Prisms throw light on developmental disorders

Neuropsychologia 45 (2007) 1921–1930

Prisms throw light on developmental disorders Rebecca L. Brookes ∗ , Roderick I. Nicolson, Angela J. Fawcett Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, United Kingdom Received 15 June 2006; received in revised form 22 November 2006; accepted 25 November 2006 Available online 30 January 2007

Abstract Prism adaptation, in which the participant adapts to prismatic glasses that deflect vision laterally, is a specific test of cerebellar function. Fourteen dyslexic children (mean age 13.5 years); 14 children with developmental coordination disorder (DCD): 6 of whom had comorbid dyslexia; and 12 control children matched for age and IQ underwent prism adaptation (assessed by clay throwing accuracy to a 16.7◦ visual displacement). All 8 DCD children, 5 of the 6 children with comorbid DCD and dyslexia and 10 of the 14 dyslexic children showed an impaired rate of adaptation, thereby providing strong evidence of impaired cerebellar function in DCD and developmental dyslexia. Taken together with other emerging evidence of overlap between developmental disorders, these findings highlight the importance of complementing research on the individual disorders with research on the commonalities between the disorders. © 2006 Elsevier Ltd. All rights reserved. Keywords: Dyslexia; Cerebellum; DCD; Learning; Adaptation; Comorbidity

1. Introduction Thirty years ago, the developmental disorders were characterised in terms of minimal brain dysfunction (Clements & Peters, 1962; Wender, 1978) or ‘soft neurological signs’ (Touwen & Sporrel, 1979). Subsequent research has focused primarily on analyses of the individual disorders, of which the most prevalent are now termed attention deficit and hyperactivity disorder (ADHD), dyslexia, developmental coordination disorder (DCD), specific language impairment (SLI), and autism. There remain significant difficulties in diagnosing these developmental disabilities with the DSM-IV (American Psychiatric Association, 1994) and the ICD-10 (World Health Organization, 1992) classifying the disorders primarily in terms of behaviour rather than underlying aetiology. It is well known that many symptoms seem to co-occur. Single-disorder theorists have attempted to find specific and positive criteria for diagnosis of a given disorder, but in general (with the possible exception of autism) such endeavours have been unsuccessful. A valuable example is the ‘phonologicalcore, variable difference’ model of dyslexia (Stanovich, 1988), which held that dyslexic children have a specific problem in

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phonological skills, and though they might have additional problems in other domains (such as motor skill) these were not ‘core’ problems. Subsequent research has revealed that almost all children with dyslexia do indeed have phonological difficulties (Vellutino, Fletcher, Snowling, & Scanlon, 2004). Unfortunately, in terms of this theoretical perspective, many children without dyslexia also have phonological difficulties (Morris et al., 1998; Stanovich, 1993; Stuebing et al., 2002). Furthermore, a high percentage of dyslexic children (at least 50 percent) show motor problems early on (Fawcett & Nicolson, 1995; Ramus, Pidgeon, & Frith, 2003a; Wolff, Michel, Ovrut, & Drake, 1990). The above analyses reflect the high ‘comorbidity’ (overlap) between dyslexia and other developmental disorders (Fletcher, Shaywitz, & Shaywitz, 1999; Gilger & Kaplan, 2001; Jongmans, Smits-Engelsman, & Schoemaker, 2003). Comorbidity problems appear to be particularly difficult in the case of DCD, which has been shown to be linked to ADHD, SLI, and dyslexia (Cantell, Smyth, & Ahonen, 2003; Dewey, Kaplan, Crawford, & Wilson, 2002; Henderson & Henderson, 2002; Kaplan, Dewey, Crawford, & Wilson, 2001; Kaplan, Wilson, Dewey, & Crawford, 1998; Macnab, Miller, & Polatajko, 2001; Visser, 2003). Gillberg (2003) refers to one grouping as deficits in attention, motor control and perception (DAMP). Hadders-Algra (2002) proposes the terminology MND (minor neurological dysfunction). Gilger and Kaplan (2001) propose the term atypical brain development (ABD).

