur via a disruption in the balance between top-down cognitive control and bottom-up stimulusdriven attention [Corbetta & Shulman, 2002]. Therefore, at high levels of anxiety, there is a switch in the processing of stimuli, leading to hyper-attentiveness toward negative information. One way in which this reduction in cognitive control is manifested in anxiety disorders is via attentional biases toward threat stimuli [Dalgleish et al., 2003; Waters, Henry, Mogg, Bradley, & Pine, 2010; Waters, Mogg, Bradley, & Pine, 2008]. In addition, such biases in attention may be related to a failure to flexibly disengage from negative stimuli [Derakshan, Ansari, Hansard, Shoker, & Eysenck, 2009a, Derryberry & Reed, 2002]. As people with ASD are often reported to have difficulties in executive functioning [Hill, 2004], and in particular cognitive flexibility, they may be vulnerable to the development of negative processing styles. Initial studies examining the association between ASDrelated cognitive factors and symptoms have suggested a link between higher intellectual functioning (Full-Scale IQ [FSIQ] > 70) and elevated levels of anxiety symptoms in ASD [Hallett et al., 2013; Sukhodolsky et al., 2008; Weisbrot, Gadow, Devincent, & Pomeroy, 2005]. However, this relationship has not been consistently reported [Simonoff et al., 2008; Strang et al., 2011]. In addition, the severity of anxiety symptoms in people with an ASD has been associated with the extent of both restricted interests [Spiker, Lin, Van Dyke, & Wood, 2012] and repetitive behaviors [Gotham et al., 2012; Rodgers, Glod, Connolly, & McConachie, 2012]. It has been suggested that repetitive behaviors may be maladaptive coping strategies to manage anxiety-associated arousal [Spiker et al., 2012]. It is also possible that greater rigidity of behavior and thought lead to more anxiety, due to everyday disruptions of routine. Social Cognition in ASD, Anxiety, and Depression Social skills deficits have been implicated in relation to heightened anxiety symptoms in ASD [Eussen et al., 2012]. For instance, greater social anxiety in adolescents with ASD has been associated with worse social skills [Bellini, 2004, 2006], measured using the Social Skills

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Rating System [Gresham & Elliott, 1990]. Interestingly, a curvilinear relationship was found between the empathy subscale and anxiety. Specifically, participants with both high and low levels of empathy had lower levels of selfreported anxiety symptoms [Bellini, 2004]. In contrast, Niditch, Varela, Kamps, and Hill [2012] examined the relationship between social understanding and anxiety in a large group of children with an ASD (n = 231), both measured via the parent rating scale from the Behavior Assessment System for Children-2 [Reynolds & Kamphaus, 2004]. Results indicated that greater social understanding was significantly correlated with higher anxiety [Niditch et al., 2012]. Fisher (2006, unpublished PhD thesis) also reported that better performance on the ToM tasks predicted more self-reported anxiety (on the Beck Anxiety Inventory [Steer & Beck, 1997]) in a group of 30 adults with ASD and average-range IQ. These studies provide preliminary evidence that differences in social cognition may influence the development of anxiety disorders in ASD. One hypothesis is that those people with an ASD who have greater ToM ability are more aware of their social difficulties, leading to increased risk for developing emotional problems [Mazurek & Kanne, 2010]. To date, there is a lack of studies investigating whether social cognition is related to symptoms of depression in children with or without ASD. However, a few studies conducted in adults with depression have reported poorer ToM performance compared with controls [Cusi, Nazarov, Macqueen, & McKinnon, 2013; Wang, Wang, Chen, Zhu, & Wang, 2008] and that poorer ToM may predict relapse of depression [Inoue, Yamada, & Kanba, 2006]. Executive Functions, Anxiety, and Depression Executive functions are a set of cognitive skills that include set shifting and set maintenance, interference control, inhibition, integration across space and time, planning, and working memory, which overlap with the top-down control of attention [Pennington & Ozonoff, 1996]. It is well documented that, in addition to deficits in social cognition, individuals with ASD demonstrate difficulties in executive functioning [Hill, 2004]. Differences in neuropsychological functioning have also been implicated in anxiety disorders in people without ASD. One leading theory of anxiety disorders [Eysenck et al., 2007] suggests that anxiety disrupts the balance between the top-down attentional control system and the bottom-up stimulus-driven attentional systems [Corbetta & Shulman, 2002], favoring stimulus-driven attention and thereby reducing control over attentional allocation. Therefore, it is possible that problems with executive control of attention in those with ASD may place them at increased risk for developing anxiety disorders.

