576343

research-article2015

JADXXX10.1177/1087054715576343Journal of Attention DisordersParke et al.

Article

Intellectual Profiles in Children With ADHD and Comorbid Learning and Motor Disorders

Journal of Attention Disorders 1­–10 © 2015 SAGE Publications Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1087054715576343 jad.sagepub.com

Elyse M. Parke1, Nicholas S. Thaler2, Lewis M. Etcoff1, and Daniel N. Allen1

Abstract Objective: Neurodevelopmental disorders, including Reading Disorder, Disorder of Written Expression, and Developmental Coordination Disorder, often co-occur with ADHD. Although research has identified increased functional impairment in the presence of these comorbid diagnoses, few direct comparisons of intellectual profiles have been conducted. Thus, the present study examined Wechsler Intelligence Scale for Children–Fourth Edition (WISC-IV) profiles of children with ADHD alone and with comorbid neurodevelopmental disorders. Method: Participants included 296 children with ADHD, ADHD with Developmental Coordination Disorder, and ADHD with Reading Disorder and/or Disorder of Written Expression. Results: Comparisons of these groups suggests children with ADHD and language-based Learning Disorders have poorer working memory than children with only ADHD. Furthermore, children with ADHD and Developmental Coordination Disorder perform relatively better on verbal compared with perceptual reasoning indexes. Conclusion: These intellectual profiles may have utility in identifying cognitive weaknesses inherent to these disorders and may be used to guide treatment intervention. (J. of Att. Dis. XXXX; XX(X) XX-XX) Keywords ADD/ADHD, IQ, WISC-IV, developmental coordination disorder, learning disabilities Children with ADHD exhibit a number of cognitive and behavioral abnormalities that are often the direct result of the disorder itself, but are also due to one or more coexisting Learning Disorders (LDs; Barkley, 2013). Research indicates that Reading Disorder (RD), Disorder of Written Expression (DWE), and Developmental Coordination Disorder (DCD) are among the most common co-occurring LDs with ADHD (Mayes & Calhoun, 2006). RD is defined as deficits in reading achievement in areas such as speed, accuracy, or comprehension (American Psychiatric Association [APA], 2000). DWE is characterized by deficits in writing, such as spelling, organization, or excessive grammatical errors (APA, 2000). DCD is characterized by deficits in the development of motor coordination, which is unexplained by the child’s intelligence, neurological, or psychiatric disorders (APA, 2013). Children with this disorder often show observable behaviors such as poor posture, clumsiness, and difficulties holding a pencil. Children with these comorbid conditions exhibit poorer outcomes across a number of domains, including academic success, social skills, and occupational outcome in adulthood (Eden & Vaidya, 2008; Glomb, Buckley, Minskoff, & Rogers, 2006). The Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; APA, 2013) has recategorized LDs into

Specific LDs with specifiers for area of academic weakness. This reconceptualization of LDs as more pervasive learning problems across numerous academic areas (Tannock, 2013), suggests that more research is needed to examine characteristics of children with multiple learning impairments. For example, few studies have examined children with both reading and writing difficulties. Furthermore, few studies have examined IQ profiles in large samples of children with ADHD and co-occurring problems with motor planning difficulties. Coordination difficulties have the potential to affect perceptual reasoning abilities (Tsai, Wilson, & Wu, 2008). Therefore, the current study examined IQ profiles in children with ADHD alone and with one or more comorbid neurodevelopmental disorders. Intellectual assessment is useful in characterizing the cognitive deficits 1

University of Nevada, Las Vegas, USA UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA 2

Corresponding Author: Daniel N. Allen, Neuropsychology Research Program, Department of Psychology, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, NV 89154-5030, USA. Email: [email protected]

Downloaded from jad.sagepub.com at UCSF LIBRARY & CKM on April 10, 2015

2

Journal of Attention Disorders 

demonstrated by these children for both clinical and research purposes. From a clinical perspective, results from intellectual assessments are commonly used to develop educational assistance plans and intervention methods, as well as measure treatment outcomes (Berninger & May, 2011). From a research perspective, results of IQ batteries, such as the Wechsler Intelligence Scale for Children (WISC), have provided insight into brain regions that are differentially affected by the disorders (Moura, Simões, & Pereira, 2014). Beyond their high prevalence in children with ADHD, Reading, Writing, and DCDs are of interest because of the potential for their unique impact on components of intellectual functioning. Academic impairments in reading and writing are thought to both relate to language abilities (Pennington & Bishop, 2009). Conversely, DCD is thought to relate more to perceptual reasoning abilities than language-based disorders (Alloway & Archibald, 2008). Identifying levels of verbal or perceptual intelligence is clinically useful as verbal skills are thought to relate more to academic and behavioral functioning than perceptual abilities (Ek, Westerlund, Holmberg, & Fernell, 2011). Thus, demonstrating the differences between languagebased and motor-related disorders could assist in characterization of intellectual abilities, specific treatment targets, and expected outcomes for each group. Currently available information for the commonly used WISC is limited in a number of respects within the ADHD literature. First, few studies have examined the impact that coexisting LDs might have on index and subtest score profiles. Those studies that do exist are limited in that many were accomplished with earlier versions of the WISC (Cheung et al., 2012; Chhabildas, Pennington, & Willcutt, 2001; Moura et al., 2014; Rucklidge & Tannock, 2002; Shanahan et al., 2006; Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005). Although many subtests from these earlier versions are retained in the WISC-IV, there have also been significant changes with successive revisions, including the collection of new normative data, addition of a number of new subtests, and the reconceptualization of IQ away from performance and verbal indexes to the four index scores. Research with other clinical populations, such as children with traumatic brain injury, suggests that these changes have altered traditional profiles obtained with earlier versions of the WISC (Allen, Thaler, Donohue, & Mayfield, 2010; Donders & Janke, 2008). In addition, some studies used small samples, with as few as 11 participants in a clinical group (Loh, Piek, & Barrett, 2011). Furthermore, many studies did not include all subtests or indexes (Jacobson et al., 2011; Loh et al., 2011; Shanahan et al., 2006; Willcutt et al., 2005), limiting the amount of information available regarding intellectual profiles in children with ADHD and comorbid LDs. Thus, there is a need for a study that addresses ADHD and LD comorbidity using all of the WISC-IV indexes and includes well-characterized

