psychology dyslexia

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Journal of Learning DisabilitiesVolume 42 Number 6

November/December 2009 511-527© 2009 Hammill Institute on

Disabilities10.1177/0022219409345013

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Authors’ Note: We thank the Academy of Finland (Project 108410), the Emil Aaltonen Foundation, and the Otologic Research Foundation for financial support. In addition, thanks go to Emma Hietarinta, Sasa Kivisaari, Minna Kuivalainen, Maisa Lehtinen, Mirva Reuhkala, and Meeri Sivonen for participating in the gathering of psychometric data. Address correspondence to Marja Laasonen, Department of Psychology, PO Box 9 (Siltavuorenpenger 20), FIN-00014 University of Helsinki, Finland; e-mail: [email protected].

Adult Dyslexia and Attention Deficit Disorder in Finland—Project DyAdd

WAIS-III Cognitive ProfilesMarja LaasonenUniversity of Helsinki, FinlandHelsinki University Central Hospital, Finland

Sami Leppämäki

Pekka TaniHelsinki University Central Hospital, Finland

Laura HokkanenUniversity of Helsinki, Finland

The project Adult Dyslexia and Attention Deficit Disorder in Finland (Project DyAdd) compares adults (n = 119, 18–55 years) with dyslexia, attention-deficit/hyperactivity disorder (ADHD), dyslexia together with ADHD (comorbid), and healthy controls with neuropsychological, psychophysical, and biological methods. The focus of this article is on the Wechsler Adult Intelligence Scale–Third Edition (WAIS-III). The clinical groups performed well compared to the norms, and they did not differ from each other. However, compared to the controls, all of them were slightly poorer in their Full IQ, and of the factors, processing speed was relatively difficult for all of them. In addition to the group comparisons, a cluster analysis based on subtest scores was conducted over the clinical groups. It did not suggest a solution that would differentiate between the clinical groups. Instead, four clusters emerged: above average, average, poor perceptual organiza-tion, and poor working memory. Thus, differentiating between these clinical groups with the WAIS-III was not possible. However, all of them shared a relative difficulty in processing speed, and group-independent clusters with perceptual or memory difficulties emerged.

Keywords: dyslexia; ADHD; intelligence; WAIS; processing speed

This article is an introduction to the project Adult Dyslexia and Attention Deficit Disorder in Finland

(Project DyAdd). In addition to the general characteriza-tion of the project, the specific focus of this article is the comparison of Wechsler Adult Intelligence Scale–Third Edition (WAIS-III; Wechsler, 2005) profiles of three clinical groups and a group of healthy controls. The clinical groups are those with dyslexia, attention-deficit/hyperactivity disorder (ADHD), and dyslexia together with ADHD (comorbid). The aim is to find characteristics that differentiate between or are shared by the groups. In addition, we inspect the pooled data of clinical groups with a cluster analysis. The aim is to clarify whether the suggested clusters reflect the clinical groups or character-istics that emerge regardless of group membership.

Background and Rationale of Project DyAdd

Several disorders of development and learning may hamper the acquisition of basic academic skills. Specific developmental disorders of scholastic skills are one such entity and include, for example, developmental dyslexia (i.e., specific reading disorder; World Health

512 Journal of Learning Disabilities

Organization, 1998). Another entity is composed of behavioral and emotional disorders, with onset usually occurring in childhood and adolescence, and includes, for example, ADHD (i.e., hyperkinetic disorders; World Health Organization, 1998). The difficulties of the affected indi-viduals emerge and are evident already in childhood but usually do not hamper this developmental period only. Instead, the disorders continue to impair adults’ academic and occupational performance. In some cases, the develop-mental impairments actually are aggravated with increas-ing age (Laasonen, Lahti-Nuuttila, & Virsu, 2002; Virsu, Laasonen, & Lahti-Nuuttila, 2003). This accentuates the true developmental nature of the disorders and emphasizes the need for adult learning disability research.

Environmental factors may have a major influence on the manifestation of learning disorders in the population. Comparable research in different countries is thus war-ranted. Only this way can we pinpoint the core deficits that are related to each condition and that span across environ-ments. For instance, the orthographic systems (correspon-dence rules between phonemes and graphemes) vary across languages. In many ways, the orthographic system in Finnish is at the other end of the continuum compared to English (as reviewed in Leinonen et al., 2001). First, Finnish has a highly shallow orthography; that is, the cor-respondence between graphemes and the 21 phonemes is almost perfect. Second, both the fixed syllabification and the main stress on the first syllable support skilled word segmentation. Third, the words can become rather long because Finnish is an agglutinating language (both prepo-sitions and possessive pronouns are expressed by adding endings to stems). Based on these characteristics, one might assume that the appearance of dyslexia may differ in Finnish compared to English. Still, many correlates of dys-lexia have shown to emerge across language environments (Paulesu et al., 2001). The full extent of the deficits that are specific to an environment or span across them are yet to be resolved, however.

Each of the separate learning disabilities complicates the lives of at least 5% of a given population (Faraone, Sergeant, Gillberg, & Biederman, 2003; Katusic, Colligan, Barbaresi, Schaid, & Jacobsen, 2001). There is also sub-stantial comorbidity; that is, a large number of people are affected by the concomitant but unrelated existence of developmental and learning disorders (Gilger, Pennington, & DeFries, 1992; Willcutt & Pennington, 2000). On the other hand, it has been suggested that the coexisting learn-ing disabilities might not be completely unrelated (i.e., comorbid). For example, dyslexia and ADHD have been found to have shared genetic influences (Gayán et al., 2005; Gilger et al., 1992; Willcutt, Pennington, & DeFries,

2000; Willcutt et al., 2002). This might result in shared characteristics at different levels of analysis. However, the focus of any given study is usually on a single disability, and no effort is invested in investigating or excluding other conditions. Thus, there is no definitive knowledge about the characteristics that are shared with or differenti-ate between these learning disabilities when they occur alone or together.

The defining behavioral or clinical neuropsychological features of both dyslexia and ADHD are gradually unfold-ing. Besides the hallmark difficulties in reading, dyslexia is most often believed to include and possibly result from difficulties in phonological processing, whereas an ADHD diagnosis is based on behavioral characteristics that reflect inattention, impulsivity, and hyperactivity. In addition to the behavioral and clinical neuropsychological character-istics, the basic cognitive processes that are related to these learning disabilities are yet to be resolved. For example, in dyslexia research, possible core impairments have been suggested in temporal processing or acuity (Laasonen, Service, & Virsu, 2001; Tallal, 1980), attention (Hari & Renvall, 2001; Hari, Renvall, & Tanskanen, 2001), short-term and working memory (Siegel, 1994), and learning (Nicolson, Daum, Schugens, Fawcett, & Schulz, 2002; Vicari, Marotta, Menghini, Molinari, & Petrosini, 2003). In ADHD research, core deficits have been suggested, for example, in executive functions (Barkley, 1997; Castellanos & Tannock, 2002; Pennington & Ozonoff, 1996; Schachar, Mota, Logan, Tannock, & Klim, 2000), delay aversion (Sonuga-Barke, 2003), regu-lation of arousal and activation (Sergeant, 2000), and temporal processing (Barkley, Murphy, & Bush, 2001; Toplak, Rucklidge, Hetherington, John, & Tannock, 2003). Thus, there seems to be some similarities between the two conditions, but the shared and differentiating characteris-tics in many cognitive areas are yet to be determined.