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Studies examining neuropsychological correlates of anxiety and depression in otherwise healthy adults have found impairments in a range of neuropsychological domains, including attention and executive functioning [see Castaneda, Tuuio-Henriksson, Marttunen, Suvisaari, & Lonnqvist, 2008, for a review]. Further, the implicated neuropsychological domains can vary across different anxiety disorders [Airaksinen, Larsson, & Forsell, 2005]. Neuropsychological studies in children and adolescents with anxiety disorders have also provided some evidence for difficulties in the domains of attention and executive functions. For instance, Toren et al. [2000] found that children with either overanxious disorder or separation anxiety performed more poorly on the Wisconsin Card Sorting Task (WCST), a test of executive functioning. Problems with verbal working memory have been identified in depression but not anxiety [Günther, Holtkamp, Jolles, Herpertz-Dahlmann, & Konrad, 2004], and possible problems with attention and executive functioning have been found in those with OCD [Ornstein, Arnold, Manassis, Mendlowitz, & Schachar, 2009]. Children with major depressive disorder have been shown to perform poorly on tests of attentional switching, while other measures of executive functioning, such as set shifting and perseverative responding, appear spared [Favre et al., 2009; Matthews, Coghill, & Rhodes, 2008]. Neuropsychological studies of childhood depression are limited and, as with the anxiety literature, report a wide array of possible correlates. Overall, children with major depressive disorder have been shown to perform poorly on tests of visual and spatial memory, processing speed, and attentional switching, while other measures of executive functioning, such as set shifting and preservative responding, appear relatively spared [Favre et al., 2009; Matthews et al., 2008]. In summary, evidence suggests that social skill deficits and perhaps social cognitive deficits may relate to elevated symptoms of anxiety in individuals with ASD, while neuropsychological studies in people with anxiety disorder and/or depression indicate problems with attention and executive functioning compared with controls. The aim of the present study is to examine the relationship between social cognition and executive functions and the severity of anxiety and depression in adolescents with ASD. We will answer this question by using latent variables of social cognition and executive functioning, indexed by well-validated neurocognitive tasks, and examine possible relationships with parent-reported anxiety and depressive symptoms.

Methods Participants This study included 90 adolescents (82 male) with an ASD selected from the population-derived Special Needs and

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Autism Project cohort (SNAP; ASD n = 100) [Baird et al., 2006]. Full details of sample selection and characteristics in SNAP can be found in Baird et al. [2006] and Charman et al. [2011]. Participants were included in this analysis if both parent questionnaires were completed (see below). ASD diagnoses were confirmed according to the ICD-10 criteria based on a full clinical assessment, including the Autism Diagnostic Interview-Revised [Lord, Rutter, & Couteur, 1994], the Autism Diagnostic Observation Schedule-Generic [Lord et al., 2000], and the Social Communication Questionnaire [Rutter, Bailey, & Lord, 2003]. This study was reviewed and approved by the South East London Research Ethics Committee (05/MRE01/67).

Measures The tasks were administered to the participants over 2 days of testing, separated by at least a week. Questionnaire Measures Strengths and Difficulties Questionnaire (SDQ; Goodman, Ford, Simmons, Gatward, & Meltzer, 2003). The SDQ is a brief screening questionnaire assessing mental health on 25 items in five domains: emotional symptoms, conduct problems, hyperactivity/ inattention, peer relationship problems, and pro-social behavior. Combining the first four domains, a total difficulties score can be established. In this study, we focused on the emotional symptoms subscale, which consists of five questions and has a maximum score of 10 and has a suggested cutoff of 5. Profile of Neuropsychiatric Symptoms (PONS; Santosh, Baird, Pityaratstian, Tavare, & Gringras, 2006). The PONS is a 62-item questionnaire that assesses the severity and impact of 31 symptoms commonly reported in children and young people with neurodevelopmental disorders. For each symptom, a brief definition is given, and the respondent is asked to report the overall frequency of that symptom (0–5) and its impact on everyday life (0–5). The two ratings are combined to provide an overall score for each symptom ranging from 0 to 10. We focused on five items related to anxiety and depression: worries, fears, depressed thoughts, low mood, and labile mood. Neuropsychological Measures The Wechsler Abbreviated Scale of Intelligence (Wechsler, 1999) was selected as a brief but reliable measure of intelligence; it contains four subtests that measure both verbal and nonverbal intelligence, as well as provide an estimate of the FSIQ.