participants to determine cognitive strengths and weaknesses in this population. Few studies have investigated cognitive profiles in children with ADHD who also have DWE, despite its high prevalence and relation to RD (Mayes & Calhoun, 2006). Previous studies suggest that there is a common cognitive mechanism underlying both reading and writing performance (Parodi, 2006; Sadoski & Paivio, 2001). Research also indicates that this diagnosis may be a direct consequence of language and word decoding deficits associated with RD (Lindstrom, 2007). Furthermore, these groups were combined due to these cognitive and academic similarities in the WISC-IV standardization process (Wechsler, 2003). As DWE commonly co-occurs with RD and both diagnoses result in weaknesses in language skills that require similar cognitive abilities (Lindstrom, 2007; Parodi, 2006), children with DWE and RD are anticipated to have similar WISC profiles. However, the extent to which verbal and working memory abilities in children with ADHD and RD or DWE are affected on the WISC-IV is yet to be determined. The presence of RD and DWE in children with ADHD is expected to affect intellectual abilities. Children with RD have limited reading development that may suppress their ability to gather and retain verbal information (Pennington & Bishop, 2009), which should theoretically be tied to lower verbal IQ. Furthermore, research suggests that reading and writing disorders may be a result of underlying neuropsychological dysfunction, specifically in verbal, working memory, and processing speed abilities (Semrud-Clikeman & Harder, 2010; Willcutt et al., 2013). For example, avoidance of and delays in comprehending written material may be due to deficits in language abilities (Pennington, 2008; Pisecco, Baker, Silva, & Brooke, 2001) as some studies find poor performance in children with RD on previous versions of WISC verbal subtests (Thomson, 2003; Willcutt et al., 2010; Willcutt et al., 2013). Working memory deficits in this population are thought to be a result of increased difficulty processing information in the phonological loop of the working memory system (Baddeley, 2003; Rucklidge & Tannock, 2002). Consistent with this theory, previous studies demonstrate that children with comorbid ADHD and RD perform significantly worse on the WISC-III freedom from distractibility index (FDI) when compared with children with only ADHD (Rucklidge & Tannock, 2002; Willcutt et al., 2005). Previous findings also indicate that there are general deficits in processing speed in ADHD both with and without the presence of LDs (Shanahan et al., 2006; Willcutt et al., 2005). Given the strong association of academic performance with verbal abilities (Hogan et al., 2010), working memory (Alloway & Alloway, 2010), and processing speed (Dunn et al., 2010), research is needed to demonstrate whether the WISC-IV is still sensitive to these cognitive weaknesses.

Downloaded from jad.sagepub.com at UCSF LIBRARY & CKM on April 10, 2015

3

Parke et al. Finally, although DCD also occurs at a high rate in ADHD, little is known regarding its impact on IQ profiles in children with both disorders. Although DCD is primarily characterized by fine and gross motor deficits, some research suggests children with DCD also exhibit deficits in perceptual abilities independent of motor functioning (O’Brien, Williams, Bundy, Lyons, & Mittal, 2008; Tsai et al., 2008). Research investigating perceptual abilities using the WISC demonstrates that children with ADHD and DCD received lower scores on their WISC-IV perceptual reasoning index (PRI), compared with children with ADHD alone (Loh et al., 2011). In addition, this study found that participants with ADHD and DCD performed significantly lower on the processing speed index (PSI) than a control group. This finding resonates with other studies indicating children with DCD generally work slower than typically developing peers (Piek, Dyck, Francis, & Conwell, 2007). However, this study used a small sample and did not include the working memory index (WMI), which is also likely affected by a diagnosis of DCD (Alloway, 2011). Perceptual difficulties in children with DCD may result in worse working memory performance by affecting the visuospatial sketchpad theorized to be a component of the working memory system (Baddeley, 2003; Piek et al., 2007). Given the limitations apparent in the existing literature and the important role that IQ testing plays in treatment and research of LD and ADHD, the current study examined WISC-IV performance in children and adolescents diagnosed with ADHD-only, compared with groups of children diagnosed with both ADHD and LDs. Given the theoretical understanding and previous findings indicating that verbal, working memory, and processing speed abilities are affected in RD and DWE, it was predicted that the children with ADHD-RD/DWE will demonstrate a profile of decreased verbal comprehension index (VCI), WMI, and PSI compared with the PRI. It was also predicted that this group will perform significantly worse on the VCI and WMI compared with ADHD-only group. Finally, given the literature supporting perceptual difficulties in children with DCD, it is predicted that children with ADHD and DCD will demonstrate a profile of decreased PRI, WMI, and PSI relative to the VCI.

Method Participants The 296 children included in this study were included in the study if (a) they had a diagnosis of ADHD-Combined (ADHD-C), ADHD-Inattentive (ADHD-I), comorbid ADHD and DCD, or comorbid ADHD and RD and/or DWE; (b) they had no comorbid pervasive developmental disorder, traumatic brain injury, or other neurological conditions; (c) had a WISC-IV full-scale IQ > 75; and (d) they were administered the WISC-IV as part of a neuropsychological evaluation.

Table 1.  Demographic and Clinical Information of the Sample. Variable Age (years) Grade WISC-IV FSIQ   Diagnosis  ADHD-I  ADHD-C  ADHD-RD/DWE  ADHD-DCD Gender  Male  Female Comorbid diagnoses   ODD (n = 16)   Anxiety disorder (n = 21)   Mood disorder (n = 11)   Adjustment disorder (n = 70)   Substance abuse (n = 5)

M (SD) 10.4 (2.8) 4.5 (2.7) 102.0 (11.3) Frequency (%) 33.8 26.4 25.7 14.2 68.9 31.1 5.4 7.1 3.7 23.6 1.7

Note. WISC-IV FSIQ = Wechsler Intelligence Scale for Children–Fourth Edition Full-Scale IQ; ADHD-I = ADHD-Predominantly Inattentive Type; ADHD-C = ADHD-Combined Type; ADHD-RD/DWE = ADHDReading Disorder/Disorder of Written Expression; ADHD-DCD = ADHD-Developmental Coordination Disorder; ODD = Oppositional Defiant Disorder.

