Different underlying neurocognitive deficits in developmental dyslexia: A comparative study

Neuropsychologia 48 (2010) 863–872

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Different underlying neurocognitive deficits in developmental dyslexia: A comparative study D. Menghini a,b , A. Finzi a , M. Benassi c , R. Bolzani c , A. Facoetti d,e , S. Giovagnoli c , M. Ruffino d,e , S. Vicari a,b,∗ a

Neuroscience Department, “Children’s Hospital Bambino Gesù”, Research Hospital, Rome, Italy Psychology Department, European University, Rome, Italy Psychology Department, University of Bologna, Italy d General Psychology Department, University of Padova, Italy e Cognitive Psychology Unit, “E. Medea” Scientific Institute, Bosisio Parini (Lecco), Italy b c

a r t i c l e

i n f o

Article history: Received 2 March 2009 Received in revised form 2 November 2009 Accepted 5 November 2009 Available online 10 November 2009 Keywords: Phonological processing Visual-spatial processing Attention Implicit learning Executive functions

a b s t r a c t The aim of this study was to investigate the role of several specific neurocognitive functions in developmental dyslexia (DD). The performances of 60 dyslexic children and 65 age-matched normally reading children were compared on tests of phonological abilities, visual processing, selective and sustained attention, implicit learning, and executive functions. Results documented deficits in dyslexics on both phonological and non-phonological tasks. More stringently, in dyslexic children individual differences in non-phonological abilities accounted for 23.3% of unique variance in word reading and for 19.3% in non-word reading after controlling for age, IQ and phonological skills. These findings are in accordance with the hypothesis that DD is a multifactorial deficit and suggest that neurocognitive developmental dysfunctions in DD may not be limited to the linguistic brain area, but may involve a more multifocal cortical system. © 2009 Elsevier Ltd. All rights reserved.

1. Introduction Learning to read is a highly complex and integrative neurocognitive process. Reading acquisition requires precise visual recognition of letters and letter combinations to convert the visual forms into their appropriate sounds using primary grapheme-phoneme mapping (e.g., Share, 1995). In addition to phonological short term memory and to phonological awareness for sounding out syllables and phonemes (for a review see Ramus, 2004), this visual-to-auditory conversion also requires selective attention to visual sub-lexical units (e.g., Cestnik & Coltheart, 1999; Facoetti, Ruffino, Peru, Paganoni, & Chelazzi, 2008; see for a recent review Perry, Ziegler, & Zorzi, 2007). This highly integrative process depends on reliable multi-faceted visual and auditory sensory processing that includes, for example, motion detection, and object discrimination in the visual domain (for a recent review see Boden & Giaschi, 2007) and sequential processing in the auditory domain (for a review see Wright, Bowen, & Zecker, 2000).

∗ Corresponding author at: Neuroscience Department, “Children’s Hospital Bambino Gesù”, Research Hospital, Piazza Sant’Onofrio 4, I-00165, Rome, Italy. Tel.: +39 06 68592475; fax: +39 06 68592450. E-mail address: [email protected] (S. Vicari). 0028-3932/$ – see front matter © 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.neuropsychologia.2009.11.003

The complexity of reading behaviour becomes apparent especially when we analyse a clinical condition such as developmental dyslexia (DD). The DSM-IV-TR explains DD as a reading achievement that falls substantially below expected levels given an individual’s age and education. The reading deficit should be sufficiently severe as to interfere with everyday activities requiring reading. Finally, the reading deficit cannot be strictly due to a sensory disorder (American Psychiatric Association, 2000). Although this definition is generally well accepted, the different underlying neurocognitive deficits in DD are still a matter of debate. There is now a strong consensus that the central difficulty in DD reflects a deficit within the language system and, more particularly, in a lower level component, phonology, which has been defined as the ability to access the underlying sound structure of words (Ramus et al., 2003; Scarborough, 1990; Shaywitz & Shaywitz, 2005; Shaywitz et al., 1998; Shaywitz, 1996; Snowling, 2000; Swan & Goswami, 1997; Wagner and Torgesen, 1987). Results from different populations with reading disability confirm that in children a deficit in phonologic analysis represents the most reliable (Fletcher et al., 1994; Stanovich & Siegel, 1994) and specific (Morris et al., 1998) correlate of DD. Phonological deficits have been also suggested to account for the phonological working memory deficits, often reported in individuals with DD (see for example Gathercole, Willi, Baddeley, & Emslie, 1994).