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Social Cognition Measures Frith-Happé animations [Abell, Happe, & Frith, 2000; Castelli, Frith, Happe, & Frith, 2002]. This task was included as a measure of ToM. Participants viewed a series of silent animations that are part of an established set of mental state cartoons. All featured two interacting “characters,” a big red triangle and a small blue triangle, moving within a white framed space. Four animations depicted ToM interactions and two animations depicted goal-directed interactions. The order of the animations was counterbalanced. The participants were required to watch the animations and describe what the two triangles were doing. The participants’ verbal responses were recorded for later transcription and scoring. Analysis focused on the ToM animations, with intentionality scores calculated based on the degree of mental state attribution (0–5). The score used in this analysis was the average intentionality across the four items, with higher scores relating to better performance. Two experimenters independently scored 72 of the 129 scripts (56%), and intraclass correlations for the mean scores were high (range 0.82–0.98; see Jones et al. [2011] for details. Strange stories [Happé, 1994]. This task was included as a general measure of mental state understanding and requires the attribution of mental states underlying nonliteral utterances (e.g. double bluffs, misunderstanding, lies, persuasion). The participants were read a series of stories, which were also written out in front of them and accompanied by an appropriate illustration. At the end of each story, they were asked a question about the text. Four of the stories had a ToM component, where the participant had to demonstrate understanding of the character’s thoughts, feelings, and intentions, and two were control “physical” stories, which did not demand mental state understanding. The order of story presentation was counterbalanced. The tasks were scored on a 0–2 scale, with 0 representing an incorrect or “don’t know” response, 1 a partial or implicitly correct response, and 2 representing a full and explicitly correct answer. The total score was the average score across the four ToM stories. Scoring was subjectively marked by two raters as per the Frith-Happé animations task (see above). False belief composite score. A composite score based on two false belief tasks was generated for use in this study. The first task was the “combined false belief story,” which was included as a measure of first- and second-order false belief understanding. The task (The Chocolate Story) was developed by Rhonda Booth (Institute of Psychiatry, London) and based on previous false belief tasks [Baron-Cohen, 1989; Bowler, 1992]. The story was divided into two sections: (a) first-order false belief

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and (b) second-order false belief. The second task [Bowler, 1992] was included as a measure of second-order false belief understanding, with the verbal demands being greater than in the combined false belief task. The false belief composite score ranged from 0 to 7, with points being awarded for both correctly passing each false belief question and for correct justification of each false belief question. The reading the mind in the eyes task [Baron-Cohen, Wheelwright, Spong, Scahill, & Lawson, 2001]. The eyes test requires the participants to understand mental/emotional state “concepts” and match them to expression of eyes from black and white photos. Participants were shown black and white photographs of just the eye region of the face of 28 people. Participants were asked to pick which of four inner state words best describes what the person in the photo is thinking or feeling. Three of the four words were distractors, while the other word was deemed “correct.” A point was awarded for each correct trial, giving a score range of 0–28. Executive Functioning Measures Opposite worlds. The opposite worlds task was taken from the Test of Everyday Attention for Children [Manly et al., 2001] and was included as a measure of interference inhibition. The task included a “same world” trial, where participants read out a series of the numbers 1 and 2; and the “opposite world” trial, where participants had to say the opposite to the number they were reading (so “2” when they read a 1, and “1” when they read a 2). Two same world trials and two opposite world trials were presented. The time taken to complete each world was recorded in seconds. The outcome variable was the subtraction of the mean same worlds completion time from the mean opposite worlds completion time, with a higher score relating to worse inhibition performance. Trail making [Reitan, 1958]. This was included as a measure of attentional switching and comprised three separate trials. For Part A1, the participant was asked to “join the dots” in numerical order 1–25. For Part A2, the participant was asked to “join the dots” in alphabetic order of 25 circles labeled A-Y. For Part B1, the participant was asked to “join the dots” by switching between 25 numbers and letters (i.e. 1-A-2-B-3-C and so on). The time taken to complete each trial was recorded in seconds. The outcome variable was the subtraction of Part A1 from Part B1, with a higher score indicating worse performance. Numbers backwards. This digit span task, taken from the Children’s Memory Scale [Cohen, 1997], was included as a measure of verbal working memory. Participants listened to the experimenter say a list of single digits at a rate of 1 per second and then had to recall them