Demographic and clinical information are presented in Table 1. As can be seen from Table 1, the sample was 69% male, between 6.0 and 16.4 years of age (M = 10.4, SD = 2.8). Ethnicity was not available for this sample. Children were selected from a series of 619 cases that were referred for neuropsychological evaluation. Children were referred for a neuropsychological evaluation because they were experiencing academic problems, but presented with multiple complaints, such as learning difficulties, attention deficits, mood and anxiety symptoms, and behavior disturbances in the home and at school. The 296 children were initially divided into four groups based on clinical diagnosis, including ADHD-I (n = 100), ADHD-C (n = 78), ADHD-DCD (n = 42), and ADHD-RD/DWE (n = 76). A mixed-model ANOVA compared the ADHD-I and ADHD-C groups on WISC-IV index performance and indicated no significant differences, F(1, 176) = 71.71, p = .40. Thus, these subtypes were collapsed into one group of participants with only ADHD (n = 178) for subsequent analyses.

Procedure The WISC-IV Verbal Comprehension, Perceptual Reasoning, Working Memory, and PSIs were used to evaluate IQ profiles. Cases for this analysis were selected from a consecutive series of 619 cases that were referred for neuropsychological

Downloaded from jad.sagepub.com at UCSF LIBRARY & CKM on April 10, 2015

4

Journal of Attention Disorders 

evaluation over a period of 9 years. Children were primarily referred for neuropsychological evaluations from a variety of referral sources, including schools, pediatricians, and neurologists. The WISC was administered by a clinical neuropsychologist or advanced doctoral students under the supervision of the neuropsychologist. The WISC-IV was administered as a baseline of intellectual functioning and was not used to diagnose ADHD and/or LDs. All assessments were administered according to standardized procedures and diagnoses of ADHD and LD, and other clinical diagnoses (e.g., Adjustment Disorder) were made by the licensed neuropsychologist. Diagnoses were made according to Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) diagnostic criteria based on parent and child interviews, neuropsychological testing, behavioral assessment, and other relevant information from medical and educational records. Children were individually assessed in a quiet room in a private practice setting. Assessments took place within 1 day with total assessment times ranging from 4 to 6 hr. Children were given breaks throughout the assessment to maintain their effort and attention toward testing materials. All of the research was conducted in accordance with local institutional review board policies.

Data Analysis Prior to analyses of the primary hypotheses, descriptive statistics were calculated for each group on demographic variables, including age, gender, type of school (public or private), and current full-scale IQ. ANOVA and chi-square analyses were used to determine whether the four groups significantly differed on these variables. The general approach to the analysis was to evaluate the study hypotheses using a mixed-model ANCOVA that contained group as a between-subjects factor, and index score as a within-subjects factor. Age and gender were used as covariates in the analyses. Given that WISC-IV profiles differences are hypothesized for each of the groups, it was anticipated that the results of this analysis would produce a significant main effect for group, a significant main effect for WISC-IV index, as well as a significant Group by WISC-IV index interaction effect. For significant results from the primary analyses, ANCOVAs and other appropriate procedures were used to examine the specific predictions made in each hypothesis by comparing index score differences within and between groups as appropriate.

Results Descriptive statistics for the ADHD and LD groups are presented in Table 1. There were significant differences among the ADHD-only, ADHD-RD/DWE, and ADHD-DCD groups with regard to gender, χ2(2) = 13.13, p < .01, and age, F(2, 293) = 4.40, p < .05. Post hoc analyses indicated

that the ADHD-DCD group had significantly fewer females than the other groups and the ADHD-RD/DWE group was significantly older than the other groups. Therefore, ANCOVAs controlling for age and gender were used. There were no significant differences among groups with regard to type of school (public/private), χ2(2) = 1.91, p = .59. Descriptive statistics for the ADHD and LD groups on the WISC-IV index scores are presented in Table 2. The ADHD-RD/DWE group evidenced a flat profile with somewhat less variability across indexes. In contrast, the ADHD-DCD group demonstrated a sloping profile with VCI being the highest score, followed by PRI and WMI, with PSI being the lowest score. There was a Group by Wechsler index interaction effect for the ADHD-DCD group compared with the ADHD-only and ADHD-RD/ DWE groups, p < .01. There was not a significant interaction between the ADHD-RD/DWE and ADHD-only groups, p = .37. Results of the ANCOVA for all groups indicated significant effects for Group, F(2, 291) = 6.63, p < .05, and for Wechsler index scores, F(3, 291) = 12.13, p < .001, as well as a significant Group by Wechsler index interaction effect, F(2, 291) = 3.62, p < .01. This interaction effect is presented in Figure 1. To further examine this interaction, univariate ANCOVAs were used to examine differences among the groups on each index score (see Table 2). Bonferroni corrections were utilized to control Type I error. ANCOVA results indicated that the VCI and WMI significantly differed among groups. Post hoc analysis indicated that the ADHD-RD/DWE group performed significantly worse than all groups on the VCI. In addition, the children with ADHD-RD/DWE also performed significantly worse on the PRI and WMI compared with the ADHD-only group. Finally, differences among the index scores for each of the groups were examined using repeated measures ANCOVA where index scores served as the repeated measures. The results of this analysis are presented in Table 3. Significant differences were present among the index scores for each group. Contrasts further indicated that for all groups, the WMI and PSI were significantly lower than the VCI and PRI. In addition, the ADHD-DCD group attained significantly lower scores on the PRI compared with the VCI and lower scores on the WMI compared with the PRI. The ADHD-DCD group also performed significantly poorer on the PSI compared with all other WISC-IV indexes. To examine subtest performance, descriptive statistics for the individual subtest scores are presented in Table 4 and Figure 2. These test scores are provided for informational purposes as we did not make specific hypotheses regarding subtests differences among the groups or conduct inferential analyses to examine these differences. However, as seen in Figure 2, all groups tended to perform worse on Block Design and Coding, which both include a strong motor component.