Also the biological correlates of the learning dis-abilities are still relatively unknown. In dyslexia research, possible candidates so far have been suggested to be, for example, the magnocellular system (Stein, 2001), serum lipid fatty acids (Richardson et al., 2000; Taylor et al., 2000), and the cerebellum (Finch, Nicolson, & Fawcett, 2002; Nicolson, Fawcett, & Dean, 2001). In ADHD research, dopaminergic and noradrenergic imbal-ances and fronto-striatal, callosal, and cerebellar abnor-malities have been found (cf. functional imaging, Bush, Valera, & Seidman, 2005; structural imaging results, Seidman, Valera, & Makris, 2005; Valera, Faraone, Murray, & Seidman, 2007). However, the roles of fatty acids (Richardson, 2006) and the magnocellular system (Stuart, McAnally, & Castles, 2001) have also been

Laasonen et al. / DyAdd: WAIS-III 513

speculated on. Thus again, there seems to be similarities between these two disabilities, but the shared and dif-ferentiating factors for these two conditions are not fully resolved.

To summarize, previous research on learning disabili-ties emphasizes the following: (a) The impairments should be assessed in adults in addition to children. (b) Research should span different (language) environ-ments. (c) One should acknowledge the possible coexis-tence of multiple learning disabilities in one individual. (d) The research should be conducted simultaneously at multiple levels. Examples of these are clinical neuropsy-chological practice, experimental studies of cognition, and biological measures.

General Aim of Project DyAdd

In Finland, we have launched Project DyAdd, in which we investigate adult dyslexia and ADHD when they appear separately or coexist. The main objectives of the project are the following. First, we investigate the neurocognitive pro-files of the disabilities with clinical neuropsychological methods (cf. results on phonological processing and achievement; Laasonen, Lehtinen, Leppämäki, Tani, & Hokkanen, 2009). Possible group differences at this level of analysis would suggest, for example, a basis for a reli-able neuropsychological assessment protocol. Second, we investigate the basic cognitive processes in these clinical groups with experimental methods. Investigated areas include learning and memory, various aspects of attention, and temporal processing acuity. The aim is to find sha red and differentiating characteristics, possible core deficits, and suggestions for future clinical neuropsycho-logical methods. The last objective is to investigate the relations between neuropsychological, experimental, and biological measures. The main biological measures in the project are the fatty acids and the role of cerebellum (cf. results on fatty acid status; Laasonen, Hokkanen, Leppämäki, Tani, & Erkkilä, 2009a, 2009b).

Current Study

The focus of this study is at the level of clinical neu-ropsychology and, more specifically, in Wechsler tests of intelligence. The specific aim is to find out whether adult participants with dyslexia, ADHD, and dyslexia together with ADHD show similarities or differences in their cog-nitive profiles. An additional aim is to clarify with a cluster analysis whether the suggested clusters reflect the clinical groups or characteristics that emerge regardless of group membership. Intelligence is estimated with the

Finnish version of the WAIS-III (Wechsler, 2005). Of the available subtests, we included those that comprise the Wechsler Abbreviated Scale of Intelligence (WASI, which is not published in Finland; cf. English version, Wechsler, 1999), and those that have posed difficulties to either dyslexic or ADHD participants. These difficulties and the alternative subtest classifications are reviewed below. (See Table 1 for WAIS-III subtests and their various clas-sifications in dyslexia and ADHD literature.)

WAIS Full, Verbal, and Performance Intelligence Quotients

At the most robust level, WAIS subtests can be con-sidered to reflect one entity (Full Intelligence Quotient, or FIQ). Alternatively, the subtests can be divided into Verbal IQ (VIQ) and Performance IQ (PIQ; for details, see Table 1). In adult dyslexia, VIQ is expected to be poorer than in controls (Scarborough, 1984). VIQ is also expected to be poorer than PIQ (Alm & Kaufman, 2002; Frauenheim & Heckerl, 1983). In adult ADHD, three meta-analytical studies suggest that FIQ, VIQ, and PIQ are all lowered compared to controls (Bridgett & Walker, 2006; Frazier, Demaree, & Youngstrom, 2004; Hervey, Epstein, & Curry, 2004). However, only some adults with ADHD seem to have lowered general ability. For example, poor FIQ is suggested to be related to comor-bid conditions in general (Bridgett & Walker, 2006). The subtype of ADHD (hyperactive-impulsive, combined, or inattentive) does not seem to make a difference, however (Frazier et al., 2004).

Ottem (2002) has suggested that VIQ and PIQ should not be compared. Instead, one should compare subtests of similar complexity across the verbal and performance domains (see Table 1). This categorization was originally based on children’s scales but can be translated to adults. In children’s dyslexia, correcting for task complexity resulted in emphasized PIQ > VIQ differences (Ottem, 2002). To our knowledge, there is no research on adult dyslexia or ADHD.

WAIS Factors and Indices, and Other Categorizations

Besides the traditional classification into FIQ, VIQ, and PIQ, the subtests of the Wechsler intelligence scales have been categorized in various ways. Here, were pres-ent classifications that have been used in either dyslexia or ADHD research.

Based on a vast amount of factor analytical studies, the WAIS subtests are most commonly divided into three


or four factors (cf. the review by Kaufman & Lichtenberger, 2006). These are verbal comprehension (VC), working memory (WM, previously called freedom from distract-ibility), perceptual organization (PO), and processing speed (PS; for details, see Table 1). Accordingly, the WAIS-III has been divided into indices by the same names as the factors above. The same factor structure that is observed in the normative sample emerges in adult dyslexia (Alm & Kaufman, 2002), and a pattern of PO > VC > WM is often found (Alm & Kaufman, 2002; Frauenheim & Heckerl, 1983; Gregg, Hoy, & Gay, 1996). In adult ADHD, WM (Bridgett & Walker, 2006; Woods, Lovejoy, & Ball, 2002) and possibly PS are low-ered (Woods et al., 2002).

Bannatyne (1974) has proposed a slightly different categorization of the Wechsler subtests. These categories are verbal conceptualization (VC, analogous to VC fac-tor), acquired knowledge (AK), spatial ability (SpatA, resembles PO), and sequential ability (SeqA, resembles WM/PS; for details, see Table 1). Learning disorder in general has resulted in a SpatA > VC = AK > SeqA pat-tern (Frauenheim & Heckerl, 1983; Kaufman & Lichtenberger, 2006; Sandoval, Sassenrath, & Penaloza, 1988). Similar pattern has been observed also in adult

dyslexia (SpatA > VC > AK ≥ SeqA; Alm & Kaufman, 2002; Frauenheim & Heckerl, 1983). To our knowledge, there are no studies on adult ADHD examining Bannatyne categories.

ACID and other subtest profiles

Certain Wechsler subtests have posed specific difficul-ties to participants with learning difficulties. Based on this, characteristic performance profiles have been sug-gested. These have been named according to the initial letters of the impaired subtests. The poor ACID (Arithmetic, Coding/Digit Symbol Coding, Information, Digit Span) profile has been found in both adult dyslexia (Alm & Kaufman, 2002; Frauenheim & Heckerl, 1983) and ADHD (Woods et al., 2002). Suggested alternatives to ACID profile are AC (Arithmetic, Coding), CAD (Coding, Arithmetic, Digit Span), and SCAD (Similarities, Coding, Arithmetic, Digit Span; Mayes & Calhoun, 2004).