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in the reverse order. Scaled scores (mean = 10, SD = 3) were calculated, with higher score reflecting better performance. Card sorting task. A card sorting task adapted from the WCST [Grant & Berg, 1948] was included as measure of cognitive set shifting. This version of the task was based on a previously adapted child-friendly version of the WCST [Hughes, Dunn, & White, 1998] and full details have been published previously [Tregay, Gilmour, & Charman, 2009]. Briefly, the task included three trials in which participants were shown a photograph of a character and a pack of 64 cards. The cards depicted single objects that varied on three dimensions: color (e.g. red or blue), shape (e.g. squares and hearts), and size (i.e. small objects or large objects). Each trial required the participants to sort by a different dimension, requiring the participants to shift cognitive set. Participants were told that the character had some favorite cards and that it was their job to work out what those cards were; they were also shown four of the cards as exemplars. They were told there was a rule about the cards that the character liked best, which they had to work out. A counterbalanced ordering system was used to dictate the rule on which the cards were sorted (e.g. color, shape, or size). The participant’s decision on the first sort was always taken as the correct answer and so dictated the type of card (e.g. red or blue) that the character favored. The trial continued until the participant was correct on six consecutive sorts or after 20 sorts had elapsed. A new character was used for each trial, but the participants were not explicitly told that the rule would be different. Scores ranged from 0 to 3 depending on the number of correct trials. Because participants tended to either pass or fail this task, scores were recoded “0” for fail (scores of 0–2) or “1” for pass (scores of 3).

sures, the SDQ and the PONS emotional items. All items were entered into an exploratory factor analysis (using Stata 11), using maximum likelihood and a promax rotation that allowed factors to be correlated. The factor analysis was constrained to two factors to see if it would generate clear anxiety and depression factors; the factor loadings, shown in Appendix A, fell in the predicted pattern and this allowed the generation of “total anxiety” and “total depression” variables. Factor 1 (total anxiety) had an eigenvalue of 3.9 and accounted for 70% of the variance, while factor 2 (total depression) has an eigenvalue of 1.1 and accounted for 22% of the variance; there was an inter-factor correlation of .54. Both factors showed good internal consistency with Cronbach’s alpha for the anxiety factor of .72 and for the depression factor .78. Following the generation of our anxiety and depression scales, SEM analysis was conducted to estimate the effects of the social cognition and executive functioning latent variables on emotional symptoms. The social cognition and executive functioning latent variables were regressed onto FSIQ. All key variables and covariates were added into an initial hypothesized model (see Fig. 1), and pathways were dropped systematically to reveal the most parsimonious model. Models were fit to raw data using full information maximum likelihood to account for data missing at random. Models were compared using chisquare likelihood ratio test of comparative model fit, comparative fit index (CFI), and root mean square error of approximation (RMSEA). Post-hoc regression analysis was conducted to investigate specific relationships between neurocognitive tasks and emotion scales in those areas found significant by the SEM analysis.

Results Descriptive Statistics

Statistical Analysis Data preparation and descriptive analysis were carried out using Stata 11 [StataCorp, 2009], and latent variable and structural equation modeling (SEM) was conducted in Mplus [Muthen & Muthen, 2012]. All variables were assessed for normality, and the variables that were found to be non-normally distributed were log-transformed to allow the use of parametric statistics. This was only necessary for the trail making task and was not required for the card sorting task as this was a categorical variable (pass/fail). Our analysis was designed to examine the independent relationship between social cognition and executive functioning and the levels of anxiety and depression within a group of adolescents with ASD. In order to do this, we generated variables for both anxiety and depression from our existing parent-report mea-

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This sample had a mean age of 15.5 years (range; 14.7– 16.8) and FSIQ of 84.5 (range; 50–119; see Table 1 for full descriptive statistics). The mean score on the anxiety scale was 8.19 (SD = 6.45; range 0–26) and on the depression scale was 5.99 (SD = 6.65; range 0–32). Higher FSIQ was significantly correlated with a lower anxiety score (r = −.24, P = .03), but was not correlated with the depression scale (r = −.01, P = .47). The Relationship Among Social Cognition, Executive Function, and Emotional Symptoms A correlation matrix for the neurocognitive tasks is displayed in Table 2 demonstrating variable correlation strength between the neurocognitive measures. This will impact on our model fit (described below), but the focus of this paper will be on the relationships between the