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Irrespective of the terms used, the ‘core’ problems which seem to occur at sub-clinical levels across the specific developmental disorders appear to involve a range of co-dependent deficits including sensori-motor coordination, attention and motor skill. One recent approach to the issue of comorbidity in ADHD (Banaschewski et al., 2005) is to attempt to distinguish between unique pathways (within-disorder) and shared pathways (between-disorder). The authors call for the development of an extensive battery of well-defined and theoretically based tasks to assist in these analyses. In this paper we present the results of one such test. Our aim was to selectively assess cerebellar function, with little interference from other brain regions that normally conspire with the cerebellum to provide optimal learning and actions. Prism adaptation, a fundamental form of motor learning, selectively linked to the cerebellum (Kane & Thach, 1989; Martin, Keating, Goodkin, Bastian, & Thach, 1996a,b; Weiner, Hallet, & Funkenstein, 1983) was used with three groups: control children, children with DCD and children with dyslexia. We preface the study with a brief overview of the theoretical approaches to DCD and to dyslexia. 1.1. Developmental dyslexia Developmental dyslexia is traditionally defined as a failure to read, write or spell despite average IQ and conventional classroom experience (World Federation of Neurology, 1968). Estimates of the prevalence of dyslexia range from 5 percent to 17 percent. Research into developmental dyslexia has made considerable progress over the last 25 years, not least due to the discovery of the importance of phonological processing (e.g., Snowling, 1987) to predicting the onset (Bradley & Bryant, 1983) and in early remediation of reading problems (Bradley, 1988). These findings provide the basis for a central theoretical model in dyslexia, namely the phonological deficit hypothesis, which proposed that the origin of dyslexia lay in the linking of graphemes (written words) to phonemes (sounds). Although phonological problems provide a good account of the literacy deficits associated with dyslexia, the reconceptualisation of dyslexia as a multi-faceted syndrome has brought with it a number of challenges to this explanation. Dyslexia is currently accounted for by three competing causal frameworks: the phonological deficit framework; the magnocellular deficit framework, which holds that the difficulties in reading arise indirectly from abnormal processing in the auditory (Tallal, Miller, & Fitch, 1993), visual or visuo-motor magnocellular pathways (Stein, 2001) (for a review see Demonet, Taylor, & Chaix, 2004); and the cerebellar deficit hypothesis. The cerebellar deficit hypothesis (Nicolson, Fawcett, & Dean, 1995, 2001) suggests that a mild cerebellar deficit leads to difficulties in any skill dependent on the cerebellum for acquisition, automatisation or execution. The hypothesis therefore breaks with single mechanism accounts and provides an ‘umbrella’ explanation for phonological skills, memory, eye-movements, and motor skills. In summary, it accounts directly for poor balance and coordination and poor handwriting in dyslexics and indirectly for cognitive skills through automatisation and

articulation, linking to other hypotheses including poor oculomotor control (Dean, 1995). The CDH is supported by a recent reconceptualisation of the role of the cerebellum, following the initial hypothesis by Ito (1990) and Leiner, Leiner, & Dow (1989, 1993) that the cerebellar circuitry is able to provide the same function for cognitive skills as it does for motor skills. There is now strong evidence from brain imaging studies and studies with cerebellar patients that the right cerebellum is related to activation in the contra-lateral cortex leading to reduced activity in areas involved in language (Ackermann & Hertrich, 2000; Fabbro, Moretti, & Bava, 2000; Justus & Ivry, 2001; Marien, Engelborghs, Fabbro, & De Deyn, 2001; Silveri & Misciagna, 2000); attention (Akshoomoff & Courshesne, 1992); verbal working memory (Desmond & Fiez, 1998; Scott et al., 2001), and most recently, FMRI data have linked the cerebellum to reading (Moretti, Bava, Torre, Antonello, & Cazzato, 2002; Stanberry et al., 2006; Turkeltaub, Eden, Jones, & Zeffiro, 2002). Direct evidence for the role of the cerebellum in dyslexia is growing and includes behavioural evidence of posture and balance deficits (Brookes & Stirling, 2005; Fawcett & Nicolson, 1992; Fawcett, Nicolson, & Dean, 1996; McPhillips & Sheehy, 2004; Moe-Nilssen, Helbostad, Talcott, & Toennessen, 2003; Needle, Fawcett, & Nicolson, 2005; Yap & van der Leij, 1994); eye-movement deficits (Stein & Fowler, 1993); and articulation problems (Fawcett & Nicolson, 2002). Other studies revealed impaired eye blink conditioning (Coffin, Baroody, Schneider, & O’Neill, 2005; Nicolson, Daum, Schugens, Fawcett, & Schulz, 2002) and motor sequence learning deficits (Nicolson et al., 1999). Further direct evidence of cerebellar dysfunction in dyslexia comes from structural (Finch, Nicolson, & Fawcett, 2002; Rae et al., 2002), histological (Rae et al., 1998) and functional (Nicolson et al., 1999) differences found in the cerebellum, in adults with dyslexia. Though, the different research groups have taken different approaches to cerebellar analyses, the findings of both structural and functional studies seem to be converging on the right cerebellum, consistent with the neuroscience literature on cortico-cerebellar connections and cognitive processes. Nicolson and colleagues employed a sequence-learning task known to activate the cerebellum (Jenkins, Brooks, Nixon, Frackowiak, & Passingham, 1994). The original PET study reported that the cerebellum was activated during the first phases of learning a new sequence. The replication of this study comparing dyslexic and control groups found similar between-group differences for both the execution of a learned task and acquisition of a novel task. Overall results revealed that activation in the dyslexic right lateral cerebellum was significantly lower than that of controls in 80 percent of cases. Eckert et al. (2003) performed structural analyses and found that the right anterior lobes of the cerebellum were significantly smaller in dyslexics than controls. The authors propose that their study indicate anomalies in the cerebellar frontal circuit. Despite this converging evidence, the cerebellar deficit hypothesis remains controversial in that its emphasis on subcortical systems runs counter to traditional causal frameworks, and because of the difficulty of deriving objective behavioural measures that isolate cerebellar performance. Furthermore, a