Without any reference to the profiles above, single subtests also have been found to pose difficulties to samples with dyslexia, ADHD, or both. In adult dys-lexia, Digit Symbol Coding, Digit Span, Arithmetic, and Block Design are often impaired (Hatcher, Snowling, &

Table 1Wechsler Adult Intelligence Scale (WAIS) Subtests and Their Classification in

Dyslexia and Attention-Deficit/Hyperactivity Disorder Literature

IQ

Verbal

Performance

Subtest

InformationComprehensionArithmetica

Similaritiesa

Vocabularya

Digit Spana

Letter-Number Sequencinga

Picture CompletionDigit Symbol

Codinga

Block Designa

Matrix Reasoninga

Picture ArrangementSymbol SearchObject Assembly

WAIS Factor

VC—WMVCVCWMWM

POPS

POPO—PS—

ACID/SCAD/CAD/AD

ACID—ACID/SCAD/CAD/AD SCAD—ACID/SCAD/CAD/AD —

—ACID/SCAD/CAD

—————

Bannatyne

AKVCAK/SeqAVCVC/AKSeqASeqAb

SpatASeqA

SpatASpatAb

——SpatA

Ottem Ranking

SimpleSimpleModerateSimpleSimpleComplex—

SimpleComplex

Moderate—Moderate—Moderate

WDI

Hold——Don’t holdHoldDon’t hold—

HoldDon’t hold

Don’t hold———Hold

a. Indicates subtests that were included in the study.b. Indicates later classification of subtests into Bannatyne categories (Kaufman & Lichtenberger, 2006).Note: IQ = intelligence quotient; WAIS factors: VC = verbal comprehension; PO = perceptual organization; WM = working memory; PS = processing speed; ACID = subtests Arithmetic, Digit Symbol Coding, Information, and Digit Span; SCAD = subtests Similarities, Digit Symbol Coding, Arithmetic, and Digit Span; CAD = subtests Digit Symbol Coding, Arithmetic, and Digit Span; AD = subtests Arithmetic and Digit Span; Bannatyne categories: VC = verbal conceptualization; SpatA = spatial ability; AK = acquired knowledge; SeqA = sequential ability; WDI = Wechsler Deterioration/Developmental Index.


Griffiths, 2002; Rack, 1997). In adult ADHD, impairments have been observed in exactly the same subtests (Bridgett & Walker, 2006; Hervey et al., 2004; Woods et al., 2002). This would suggest an ABCD profile.

The Wechsler Deterioration Index (WDI) was tradi-tionally an index of cognitive impairment, and it was assumed to reflect brain injury (Livesay, 1986). In the WDI, the subtests are categorized into “hold” and “don’t hold” subtests. Only the “hold” subtests are expected to be resistant to brain injury and the cognitive deterioration that results from it (see Table 1). Bowers and his col-leagues renamed the WDI the Wechsler Developmental Index (Bowers, Risser, Suchanec, & Tinker, 1992) and suggested that the classification might reflect also the uneven development of cognitive skills. In their studies with children, the WDI did not predict the nature of the learning disability, nor its severity, but it did distinguish children with ADHD and children who were nondisabled from each other (Bowers et al., 1992). To our knowledge, there is no similar research on adult dyslexia or ADHD.

Current Questions

Based on the literature review above, adult ADHD is suggested to result in impaired FIQ, VIQ, and PIQ in general; poor performance in factors WM and PS in par-ticular; and especially difficulties in the single subtests of Arithmetic, Digit Span, Digit Symbol Coding, and Block Design. Adult dyslexia is suggested to result in lowered VIQ, especially difficulties in the WM factor, and impair-ments on the same subtests as ADHD. Thus, overlap of the difficulties in these two conditions seems to be sub-stantial. Further, it has been suggested that comorbidity could mediate the WAIS difficulties in at least the ADHD group (Bridgett & Walker, 2006). The majority of previ-ous studies have had only one clinical group and a group of healthy controls, however. Two clinical groups, one with dyslexia and another with ADHD, or even a third comorbid group, have not been included. Therefore, the shared and differentiating characteristics of these clinical groups are not fully resolved.

In the present study, we first set out to confirm the previously observed difficulties in adults with dyslexia or ADHD, as compared to a group of healthy controls, in a Finnish sample. A small comorbid group (dyslexia together with ADHD) is included as a third clinical group in order to clarify the previous results on dyslexia and ADHD when they exist alone. WAIS-III perfor-mance is investigated at the levels of intelligence quo-tients, factors, and subtests. The profiles are discussed also in light of Ottem ranking, Bannatyne categories,

ACID derivatives, and WDI. Second, as a novel aspect, we compare the performance of the three clinical groups of adults to each other with these same measures. The aim is to find differentiating and shared characteristics of the conditions, both when they appear alone or coexist. Third, we try to differentiate, with a cluster analysis, whether the participants cluster together based on their group membership or performance characteristics that emerge regardless of the clinical group. With all these analyses, we try to clarify whether the clinical groups of adults have group-specific characteristics compared to the controls or to other clinical groups. Alternatively, they could all share difficulties that would emerge in the group comparisons. In cluster analysis, these and other shared characteristics would be expected to result in clusters that do not reflect the group membership.

Method

Sample and Selection Criteria

The demographic characteristics of the participants are presented in Table 2. The groups did not differ in their age, F(3, 115) = 1.847, p = .142; gender, χ2(3) = 2.292, p = .514; educational level, χ2(6) = 8.155, p = .227; or handedness, χ2(6) = 3.179, p = .786. This was achieved by screening the participants into balanced cohorts according to the first three characteristics. Parti-cipants were volunteers. Informed consent was obtained from all participants, and the project was accepted by the appropriate ethical committee of Helsinki University Central Hospital. The examiner was blind to the group of the participant.

Participants with dyslexia and ADHD were required to have a prior diagnosis as an inclusion criterion. The par-ticipants in the ADHD and comorbid groups were diag-nosed according to the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 1994) criteria using Conners’ Adult ADHD Diagnostic Interview for DSM-IV (Epstein, Johnson, & Conners, 2001) by a medical doc-tor specialized in neuropsychiatry (authors Sami Leppämäki or Pekka Tani in most cases). Confounding psychiatric disorders were excluded by the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I; First, Spitzer, Gibbon, & Williams, 1996) and SCID-II interviews (First, Gibbon, Spitzer, Williams, & Benjamin, 1997). Thus, hyperactivity was not a required character-istic, but those with only inattention were included. Therefore, in this article, the label ADHD refers both to those with attention deficit disorder and to those with


ADHD. Diagnosis of dyslexia and a history of reading difficulties were exclusion criteria for the ADHD group. Most (n = 28) of the participants with ADHD were recruited from Helsinki University Central Hospital, Department of Neuropsychiatry, and the remaining from a private practice of a specialist having experience in ADHD.

Diagnosis of ADHD, diagnosis of dyslexia, a history of reading difficulties, and a history of ADHD-related difficulties were exclusion criteria for the control group. Recruitment sources for the control group were the University of Helsinki; large civil service departments, with heterogeneous staff, of the City of Espoo; student organizations at the University of Helsinki; and voca-tional high schools and vocational schools in the metro-politan area of Helsinki.