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Phonological deficits in DD are also supported by a large number of anatomical and neuroimaging studies that report brain abnormalities located mainly in the left posterior temporal areas (Galaburda, Sherman, Rosen, Aboitiz, & Geschwind, 1985; Geschwind & Galaburda, 1985; Paulesu et al., 1996, 2001; for a review see Ramus, 2004). In addition to the phonological deficit, several studies have provided evidence for further cognitive impairments associated with DD (see Stein & Walsh, 1997 for a review; Nicolson, Fawcett, & Dean, 2001). In particular, visual-spatial deficits have been found in DD, as documented by deficits in visual recognition tasks (Geiger et al., 2008) or in mental rotation tasks (Rüsseler, Scholz, Jordan, & Quaiser-Pohl, 2005). Furthermore, impaired perceptual performance in different motion detection tasks has been reported in dyslexics (e.g., Cornelissen, Richardson, Mason, Fowler, & Stein, 1995; Demb, Boynton, Best, & Heeger, 1998; Eden et al., 1996), thus suggesting a possible magnocellular-dorsal (MD) pathway impairment in DD (see for a recent review, Boden & Giaschi, 2007). However, it should also be recognized that physiological and psychophysical findings have been failures to replicate (see for a review, Skottun, 2000) and the hypothesis for a MD pathway deficit in DD remains controversial. Reading acquisition (i.e., phonological decoding) is an attention demanding process even in skilled adult readers (Reynolds & Besner, 2006). In particular, graphemic parsing, that is the segmentation of a letter string into its constituent graphemes (Perry et al., 2007), requires an efficient orienting of visual-spatial attention (Cestnik & Coltheart, 1999; Facoetti et al., 2006; for a review see, Hari & Renvall, 2001). Moreover, auditory attention is an essential component to focus resources on relevant acoustic information during phoneme–grapheme conversion processes. Notably, impaired visual-spatial attention and auditory attention have been repeatedly described in DD (e.g., Bosse, Tainturier, & Valdois, 2007; Cestnik & Coltheart, 1999; Facoetti et al., 2003, 2006; Geiger et al., 2008). In particular, deficits have been found in DD when dyslexics with non-word reading impairment performed visual serial search tasks (Buchholz & McKone, 2004; Jones, Branigan, & Kelly, 2008; Roach & Hogben, 2007) or during tasks involving attention to auditory stimuli (Dufor, Serniclaes, Sprenger-Charolles, & Démonet, 2007). Behavioural studies in individuals with DD have also documented deficits in implicit and procedural learning abilities (Bennett, Romano, Howard, & Howard, 2008; Stoodley, Ray, Jack, & Stein, 2008; Vicari, Marotta, Menghini, Molinari, & Petrosini, 2003; Vicari et al., 2005). Similarly, an automaticity deficit has also been demonstrated in dyslexics (Nicolson & Fawcett, 1990; Nicolson et al., 2001). Neuroimaging studies exploring brain activity in dyslexics performing implicit learning and automatization tasks have been documented notable cerebellar dysfunctions in these individuals (Menghini, Hagberg, Caltagirone, Petrosini, & Vicari, 2006; Nicolson et al., 1999). Moreover, Nicolson and Fawcett (2007) recently systematized findings on this topic into a “neural system” approach. They suggested that in DD the procedural learning system, which is sustained by prefrontal language areas, basal ganglia, parietal and cerebellar regions, is specifically impaired. Finally, it has been suggested that executive function deficits are present in DD. For example, deficits in both verbal and figural fluency ability (Reiter, Reiter, Tucha, & Lange, 2005), in response inhibition (Kelly, Best, & Kirk, 1989; Reiter et al., 2005) and in the Wisconsin Card Sorting Test (Helland & Asbjørnsen, 2000) have been documented in individuals with DD. In sum, a multiple neurocognitive deficit model seems necessary to understand DD (Pennington, 2006). Although many studies have proved the existence of individual neurocognitive deficits in DD, only a few have tested these different deficits simultaneously in a single study. For example, a paradigmatic study was conducted

by Ramus et al. (2003) to test the phonological, magnocellular, and cerebellar theories of DD. All 16 adults with DD included in the study showed phonological deficits related to literacy impairments. Other disorders, when present, were interpreted as a mere aggravation, and only associated with the basic phonological deficits. The same research group replicated the previous study on 23 children with DD using similar tasks (White et al., 2006). Phonological deficits were reported by 50% of children with DD and only visual difficulties were present in a small subgroup of children. In these two studies the authors focused on individual variations in DD and used measures taken from a wide range of theories. However, many theoretical and methodological criticisms were advanced (Bishop, 2006; Nicolson & Fawcett, 2006; Tallal, 2006). In particular, the tasks chosen to assess the different cognitive abilities were not uniformly distributed. For instance, while the phonological theory was extensively tested, the cerebellar theory was investigated in only a few basic tasks. Moreover, the hypothesis of attentional-parietal deficits in DD was not evaluated in these two studies (Ramus et al., 2003; White et al., 2006). It is worth noting that in their first work Ramus et al. (2003) tested 16 university students who could have compensated their deficit and whose reading could have been very different from that of children. Instead, in the second study (White et al., 2006) phonological and sensorimotor abilities were investigated in a sample of 23 children with DD. This sample may have been too small to test so many different neuropsychological domains. Based on the above, the role of phonological deficits in DD is still unclear. Indeed, as Castles and Coltheart state in their recent review, “no study has provided unequivocal evidence that there is a causal link from competence in phonological awareness to success in reading” (Castles & Coltheart, 2004, pp. 77). The present study was designed to verify this multifactorial hypothesis by simultaneously testing different neurocognitive domains in the same sample of children using numerous tasks. In particular, we assessed participants’ neuropsychological profile by evaluating phonological and non-phonological skills. Regarding phonological capacities, the ability to access the sound structure of words has been evaluated using a measure of phonological fluency, a spoonerism task and a non-word repetition task. Tasks exploring spatial perception, spatial rotation and motion coherence as well as spatial and auditory attention have been also included in the study. Implicit learning abilities have been studied using a serial reaction time task. Finally, to assess executive functions a categorical fluency task and a widely used task, Wisconsin Card Sorting Test, have been utilized. Starting from the consideration that each task used in this study required multiple cognitive abilities that were not limited to a single area, we used general linear model analysis (GLM) to analyse differences between groups, taking into account the reciprocal interaction between cognitive domains. In addition, to obtain more representative results we extended our investigation to a large sample of children with DD in different phases of development. Moreover, we investigated the predictive value of non-phonological cognitive functions with respect to word and non-word reading in dyslexic children. If non-phonological cognitive functions were independent non-speech mechanisms involved in sub-lexical processing, then measures of these non-phonological cognitive functions should predict non-word reading even when age, IQ and phonological skills are controlled for. 2. Methods 2.1. Participants The study included 125 children and adolescents: 60 with DD and 65 normal readers (NR). Demographic data, global cognitive profile, and reading abilities of both groups are reported in Table 1. An inefficiency reading index, calculated as