Downloaded from jad.sagepub.com at UCSF LIBRARY & CKM on April 10, 2015

5

Parke et al. Table 2.  Descriptive Statistics for the WISC-IV Index Scores and Between-Subjects Effects. ADHD-only WISC-IV indexes VCI PRI WMI PSI

ADHD-RD/ DWE

ADHD-DCD

M

SD

M

SD

M

SD

F

p

Post hoc

105.5 104.9 99.1 97.6

13.5 12.0 12.4 13.4

111.8 105.5 97.5 92.9

13.7 11.0 9.0 10.1

100.9 100.2 95.9 94.2

11.0 11.1 12.1 11.2

8.0 5.7 3.2 2.1

.001 .001 .02 .13

ADHD-RD/DWE < ADHD, ADHD-DCD ADHD-RD/DWE < ADHD ADHD-RD/DWE < ADHD —

Note. WISC-IV = Wechsler Intelligence Scale–Fourth Edition; ADHD-DCD = ADHD-Developmental Coordination Disorder; ADHD-RD/DWE = ADHD-Reading Disorder/Disorder of Written Expression; VCI = verbal comprehension index; PRI = perceptual reasoning index; WMI = working memory index; PSI = processing speed index.

120

115

Standard Scores

110

105

ADHD only

100

ADHD-DCD ADHD-RD/DWE

95

90

85

80 VCI

PRI

WMI

PSI

WISC-IV Indexes

Figure 1.  WISC-IV index performance.

Note. WISC-IV = Wechsler Intelligence Scale–Fourth Edition; VCI = verbal comprehension index; PRI = perceptual reasoning index; WMI = working memory index; PSI = processing speed index; ADHD-DCD = ADHD-Developmental Coordination Disorder; ADHD-RD/DWE = ADHD-Reading Disorder/Disorder of Written Expression. Error bars represent standard errors.

Discussion The present study investigated WISC-IV profiles in children with ADHD and comorbid LDs. Findings suggest differences in intellectual profiles of children with ADHD with and without different comorbid LDs. The ADHD-RD/ DWE group demonstrated weaker processing speed and working memory relative to perceptual and verbal indexes.

The ADHD-DCD group exhibited more variability among indexes than the ADHD-only and comorbid, languagebased LDs groups. Within the ADHD-DCD group, verbal comprehension was the highest score, followed by perceptual reasoning, working memory, and processing speed. The present study provides some insight into verbal and perceptual reasoning abilities in children with ADHD and

Downloaded from jad.sagepub.com at UCSF LIBRARY & CKM on April 10, 2015

6

Journal of Attention Disorders 

Table 3.  Descriptive Statistics for the WISC-IV Index Scores and Within-Subjects Effects. VCI Group ADHD-only ADHD-DCD ADHD-RD/DWE

PRI

WMI

PSI

M

SD

M

SD

M

SD

M

SD

F

P

Post hoc

105.5 98.3 95.3

13.5 10.1 12.5

104.9 100.1 100.6

12.0 15.0 13.8

99.1 97.5 92.9

12.4  9.0 10.1

97.6 95.9 94.2

13.4 12.1 11.2

25.5 30.0 7.0

.001 .001 .001

PSI, WMI < PRI, VCI PSI < WMI < PRI < VCI PSI, WMI < PRI, VCI

Note. WISC-IV = Wechsler Intelligence Scale–Fourth Edition; VCI = verbal comprehension index; SD = standard deviation; PRI = perceptual reasoning index; WMI = working memory index; PSI = processing speed index; ADHD-DCD = ADHD-Developmental Coordination Disorder; ADHD-RD/DWE = ADHD-Reading Disorder/Disorder of Written Expression.

Table 4.  Descriptive Statistics for the WISC-IV Subtest Scores. ADHD-only WISC-IV Subtests Vocabulary Similarities Comprehension Block design Matrix reasoning Picture concepts Letter number sequencing Digit span Symbol search Coding

ADHD-DCD

ADHD-RD/DWE

M

SD

M

SD

M

SD

11.2 11.0 10.8 10.0 11.2 11.3 10.3 9.6 10.0 9.1

2.5 3.1 2.7 2.7 2.7 2.4 2.5 2.7 2.5 2.8

12.3 12.5 11.7 10.3 11.0 11.4 9.8 9.6 9.5 8.1

2.7 2.8 2.6 3.2 2.3 2.5 2.4 2.0 2.4 2.5

10.1 10.3 10.1 9.5 9.8 10.4 10.0 8.6 9.7 8.4

2.3 2.4 2.3 2.7 2.6 2.2 2.7 2.4 2.0 2.5

Note. WISC-IV FSIQ = Wechsler Intelligence Scale for Children–Fourth Edition Full-Scale IQ; SD = Standard Deviation; ADHD-DCD = ADHD-Developmental Coordination Disorder; ADHD-RD/DWE = ADHD-Reading Disorder/Disorder of Written Expression.