The participants in the dyslexia and comorbid groups were required to have a prior diagnosis of dyslexia as an inclusion criterion. They were diagnosed by appropriate specialists, such as doctors, psychologists, speech and language pathologists, special education teachers, or reading and writing representatives. Their diagnosis was based on achievement criteria that varied slightly. Therefore, the current phonological processing and read-ing status of each participant in the dyslexia and comor-bid groups was checked against the age-corrected values of our previous (Laasonen, 2002) and current control data. Participant in these two groups performed below –1 standard deviation in phonological processing and read-ing, as assessed with phonological naming speed (rapid alternate stimulus naming speed/accuracy; Wolf, 1986), phonological awareness (phonological synthesis accu-racy; Laasonen, Service, & Virsu, 2002), phonological

memory (WAIS digit span forward length; Wechsler, 2005), and reading (oral reading speed/accuracy; task details in Laasonen, Service, et al., 2002, and tasks in the appendix). One participant with diagnosed dyslexia and a history of reading difficulties and one with a comorbid diagnosis were impaired only in phonological process-ing. We chose to include these two participants in the dyslexia and comorbid groups because it has been sug-gested that childhood dyslexia could manifest merely in phonological difficulties in adulthood (Daryn, 2000; Felton, Naylor, & Wood, 1990). A diagnosis of ADHD and a history of ADHD-related difficulties were exclu-sion criteria for the dyslexia group. The majority of those with dyslexia were recruited through HERO (a diverse learners’ association in Helsinki). Other recruitment sources were the University of Helsinki; large civil ser-vice departments, with heterogeneous staff, of the City of Espoo; student organizations at the University of Helsinki; and vocational high schools and vocational schools in the metropolitan area of Helsinki.

Finnish as mother tongue and age 18 to 55 years were further inclusion criteria. General exclusion criteria were brain injury, a somatic or psychiatric condition affecting cognitive functions (including major depression), medi-cation affecting cognitive functions, and substance abuse. Those with ADHD participated in the project unmedi-cated. If they had a prescription for methylphenidate, a washout period of at least 1 week was required before and during the appointments. ADHD participants with medication with a longer half-life were excluded from the project. Those with a WASI FIQ (Wechsler, 2005) less than 70 (that is, less than –2 standard deviations) were also excluded from every group, due to the

Table 2Demographic Characteristics of the Participants

Group

Control Attention-Deficit/ (n = 40) Hyperactivity Disorder (n = 30) Dyslexia (n = 40) Comorbid (n = 9)

Age in years, M (SD) 37.15 (11.70) 31.60 (8.17) 35.45 (10.27) 32.56 (10.31)Gender, n (%)

Female 20 (50%) 12 (40%) 21 (53%) 6 (67%)Male 20 (50%) 18 (60%) 19 (48%) 3 (33%)

Handedness, n (%) Right 34 (85%) 26 (87%) 37 (93%) 9 (100%)Left 5 (13%) 3 (10%) 3 (8%) 0 (0%)Ambidextrous 1 (3%) 1 (3%) 0 (0%) 0 (0%)

Educational level, n (%) Basic 14 (35%) 18 (62%) 18 (45%) 6 (67%)Middle 12 (30%) 4 (14%) 13 (33%) 2 (22%)High 14 (35%) 7 (24%) 9 (23%) 1 (11%)


International Statistical Classification of Diseases and Related Health Problems (10th revision) criteria for spe-cific reading disorders (World Health Organization, 1998). This resulted in excluding 1 participant in the dyslexia group. Three participants (1 control and 2 with ADHD) dropped out of the study, and their incomplete data were excluded from the analyses.

Method

The full Project DyAdd comprised five appointments. The first phase, clinical neuropsychological assessment, consisted of two appointments, each lasting approximately 3 hours, separated by a median of 7 days (M = 8.7, SD = 5.0 days). Blood samples were collected between these two appointments to rule out endocrinopathies (e.g., dys-function of the thyroid gland), diabetes, renal dysfunction, abuse of alcohol, and similar somatic states that might compromise cognitive functions. The tests included hemo-globin, red blood cells, white blood cells, platelet count, thyroid stimulating hormone, serum creatinine, alanine aminotransferase, gamma-glutamyltransferase, and fasting blood glucose. The second phase, experimental methods, consisted of two appointments, the first session lasting 2 hours and the second lasting 3 hours, separated by at least 14 days (median = 19, M = 21.11, and SD = 12.19 days).

The full neuropsychological assessment battery of the first phase of Project DyAdd and its alternating orders are presented in the appendix. Before the appointments, the participants received several questionnaires and symptom checklists by mail, filled them out at home, and returned them at their first visit. The neuropsychological assessment included various paper-and-pencil tasks in the cognitive domains of visuospatial, visuoconstruc-tive, and psychomotor basic functions; intelligence, memory, attention, and executive functions; phonologi-cal processing; and reading, spelling, and arithmetic. Computerized tests were administered in the domains of attention and executive functions. All tasks were admin-istered in Finnish. The current article focuses on the WAIS-III (Wechsler, 2005), and we included the follow-ing subtests: Similarities, Vocabulary, Arithmetic, Digit Span, Letter-Number Sequencing, Block Design, Matrix Reasoning, and Digit Symbol Coding.

Statistical Analyses

There were no poor univariate outliers in the depen-dent variables (criterion: z score over all the groups less than –3.29, p < .05). There were no multivariate outliers

at the level of subtests, as assessed with Mahalanobis distance. The normality of the variable distributions was investigated with the Shapiro-Wilk test and homogeneity of variance with Levene’s test. There were distributional limitations in the VC factor and subtests Vocabulary and Similarities. The variances differed between the groups in Block Design. Therefore, nonparametric analyses (Kruskall-Wallis ANOVA and Mann-Whitney/Wilcoxon’s rank-sum test) and analyses without the homogeneous variance assumption were conducted (Welch test). The results of these additional analyses are reported only if they resulted in qualitatively different results compared to the parametric analyses. The overall group differences were tested with factorial and repeated measures ANOVA and Bonferroni-corrected post hoc tests. The significance level was set at .05.

The classification of the subtests is presented in Table 1. The first level of analysis was that of the intelligence quotients. FIQ was estimated based on an average over all included subtest standard scores. This average was converted to a quotient value with a mean of 100 and a standard deviation of 15. VIQ was estimated similarily over all included verbal subtests, and PIQ over all included performance subtests. Then the analyses were conducted at the level of factors. Again, the averages of the included subtest standard scores were converted to a score with a mean of 100 and a standard deviation of 15. The verbal factor, VC, was composed of Similarities and Vocabulary. The verbal factor, WM, was an average of Arithmetic, Digit Span, and Letter-Number Sequencing. The performance factor, PO, was composed of Block Design and Matrix Reasoning. The performance factor, PS, reflected Digit Symbol Coding. The last level of analysis was that of the single subtests. In all cases, the standard scores were converted from the raw scores based on the general norms (Wechsler, 2005).

Cluster analysis was based on all included subtest stan-dard scores. Ward’s hierarchical agglomerative method was used as the grouping procedure. This method joins together the most similar cases, and thereafter clusters, by using analysis of variance to assess the distance (Morris, Blahsfield, & Satz, 1981). Squared Euclidean distance was used as the similarity measure. Euclidian distance is the geometric distance between objects in a multidimen-sional space, and if squared, it places greater weight on objects that are further apart. A dendrogram, a graph that indicates the relative similarity of the cases and cohesive-ness of the clusters, together with sudden changes in cluster distance coefficients were used as criteria for the number of clusters in an acceptable solution.


Results

In this section, we first describe the group comparisons in intelligence quotients, factors, and subtests. Then, we describe the cluster solution based on the subtest perfor-mance conducted over all the clinical groups.

Intelligence Quotients

The estimated intelligence quotients of the groups are presented in Figure 1. In a 4 × 1 ANOVA with FIQ, the main effect of group was statistically significant, F(3, 115) = 5.62, p < .01, partial η2 = .13. This was explained by the poorer performance of both the groups with ADHD (p < .01) and dyslexia (p < .01), compared to the control group. There was a trend for a poorer perfor-mance also in the comorbid group (p = .09).