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Figure 1. Hypothesized model of the relationship between executive functioning and social cognition with emotional symptoms. T1: Trail Making Task, T2: Card Sort Task, T3: Opposite Worlds, T4: Numbers Backwards, T5: Frith-Happé animations, T6: Strange Stories, T7: Eyes Task, T8: False Belief Composite, Q1: SDQ: Many worries, often seems worried, Q2: SDQ: Nervous or clingy in new situations, easily loses confidence, Q3: SDQ: Many fears, easily scared, Q4: PONS: worries, Q5: PONS: fears, Q6: SDQ: Often unhappy, down-hearted or tearful, Q7: PONS: low mood, Q8: PONS: Depressed thoughts, Q9: PONS: Labile mood; SDQ = Strengths and Difficulties Questionnaire: PONS = Profile of Neuropsychiatric Symptoms. overall neurocognitive domains of executive functioning and social cognition and anxiety/depression rather than individual tasks. Correlations between individual neurocognitive tasks and the anxiety and depression scales are presented in Table 3. Our initial hypothesized full model is presented in Figure 1; this predicted independent relationships between both executive functioning and social cognition with both anxiety and depression. The model further allowed correlations between the two neurocognitive latent variables and also the emotional latent variables. This model had adequate model fit statistics (x2 (128) = 185.9, CFI = .901, RMSEA = .071) and suggested that executive functioning was inversely related to anxiety (standardized coefficient = −.50, P = .04). Nonsignificant pathways from social cognition to both anxiety and depression and from

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executive functioning to depression were identified. These nonsignificant pathways were excluded in sequence, with model comparisons using the likelihood ratio χ2 test to accept or reject more parsimonious models. In a model that included pathways between FSIQ and anxiety/depression, there was very poor model fit and no significant associations between FSIQ and either anxiety or depression. The final model (see Fig. 2) demonstrated adequate model fit (x2 (132) = 186, CFI = .904, RMSEA = .068) and indicated a significant association between poorer executive functioning and higher anxiety (standardized coefficient = −.35, sig. P < .01). The anxiety and depression scales were correlated (standardized coefficient = .35, P < .01). In addition, given previous findings, we used the fracpoly command in Stata to investigate whether there

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Table 1.

Descriptive Statistics, Mean (SD)

Variable

Mean

SD

Range

Age (years) Full-Scale IQ SCQ SDQ—Total SDQ—Emotional difficulties Triangles—Intentionality (83/90) Strange stories (81/90) Eyes task (85/90) False belief composite (79/90) Trail making (82/90) Opposite worlds (90/90) Numbers backwards (79/90) PONS: Worries PONS: Fears PONS: Low mood PONS: Depressed thoughts PONS: Labile mood

15.5 84.5 23.4 16.7 3.6 2.9 .85 17.1 5.3 3.9 8.2 7.1 2.7 2.9 2.2 1.7 1.8

.47 17.2 7.3 6.8 2.4 .95 .51 4.3 1.6 .63 6.5 3.6 2.6 3.0 2.4 2.5 2.5

14.7–16.8 50–119 2–37 3–29 0–9 0–4.8 0–2 6–25 0–7 2.4–5.4 −3.7–34.5 1–18 0–10 0–10 0–10 0–10 0–10

Note. Number in brackets indicates the number of participants who completed each task. PONS, Profile of Neuropsychiatric Symptoms; SCQ, Social Communication Questionnaire; SDQ, Strengths and Difficulties Questionnaire.

were any nonlinear relationships between social cognition and either anxiety or depression. We found no significant nonlinear relationships between the scores on the cognitive tasks and either anxiety or depression (ranges: β = 4.1–30.5; P = .07–.53). However, there was a trend for a nonlinear (second-order polynomial) relationship between depression score and the false belief composite score (β = 30.5; P = .07). Post-Hoc Analysis of the Association Between Executive Functions and Anxiety Post-hoc regression analysis revealed that higher anxiety was related to poorer performance on the opposite worlds task (β = −.23; P = .03), the card sort task (β = −3.5; P = .03), and the trail making task (β = −3.4; P < .01). However, no significant relationship was found between numbers backwards and anxiety (β = −.10; P = .61). After correcting for FSIQ, the relationships remained in the same direction but with higher alpha values: opposite worlds task (β = −.19; P = .08), card sort task (β = −3.2; P = .07), and the trail making task (β = −3.2; P = .01).