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long-standing issue in the discipline is the high ‘comorbidity’ between different developmental disorders, as discussed earlier. Critics suggest that the apparent cerebellar findings are attributable to comorbid sub-types of dyslexia/ADHD (Wimmer, Mayringer, & Raberger, 1999) or dyslexia/DCD (Ramus, 2003). 1.2. Developmental coordination disorder In many ways, association of cerebellar deficit with motor disorder is more natural than the link to dyslexia, given the established role of the cerebellum in motor skill automatisation and coordination (Holmes, 1939). Developmental coordination disorder is defined as “a marked impairment in the development of motor coordination” (DSM-IV, American Psychiatric Association, 1994, p. 53). Researchers of DCD have observed literacy and learning deficits similar to those displayed in dyslexia (Geuze, Jongmans, Schoemaker, & Smits-Engelsman, 2001), together with a high comorbidity between DCD and dyslexia (Jongmans et al., 2003; Kaplan et al., 2001). Jongmans et al. (2003) established that children with combined DCD and dyslexia were more impaired than DCD-only children on perceptual-motor ability and on manual dexterity and balance tasks but not ball-skill tasks. O’Hare and Khalid (2002) reported a high incidence of reading, phonological, and writing problems in a group of 23 children with DCD, and speculated that the underlying cause might be cerebellar deficit. Both Piek and Dyck (2004) and Visser (2003) mention cerebellar deficit as a possible underlying cause of these comorbid difficulties. In a review by Diamond (2000), interactions between the pre-frontal cortex and the cerebellum are noted as possible explanations of ADHD and of DCD. It is surprising therefore that despite the large numbers of studies reported in the DCD literature, including those implicating the cerebellum (Lundy-Ekman, Ivry, Keele, & Woollacott, 1991), cerebellar deficit is not currently a major explanatory construct in DCD research. 1.3. Prism adaptation One form of learning strongly associated with cerebellar function is prism adaptation (Held, 1965)—a fundamental form of sensori-motor learning where visual gaze is disturbed via laterally displacing prisms and proprioception has to adapt to the new parameters. This occurs automatically within a relatively short time. The mechanism involved is thought to be long-term depression of output of cerebellar Purkinje cell/parallel fibre complexes modulated via error signals from the climbing fibres originating in the brain stem (Ito, 1989, 2001). Evidence for the role of the cerebellum in prism adaptation has come from studies in humans with cerebellar disorders on target pointing, throwing tasks (Kane & Thach, 1989), and anticipatory muscle adaptation (Lang & Bastian, 1999). Prism adaptation in monkeys has been shown to be selectively disrupted by lesions to the cerebellar cortex, a major site for visual inputs, whereas lesions to vermian lobules VI/VII, oculomotor

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site, have been shown to induce ataxia but not disrupt prism adaptation (Baizer, Kralj-Hans, & Glickstein, 1999). Similarly, cerebellar damage to the right sided posterior inferior cerebellar artery territory has been shown to impair adaptation but not previously learned throwing performance (Martin et al., 1996a,b). Furthermore, unilateral lesion of anterior cerebellum leads to ipsidirectional impairment of prism adaptation (Pisella et al., 2005). There is no doubt that prism adaptation is not a simple process (Redding, Rossetti, & Wallace, 2005) and other brain regions which receive cerebellar outputs are involved at some point in the process [e.g. the posterior parietal cortex (Clower, West, Lynch, & Strick, 2001) and ventral pre-motor cortex (Dum & Strick, 2003)]. However, there is equally little doubt that “The cerebellum is an essential component in the network for both types [walking and reaching] of prism adaptation” (Morton & Bastian, 2004). The present study was therefore designed to address three key issues: whether children with DCD show a deficit in prism adaptation (compared with an age- and IQ-matched control group)—a key prediction if cerebellar impairment is involved in DCD; whether dyslexic children show a prism adaptation deficit; and whether there appear to be any systematic differences between prism adaptation in the two experimental groups. 2. Methods 2.1. Participants Participants were 14 dyslexic adolescents, 12 control adolescents, and 14 children with prior diagnoses of DCD. Six of the 14 DCD group had literacy deficits (defined in the same way as the dyslexic group), so that they were technically comorbid dyslexia/DCD. This allows a preliminary analysis of the effects (if any) of comorbidity on the prism adaptation. The participants were recruited through the University of Sheffield Dyslexia Research Group panel and from a specialist school for developmental disorders. All children with dyslexia were screened to ensure they satisfied standard UK exclusionary criteria of IQ above 90 and reading age at least 18 months below their chronological age and had no additional diagnoses of other developmental disorders.1 Controls satisfied the criterion of reading skills no worse than 6 months below their age and had an IQ above 90. The DCD groups were diagnosed by occupational therapists and educational psychologists for poor coordination before being referred to the specific learning disabilities school. All DCD children satisfied the DSM IV criteria of “a marked impairment in the development of motor coordination” (American Psychiatric Association, 1994). Although IQ is not included in the exclusionary criterion for DCD, all participants had IQ ≥ 89. Information on diagnostic instruments, provided to us by the school, stated that all DCD children had been diagnosed with DCD on the basis of one or a combination of instruments including the WISC-III (1993) criteria for DCD, the VMI (Beery, 1998), and the Movement ABC (Henderson & Sugden, 1992). Demographic and psychometric data are displayed in Table 1. Six additional participants did not meet the exclusion criteria for any of the main experimental groups: two remediated dyslexics, two DCD participants with poor reading >6 months and <18 months, and two DCD participants with and IQ <89. These participants are considered in a separate analysis.