In a repeated measures 4 × 2 ANOVA, Group × Quotient Type (VIQ, PIQ), both the between-subjects effect of group, F(3, 115) = 5.48, p < .01, partial η2 = .13, and its interaction with the within-subjects effect of quotient type, F(3, 115) = 2.90, p < .05, partial η2 = .07, were statistically significant. The within-subjects effect of quotient type was not significant, however. In repeated measures 1 × 2 ANOVAs for each separate group, the main effect of quo-tient type reached significance only in the dyslexia group, F(1, 39) = 5.89, p < .05, partial η2 = .13. In them, VIQ was significantly poorer than PIQ (p < .05).

In a 4 × 1 ANOVA with VIQ, the significant main effect of group, F(3, 115) = 5.59, p < .01, partial η2 = .13, was

explained by the poorer performance of the groups with both ADHD (p < .05) and dyslexia (p < .01), compared to the controls. In 4 × 1 ANOVA with PIQ, the similarly sig-nificant main effect of group, F(3, 115) = 4.40, p < .01, partial η2 = .10, was explained by poorer performance in the ADHD group (p < .01) compared to the control group.

Factors

The estimated WAIS factors of the groups are pre-sented in Figure 2. In a repeated measures 4 × 4 ANOVA (Group × Factor), only the between-subjects effect of group was significant, F(3, 115) = 6.80, p < .001, partial η2 = .15. The within-group effect of factor and its interac-tion with group were not significant. In repeated mea-sures 1 × 4 ANOVAs for each separate group, the main effect of factor type reached significance only in the dys-lexia group, F(3, 117) = 2.82, p < .05, partial η2 = .07. In them, WM was significantly poorer than PO (p < .05).

In 4 × 1 ANOVAs with the factors, the main effect of the group was significant in all cases, except for PO. Differences in VC, F(3, 115) = 4.31, p < .05, partial η2 = .10, were explained by poorer performance of groups with ADHD (p < .05) and dyslexia (p < .05), differences in WM, F(3, 115) = 4.55, p < .01, partial η2 = .11, by poorer performance of the dyslexia group (p < .01), and differences in PS, F(3, 115) = 7.45, p < .001, partial η2 = .16, by poorer performance of all the clinical groups (all ps < .01) compared to the control group.

Figure 1Estimated Wechsler Adult Intelligence Scale–Third Edition (WAIS-III) Full (FIQ), Verbal (VIQ), and Performance Intelligence Quotients (PIQ) in the

Control, Attention-Deficit/Hyperactivity Disorder (ADHD), Dyslexia, and Comorbid (dyslexia

with ADHD) Groups

Note: The values represent the group means, and the bars represent 1 standard error of measurement. The horizontal line indicates the norm mean of 100 (SD = 15).

Figure 2Estimated Wechsler Adult Intelligence Scale–Third Edition (WAIS-III) Verbal Comprehension (VC),

Working Memory (WM), Perceptual Organization (PO), and Processing Speed (PS) Factors in the

Control, Attention-Deficit/Hyperactivity Disorder (ADHD), Dyslexia, and Comorbid (dyslexia with

ADHD) Groups

Note: The values represent the group means, and the bars represent 1 standard error of measurement. The values are expressed as factor/index scores. The horizontal line indicates the norm mean of 100 (SD = 15).


Table 3Wechsler Adult Intelligence Scale–Third Edition Estimated Intelligence

Quotients and Subtest Standard Scores of the Participants

Group

Attention-Deficit/ Control Hyperactivity Disorder Dyslexia Comorbid

Mean (SD) Range Mean (SD) Range Mean (SD) Range Mean (SD) Range

Full Intelligence Quotient 110.26 (7.68) 94-124 103.35 (10.92)a 85-128 103.43 (7.91)a 90-120 102.30 (9.49) 89-119Verbal Intelligence Quotient 110.33 (8.33) 91-129 104.00 (11.74)a 82-134 102.03 (9.52)a 75-118 101.89 (9.31) 86-116Performance Intelligence 110.12 (8.74) 93-127 102.22 (11.13)a 83-123 105.75 (8.65) 87-125 102.97(10.56) 88-123 QuotientArithmetic 12.18 (2.40) 6-17 10.90 (3.17) 5-19 10.83 (2.50) 6-16 9.98 (2.26) 7-13Similarities 12.40 (2.53) 8-19 10.83 (1.91)a 7-14 11.03 (2.57)a 6-17 10.33 (1.94)a 8-13Vocabulary 12.03 (1.76) 7-16 10.47 (2.50)a 4-15 10.83 (2.52)a 5-16 10.89 (2.93) 6-13Digit Span 12.65 (2.71) 8-19 11.37 (3.47) 6-19 10.13 (3.04)a 3-18 10.89 (2.47) 8-16Letter-Number Sequencing 11.08 (2.46) 7-17 10.43 (3.98) 2-18 9.23 (2.59)a 3-14 9.89 (2.71) 6-14Digit Symbol Coding 12.55 (2.12) 9-19 10.73 (2.05)a 7-14 10.88 (2.30)a 5-16 9.67 (1.87)a 7-13Block Design 11.65 (2.36) 6-16 10.17 (3.38) 2-15 10.85 (2.37) 6-15 10.78 (4.24) 5-19Matrix Reasoning 11.88 (2.64) 6-18 10.43 (3.02) 5-16 11.73 (2.49) 5-18 11.33 (2.40) 7-15

a. Indicates a significant difference compared to the control group in Bonferroni-corrected post hoc tests. No differences were found between the clinical groups.

Subtests

The subtest standard scores of the participants are presented in Table 3. In a repeated measures 4 × 8 ANOVA (Group × Subtest), both the between-subjects effect of group, F(3, 115) = 5.62, p < .01, partial η2 = .13, and the within-subjects effect of subtest, F(7, 805) = 2.50, p < .05, partial η2 = .02, were statistically signifi-cant. In repeated measures 1 × 8 ANOVAs for each sepa-rate group, the main effect of subtest reached significance in the controls, F(7, 273) = 2.82, p < .01, partial η2 = .07, and in the dyslexia group, F(7, 273) = 4.65, p < .001, partial η2 = .11. In controls, Letter-Number Sequencing was poorer than Digit Span (p < .01). Also in the dys-lexia group, Letter-Number Sequencing was poorer than Vocabulary (p < .05), Similarities (p < .05), Arithmetic (p < .05), and Block Design (p < .001).

In 4 × 1 ANOVAs, the main effect of group was sig-nificant in all cases, except Block Design and Matrix Reasoning. The group differences in other subtests were analyzed further. Although the main effect of group was statistically significant in the Arithmetic subtest, F(3, 115) = 2.69, p < .05, partial η2 = .07, the post hoc com-parisons were nonsignificant. Similarities, F(3, 115) = 3.91, p < .05, partial η2 = .10, was poorer in all of the clinical groups compared to the group of controls (Mann-Whitney U for the group with ADHD = 382.0, p < .01; dyslexia = 582.0, p < .05; and comorbid = 91.5, p < .05). Vocabulary, F(3, 115) = 3.06, p < .05, partial η2 = .07,

was poorer in those with dyslexia (Mann-Whitney U = 517.5, p < .01) and ADHD (p < .05) compared to the controls. Digit Span, F(3, 115) = 4.75, p < .01, partial η2 = .11, and Letter-Number Sequencing, F(3, 115) = 2.69, p < .05, partial η2 = .07, were poorer in the dyslexia group compared to the controls (p < .01 and p < .05, respectively). Digit Symbol Coding, F(3, 115) = 7.45, p < .001, partial η2 = .16, was poorer in all the clinical groups compared to the controls (all ps < .01).