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the ratio between word or non-word reading speed (in seconds) and accuracy rate (number of words or non-words read correctly by the total number of words or non-words read), was considered. Children with DD were tested at the Children’s Hospital Bambino Gesù in Santa Marinella (Rome, Italy); NRs who comprised the control group were tested individually in a quiet room at their school. In the group with DD we included only participants whose word or non-word reading speed and/or accuracy level was at least 2 standard deviations below the mean for their chronological age. Speed (in seconds) and errors (each incorrect word or non-word was calculated as one error) were assessed using the “Battery for the Diagnosis of Dyslexia” (Sartori, Job, & Tressoldi, 1995). Moreover, no child with DD had undergone intensive or specific reading training. The presence of attention deficit or hyperactivity disorder (ADHD) was excluded based on the DSM-IV recommendations (American Psychiatric Association, 2000). None of the children in our sample with DD showed co-morbidity with ADHD. The control group was matched with the group with DD for chronological age and non-verbal intelligence level. Criteria for inclusion in the control sample were the following: (i) no reading delay on word and non-word reading tests; (ii) performance in the normal range (above 10th percentile = score of 22) on Raven’s Coloured Progressive Matrices (Raven, 1994), a test designed to measure the ability to form perceptual relations and to reason by analogy, independently of language and formal schooling; (iii) normal or corrected to normal visual acuity; (iv) no ADHD. Informed consent was obtained from the children and their parents.

al., 2004) and a non-word repetition task (NWRT; Vicari, 2008). FAS is a verbal fluency measure. In this task participants were asked to verbalize as many words as possible beginning with a given phoneme (F, A and S). The time limit was 1 min for each phoneme. Raw scores (number of correct words) were summed for each of the three trials; the total score was used for data analysis. SPOON is a widely used phonological awareness test. The examiner pronounced two words aloud and participants had to swap the initial phonemes to form two new real words. They were asked to transpose the beginning sounds of the two words as quickly as possible (time limit to complete a single trial: 1 min; number of trials: 15). The number of correct answers (SPOON A: maximum score: 30) and the time (SPOON S) taken to complete the entire test (15 trials) were recorded. NWRT involves the encoding, storage, processing, and reproduction of a novel sequence of speech sounds and is biased towards explicit phonological processing (Castro-Caldas, Petersson, Reis, Stone-Elander, & Ingvar, 1998; Klein, Watkins, Zatorre, & Milner, 2006). The test comprised 40 phonotactically legal non-words: 10 had two syllables (e.g., MIPO), 10 three syllables (e.g., BIDANA), 10 four syllables (e.g., RAGONOPO) and 10 five syllables (e.g., CATAMOGATO). In every list 5 items had high (e.g., PEPARONI) and 5 items had low (e.g., RABU) wordlikeness; this was given by the number of items that could be obtained by changing one letter of the string, but maintaining its position with respect to the other phonemes. Participants had to repeat the non-words. Each non-word was presented only once, and they were given one chance to repeat it after the examiner. Their response was scored as either fully correct or incorrect (maximum score = 40).

2.2. Design and materials

2.6. Visual-spatial perception tasks

The neuropsychological battery was administered to the children individually in either 3 or 4 testing sessions carried out on separate days. The tasks involved general intelligence as well as reading, phonological, attention, executive, visualspatial, and implicit learning abilities. Each session lasted approximately one hour and a half. Intelligence tests and reading tests were administered in the first session and the remaining tasks in the other sessions. All tasks were administered in a pseudorandom way. The tests comprising the battery administered to each child are described below.

The visual-spatial perception abilities were evaluated using a subtest of the Visual Perception Test – subtest 2 (VPT2; Hammill, Pearson, & Voress, 1994). VPT2 is a visual-spatial ability task investigating, in particular, perceptual and discrimination capacities in the visual domain. Participants were asked to match one linear design to a multiple-choice display consisting of an array of vertically arranged figures. In each of 25 items, the wrong alternatives differed from the target due to minor changes in orientation or spatial relations between constitutive elements (maximum score = 25). Visual-spatial imagery and mental rotation abilities were investigated using the Spatial Rotation Test (SRT; Vicari, Bellucci, & Carlesimo, 2006) and the Stick (STICK; Carlesimo, Perri, Turriziani, Tomaiuolo, & Caltagirone, 2001). In each trial of SRT, participants were instructed to create a visual image by mentally rotating geometric drawings. The image obtained had to be matched with one of five alternatives drawn on a sheet of paper (maximum score = 27). In each trial of STICK, participants were presented with a line drawing of an L- or an S-shaped stick with a full or an empty circle at the two ends. They had to indicate which of four similarly shaped sticks, rotated from 45 to 270◦ on a horizontal plane, would match the stimulus stick after appropriate mental rotation based on the respective location of the full and the empty circles (maximum score = 10).