comorbid LD. There was a significant group by WISC-IV index interaction that was primarily accounted for by the ADHD-DCD group demonstrating a unique intellectual profile. This group demonstrated worse performance on the PRI and WMI than the ADHD-only group. The current study also suggests that clinicians may observe weaknesses in verbal abilities in ADHD and comorbid RD and/or DWE relative to children with ADHD and motor problems. Prior research indicates that academic difficulties in children with language-based LDs may reflect broader weaknesses across multiple cognitive domains (McGrath et al., 2011; McCardle, Scarborough, & Catts, 2001; Moll, Göbel, Gooch, Landerl, & Snowling, 2014) and specifically language skills (Pennington, 2008; Pisecco et al., 2001). Previous studies indicate that verbal abilities underlie other neuropsychological abilities, such as perceptual reasoning (Allen et al., 2010). For example, Matrix Reasoning on the WISC-IV is thought to require fluid reasoning and language abilities to manipulate perceptual patterns (Keith, Fine, Taub, Reynolds, & Kranzler, 2006). Thus, language difficulties characteristic of reading and writing disorders, as indicated in prior research (Bennett, McHale, & Soper, 2011; Pennington, 2008), may affect IQ functioning across

indexes. This population may benefit from addressing underlying neurocognitive weaknesses, which may generalize to their academic performance (Chenault, Thomson, Abbott, & Berninger, 2006). Study findings also suggest that children with ADHD and DCD tend to have higher verbal skills relative to their perceptual abilities. Relative weaknesses in perceptual abilities compared with verbal reasoning is consistent with previous studies (Loh et al., 2011; Tsai et al., 2008) and could be beneficial for clinicians to consider when assessing and treating children with ADHD and DCD. For example, this population may benefit from targeting perceptual reasoning by developing strategies for processing complex puzzles, mazes, and reinforcement for recognizing visual patterns (Kurtz, 2006). This group also performed significantly worse on the PSI compared with all other WISC-IV indexes. This finding is consistent with previous research indicating slowed cognitive processing and resulting motor output in children with both ADHD and DCD (Piek et al., 2007). Although the profile of decreased working memory and processing speed has been largely supported separately in the ADHD (Mayes, Calhoun, Chase, Mink, & Stagg, 2009; Wechsler, 2003) and LD literature (De Clercq-Quaegebeur

Downloaded from jad.sagepub.com at UCSF LIBRARY & CKM on April 10, 2015

7

Parke et al.

14

13

12 ADHD only

Scaled Scores

11

10

ADHD-DCD

9 ADHDRD/DWE 8

7

6 VO

SI

CO

BD

MR PCon

LN

DS

SS

CD

Subtests

Figure 2.  WISC-IV subtest performance grouped by index.

Note. WISC-IV = Wechsler Intelligence Scale–Fourth Edition; VO = Vocabulary; SI = Similarities; CO = Comprehension; BD = Block Design; MR = Matrix Reasoning; PCon = Picture Concepts; LN = Letter Number Sequencing; DS = Digit Span; SS = Symbol Search; CD = Coding; ADHD-DCD = ADHD-Developmental Coordination Disorder; ADHD-RD/DWE = ADHD-Reading Disorder/Disorder of Written Expression. Error bars represent standard errors.

et al., 2010; Piek et al., 2007; Wechsler, 2003), few studies examined a complete WISC-IV profile in children with both disorders. The current study indicates that children with ADHD and comorbid RD, DWE, or DCD also have relatively lower working memory and processing speed than their verbal and perceptual reasoning abilities. This finding is clinically relevant, as IQ is among the best predictors of academic performance (Mayes, Calhoun, Bixler, & Zimmerman, 2009) and weaknesses in working memory and processing speed are associated with specific neuropsychological weaknesses (Willcutt et al., 2010; Zayed, Roehrig, Arrastia-Lloyd, & Gilgil, 2013), behavioral symptoms (Thaler, Bello, & Etcoff, 2013), and treatment recommendations (Jacobson et al., 2011; Katz, Brown, Roth, & Beers, 2011). Although decreased working memory and processing speed are not specific to ADHD or LD (Mulder, Pitchford, & Marlow, 2011), this cognitive profile has unique implications for academic outcomes in this clinical population. For example, slowed processing speed indicates that more mental effort is required to perform simple tasks, with less cognitive resources available for higher levels of learning and

cognitive processing, such as reading comprehension (Jacobson et al., 2011) and perceptual abilities (Shanahan et al., 2006). This rate of processing can also result in poor note taking because of slowed motor output and processing of competing visual and auditory information (Evans, Pelham, & Grudberg, 1995). Thus, children with ADHD and LD may increasingly fall behind in their learning because of decreased reserves of cognitive resources and slowed academic fluency skills (Jacobson et al., 2011). Therefore, these underlying weaknesses in processing speed could exacerbate reading, writing, and perceptual difficulties already present in children with ADHD and LD. Furthermore, working memory abilities predict performance in reading, writing, and math skills (Bull & Scerif, 2001; Swanson & Sachse-Lee, 2001). For example, working memory is necessary for the development of reading comprehension because one needs immediate attention to encode and manipulate initial instruction of basic academic skills (Alloway & Alloway, 2010). Thus, recommendations and accommodations can target these underlying weaknesses in cognitive abilities of processing speed and working memory difficulties in addition to academic problems

Downloaded from jad.sagepub.com at UCSF LIBRARY & CKM on April 10, 2015

8

Journal of Attention Disorders 

associated with specific LDs. For example, many children with ADHD and LD benefit from having more time to complete assignments and tests, as well as allowing them to write less or solve fewer homework problems (Braaten & Willoughby, 2014). Working memory difficulties are particularly relevant to children with ADHD and language-based LDs. Previous research suggests that this population has increased difficulties due to the combination of inattention and difficulty processing auditory information in the phonological loop (De Clercq-Quaegebeur et al., 2010; Rucklidge & Tannock, 2002; Thomson, 2003). The current results also reflect decreased working memory in children with comorbid ADHD, RD, and/or DWE compared with children with only ADHD. These findings suggest children with ADHD, RD, and DWE may need more accommodations than children with ADHD without a comorbid LD, for academic difficulties associated with working memory deficits. For example, they may need increased assistance and cognitive training on processing multistep auditory directions, as other studies suggest (Chenault et al., 2006; Horowitz-Kraus, 2013). A limitation of this study is that we did not have children diagnosed with the ADHD-Hyperactive subtype and so could not examine their WISC-IV profiles. However, this subtype is relatively rare (Rasmussen et al., 2002) and some researchers have questioned the validity of this subtype (Willcutt et al., 2012) and claimed that symptoms of inattention are the core deficit in ADHD accounting for other intellectual and cognitive deficits (Halperin et al., 1990), which was the main focus of this study. It should also be noted that the DSM-5 (APA, 2013) was recently published, but the current study used DSM-IV classifications for diagnoses. Although some changes were made to these diagnoses in the DSM-5 (e.g., renamed ADHD subtypes as presentations, grouped LDs into Specific LDs), the symptoms required to diagnose each disorder are largely unchanged and so the current results should generalize well to children with DSM-5 diagnoses. Finally, a limitation of the current study is that demographic information, such as ethnicity was not available for the population. Despite the aforementioned limitations, the current study provides support for distinct intellectual profiles in children with ADHD and comorbid LDs. Future research is needed to determine the value of using these cognitive profiles to predict academic, behavioral, and social outcomes within each group. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