Cluster Analysis

Only the clinical groups were entered into a cluster analysis that was based on the subtest standard scores. The cluster analysis resulted in four clusters that were named average (n = 44; 56%), poor PO (n = 16; 20%), above average (n = 10; 13%), and poor WM (n = 9; 11%). Their profiles are depicted in Figure 3, and the cluster differences in WAIS-III subtests are presented in Table 4. There was no relation between the membership of a clinical group and a cluster, χ2(6) = 5.80, ns (for details, see Table 5).

Discussion

This study is the first for Project DyAdd in which we investigate the similarities and differences between par-ticipants with dyslexia, ADHD, dyslexia together with ADHD (a small comorbid group), and healthy controls,


using clinical neuropsychological, experimental psy-chophysical, and biological methods. The specific focus of this study was in characterizing the cognitive profiles of the groups with WAIS-III. We first set out to confirm the previously observed difficulties compared to a group of healthy controls. The second and novel aim was to compare the performance of the three clinical groups to each other. The last approach was to search for perfor-mance profiles using a cluster analysis conducted over all the clinical groups. With these methods, we tried to clarify whether the clinical groups had shared or group-specific characteristics compared to the controls or to each other.

Clinical Groups Compared to Controls

All the clinical groups performed well compared to the general norms: The mean of every quotient of a clinical group was above the normative mean. Only 1 participant out of the full sample was excluded based on poor FIQ (less than 70). Because the data were not cor-rected to a great extent, this suggests that developmental impairment is not necessarily related to low WAIS-III performance. The good level of performance in all groups could also reflect selective sampling. All of the current participants came from the capital area. In the Finnish standardization sample of WAIS-III, the average FIQ in the metropolitan area was 110, whereas in other areas it ranged between 94 and 102 (Wechsler, 2005), and the high IQs of the current control group reflect this geo-graphic advantage. Another contributing factor was the

level of education, which served as the only variable on socioeconomic status. Our data included more partici-pants with a higher educational level than the standard-ization sample, as is often the case with people living in the capital area. As higher level of education was related to a higher FIQ in the standardization sample (i.e., 110 contrasted to the mean of 100), this factor can explain the higher-than-expected FIQs of the current groups. Thus, the good level of performance of the clinical groups may not generalize to other samples with less education or lower socioeconomic status. However, the relative differences between the groups and the qualita-tive characteristics of their profiles may be more univer-sal. These are discussed in more detail below.

Shared Characteristics in the Clinical Groups

The pattern of results suggests a shared relative diffi-culty in a factor that reflects processing speed. This is an often reported finding in learning difficulty (Alm & Kaufman, 2002; Frauenheim & Heckerl, 1983; Kaufman & Lichtenberger, 1999, 2006) and ADHD literature (Woods et al., 2002). Accordingly, impaired processing speed has been suggested to be the shared cognitive dif-ficulty in dyslexia, ADHD, and their comorbid combina-tion (Shanahan et al., 2006). The deficit has been suggested to be more emphasized in dyslexia and to be related to output rather than input in ADHD (Shanahan et al., 2006). This reasoning has been based on results obtained with clinical neuropsychological methods, such as Stroop (Shanahan et al., 2006), Trail Making Test (Chhabildas, Pennington, & Willcutt, 2001), and rapid automatized naming (Bidwell, Willcutt, DeFries, & Pennington, 2007). In our previous data with dyslexic participants and fluently reading participants, the PS fac-tor of WAIS correlated positively and statistically sig-nificantly with various summary variables that reflected other areas of processing speed (unpublished results of the data in Laasonen, 2002). These included naming speed, reading speed, and nonverbal perceptual temporal processing speed. Confrontation naming and reading rely most probably on both input and output speed. The temporal processing tasks reflect mostly input speed, however. Thus, at least in dyslexic and fluently reading participants, PS impairment seems to be related to both input and output and to both nonverbal and verbal pro-cessing speed.

The groups with ADHD and dyslexia shared some additional verbal characteristics that did not emerge in the small comorbid group. Although the verbal relative impairment of the dyslexia group was fully expected,

Figure 3Factor Profiles of the Four Cluster Solution Based

on Wechsler Adult Intelligence Scale–Third Edition (WAIS-III) Subtest Standard

Scores

Note: The solid and dashed lines indicate the norm mean (100) and ±1 standard deviations (15). VC = verbal comprehension; WM = working memory; PO = perceptual organization; PS = processing speed.


Table 4Differences Between the Clusters in Wechsler Adult Intelligence Scale–Third Edition Subtests

Subtest F(3, 75) p Post hoc, p < .05

Arithmetic 26.48 < .0001 Poor WM < poor PO < average < above averageSimilarities 17.87 < .0001 Poor WM = poor PO < average < above averageVocabulary 13.45 < .0001 Poor WM = poor PO < average = above averageDigit Span 29.74 < .0001 Poor WM < poor PO = average < above averageLetter-Number Sequencing 26.73 < .0001 Poor WM < poor PO = average < above averageDigit Symbol Coding 4.20 .008 Poor WM < average = above average; poor PO < above average Block Design 39.48 < .0001 Poor PO < poor WM = average < above averageMatrix Reasoning 27.07 < .0001 Poor PO < poor WM = average < above average

Note: The F values refer to ANOVAs conducted for each subtest. The post hoc significances refer to Bonferroni-corrected post hoc tests (most of the subtests) or Mann-Whitney/Wilcoxon’s rank-sum test (Similarites, Arithmetic, and Digit Symbol Coding). PO = perceptual organization; WM = working memory.

Table 5Cross-Tabulation of Cluster and Clinical Group Memberships

Cluster

Group

Poor Perceptual Organization

Poor Working Memory

Average

Above Average

Total

Attention-deficit/hyperactivity disorder 8 4 12 6 30 Dyslexia 6 4 27 3 40 Comorbid 2 1 5 1 9 Total 16 9 44 10 79

language-related relative difficulties in the ADHD group were perhaps surprising. However, this pattern of results may be explained by other specific difficulties that pro-duce secondary relative impairments in language-related tasks in those with ADHD. ADHD has been shown to relate also to other verbal difficulties in, for example, organizing and monitoring verbal narrative (Purvis & Tannock, 1997) and naming speed (as described above). It is suggested that these difficulties cannot be explained by comorbid factors, such as dyslexia or specific lan-guage impairment (Purvis & Tannock, 1997). Instead, we suggest that they might be explained, for example, by a more general impairment in executive functions. Thus, the basic cognitive difficulty that explains the similarly poor scores in dyslexia and ADHD may vary.

Specific Characteristics in the Clinical Groups

ADHD. In addition to the shared relative difficulties, there was only one group-specific difficulty when com-pared to the current controls: PIQ was lower in the ADHD group than in the controls. PIQ is known to be lowered in various conditions, including chronic alco-hol abuse and neurological disorders. These include, for example, traumatic brain injury, Alzheimer’s, Huntington’s, and Parkinson’s diseases (Psychological

Corporation, 1997). These conditions were excluded in our sample, but PIQ can be affected by such a spectrum of conditions that it is not surprising that it was lower also in the current group with ADHD. Many of the previ-ously found difficulties did not emerge in the current sample. We suggest, following Bridgett and Walker (2006), that the earlier results may have been affected by comorbid factors in the ADHD group.