1

Participants from the specialist school were routinely screened by the school for comorbid developmental disorders. Participants from the dyslexia research panel were referred by local schools in the Sheffield area following specific diagnoses by educational psychologists of dyslexia without additional clinical conditions.

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Table 1 Participant data Group

Controls Dyslexics DCD (a) DCD (b)

Gender

Seven males, five females Ten males, four females Eight males Five males, one female

Age

Reading age discrepancy (years)

IQ

Mean (SD)

Range

Mean (SD)

Mean (SD)

Range

13.6 (1.8) 13.5 (1.4) 12.5 (2.8) 13.9 (0.9)

9.0–15.4 11.0–15.5 7.5–15.3 12.6–15.3

+3.1 (1.8) −4.3 (1.6) +0.8 (1.4) −3.2 (0.6)

115 (16) 105 (11.1) 109 (16.1) 97.4 (2.6)

92–141 90–124 89–135 95–101

DCD (a) refers to those in the DCD group without literacy deficits and DCD (b) the subgroup with DCD and dyslexia.

2.2. Ethics All research was approved by the ethics committee at the Department of Psychology, University of Sheffield. All participants and their parents were fully informed about the study procedure before consenting to take part and were aware of their right to withdraw at anytime. Full debriefs and explanations of the prism adaptation paradigm were given post-experiment.

We conducted a linear regression between time and horizontal displacement for the first seven throws (fast phase), taking the intercept as −60, the theoretical initial displacement. We took this slope as the rate of adaptation (b) which can be summarised as adaptation rate equation : y = b∗ t − 60 where y is the displacement on throw t (prism phase), b the adaptation rate and t is the time (number of adaptation trials).

2.3. IQ and reading skills All groups were tested on literacy and IQ using the same instruments. IQ scores were calculated using the WISC-III (Wechsler, 1992) test of intelligence. Reading was assessed using WORD (Wechsler, 1993) reading scale. Reading age discrepancies were calculated by subtracting the participant’s chronological age from these scores.

2.4. Experimental procedure Following the procedure in Martin et al. (1996a,b), participants were instructed to perform 30 trials, throwing clay balls (weighing 10 g) underarm at a target board 2 m wide by 0.8 m high whilst wearing clear goggles which covered the front and periphery of their vision and with their arms occluded by a plastic screen. The target, constructed on a board of 1 cm2 with X and Y axes running through the centre, was placed at shoulder height, 2 m away from the participant. Following the baseline performance condition, subjects donned 30 diopter fresnel plastic lenses (mounted in goggles otherwise equivalent to the clear ones) which displaced their gaze to the right, making the target appear to be 60 cm to the left of its actual position, and repeated the procedure. The experimenter noted the grid reference for each throw. The horizontal distance from the target (cm) was plotted against the trial number. The initial aim wearing prisms is theoretically displaced by 60 cm to the left, and adaptation takes place as participants’ throws move in the direction of the target. Immediately postadaptation, the removal of the prisms causes the participant’s throw to deviate in the opposite direction, the negative after effect.

2.5.3. Percentage return Percentage return was calculated using the method outlined in (Lang & Bastian, 1999), calculating actual return in relation to the expected distance to return: percentage return =

−60 − r × 100 −60 − d

where r is mean impact displacement on the last 10 trials of adaptation and d is the performance displacement (mean displacement of last 10 pre-adaptation trials). Adaptation gives the rate of return to ‘baseline’ immediately after the prisms are worn, whereas percentage return gives the degree to which the adaptation actually reaches the previous baseline after many adaptation trials. 2.5.4. After effect Negative after effects occur on removal of prisms, post-adaptation. Percentage after effect is the degree to which a participant’s throw, on having adapted, deviates in the opposite direction. This was calculated as below, where p is the 100 first throw post-prism phase:percentage after effect = (−60−d)(p−d) .