2.3. General intelligence General intelligence was evaluated by means of the Coloured Progressive Matrices (CPM; Raven, 1994) and of the Wechsler Intelligence Scale for Children (WISC-r; Wechsler, 1993). CPM is designed to measure the ability to form perceptual relations and to reason by analogy, independently of language and formal schooling. WISC-r assesses general intellectual functions. 2.4. Reading abilities Speed and accuracy of reading were assessed using the “Battery for the Diagnosis of Dyslexia” (Sartori et al., 1995). Two subtests were chosen. In the first subtest participants had to read aloud 4 lists of 28 concrete and abstract words with high or low frequency (length from 4 to 8 letters). In the second task they had to read 3 lists of 16 orthographic strings (non-word length from 5 to 9 letters) that were similar to real Italian words. Speed (in seconds) and errors (each incorrect word or non-word was calculated as one error) were computed for each task. The ratio between word or non-word reading speed (in seconds) and accuracy rate (number of words or non-words read correctly by the total number of words or non-words read) was also obtained. 2.5. Phonological tasks The phonological abilities were evaluated using a phonological fluency task (FAS; Marotta, Trasciani, & Vicari, 2004), a spoonerism task (SPOON; Marotta et

2.7. Motion perception task Motion detection was evaluated by means of a modified version of the Random Dot Kinematogram (RDK; Benassi, Rydberg, Belli, & Bolzani, 2003). On a black background (0.2 cd/m2 ), 150 high luminance dots (luminance 51.0 cd/m2 ) could move coherently at a constant speed (6.1◦ /s) in one of the eight directions of the space (4 cardinal and 4 oblique). Dots were displayed on a computer screen at a distance of 130 cm from the participants and subtended a visual angle of 5◦ . To obtain a selective response of MD pathway (Demb et al., 1998) each dot was presented at low luminance level (mean luminance 5 ± 5 cd/m2 ). Moreover, to avoid the possibility of tracking, each dot had a limited lifetime of 4 animation frames (duration = 200 ms). The task consisted of 7 levels of difficulty. Coherent motion percentage was defined as the total number of dots moving together in a single direction in either of the primary horizontal axes between consecutive frames. The non-coherent dots moved randomly between frames in a Brownian manner. Starting from a condi-

Table 1 Demographic data and cognitive (A) and reading (B) measures of children with developmental dyslexia (DD) and normal readers (NR).

(A) Demographic data and cognitive profile Sex – F/M Chronological age – mean (range) IQ (WISC-r) – mean (SD) CPM – mean (SD) Educational level (Primary School) – children Educational level (Secondary School) – children

DD (n = 60)

NR (n = 65)

27/33 11.43 (8–17) 103 (11.28) 29.28 (4) 28 32

28/37 11.94 (8–16) 29.83 (3.53) 22 43

10.52 (7.68) 171 (82) 193.88 (110.65) 13 (6.2) 110 (43) 160.1 (78.71)

1.38 (1.28) 72 (22) 73.31 (23.23) 3.4 (2.33) 57 (20) 61.62 (24.18)

(B) Reading score Word reading (112 words)

Non-word reading (48 non-words)

Errors – mean (SD) Speed in seconds – mean (SD) Inefficiency index – mean (SD) Errors – mean (SD) Speed in seconds – mean (SD) Inefficiency index – mean (SD)

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tion of 100% coherence (all the dots moved coherently in one direction), at each step the percentage of the coherence decreased by 63% compared with the previous step. Therefore, participants performed at an increasing level of difficulty, while the global coherence of the moving stimuli decreased. Participants were asked to stop the stimulus by pressing the spacebar just when the motion was perceived and then to indicate the direction of the perceived motion by pressing an appropriate key. After 5 s, also if the spacebar was not pressed, the stimulus stopped anyway and participants were told to guess when necessary. Number of correct detections (RDK A: maximum score = 42) and response time (RDK S) were recorded for each participant. RDK S was considered a measure of the time needed to detect the motion.

or green) and numbers (1, 2, 3 or 4). Participants had to sort each response card under the stimulus card they thought was correct. After each sort they were told whether or not their response was correct. No other instructions were given during the test. After 10 consecutive correct responses the criterion was changed without any forewarning or comment by the examiner. The test continued until participants had either completed the 6 categories or had used all 128 cards. The WCST provides several objective scores of overall success and specific sources of difficulty. The number of perseveration errors (WCST PE) and of categories completed (WCST NC) was calculated in the present study.