References Allen, D. N., Thaler, N. S., Donohue, B., & Mayfield, J. (2010). WISC-IV profiles in children with traumatic brain injury: Similarities to and differences from the WISC-III. Psychological Assessment, 22, 57-64. doi: 10.1037/a0016056 Alloway, T. P. (2011). A comparison of working memory profiles in children with ADHD and DCD. Child Neuropsychology, 17, 483-494. doi: 10.1080/09297049.2011.553590 Alloway, T. P., & Alloway, R. G. (2010). Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology, 106, 20-29. doi: 10.1016/j.jecp.2009.11.003 Alloway, T. P., & Archibald, L. (2008). Working memory and learning in children with developmental coordination disorder and specific language impairment. Journal of Learning Disabilities, 41, 251-262. doi: 10.1177/0022219408315815 American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing. Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4, 829-839. doi: 10.1038/nrn1201 Barkley, R. A. (2013). Distinguishing sluggish cognitive tempo from ADHD in children and adolescents: Executive functioning, impairment, and comorbidity. Journal of Clinical Child & Adolescent Psychology, 42, 161-173. doi: 10.1080/15374416.2012.734259 Bennett, T. M., McHale, T. J., & Soper, H. V. (2011). Disorders of written language. In C. A. Noggle, R. S. Dean, & A. M. Horton (Eds.), The encyclopedia of neuropsychological disorders (pp. 262-265). New York, NY: Springer. Berninger, V. W., & May, M. O. M. (2011). Evidencebased diagnosis and treatment for specific learning disabilities involving impairments in written and/or oral language. Journal of Learning Disabilities, 44, 167-183. doi: 10.1177/0022219410391189 Braaten, E., & Willoughby, B. (2014). Bright kids who can’t keep up: Help your child overcome slow processing speed and succeed in a fast-paced world. New York, NY: Guilford. Bull, R., & Scerif, G. (2001). Executive functioning as a predictor of children’s mathematics ability: Inhibition, switching, and working memory. Developmental Neuropsychology, 19, 273-293. doi: 10.1207/S15326942DN1903_3 Chenault, B., Thomson, J., Abbott, R. D., & Berninger, V. W. (2006). Effects of prior attention training on child dyslexics’ response to composition instruction. Developmental Neuropsychology, 29, 243-260. doi: 10.1207/s15326942dn2901_12 Cheung, C. H., Wood, A. C., Paloyelis, Y., Arias-Vasquez, A., Buitelaar, J. K., Franke, B., & Kuntsi, J. (2012). Aetiology for the covariation between combined type ADHD and reading difficulties in a family study: The role of IQ. Journal of Child Psychology and Psychiatry, 53, 864-873. doi: 10.1111/j.14697610.2012.02527.x

Downloaded from jad.sagepub.com at UCSF LIBRARY & CKM on April 10, 2015

9

Parke et al. Chhabildas, N., Pennington, B. F., & Willcutt, E. G. (2001). A comparison of the neuropsychological profiles of the DSM-IV subtypes of ADHD. Journal of Abnormal Child Psychology, 29, 529-540. doi: 10.1023/A:1012281226028 De Clercq-Quaegebeur, M., Casalis, S., Lemaitre, M. P., Bourgois, B., Getto, M., & Vallée, L. (2010). Neuropsychological profile on the WISC-IV of French children with dyslexia. Journal of Learning Disabilities, 43, 563-574. doi: 10.1177/0022219410375000 Donders, J., & Janke, K. (2008). Criterion validity of the Wechsler Intelligence Scale for Children–Fourth Edition after pediatric traumatic brain injury. Journal of International Neuropsychological Society, 14, 651-655. doi: 10.1017/ S1355617708080752 Dunn, D. W., Johnson, C. S., Perkins, S. M., Fastenau, P. S., Byars, A. W., Degrauw, T. J., & Austin, J. K. (2010). Academic problems in children with seizures: Relationships with neuropsychological functioning and family variables during the 3 years after onset. Epilepsy & Behavior, 19, 455-461. doi: 10.1016/j.yebeh.2010.08.023 Eden, G. F., & Vaidya, C. J. (2008). ADHD and developmental dyslexia: Two pathways leading to impaired learning. Annals of the New York Academy of Sciences, 11, 316-327. doi: 10.1196/annals.1416.022 Ek, U., Westerlund, J., Holmberg, K., & Fernell, E. (2011). Academic performance of adolescents with ADHD and other behavioural and learning problems—A populationbased longitudinal study. Acta Paediatrica, 100, 402-406. doi: 10.1111/j.1651-2227.2010.02048.x Evans, S. W., Pelham, W., & Grudberg, M. V. (1995). The efficacy of note taking to improve behavior and comprehension of adolescents with attention deficit hyperactivity disorder. Exceptionality, 5, 1-17. Retrieved from http://www.researchgate.net/publication/230743652 Glomb, N. K., Buckley, L. D., Minskoff, E. D., & Rogers, S. (2006). The learning leaders mentoring program for children with ADHD and learning disabilities. Preventing School Failure: Alternative Education for Children and Youth, 50, 31-35. doi: 10.3200/PSFL.50.4.31-35 Halperin, J. M., Neweorn, J. H., Sharma, V., Healey, J. M., Wolf, L. E., Pascualvaca, D. M., & Schwartz, S. (1990). Inattentive and noninattentive ADHD children: Do they constitute a unitary group? Journal of Abnormal Child Psychology, 18, 437-449. doi: 10.1007/BF00917645 Hogan, M. J., Parker, J. D., Wiener, J., Watters, C., Wood, L. M., & Oke, A. (2010). Academic success in adolescence: Relationships among verbal IQ, social support and emotional intelligence. Australian Journal of Psychology, 62, 30-41. doi: 10.1080/00049530903312881 Horowitz-Kraus, T. (2013). Differential effect of cognitive training on executive functions and reading abilities in children with ADHD and in children with ADHD comorbid with reading difficulties. Journal of Attention Disorders. Advance online publication. doi: 10.1177/1087054713502079 Jacobson, L. A., Ryan, M., Martin, R. B., Ewen, J., Mostofsky, S. H., Denckla, M. B., & Mahone, E. M. (2011). Working memory influences processing speed and reading fluency in ADHD. Child Neuropsychology, 17, 209-224. doi: 10.1080/09297049.2010.532204