Discussion The primary aim of this study was to examine whether difficulties in social cognition and/or executive functioning are related to higher levels of anxiety and depression, and might therefore be associated with the increased prevalence of emotional disorders in adolescents with

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ASD. Results indicated that poor executive functioning was significantly related to greater symptoms of anxiety but not depression. However, we found no relationship between social cognition and either anxiety or depression. To our knowledge, this is the first study to use neurocognitive measures of social cognition and executive functioning to investigate affective symptoms in ASD. However, research in children with anxiety disorders has previously suggested an association between reduced performance on tasks of executive functioning, attentional switching, and verbal working memory, and the presence of greater levels of anxiety [Günther et al., 2004; Toren et al., 2000]. Our findings support a relationship between poor executive functioning and higher levels of anxiety in those with ASD. In the present analyses, the path from executive function to depression fell short of statistical significance, with the only significant relationship being through its correlation with anxiety. However, this finding should be treated with caution. The present sample size is moderate only and is not powered to identify a small effect size, although the same applies for our finding relating to social cognition, which also requires further investigation. Furthermore, the rates of depression in the SNAP study may be more limited than anxiety. At age 12, when DSM-IV disorders were measured, anxiety disorders were considerably elevated (41.9%) while depression was much less common (1.4%) [Simonoff et al., 2008]. Rates of depression are known to increase in adolescence in the general population, but there are at present no data to test this longitudinal relationship in people with ASD. Nevertheless, there was considerable range in the depression scores in this study, which should have allowed us to adequately detect continuous relationships between neurocognitive measures and depressive symptoms. However, the present latent depression variable does not directly relate to a recognized measure for which norms and clinical cutoffs are available. Our executive function latent variable included a number of tasks, covering domains such as attentional switching and set shifting, interference inhibition, and working memory. Furthermore, post-hoc analysis suggested that each of these domains, with the exception of working memory, may contribute to the relationship between executive functions and anxiety. Our finding that reduced performance in these areas is related to greater anxiety is consistent with the attentional control theory of anxiety. This theory suggests that under high levels of anxiety, top-down control of attention is reduced, allowing attentional resources to be reallocated toward threatening cues in the environment [Derakshan et al., 2009a, 2009b; Eysenck et al., 2007]. The model tested within this study assumes that poorer executive functioning leads to greater anxiety symptom

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Table 2.

Correlations Between Neurocognitive Measures Trail making

Trail making Card sort Opposite worlds Numbers backwards Triangles Strange stories Eyes False belief FSIQ

Card sort

Opposite worlds

Numbers backwards

Triangles

Strange stories

Eyes

False belief

FSIQ

1.0 .23 (.15) .36* (.23) .37* (.21) .06 (−.06) .09 (−.12) .22 (.02) .18 (−.02) .46*

1.0 .38* (.26) .16 (.03) .24* (.07) .23 (.06) .19 (−.04) .12 (−.04) .40*

1.0 .34* (.17) .13 (-.05) .25 (.04) .24 (.01) .04 (−.21) .42*

1.0 .15 (−.07) .29 (.06) .33* (.03) .06 (−.16) .51*

1.0 .18 (−.04) .46* (.30) .07 (−.11) .47*

1.0 .27 (.02) .24 (−.02) .53*

1.0 .21 (−.05) .55*

1.0 .52*

1.0

Notes. Coefficients in brackets are partialled for Full-Scale IQ (FSIQ). *Sig ≤ .01.