2.8. Visual-spatial and auditory attention tasks

Implicit memory abilities were evaluated by means of the Serial Reaction Time Task (SRTT; Vicari et al., 2005). The procedure of the SRTT has already been described in detail (Vicari et al., 2005). Briefly, the task consisted of a reaction-time keypress response that participants were requested to perform whenever one of four visually presented empty boxes (baseline) changed colour and turned red (stimulus). Following all stimulus presentations responses had to be as quick and accurate as possible. Participants were told to use the index or the middle finger of their right or left hand. Each button was associated with one finger and corresponded to one of the four boxes. The whole procedure consisted of six blocks. Two of the blocks were characterized by a pseudo-random presentation of 54 trials (blocks R1 and R2), and four of them (blocks S1–S4) by a repeated presentation of a nine-element sequence (positions: BVNCVBCNV). Participants were not informed about the presence of the repeated sequence. To exclude any potential explicit awareness effect, at the end of the sixth block participants were asked whether the red square presentation was patterned. They were also requested to reproduce the sequence on the keyboard. Data were analysed by computing the median (in ms) of the reaction time (RT) in each block.

Selective visual-spatial attention was evaluated using the Map Mission (MAP; Manly et al., 2002). In this subtest of the Test of Everyday Attention for Children, participants were presented with a colour-printed A3-laminated city map. Eighty targets representing restaurants (i.e., small knife and fork symbols) were randomly distributed across the map. Distracting symbols of the same size, such as supermarket trolleys, cups, or cars, were also present. Participants used a pen to circle as many targets as possible in 1 min. The performance score was the number of target symbols correctly marked by the participants (maximum score = 80). Sustained auditory attention was investigated using the Code Transmission (CODE; Manly et al., 2002). In this task, which was also a subtest of the Test of Everyday Attention for Children, participants were asked to monitor a stream of monotonous digits (presented at a rate of one every 2 s) for the occurrence of a particular target sequence (e.g., 5, 5) and then to report the digit that occurred immediately before the target sequence. Following a practice sequence to ensure comprehension, 40 targets were presented over the 12 min of the task. The number of targets correctly detected was recorded as a measure of performance accuracy (maximum score = 40). 2.9. Executive function tasks The executive function abilities were evaluated using the Category Fluency Test (CAT; Riva, Nichelli, & Devoti, 2000) and the Wisconsin Card Sorting Test (WCST; Heaton, Chelune, Talley, Kay, & Curtiss, 2000). CAT involves verbal executive control functioning. Children were asked to generate words in a particular category (e.g. animals, clothes, fruits, and toys). Deviations from the test rules, including repetitions (perseveration errors) and words not identifiable as an example of the category, were considered errors. All words generated by the participants were recorded by the examiner, and the number of valid responses produced during the time limit was calculated (excluding repetitions and errors). The number of words generated for each category and the total number of responses were calculated. WCST is a widely used executive function measure. The WCST consisted of 4 cards with different shapes (crosses, circles, triangles or stars) in various colours (red, blue, yellow,

Table 2 Group effects and covariate Age effects reported for each of the univariate tests. The evaluated F value (degrees of freedom) and the corresponding P and the percentage of variance accounted for Effect Size reported for each effect. Task

FAS SPOON A SPOON S NWRT MAP CODE SRT VPT2 STICK SRTT CAT WCST PE WCST NC RDK A RDK S

Age

Group

F(1,121)

P

Effect size (%)

F(1,121)

P

Effect size (%)

6.76 11.44 19.81 11.65 113.82 5.29 13.47 8.21 29.81 0.003 14.64 11.66 1.83 1.37 1.05

0.01 0.001 <0.001 0.001 <0.001 0.023 <0.001 <0.001 <0.001 0.958 <0.001 0.001 0.179 0.244 0.308

5.3 8.6 14.1 8.8 48.5 4.2 10 6.4 19.8 0 10.8 8.8 1.5 1.1 0.9

9.56 61.65 170.04 127.24 9.87 32.12 6.79 7.42 2.13 0.83 25.06 1.10 9.74 5.44 1.10

0.002 <0.001 <0.001 <0.001 0.002 <0.001 0.01 0.01 0.15 0.364 <0.001 0.296 0.002 0.021 0.297

7.3 33.8 58.4 51.3 7.5 21.0 5.3 5.8 1.7 0.7 17.2 0.9 7.4 4.3 0.9

FAS = Phonological Fluency Test; SPOON A = Spoonerism Accuracy; SPOON S = Spoonerism Speed; NWRT = Non-word Repetition Test; MAP = Map Mission; CODE = Code Transmission; SRT = Spatial Rotation Test; STICK = Stick Test; VPT2 = Visual Perception Test – subtest 2; SRTT = Serial Reaction Time Task; CAT = Semantic Fluency Test; WCST PE = Wisconsin Card Sorting Test Perseverative Errors; WCST NC = Wisconsin Card Sorting Test Number Categories; RDK A = Random Dot Kinematogram Accuracy; RDK S = Random Dot Kinematogram Speed.

2.10. Implicit memory learning task

2.11. Statistical analysis First, performances of the group with DD and the NR group were transformed into z-scores. The mean and the standard deviation of the NR group were respectively always 0 and 1 (so they are not reported). All cognitive task measures were included in a General Linear Model (GLM) and were analysed by means of Multivariate Analysis of Covariance (MANCOVA) with Group as effect and Age as covariate. The GLM multivariate procedure allowed testing the effect of factor variables (groupings) on the means of several interrelated variables by taking into account their joint distribution (Norusis, 2004). The eta squared value was used to evaluate the percentage of variance accounted for effect size. A further analysis of the different levels of the RDK task was made using analysis of variance for repeated measures. More stringently, to determine whether, in children with DD, reading abilities were predicted by non-phonological abilities independently from age, IQ and phonological skills, a hierarchical regression analysis with 4 steps was computed. Precisely, the dependent variable was the inefficiency reading index for the word or for the non-word, and the predictors were Age (entered at step 1), CPM scores (at step 2), the phonological task in which DD showed the strongest deficit (at step 3) and the tasks in which DD showed the strongest deficit in other non-phonological cognitive domains (at step 4). Finally, to determine the number of children with DD who have a deficit on specific task, a criterion for deviance was adopted. The threshold for deviance chosen in the present study was 1.65 SD below the mean of the control group, which corresponds to the 5th percentile in a normal distribution.