Katz, L. J., Brown, F. C., Roth, R. M., & Beers, S. R. (2011). Processing speed and working memory performance in those with both ADHD and a reading disorder compared with those with ADHD alone. Archives of Clinical Neuropsychology, 26, 425-433. doi: 10.1093/arclin/acr026 Keith, T. Z., Fine, J. G., Taub, G. E., Reynolds, M. R., & Kranzler, J. H. (2006). Higher order, multisample, confirmatory factor analysis of the Wechsler Intelligence Scale for Children– Fourth Edition: What does it measure? School Psychology Review, 35, 108-127. Kurtz, L. A. (2006). Visual perception problems in children with AD/ HD, autism and other learning disabilities: A guide for parents and professionals. London, England: Jessica Kingsley Publishers. Lindstrom, J. H. (2007). Determining appropriate accommodations for postsecondary students with reading and written expression disorders. Learning Disabilities Research & Practice, 22, 229-236. doi: 10.1111/j.1540-5826.2007.00251.x Loh, P. R., Piek, J. P., & Barrett, N. C. (2011). Comorbid ADHD and DCD: Examining cognitive functions using the WISC-IV. Research in Developmental Disabilities, 32, 1260-1269. doi: 10.1016/j.ridd.2011.02.008 Mayes, S. D., & Calhoun, S. L. (2006). Frequency of reading, math, and writing disabilities in children with clinical disorders. Learning and Individual Differences, 16, 145-157. doi: 10.1016/j.lindif.2005.07.004 Mayes, S. D., Calhoun, S. L., Bixler, E. O., & Zimmerman, D. N. (2009). IQ and neuropsychological predictors of academic achievement. Learning and Individual Differences, 19, 238-241. doi: 10.1016/j.lindif.2008.09.001 Mayes, S. D., Calhoun, S. L., Chase, G. A., Mink, D. M., & Stagg, R. E. (2009). ADHD subtypes and co-occurring anxiety, depression, and oppositional-defiant disorder: Differences in Gordon diagnostic system and Wechsler working memory and processing speed index scores. Journal of Attention Disorders, 12, 540-550. doi: 10.1177/1087054708320402 McCardle, P., Scarborough, H. S., & Catts, H. W. (2001). Predicting, explaining, and preventing children’s reading difficulties. Learning Disabilities Research & Practice, 16, 230-239. doi: 10.1111/0938-8982.00023 McGrath, L. M., Pennington, B. F., Shanahan, M. A., SanterreLemmon, L. E., Barnard, H. D., Willcutt, E. G., . . . Olson, R. K. (2011). A multiple deficit model of reading disability and attention-deficit/hyperactivity disorder: Searching for shared cognitive deficits. Journal of Child Psychology and Psychiatry, 52, 547-557. doi: 10.1111/j.1469-7610.2010.02346.x Moll, K., Göbel, S. M., Gooch, D., Landerl, K., & Snowling, M. J. (2014). Cognitive risk factors for specific learning disorder processing speed, temporal processing, and working memory. Journal of Learning Disabilities. Advance online publication. doi: 10.1177/0022219414547221 Moura, O., Simões, M. R., & Pereira, M. (2014). WISC-III cognitive profiles in children with developmental dyslexia: Specific cognitive disability and diagnostic utility. Dyslexia, 20, 19-37. doi: 10.1002/dys.1468 Mulder, H., Pitchford, N. J., & Marlow, N. (2011). Inattentive behaviour is associated with poor working memory and slow processing speed in very pre-term children in middle childhood. British Journal of Educational Psychology, 81, 147-160. doi: 10.1348/000709910X505527