Table 3. Correlations Between Neurocognitive Measures and Anxiety and Depression

Trail making Card sort Opposite worlds Numbers backwards False belief Triangles Strange stories Eyes

Anxiety

Depression

−.34** (−.25*) −.24* (−.14) −.23* (−.16) −.10 (−.05) −.24* (−.20) −.01 (.02) −.02 (.11) −.13 (.01)

−.05 (−.01) −.23* (−.10) −.09 (−.10) −.01 (.05) −.10 (−.10) −.05 (−.01) −.01 (.003) −.01 (.03)

Notes. Coefficients in brackets are partialled for FSIQ; **Sig ≤ .05. **Sig ≤ .01.

severity. This may be because poorer top-down control may lead to increased cognitive biases, which have been associated with anxiety in the non-ASD adolescent population [Lau et al., 2012]. However, this study was not designed, nor did it have adequate statistical power, to examine bidirectional effects between neurocognitive tasks and anxiety. There is evidence to suggest that state anxiety [Bishop, Duncan, & Lawrence, 2004; Derakshan et al., 2009b] and high arousal [Jefferies, Smilek, Eich, & Enns, 2008] may mediate cognitive performance and attentional allocation toward threat. Unfortunately, this study did not include a measure of either state anxiety or arousal to test this hypothesis. Therefore, future studies may benefit from including physiological measures of arousal. This will allow for a greater understanding of the link among cognition, physiology, and behavior in those with ASD.

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Previous literature has suggested a role for differences in social functioning and understanding in the development of anxiety disorders in people with ASD [Bellini, 2004; Niditch et al., 2012]. We hypothesized that a higher mentalizing score, which might be related to greater awareness of “social failures,” may be one plausible reason for the high levels of anxiety and depression in people with ASD. However, using a latent variable approach, including four well-validated and commonly used measures of social cognition, we failed to find a significant association with either anxiety or depression. Furthermore, we found very low correlations between individual measures of social cognition and both the anxiety and depression scales. This result appears to be at variance with other studies suggesting that deficits in social skills may relate to increased anxiety in ASD [Eussen et al., 2012; Niditch et al., 2012]. However, it is possible that the questionnaire measures used in these studies, while related to social cognition, are instead a surrogate measure for the impact of ASD on overall social functioning. This may include the impact of other symptoms associated with anxiety in ASD, such as restrictive and repetitive behaviors [Gotham et al., 2012; Rodgers et al., 2012; Spiker et al., 2012]. Furthermore, previous research exploring the relationship between social functioning and emotional symptoms did not account for executive functioning [Bellini, 2004, 2006], as we have done in the present analyses. In addition, our findings are in keeping with twin modeling that has suggested only relatively weak phenotypic and genetic links between anxiety and social symptoms of ASD in a general population sample [Hallett, Ronald, Rijsdijk, & Happé, 2012].

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Figure 2. Final model depicting the significant relationship between executive functioning and anxiety symptoms. * Sig ≤ .05 ** Sig ≤ .01, T1: Trails B, T2: Card Sort Task, T3: Opposite Worlds, T4: Numbers Backwards, T5: Frith-Happé animations, T6: Strange Stories, T7: Eyes Task, T8: False Belief Composite, Q1: SDQ: Many worries, often seems worried, Q2: SDQ: Nervous or clingy in new situations, easily loses confidence, Q3: SDQ: Many fears, easily scared, Q4: PONS: worries, Q5: PONS: fears, Q6: SDQ: Often unhappy, down-hearted or tearful, Q7: PONS: low mood, Q8: PONS: Depressed thoughts, Q9: PONS: Labile mood; SDQ = Strengths and Difficulties Questionnaire: PONS = Profile of Neuropsychiatric Symptoms.

It is important to note that our social cognition latent variable contained measures that tap different elements of social cognition. For instance, the “reading the mind in the eyes task” may measure more social perceptual processes, while the “strange stories” task focuses more on mental state understanding. However, even when examined at an individual task level, there are no associations with either anxiety or depression scales. Strengths, Limitations, and Future Directions In this study, we present data from a well-characterized group of adolescents, with confirmed ASD diagnoses, and use a series of well-validated neurocognitive measures. This was the first study to examine executive functions and ToM as possible neurocognitive correlates of anxiety

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in ASD. Using SEM, we were able to account for social cognition and executive functioning in one model while also controlling for the effects of IQ. This allows us to be more confident than previous studies, which used only questionnaire measures, about which factors do and do not seem to relate to elevated anxiety in ASD in adolescence. The inclusion of objective cognitive measures is also important as questionnaires are known to be limited by rater “halo effects” [Nisbett & Wilson, 1977], which may account for some of the significant betweenquestionnaire correlations reported in previous studies. Against these strengths, a number of limitations should be considered. This study focused on the relationship between neurocognitive functioning and symptoms, rather than disorders, of both anxiety and depression in a population-based sample. Therefore, we cannot