2.5. Analyses Two measures of adaptation were calculated. First, the rate at which participants adapted (adaptation rate), and second the degree to which participants returned to pre-adaptation performance levels (percent return). Measures of pre-prism performance and negative after-effects were also taken. 2.5.1. Performance The participants’ throwing ability was calculated as the average horizontal error (cm) from the target centre over the last 10 trials in the pre-prism condition. 2.5.2. Adaptation rate Changes in adaptation are initially rapid and then diminish as adaptation approaches optimum. Examination of the results revealed that adaptation was essentially complete after around seven throws for most participants, with these initial seven throws being crucial for measuring the rate of adaptation (see Fig. 1). It is conventional to fit the adaptation by an exponential or inverse power function, or via two straight lines reflecting the ‘fast phase’ and the ‘slow phase’.

Fig. 1. Initial adaptation rate for each group. The graph illustrates average displacement for each group from throw (post-prism) 1–30. Initial rate of adaptation (linear regression) is indicated by the bold lines. From left to right the lines of best fit are for the control, dyslexic, DCD, and DCD/dyslexic groups, respectively.  = control group; – = dyslexic group;  = DCD group;  = DCD/dyslexia group.

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3. Results

3.4. After effects

3.1. Performance without prisms

There was no significant difference between groups in terms of after-effects (F = 1.7, p = .177). Interestingly, despite showing lower adaptation rates, the DCD/dyslexic and dyslexic subjects showed a greater after-effect relative to the controls (mean after effects: controls, 40 percent; dyslexic, 50 percent; pure DCD 35 percent; DCD/dyslexic 52 percent).

None of the groups differed significantly in their ability to throw on target without prisms (F(3) = .86, p = .471; see Table 2 for all group means and SDs). There was however a moderate difference between the control and pure DCD group (0.9 SD), probably not significant due to low power.

3.5. Individual analyses 3.2. Adaptation rate The critical measure of prism adaptation is the adaptation rate. The mean group results (displayed in Fig. 1) for the adaptation rate were as follows: • • • •

control group: y = 9.8t − 60; dyslexic group: y = 6.8t − 60; pure DCD group: y = 5.6t − 60; DCD + dyslexia group: y = 4.5t − 60.

This indicates that whereas on average the control group adapted by 9.8 cm every throw, the dyslexic and DCD groups adapted only 6.8 and 5.6, and 4.5 cm, respectively. A one-factor, between subjects ANOVA showed that controls adapted significantly faster than all three of the other groups (F(3) = 9.6, P < .0001). A pairwise comparison confirmed deficits for the dyslexic group compared to controls (mean difference = 2.7 p < .01), similar results were found for the pure DCD group (mean difference = 4.1, p < .001) and the comorbid DCD/dyslexia group (mean difference = 5.5, p < .001). There was also significant difference between the dyslexic and DCD group with dyslexia. Compared with the dyslexic group the DCD/dyslexia group showed a mean deficit of 2.8 (p < .05). Additional between groups comparison of effect sizes (Cohen, 1988) were calculated for the three clinical groups by subtracting their mean from the control group mean and dividing by the control group standard deviation. Examination of effect sizes showed mean adaptation rates 1.3, 1.8, and 2.3 SD below the control mean for the dyslexic, pure DCD, and DCD/dyslexia groups, respectively.

The above analysis indicated poorer adaptation rate for the dyslexic group, pure DCD, and DCD/dyslexia group. Given the considerable heterogeneity within developmental disorders, an analysis of individual data was undertaken for the primary measure, rate of adaptation. The criterion for having adapted at a normal rate was taken as being within 1 SD of the control mean (9.8 ± 2.3). Ten out of 12 controls (83 percent) adapted at a normal rate, whereas only 5/14 dyslexics (35.7 percent), 1/8 ‘pure’ DCD subjects (12.5 percent), and 1/6 DCD/dyslexia subjects (16.6 percent) adapted at a rate within 1 SD of the control mean. Two control participants showed ‘abnormal’ levels of adaptation. For comparability with previous examinations of cerebellar function in dyslexia, we followed the procedure of Ramus et al. (2003a,b), and undertook a secondary re-calculation of the data having adjusted the control mean and SD without the ‘abnormal’ participants and taking the cut-off as −1.65 SD In the adjusted analysis normal adaptation was within 1.65 SD of 10.7 ± 1.6 cm per throw. The results showed that 10/14 (71.4 percent) of dyslexics, 100 percent of pure DCD children, and 5/6 (83.3 percent) of DCD/dyslexic children showed abnormal rates of adaptation. The individual effect sizes are displayed in Fig. 2. 3.6. Analysis of the effects of age and IQ In view of the lower average age of the pure DCD group and the slight differences in mean IQ across groups, it is valuable to examine the effect of age and IQ variation upon the main effect. An analysis of covariance (ANCOVA) for adaptation rate holding age and IQ as covariates showed no significant effects of IQ or age on adaptation rate (IQ: F(1) = 1.8, p = .19; age: F(1) = 3.5,