Downloaded from jad.sagepub.com at UCSF LIBRARY & CKM on April 10, 2015

10

Journal of Attention Disorders 

O’Brien, J. C., Williams, H. G., Bundy, B., Lyons, J., & Mittal, A. (2008). Mechanisms that underlie coordination in children with developmental coordination disorder. Journal of Motor Behavior, 40, 43-61. doi: 10.3200/JMBR.40.1.43-61 Parodi, G. (2006). Reading–writing connections: Discourseoriented research. Reading and Writing, 20, 225-250. doi: 10.1007/s11145-006-9029-7 Pennington, B. F. (2008). Diagnosing learning disorders: A neuropsychological framework. New York, NY: Guilford Press. Pennington, B. F., & Bishop, D. V. M. (2009). Relations among speech, language, and reading disorders. Annual Review of Psychology, 60, 283-306. doi: 10.1146/annurev. psych.60.110707.163548 Piek, J. P., Dyck, M. J., Francis, M., & Conwell, A. (2007). Working memory, processing speed and set-shifting in children with developmental coordination disorder and attention deficit hyperactivity disorder. Developmental Medicine & Child Neurology, 49, 678-683. doi: 10.1111/j.14698749.2007.00678.x Pisecco, S., Baker, D. B., Silva, P. A., & Brooke, M. (2001). Boys with reading disabilities and/or ADHD distinctions in early childhood. Journal of Learning Disabilities, 34, 98-106. doi: 10.1177/002221940103400201 Rasmussen, E. R., Neuman, R. J., Heath, A. C., Levy, F., Hay, D. A., & Todd, R. D. (2002). Familial clustering of latent class and DSM-IV defined attention deficit/hyperactivity disorder (ADHD) subtypes. Journal of Child Psychology and Psychiatry, 45, 589-598. doi: 10.1111/1469-7610.00229 Rucklidge, J. J., & Tannock, R. (2002). Neuropsychological profiles of adolescents with ADHD: Effects of reading difficulties and gender. Journal of Child Psychology and Psychiatry, 43, 988-1003. doi: 10.1111/1469-7610.00227 Sadoski, M., & Paivio, A. (2001). Imagery and text: A dual coding theory of reading and writing. Mahwah, NJ: Lawrence Erlbaum. Semrud-Clikeman, M., & Harder, L. (2010). Neuropsychological correlates of written expression in college students with ADHD. Journal of Attention Disorders, 15, 215-223. doi: 10.1177/1087054709359169 Shanahan, M. A., Pennington, B. F., Yerys, B. E., Scott, A., Boada, R., Willcutt, E. G., . . .DeFries, J. C. (2006). Processing speed deficits in attention deficit/hyperactivity disorder and reading disability. Journal of Abnormal Child Psychology, 34, 585602. doi: 10.1007/s10802-006-9037-8 Swanson, H. L., & Sachse-Lee, C. (2001). Mathematical problem solving and working memory in children with learning disabilities: Both executive and phonological processes are important. Journal of Experimental Child Psychology, 79, 294-321. doi: 10.1006/jecp.2000.2587 Tannock, R. (2013). Rethinking ADHD and LD in DSM-5 proposed changes in diagnostic criteria. Journal of Learning Disabilities, 46, 5-25. doi: 10.1177/0022219412464341 Thaler, N. S., Bello, D. T., & Etcoff, L. M. (2013). WISC-IV profiles are associated with differences in symptomatology and outcome in children with ADHD. Journal of Attention Disorders, 17, 291-301. doi: 10.1177/1087054711428806 Thomson, M. (2003). Monitoring dyslexics’ intelligence and attainments: A follow-up study. Dyslexia, 9, 3-17. doi: 10.1002/dys.232

Tsai, C. L., Wilson, P. H., & Wu, S. K. (2008). Role of visualperceptual skills (non- motor) in children with developmental coordination disorder. Human Movement Science, 27, 649-64. doi: 10.1016/j.humov.2007.10.002 Wechsler, D. (2003). Wechsler intelligence scale for children–Fourth Edition (WISC-IV). San Antonio, TX: The Psychological Corporation. Willcutt, E. G., Betjemann, R. S., McGrath, L. M., Chhabildas, N., Olson, R. K., DeFries, J. C., & Pennington, B. F. (2010). Etiology and neuropsychology of comorbidity between RD and ADHD: The case for multiple-deficit models. Cortex: A Journal Devoted to the Study of the Nervous System and Behavior, 46, 1345-61. doi: 10.1016/j.cortex.2010.06.009 Willcutt, E. G., Nigg, J. T., Pennington, B. F., Solanto, M. V., Rohdem, L. A., Tannock, R., . . . Lahey, B. B. (2012). Validity of DSM–IV attention deficit/hyperactivity disorder symptom dimensions and subtypes. Journal of Abnormal Psychology, 121, 991-1010. doi: 10.1037/a0027347 Willcutt, E. G., Pennington, B. F., Olson, R. K., Chhabildas, N., & Hulslander, J. (2005). Neuropsychological analyses of comorbidity between reading disability and attention deficit hyperactivity disorder: In search of the common deficit. Developmental Neuropsychology, 27, 35-78. doi: 10.1207/ s15326942dn270 Willcutt, E. G., Petrill, S. A., Wu, S., Boada, R., DeFries, J. C., Olson, R. K., & Pennington, B. F. (2013). Comorbidity between reading disability and math disability concurrent psychopathology, functional impairment, and neuropsychological functioning. Journal of Learning Disabilities, 46, 500516. doi: 10.1177/0022219413477476 Zayed, A. M., Roehrig, A. D., Arrastia-Lloyd, M. C., & Gilgil, N. M. (2012). Phonological awareness and working memory in Arabic-speaking Egyptian preschool children at risk for dyslexia. International Journal of Psychological Studies, 5, 139149. Retrieved from http://www.ccsenet.org/journal/index. php/ijps

Author Biographies Elyse M. Parke, MA, is a doctoral student in clinical psychology at the University of Nevada, Las Vegas. Her primary research interest is neuropsychological functioning in individuals with neurodevelopmental disorders. Nicholas S. Thaler, PhD, is a postdoctoral neuropsychology fellow at the UCLA Semel Institute for Neuroscience and Human Behavior. His research interests include the neuropsychological and neurological underpinnings of traumatic brain injury and ADHD. Lewis M. Etcoff, PhD, ABN, has been in private practice for 31 years in Las Vegas, Nevada. He restricts his practice to the neuropsychological and clinical psychological assessments of school age children and young adults. He is an adjunct psychology faculty member at the University of Nevada, Las Vegas. Daniel N. Allen, PhD, is the Lincy professor of psychology at the University of Nevada, Las Vegas. His research focuses on neuropsychological aspects of neurodevelopmental disorders and mental illness, including schizophrenia, bipolar disorder, and substance use disorders.

Downloaded from jad.sagepub.com at UCSF LIBRARY & CKM on April 10, 2015