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necessarily generalize these findings to those with clinical diagnoses of anxiety/depression. However, as previously reported by Simonoff et al. [2012], a high percentage of this ASD group (33%) met the clinical cutoff for emotional problems on the SDQ at ages 14–16 years [Simonoff et al., 2012]. In addition, there may be additional variability in neuropsychological correlates depending on the specific anxiety disorder being studied [Airaksinen et al., 2005]. As our latent variables for both anxiety and depression were continuous and not directly related to distinct disorders, we were unable to account for this. Future research would benefit from classifying the ASD group according to clinically diagnosed anxiety or depressive disorders. The emotional symptom measures used in this study were also not ideal as they included a limited number of items. The questions regarding anxiety used here focus on the cognitive components of anxiety such as fears and worries, but not behavioral aspects such as avoidance, or the physiological symptoms that may accompany some anxiety disorders such as panic and agoraphobia. This focus on cognitive components may have increased our ability to find associations with neurocognitive variables but may limit the generalizability of our results to anxiety disorders as defined clinically. Similarly, the lack of questions regarding social anxiety may have limited our ability to detect a relationship between anxiety and the social cognition latent variable. Future research would benefit from using a wider range of anxiety and depression symptoms. While there is currently a lack of ASD-specific emotional measures, recent work has suggested that some existing scales may be adequate for assessing anxiety in ASD [Hallett et al., 2013; Storch et al., 2012].However, there has been no such exploration of measurement issues for co-occurring depression in ASD. Future studies would benefit from including a more comprehensive evaluation of emotional symptoms in addition to detailed cognitive assessment. It is also important to note that, based on work in children with anxiety but without ASD, a number of other, noncognitive, pathways to anxiety have been suggested [Rapee, Schniering, & Hudson, 2009]. These include problems with regulating temperament and difficulties with physiological arousal. The present lack of association between social cognition and anxiety may suggest that interventions that focus on improving social cognition may not be of benefit for co-occurring anxiety problems. However, further research taking advantage of longitudinal methodology is required to examine these relationships further. If replicated, these findings may have important implications for the development of more effective assessment measures and treatments for co-occurring anxiety disor-

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ders in children with ASD. For instance, understanding risk factors for those who will develop anxiety problems among those with ASD may allow for better assessment and earlier intervention. Furthermore, it may be important to adapt treatment approaches to account for inflexible cognitive styles, or possibly interventions focusing on cognitive remediation targeting neuropsychological difficulties alongside a psychological therapy may prove to be beneficial. In conclusion, our study examined whether social cognition or executive functioning was related to symptoms of anxiety and depression in adolescents with ASD. We failed to find support for the hypothesis that better social cognition would lead to greater anxiety, but found that poorer executive functioning relates to greater anxiety. In addition, we found no evidence to suggest that either executive functioning or social cognition may be related to depressive symptomatology in ASD. Further research is required to specify the nature of the relationship between executive functioning and anxiety in ASD.

Acknowledgments The study was funded by the Medical Research Council (G0400065). We are grateful to the adolescents and families who took part in the study. We would like to thank Paramala Santosh for permission to reprint the relevant items from the PONS. A.P. receives royalties from the Social Communication Questionnaire and F.H. received a one-off consultancy payment from Novartis in March 2011. There are no other conflicts of interest, financial or otherwise.

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Appendix A Table A1.

Factor Analysis: Rotated Factor Loading and Unique Variances

Item SDQ: Often complains of headaches, stomach aches, or sickness SDQ: Many worries, often seems worried SDQ: Often unhappy, down-hearted, or tearful SDQ: Nervous or clingy in new situations, easily loses confidence SDQ: Many fears, easily scared PONS: Worries PONS: Fears PONS: Low mood PONS: Depressed thoughts PONS: Labile mood

Factor 1 (anxiety)

Factor 2 (depression)

Uniqueness

0.46 0.66 −0.13 0.69 0.89 0.59 0.88 0.13 −0.03 0.06

0.02 0.21 0.87 −0.005 −0.09 0.35 −0.08 0.82 0.87 0.68

0.78 0.38 0.34 0.52 0.29 0.32 0.28 0.20 0.24 0.49

PONS, Profile of Neuropsychiatric Symptoms; SDQ, Strengths and Difficulties Questionnaire.

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