3.3. Adaptation (percentage return) The percentage return to baseline performance was higher in controls (78 percent return) than in the dyslexic (59 percent) or pure DCD (62 percent) group. This was not however the case for the DCD/dyslexia group (80 percent). A between groups ANOVA found that, overall, this difference was significant at the 0.05 level (F(3) = 3.4). Pairwise analyses confirmed this for the control versus dyslexic groups (mean difference = 23.7 percent, p < .01). Effect size analyses revealed that the dyslexic and pure DCD group showed deficits of 1.4 and 1.2 SD below the control mean, respectively. The DCD/dyslexic group showed a difference of −0.2 above the control mean.

Fig. 2. Individual effect sizes for adaptation rates. Effect sizes have been calculated using the approach of Ramus et al. (2003a,b). See text for details. Red line indicates cut-off for normal adaptation of −1.65 SD

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Table 2 Mean performance by group and task

Controls Dyslexics DCD (a) DCD (b)

Performance average error (cm)

Adaptation rate (cm per throw)

Percent return

After-effect

9.8 (5.3) 10.9 (6.5) 14.4 (8.0) 10.9 (6.2)

9.8 (2.3) 6.8 (2.3) 5.6 (2.1) 4.5 (2.5)

78 percent (13.8) 58.5 percent (18.8) 62.1 percent (26.5) 80.2 percent (13.4)

40.2 percent (21.1) 49.7 percent (14.8) 35.6 percent (17.3) 52.0 percent (13.9)

Standard deviations are given in parentheses. (a) refers to those in the DCD group without literacy deficits and (b) the subgroup with DCD and dyslexia.

Table 3 Data for excluded participants

Remediated dyslexic 1 Remediated dyslexic 2 DCD + NSPR 1 DCD + NSPR 2 DCD + GLD DCD + GLD a

IQ

Age

RA disca (years)

Performance average error (cm)

Adaptation rate

Percent return

After effect

118 102 117 96 73 84

14.9 12.8 11.2 7.6 12.1 16.3

+3.1 +0.1 −1.3 −1.3 −2.2 −2.7

8.3 4.2 5.8 8.8 11.6 4.7

6.7 8.3 3.4 6.0 0.9 0.4

84.1 17.4 45.0 55.1 39.4 27.4

61.3 39.6 45.6 32.6 70.7 45.1

Reading age discrepancy = chronological age-spelling age.

p = .07), and that the group difference was still significant when these were included in the model (F(3) = 8.4, P < .0001). 3.7. Excluded participants Six participants did not meet the strict exclusion criteria for either the dyslexic, control, or DCD groups. Two participants with previous diagnoses of dyslexia had now reached reading levels commensurate with their age, they are thus termed remediated dyslexics. In addition, two DCD participants showed reading deficits of 1.3 years, placing them outside the criteria for either ‘pure’ DCD or DCD with comorbid dyslexia (DCD with non-significant poor reading: DCD + NSPR). A further two DCD participants had reading difficulties and IQ below 89 (DCD with general learning difficulties: DCD + GLD). All excluded participants were examined separately of the main group analyses (see Table 3 for details). In examining adaptation rates, compared with control mean and standard deviation, 9.8 ± 2.3, the data from the excluded participants yielded effect sizes of −1.3 and −0.7 for the remediated dyslexics, −2.8 and −1.6 for the DCD participants with non-significant reading deficits, and −3.9 and −4.1 for the DCD participants with IQ < 89. 4. Discussion This study addressed differences in prism adaptation between four groups: dyslexic, control, DCD and comorbid DCD/dyslexia, and examined whether there were individual differences within the groups. Analysis of the prism adaptation rate indicated that the dyslexic, DCD and DCD/dyslexic groups adapted significantly more slowly than the controls. There was also a significantly smaller magnitude of return to baseline performance for the dyslexic group, demonstrating poorer learning over the course of the adaptation not merely slower adaptation in the first few post-adaptation trials.