3. Results 3.1. Neuropsychological evaluation GLM multivariate analysis of covariance was performed using Age as covariate, Group as between-subject factor, and z-scores of cognitive task measures as dependent variables. Result documented a significant multivariate Group effect [F(15,107) = 18.5; P < 0.001; variance accounted for effect size = 72%] and a covariate Age effect [F(15,107) = 10.2; P < 0.001; variance accounted for effect size = 59%]. Table 2 shows age and group effects for each cognitive task in children with DD compared with NR. The evaluated F value and the corresponding P and the percentage of variance accounted for Effect Size are reported for each effect. Fig. 1 shows z-scores of children with DD calculated for each task. The Age covariate effect was significant in all phonological tasks, attention tasks, visual perception tasks, the CAT, and in the WCST

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PE. In contrast, in the SRTT, the WCST NC, and the RDK tests (both accuracy and speed), the effect of Age was not significant (Table 2). Results concerning the Group effect for each task are presented below. Relating to the Group effect and in line with Cohen’ convention (1988), high percentages of variance accounted for Effect Size of SPOON A, SPOON S, NWRT, CODE and CAT. Conversely, low percentages of variance accounted for Effect Size of SRT, VPT2 and RDK A. 3.2. Phonological tasks Children with DD showed significant deficits on all phonological tasks considered (mean z-score ± SD: FAS: −0.70 ± 1.25; NWRT: −3.04 ± 1.86; SPOON A: −2.21 ± 1.95; SPOON S: −3.78 ± 2.12). 3.3. Visual-spatial perception tasks Also in the visual and spatial perception measures children with DD exhibited significant deficits, except on the STICK test (mean z-score ± SD: SRT: −0.69 ± 1.52; VPT2: −0.77 ± 1.69; STICK: −0.33 ± 1.0). 3.4. Motion perception task As regards accuracy on motion perception task (RDK A, i.e., the total number of correct detections in the whole test) the differences between groups were significant, whereas no differences in RDK speed (in DD mean z-score ± SD: RDK A: −0.44 ± 0.94; RDK S: 1.10 ± 1.00). A separate analysis of variance for repeated measures was carried out on motion accuracy and response speed at the different levels of difficulty. A significant interaction between difficulty and groups was found only for RDK A [F(6,117) = 2.22, P = 0.046]. Fig. 2 showed the number of correct detections (RDK A) in children with DD and NR at different levels of difficulty. Children with DD made fewer correct motion recognitions than NR children at intermediate task levels of difficulty (levels 3, 5 and 6 – all P < 0.05), corresponding to noise ratio between 40% and 10%.

Fig. 2. Motion perception abilities in RDK test: number of correct detections in children with developmental dyslexia (DD – white circles) and normal readers (NR – dark square) at different percentage of coherence (*P ≤ 0.05).

3.6. Executive function tasks As regards executive function, on the CAT test, children with DD produced fewer words than NR children. On WCST NC, children with DD completed fewer categories than NR children. However, no significant differences were found in the WCST PE between children with DD and NR children (in DD mean z-score ± SD: CAT: −1.03 ± 1.15; WCST NC: −0.80 ± 1.59; WCST PE: −0.26 ± 1.00). 3.7. Implicit memory learning task Finally, in the GLM procedure, no significant difference was found in the SRTT between children with DD and NR children, considering the difference between RTs of the last pseudo-random block (R2) and the last sequenced block (S4) as an index of visual-motor sequence learning (in DD mean z-score ± SD: SRTT: −0.17 ± 1.09).

3.5. Visual-spatial and auditory attention tasks In the MAP task a significant deficit was documented in children with DD. Differences between children with DD and NR children were also found on the CODE task (in DD mean z-score ± SD: MAP: −0.58 ± 0.89; CODE: −1.93 ± 2.38).

Fig. 1. The z-scores of children with developmental dyslexia (DD) calculated for each cognitive task.

3.8. The relationship between non-phonological cognitive abilities and reading To determine predictive relationships between neurocognitive abilities and reading in a more stringent way, hierarchical regression analyses with 4 steps was computed. An analysis was calculated considering the inefficiency reading index for word as the dependent variable. Age was entered at step 1, CPM scores at step 2, the phonological task in which DD showed the strongest deficit at step 3 (SPOON S) and the tasks in which DD showed the strongest deficit in other non-phonological cognitive domains were entered at step 4 (CODE, VPT2, CAT and RDK A) as predictors. Overall, in children with DD the regression model accounted for 57.4% of the variance in word reading inefficiency. As reported in Table 3, the age measure entered first accounted for 21.9% of variance [F(1,58) = 16.26; P < 0.001], while the CPM scores were not significant. SPOON S scores accounted for 10.7% of the unique variance [F(1,56) = 9.13; P = 0.004]. More importantly, the other non-phonological cognitive measures entered last accounted for 23.3% of unique variance in word reading inefficiency [F(4,52) = 7.1; P < 0.001] (see Table 3). The same hierarchical regression equation was computed using the inefficiency reading index for non-word as the dependent variable. Age was entered at step 1, CPM scores at step 2, SPOON S scores at step 3 and CODE, VPT2, CAT and RDK A scores at step 4 as predictors. In children with DD, overall, the regression model accounted for 48.5% of the variance. The age measure entered