Dyslexia. In addition to the shared characteristics, the dyslexia group had only verbal relative difficulties. Dyslexia is by definition related to behavioral difficulties in reading. It is also rather commonly accepted that dif-ficulties in phonological processing can cause reading difficulties (Bradley & Bryant, 1983). Further, these phonological difficulties seem to emerge regardless of language and orthography (Paulesu et al., 2001). Phonological processing can be seen as a higher con-struct that encompasses multiple areas, one of which is phonological memory (Torgesen, Wagner, & Rashotte, 1994). Digit span forward is a commonly used estimate of phonological memory, and it is a subcomponent of the subtest Digit Span and thus the factor WM. Therefore, the dyslexia-specific relative difficulties in WM were fully expected.


Subtests Profiles

According to the Ottem (2002) ranking system, sim-ple verbal (Similarities and Vocabulary) and complex performance (Digit Symbol Coding) tasks differentiated all the clinical groups from the controls, whereas only the dyslexia group could be differentiated based on the complex verbal tasks (Digit Span and perhaps also Letter-Number Sequencing). No differences were found when the clinical groups were compared to each other.

The group differences in the WAIS-III factors above were reflected also in the Bannatyne (1974) categoriza-tion. Certain subtests belonging to verbal comprehen-sion posed relative difficulties to all the groups (Similarities) and some especially to the groups with ADHD and dyslexia (Vocabulary). Subtests belonging to acquired knowledge (Vocabulary) were relatively impaired in the groups with ADHD and dyslexia. Sequential ability subtests were relatively impaired in all groups (Digit Symbol Coding) and especially in those with dyslexia (Digit Span, Letter-Number Sequencing). Spatial ability was not difficult for the current clinical groups. Again, no differences were found between the clinical groups.

The subtest differences were inspected also in relation to the subtests composing the ACID derivatives CAD, AD, and SCAD. The main effect of group was signifi-cant in all these subtests (see Table 3), but the dyslexia group was the only one that had relative difficulties in D (i.e., Digit Span). In Arithmetic, the significant main effect was not reflected in the post hoc tests. Thus, a relative SC(A) profile was a shared characteristic for all the clinical groups, whereas only the dyslexia group had the relative SC(A)D profile. Contrary to the expecta-tions, the Block Design subtest did not differentiate the clinical groups from the controls. Thus, the ABCD impairment suggested in the introduction was not con-firmed. Instead, the dyslexia group had a VSDLC pro-file, the ADHD group a VSC profile, and the comorbid group a SC profile, compared to the controls. The clini-cal groups did not differ from each other.

Compared to the WDI classification, no clear pattern emerged. Verbal task in the “hold” category (Vocabulary) posed relative difficulties to the ADHD and dyslexia groups. Verbal subtests in the “don’t hold” category were relatively impaired in all (Similarities) but especially in the dyslexia group (Digit Span). Performance subtests belonging to the “hold” category did not differentiate the groups from each other. However, some in the “don’t hold” category were relatively impaired in all the clinical groups (Digit Symbol Coding). All of these differences emerged only when compared to the controls.

Taken together, also in these complementary approaches, the dyslexia group was the most and the comorbid group the least affected compared to the controls. Differentiating one clinical condition from another based on these pro-files does not appear very useful because all the differ-ences emerged when compared to the group of controls.

Comparisons Between the Clinical Groups

The second aim of this study was to compare the per-formance of the three clinical groups head-to-head to each other with these same measures. Besides the shared and specific differences that emerged compared to the controls, no significant differences or even trends for them were found between the clinical groups. Previous research has not concentrated on comparing develop-mentally impaired groups on measures of intelligence. However, the suggested behavioral and cognitive diffi-culties related to these disorders could have been expected to affect the WAIS-III subtest performance to such an extent that it would have led to differences between the clinical groups. This was not the case, how-ever, and thus the groups could be characterized only in relation to the controls.

Performance Profiles Irrespective of the Clinical Group

An additional approach in the current study was to search for performance profiles with a cluster analysis conducted over all the clinical groups. We hypothesized that if the clinical groups had specific characteristics that separated them from one another, these characteristics would result in clusters that reflect the group member-ship. Alternatively, all the clinical groups could share difficulties. In cluster analysis, these shared characteris-tics would then result in clusters that do not reflect the group membership.

The analysis resulted in four clusters. One of them (the cluster above average) grouped individuals who on average performed above +1 SD in almost every subtest. In every comparison except one, they were better than participants in other clusters. The majority of this clus-ter’s members had ADHD only. Another cluster was within +/–1 SD of the mean in every subtest (the cluster average). The majority of the members had dyslexia. This was the largest cluster, and therefore, there were more participants with ADHD in this cluster compared to the other clusters (i.e., 12 vs. 8, 6, and 4). In addition to these two “unimpaired” clusters, two groups emerged that had rather specific difficulties. The first was defined by relative difficulties in tasks composing the PO factor


(the cluster poor PO). The second cluster depicted diffi-culties in tasks composing the WM factor (the cluster poor WM). Participants in these two clusters came rather evenly from the groups with dyslexia and ADHD only.

In the original group comparisons, factor PO did not differentiate the clinical groups from the controls or from each other. Nor did the subtests belonging to the PO factor pose difficulties to any of the clinical groups. Here, one of the clusters had specific relative difficulties in the subtests of PO, that is, Block Design and Matrix Reasoning. Thus, participants in the original clinical groups had difficulties that were not frequent or severe enough to emerge in the original group comparisons. Instead, these affected participants were scattered across the clinical groups. In the original group comparisons, factor WM was relatively impaired only in the dyslexia group. Here, participants from other clinical groups clus-tered together based on their impaired WM. Thus, poor WM was not related only to dyslexia.

Although there seemed to be some group biases in the cluster memberships, the solution was shown to be inde-pendent of the original group membership. In other words, dyslexic readers did not group together in one cluster, those with ADHD in another, and the comorbid group in a third cluster. Instead, the clusters reflected group-inde-pendent characteristics that emerged in the current pooled learning difficulty sample. This indicates that the partici-pants shared characteristics across the clinical groups. Further, these shared characteristic defined them better than those that related to their diagnostic groups.

Comorbidity Between Dyslexia and ADHD

The small comorbid group of this study was the least affected in the group comparisons and exhibited no group-specific characteristics. They had relative diffi-culties in only those tasks that both the groups with dyslexia and ADHD only were impaired in. There are only few studies with groups of dyslexia, ADHD, and their comorbid combination. In one of them, a double dissociation was found between dyslexia and ADHD. Dyslexic and comorbid boys were impaired in phono-logical processing compared to controls and those with ADHD only, whereas the ADHD only group was impaired in executive functions (Pennington, Groisser, & Welsh, 1993). That is, the comorbid group resembled more the dyslexia group. However, in a later study, ADHD in children was associated with impaired execu-tive functions, more specifically inhibition difficulties, and dyslexia with a working memory impairment, whereas the comorbid group was impaired in almost all tasks (Willcutt et al., 2001).