Individual analyses allowed us to examine the prevalence of the dysfunction. The criterion for having adapted at the normal rate was set as within 1 SD of the control mean (9.8 ± 2.3). We found that 64.3 percent of dyslexics, 87.5 percent of DCD participants, and 83.3 percent of DCD/dyslexic participants displayed abnormal adaptation, compared with only 17 percent of controls. An alternative analysis, eliminating outlying controls and taking the criterion of 1.65 SD following Ramus et al. (2003a,b) led to 71 percent of the dyslexic group, 100 percent of our DCD group, and 83.3 percent of the DCD/dyslexia group showing an adaptation deficit. The results show that the adaptation rate effect sizes are large, and comparable with effect sizes on processing speed and cerebellar tests obtained in other groups of dyslexic children (Fawcett, Nicolson, & Dean, 1996). These findings differed from Ramus et al., who found a 25 percent incidence of cerebellar problems in dyslexic adults. This probably reflects the more specific test of cerebellar function used here, but may be also reflect a lack of sensitivity in the Ramus et al. (2003a,b) study owing to the participation of adults rather than children. An important point considering that many developmental disorders show a reduction in behavioural signs and their severity following puberty (Cantell et al., 2003; HaddersAlgra, 2002). In terms of the three main groups (dyslexia, DCD and DCD/dyslexia) there was therefore a high prevalence, with all but 5 of the 28 participants showing the adaptation dysfunction. Four of the five normally adapting participants within these groups were dyslexic, indicating that there is a strong prevalence of prism adaptation dysfunction in DCD with or without comorbid dyslexia. Unlike adaptation rate, the results for percentage return to baseline were less consistent with groups. The group analyses revealed effect sizes over 1 SD below the control mean for the ‘pure’ dyslexic and DCD groups but not for the combined DCD/dyslexic group. A closer look at the results revealed that 3/6 participants in this group displayed a return to baseline lower than the control average and 3/6 participants displayed a return

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to baseline greater than the control average. It would seem that the small size of this group confounds the interpretation of the group result. However, it demonstrates an important point: that developmental disorders are by their very nature heterogeneous on some measures. Therefore, measures which appear consistent, such as the fast-phase adaptation rate, are worthy of further attention. By contrast, there was no difference in the after-effects, postadaptation. All four groups showed some after effect, with a mean of around 21–31 cm compared with the theoretical maximum of 60. Weiner et al. (1983) characterize the after effect as an indicator that adaptation has taken place. This finding is consistent with research which shows that the after effect is often less pronounced than its theoretical maximum (Fernandez-Ruiz & Diaz, 1999; Redding & Wallace, 1988). Although Weiner et al. (1983) describe the after effect as the ‘true’ measure of adaptation and other researchers have found deficits in after-effects for cerebellar patients when simultaneously walking (Morton & Bastian, 2004), the reduced after effect is consistent with a deficit in the visuo-motor system. According to Clower and Boussaoud (2000) and Redding and Wallace (1990, 1994) there is a clear difference in adaptation and after effect dependent upon whether visual information is directly available. This is linked to two possible systems for adaptation: First, a visual-motor system, where recalibration of the motor system to the gaze distortion is based on visual feedback from the limb in a pointing task or the ball hitting the target in a throwing task. Second there is a proprioceptive-motor system where realignment to the target is guided by felt position of the limb. Redding and Wallace suggest that the choice of system rests solely on the information available and that adaptation can be achieved through both systems, however perceptual recalibration yields a greater magnitude of after effect. Given this indication of a deficit in the visuo-motor system, based on our experimental procedure (feedback from impact locations), it is not only unsurprising that the after-effect appeared reduced across groups but also that the DCD and DCD/dyslexic participants showed the highest magnitude of adaptation deficits. Interestingly, there was a dissociation between throwing performance which is based on the last 10 throws of condition (1) and adaptation rate based on the first seven throws of condition (2). This dissociation is valuable theoretically in that it allows us to evaluate the possibility of adaptation rate difference being due to some general attentional difference. If there were some difference in attention or distractibility between groups, one would expect it to show up near the end of a homogeneous set of throws, rather than at the very start of a new set. Given that the opposite effect occurred, it is difficult to argue that the difference in adaptation rate was due to distractability in the clinical groups. The dissociation is in fact consistent with Martin et al. (1996a,b) who found a similar pattern of dysfunction in patients with damage to the right cerebellar region around the posterior inferior cerebellar artery which is active during language processing, articulatory rehearsal and non-motor learning (Fiez, Petersen, Cheney, & Raichle, 1992; Paulesu, Frith, & Frackowiak, 1993; Raichle et al., 1994; Scott et al., 2001).