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Table 3 Percentage of variance (R2) and P value in word and non-word reading inefficiency explained by the different predictors in the hierarchical regression analysis with 4 steps in children with developmental dyslexia (DD). Step

4

Task

DD Word

1 2 3

Table 4 For each task, number and percentage of children with developmental dyslexia (DD) having a performance beyond the 5th percentile.

Non-word

R2

P

R2

P

Age CPM SPOON S

0.219 0.014 0.107

<0.001 n.s. 0.004

0.165 0.032 0.087

0.001 n.s. 0.011

CODE VPT2 CAT RDK A

0.233

<0.001

0.193

0.002

CPM = Coloured Progressive Matrices; SPOON S = Spoonerism Speed; CODE = Code Transmission; VPT2 = Visual Perception Test – subtest 2; CAT = Semantic Fluency Test; RDK A = Random Dot Kinematogram Accuracy.

SPOON S NWRT SPOON A CODE CAT SRT WCST NC FAS VPT 2 MAP RDK A STICK RDK S SRTT WCST PE

5th percentile #

%

52 45 33 26 20 16 16 14 12 10 7 6 6 4 3

86.7 75.0 55.0 43.3 33.3 26.7 26.7 23.3 20.0 16.7 11.7 10.0 10.0 6.7 5.0

first accounted for 16.5% of unique variance in non-word reading inefficiency [F(1,58) = 11.48; P = 0.001], while the CPM scores were not significant. SPOON S accounted for 8.7% of the unique variance [F(1,56) = 6.8; P = 0.011], while the other non-phonological cognitive measures entered last accounted for 19.3% of unique variance in non-word reading inefficiency [F(4,52) = 4.8; P = 0.002] (see Table 3).

FAS = Phonological Fluency Test; SPOON A = Spoonerism Accuracy; SPOON S = Spoonerism Speed; NWRT = Non-word Repetition Test; MAP = Map Mission; CODE = Code Transmission; SRT = Spatial Rotation Test; STICK = Stick Test; VPT2 = Visual Perception Test – subtest 2; SRTT = Serial Reaction Time Task; CAT = Semantic Fluency Test; WCST PE = Wisconsin Card Sorting Test Perseverative Errors; WCST NC = Wisconsin Card Sorting Test Number Categories; RDK A = Random Dot Kinematogram Accuracy; RDK S = Random Dot Kinematogram Speed.

3.9. Deviance analysis

considered impaired if beyond the 5th control percentile. Fig. 3 showed the number of deficits present in children with DD and NR. The majority of NR (34 children) did not show any neuropsychological deficit. However, it must be noted that a consistent part on NR displayed one deficit or two (respectively, 14 and 12 children). Differently, the majority of children with DD exhibited multiple deficits. Namely, 13 dyslexics were found to have impairments on four tasks and 12 on five. More in details, the number and percentage of DD children having in each task a performance beyond the 5th NR percentile were reported in Table 4.

One of the goals of the present study was to verify the presence of multifactorial deficits in DD by simultaneously testing different neurocognitive domains. Thus, in addition to the group comparison analyses, a criterion for deviance, to determine in which domains participants show remarkable deficits, was also adopted. As reported above, performances of the group with DD and the NR were transformed into z-scores based on the mean and the standard deviation of the NR children. A performance was thus

Fig. 3. The distribution of neuropsychological deficits in children with developmental dyslexia (DD) and in normal readers (NR). The X-axis represents the number of children with one or more neuropsychological deficits (respectively, NR on the left side and children with DD on the right side of the graph). The Y-axis represents the number of neuropsychological deficits found in children with DD and NR.