Willcutt and his colleagues have reviewed alternative hypotheses and predictions that would explain the diffi-culties of the comorbid group with both dyslexia and ADHD (Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005). The phenocopy hypothesis suggests that one difficulty (e.g., dyslexia) may result in an impres-sion or phenotype of another difficulty (e.g., ADHD) without the typical etiology of the second difficulty. Therefore, the comorbid group should exhibit basic-level cognitive difficulties of the primary impairment, that is, either dyslexia or ADHD. In the current study, this was not the case at the group level. The cognitive subtype hypoth-esis suggests that comorbid cases represent a subtype with different etiology that is distinct from dyslexia and ADHD only. Therefore, the comorbid group should show an impairment that differs from the sum of the difficulties existing alone. This was not the case in the current study, although the comorbid group did appear to be the least affected. The common etiology hypothesis suggests that both dyslexia and ADHD share etiological influences. The cross-assortment hypothesis suggests that people with dyslexia and ADHD tend to find spouses with similar conditions to their own, and therefore also have children with a partner who has the same problem more often than would be expected by chance. These two hypotheses sug-gest that the comorbid group should have the difficulties that the groups with dyslexia and ADHD only have. The latter hypothesis suggests a full summation of the difficul-ties, whereas the former suggests a summation in at least one domain. In the current study, the very small comorbid group did have a shared relative impairment in processing speed with the other clinical groups, but not in other domains. In addition, they had relative difficulties only in areas that both the groups with dyslexia and ADHD only were impaired in. This provides support for the common etiology hypothesis. In Willcutt et al.’s (2005) research with children, the comorbid group exhibited a combina-tion of the difficulties found in the groups with dyslexia and ADHD only. They concluded that the results did not support the phenocopy or cognitive subtype hypotheses but, instead, favored the common etiology hypothesis. This was the case also in the current study.

Conclusion

The current study of Project DyAdd focused on the WAIS-III profiles of adults with dyslexia, ADHD, and their comorbid combination. The results suggest that these groups share a relative impairment in general ability and more specifically in processing speed. A cluster analysis resulted in group-independent clusters, which indicates


also shared characteristics across the clinical groups. In addition to the shared characteristics, the dyslexia group had specific relative difficulties in the WM factor, and those with ADHD only could be characterized by rela-tively poor PIQ. The profile of the small comorbid group was the least affected. They had relative difficulties only in tasks that both the dyslexia and ADHD only groups were impaired in. Upcoming publications of Project DyAdd will describe phonological processing, achievement, and fatty acid status in these same groups (Laasonen, Hokkanen, et al., 2009a, 2009b; Laasonen, Lehtinen, et al., 2009).

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AppendixAssessment Battery of Project DyAdd, First

Phase Appointments

0. Before the appointments, participants answered to several questionnaires

a. Adult Reading History Questionnaire (ARHQ; Lefly & Pennington, 2000)b. Brown Attention Deficit Disorder Scales (BADDS; Brown, 2001)c. Wender Utah Rating Scale (WURS; Ward, Wender, & Reimherr, 1993)d. Adult Problem Questionnaire (APQ; De Quiros & Kinsbourne, 2001)

1. Beck Depression Inventory II (BDI-II; Beck, Steer, & Brown, 2004)2. Basic functions

a. Visuospatial (Clock Drawing Test) and Visuoconstructive (3-D Copy)b. Psychomotor (Finger Tapping, Luria: Dynamic and Recipro- cal Coordination, Motor Control)

3. WAIS III (Wechsler, 2005)4. WMS III (Wechsler, 2008)

a. Immediate (Logical Memory I, Visual Reproduction I, Word Lists I, Digit Span, Spatial Span, Letter-Number Sequencing)b. Delayed (Logical Memory II, Visual Reproduction II, Word Lists II)

5. Attentiona. Color Trails Test (D’Elia, Staz, Uchiyama, & White, 1996) and Dual Taskb. Word Fluency and Stroop

6. Computerized tasksa. Continuous Performance Test (Conners, 2004)b. Cantab (Big/Little Circle, Intra-Extra Dimensional Set Shifting, Matching to Sample Visual Search, Reaction Time, Rapid Visual Information Processing, Stockings of Cambridge, Spatial Working Memory; Cambridge Neuropsychological Test Automated Battery, 2004)

7. Phonological processinga. Awareness (Phoneme Synthesis, Laasonen, 2002; and Pig Latin, Nevala, Kairaluoma, Ahonen, Aro, & Holopainen, 2006)b. Memory (Pseudoword Span and Learning: i. Immediate, ii. Delayed 1, iii Delayed 2; Service, Maury, & Luotoniemi, 2007)c. Naming (Rapid Alternating Stimulus; Wolf, 1986)

(continued)

Appendix (continued)

8. Reading and spellinga. Technical reading (Word List and Pseudoword List Reading, Nevala et al., 2006; reading aloud a narrative text, Laasonen, 2002; segregating word chains and searching for misspellings, Holopainen, Kairaluoma, Nevala, Ahonen, & Aro, 2004)b. Reading comprehension (forced choice task, Nevala et al., 2006; searching for incorrect words within a story, Holopainen et al., 2004) c. Spelling (Pseudoword Writing, Holopainen et al., 2004)

9. Arithmetic (RMAT: a paper-and-pencil numeracy battery; Räsänen, 2004)

Note: The single tasks tapping to each domain are described in paren-theses. Tasks details that are not indicated in the table can be found in Lezak, Howieson, Loring, Hannay, and Fischer, 2004. Possible pre-sentation alternatives were 5a-4a-2b-7bi-7c-9-4b-2a-7a-8 → 5b-3-7bii-1-7biii-6; 4a-2b-7bi-7c-9-4b-2a-7a-8-5a → 3-7bii-1-7biii-6-5b; 5b-3-7bi-1-6 → 5a-4a-2b-7bii-7c-9-7biii-4b-2a-7a-8; and 3-7bi-1-7biii-6-5b → 4a-2b-7bii-7c-9-7biii-4b-2a-7a-8-5a.


Castellanos, F. X., & Tannock, R. (2002). Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes. Nature Reviews Neuroscience, 3, 617–628.

Chhabildas, N., Pennington, B. F., & Willcutt, E. G. (2001). A com-parison of the neuropsychological profiles of the DSM-IV sub-types of ADHD. Journal of Abnormal Child Psychology, 29, 529–540.

Conners, C. K. (2004). The Conners Continuous Performance Test–II. Toronto, Canada: Multi-Health Systems.

Daryn, E. (2000). Lessons from the follow-up of developmental dys-lexics. Medical Hypotheses, 54, 434–437.

D’Elia, L. F., Satz, P., Uchiyama, C. L., & White, T. (1996). Color Trails Test: Professional manual. Odessa, FL: Psychological Assessment Resources.

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Marja Laasonen, PhD, is a postdoctoral researcher in the Department of Psychology, University of Helsinki, and a neuropsychologist in the Department of Phoniatrics, Helsinki University Central Hospital, Helsinki, Finland. Her research interests include dyslexia, ADHD, and orofacial clefts.

Sami Leppämäki, MD, PhD, is the deputy chief physician of the Clinic for Neuropsychiatry, Department of Psychiatry, Helsinki University Central Hospital. His research interests include affec-tive disorders and developmental neuropsychiatric disorders.

Pekka Tani, MD, PhD, is the clinical director of the Clinic for Neuropsychiatry, Department of Psychiatry, Helsinki University Central Hospital, and an adjunct professor of psychiatry in the Department of Psychiatry, University of Helsinki, Finland. His research interests include developmental neuropsychiatric disorders and sleep disorders in psychiatry.

Laura Hokkanen, PhD, is an adjunct professor of clinical neuropsychology in the Department of Psychology, University of Helsinki, Finland. Her research interests include learning disabilities, memory disorders, and dementia.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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