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Given the involvement of the entire sensori-motor system in the prism adaptation paradigm, it is always difficult to rule out completely what Zeffiro and Eden (2001) refer to as the ‘innocent bystander’ hypothesis, namely that the cerebellum is performing within normal limits but that sensory and/or motor input to it is in some sense degraded (see also Bishop, 2002). However, there is no speeded component to this task, and no magnocellular component. Furthermore, the mean magnitude of the AR deficit is of the order of 30 percent (dyslexia only) to 50+ percent (dyslexia plus DCD). This is surely very much more than could be accounted for by slight inaccuracies in sensory and/or proprioceptive information. The present findings, therefore, provide further strong support for the cerebellar deficit hypothesis (Nicolson et al., 1995, 2001), and are not easily explained by any alternative hypothesis. The even stronger results found in the DCD participants provide, to our present knowledge, the first direct demonstration of cerebellar dysfunction in DCD, confirming the conjectures of Piek and Dyck (2004) and Visser (2003). At the cognitive level this points to difficulties in automatisation, as advocated by Visser (2003), and suggests that the automatisation deficit hypothesis proposed to account for deficits in dyslexia may also provide a parsimonious explanation for the range of problems and comorbidities with DCD. The performance of the comborbid DCD/dyslexia group would have been of particular interest if there had been a dissociation in AR between the group with pure dyslexia and the group with pure DCD. The fact that both the latter groups showed a high incidence of RA problems makes the high AR problem incidence for the comorbid group unremarkable. Nonetheless, the results for the comorbid group may be taken as providing a third line of converging evidence in this study. A post hoc analysis of two remediated dyslexics, two DCD participants with poor reading (but not sufficient to satisfy our criteria for comorbid dyslexia), and two participants with DCD and IQs below 89, revealed significant deficits in adaptation rate for all but one remediated dyslexic who had an effect size of −0.7. This therefore reveals deficits even for those who did not fit precisely into a categorical group, reflecting the difficulty with diagnostic criteria; and most importantly, shows that underlying deficits are still present even when the individual has improved their skills in one symptom (reading). These findings affirm the call by Banaschewski et al. (2005) for better and more theoretically driven diagnostic batteries. A key motivation for our study was to elucidate the nature of the symptom overlap between DCD and developmental dyslexia. The results are clear-cut, suggesting strong overlap between both groups. It is important to acknowledge that this was a smallscale study with limited numbers of participants, and that the hand-occluded throwing paradigm is intrinsically ‘noisy’, generating relatively low quality data. While suggesting the need for caution in generalising these results, the high incidence of the difficulties and the significant differences obtained highlight the potential of the technique. Furthermore, the existence of converging data with different groups of participants with dyslexia and on a range of tests – structural, functional and behavioural – provides support for the interpretation taken here.

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It should be emphasised that we are not suggesting that all dyslexic children will show clear cerebellar problems. In this study 10 out of 14 (71 percent) showed AR abnormality using the criterion adopted. Studies of motor skill in dyslexic children suggest a prevalence of around 60 percent (Ramus, 2003) to 80 percent (Fawcett et al., 1996). The prevalence identified depends crucially on the sensitivity of the tests used and the stringency of the criterion adopted. Nor are we suggesting that children with dyslexia and children with DCD who do suffer from some form of ‘cerebellar’ impairment do in fact suffer from the same cerebellar impairment. Indeed, considering the spread of motor, language and learning functions throughout the cerebellar hemispheres (Desmond & Fiez, 1998), one might expect that the cerebellar deficit in DCD would be in a cerebellar circuit relating to motor skill (e.g. the primary motor cortex–cerebellar cortex lobules IV and VI), whereas for dyslexia it would be a circuit involved in phonology (see Eckert et al., 2003), involving language areas of the cerebellum (lobules VI and VIIIB in the neocerebellum and the cerebellar vermis). We suggest that this study may provide an early step in an attempt to unravel the complex interdependencies between different brain regions within and between developmental disorders. Our use of a task chosen on theoretical grounds to isolate the function of a single brain region appears to have been successful in establishing clear cerebellar involvement in both DCD and in dyslexia. We do not claim that the cerebellum is the only structure functioning abnormally in either of these disorders, and consider it likely that different subtypes may reflect dysfunction of different parts of the cerebellum probably in conjunction with dysfunction of other brain structures. These issues will surely be best addressed if we develop specific tools designed to isolate specific brain structures and processes, and capable of determining the unique and shared pathways for the various developmental disorders. Such an endeavour will lead to a radical reconceptualisation of our understanding of development and its disorders. In conclusion, the study has led to three key findings. First, the slower prism adaptation data are consistent with the cerebellar deficit hypothesis of dyslexia (Nicolson et al., 2001) and are difficult to accommodate in any other framework. Second, we expand on current understandings of DCD, identifying a link with the cerebellum, which may provide a fruitful starting point for the development of causal models. Third, we shed light on the comorbidity between dyslexia and DCD, revealing the possibility that apparently separate development disorders, as assessed by behaviour-based tests, may show surprising commonalities when assessed by brain-based tests. These combined results suggest that it is timely to attempt to develop a causal framework, encompassing brain systems, behaviour and learning, that is sufficiently broad to include all the developmental disabilities. Acknowledgements The authors would like to acknowledge Izzy Foustanos, Charlotte Carslaw, Sarah Wonders, and Matthew Bold, for their contributions to the data collection.

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