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Even if the highest percentage of children with DD had deficits on phonological tasks, such as on SPOON and NWRT, almost half of children exhibited deficits on an attentional task (i.e., CODE) and a third on an executive function task (i.e. CAT). Besides the FAS, almost 20% of children with DD showed deficits in visual-spatial tasks (i.e. SRT and VPT) and in another executive function task (WSCT NC). The frequency of occurrence of children with DD who only exhibited a phonological deficit was 18.3% (11/60), an attention deficit was 1.6% (1/60) and a motion perception deficit was 1.6% (1/60). The frequency of occurrence of children with DD who did not exhibit deficits was 1.6% (1/60). However, the most of the remaining children with DD (46/60) showed other deficits in addition to phonological deficit (76.6%). For instance, 16.6% (10/60) displayed executive deficits, 13.3% (8/60) visual-spatial perception deficits, attention and executive deficits, 8.3% (5/60) attention and perceptual deficits and 8.3% attention and executive deficits. The remaining children with DD (18/60) presented other combinations of deficits and they can be clustered in ten groups (maximum 4 children each group). 4. Discussion The main aim of the present study was to explore different cognitive domains in a wide sample of children with DD to assess their neuropsychological profiles. Impairment in most of the neuropsychological areas observed in our children with DD was not justified by the relation between cognitive variables. In fact, GLM allowed us to analyse differences between groups taking into account possible reciprocal correlations between variables. In agreement with the existing literature on DD (e.g. Paulesu et al., 1996; Ramus et al., 2003; Snowling, 2000), we observed a phonological deficit in all domains tested: phonological awareness, memory and fluency abilities. Visual-spatial and motion perception abilities were also impaired, as well as attention and executive functions. In contrast, with previous reports, however, the implicit memory capacities of the group with DD were relatively preserved. Based on these results it can be argued that DD is a complex disorder caused by heterogeneous impairments in neuropsychological functioning and that different cognitive deficits in different individuals can lead to similar poor reading abilities, thus supporting the multiple deficit models of DD (Pennington, 2006). To support this interpretation we conducted also a deviance analysis, which confirmed the high presence of phonological impairments in our children with DD as well as more diffused cognitive impairments in attentional, executive function and visual-spatial tasks. In fact, the frequency of occurrence of children with DD who only exhibited a phonological deficit was 18.3% while the most of the children with DD (76.6%) showed other deficits in addition to phonological deficit. For instance, 16.6% displayed executive deficits, 13.3% visual-spatial perception deficits, attention and executive deficits, 8.3% attention and perceptual deficits and 8.3% attention and executive deficits. The finding of reduced visual processing abilities in DD is not new (for a review see, Stein & Walsh, 1997). Converging results consistently support the hypothesis of a relationship between DD and poor performances on visual-spatial and motion perception tasks (Felmingham & Jakobson, 1995; Talcott et al., 1998; Talcott, Hansen, Assoku, & Stein, 2000). In the present study we confirmed deficits in visual-perceptual, mental rotation and motion coherence tasks. For what concern the motion coherence task, deficits were particularly evident at intermediate task levels of difficulty, attesting that dyslexics were less sensitive to coherent motion when signal to noise ratio is between 40% and 10%. We suppose that at the intermediate levels task requires high retino-cortical and dorsal stream spatial sampling of stimuli. Thus, only children with pre-

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served MD pathway spatial sampling rate can successfully detect motion in presence of an increased proportion of noise. To interpret this sort of data, Talcott et al. (2000) have hypothesized that dyslexics may have fewer motion detectors, thus undersample spatially diffuse dynamic stimuli. However, an alternative explanation has been posed by Sperling, Lu, Manis, and Seindemberg (2005, 2006) who considered visual deficits found in individuals with DD more related to the difficulty with noise exclusion and focusing on relevant factors than to an impairment of the MD pathway motion detectors. In addition to a MD deficit hypothesis, our results could also be explained by the attentional deficit hypothesis if we interpreted the difficulties of dyslexics as the consequence of a deficit in extracting signal from noise in complex visual environments. In fact, Hari and Renvall (2001) suggest that a possible explanation for the multisensory perceptual noise exclusion impairments found in DD is a primary attentional deficit. Indeed, spatial attention modulates perceptual noise exclusion, optimizing the perceptual filter, so that signal is processed and noise is excluded (e.g., Dosher & Lu, 2000). On the other hand, it seems unlikely that a noise exclusion deficit would cause the attentional orienting deficit shown by dyslexics, since some cueing task studies do not involve noisy stimuli (e.g., Facoetti et al., 2003). To support the attentional hyphotesis, evidences documented in individuals with DD the reduced capacity to allocate visual and auditory attention often reported in individuals with DD (e.g., Buchholz & McKone, 2004; Casco & Prunetti, 1996; Dufor et al., 2007; Facoetti, Paganoni, Turatto, Marzola, & Mascetti, 2000; Ruddock, 1991; Valdois, Bosse, & Tainturier, 2004). As far as executive function capacities in DD are concerned, the few studies conducted so far have demonstrated reduced poor categorial fluency and strategy formation, and lack of planning, monitoring, and revising during problem solving (e.g., Condor, Anderson, & Saling, 1995; Mati-Zissi, Zafiropoulou, & Bonoti, 1998; Reiter et al., 2005). In particular, individuals with DD have difficulty on tasks, such as the WCST, that require the ability to use external cues to guide behaviour, self-monitoring, and responses shifting (Goldstein & Green, 1995). Moreover, failure to inhibit information, such as irrelevant contexts and distractors, was also found in children with DD (Brosnan et al., 2002). In the present study the participants with DD showed unimpaired implicit memory abilities and only the 6.7% of children exhibited a performance beyond the 5th NR percentile. In light of previous reports, this is an unexpected result. In fact, implicit memory impairments in different developmental samples (i.e. children, adolescents, and adults) were previously documented with different experimental procedures, namely, visual-spatial tasks, motor sequences, or procedural learning tasks (Howard, Howard, Japikse, & Eden, 2006; Menghini et al., 2006; Stoodley, Harrison, & Stein, 2006; Vicari et al., 2003, 2005). However, other studies have documented opposite results (Kelly, Griffiths, & Frith, 2002; Waber et al., 2003). Thus, the presence of implicit learning impairments in dyslexics is still a matter of debate. Although these contrasting results may be due to methodological issues concerning heterogeneity of tasks and sample selection, we would suggest a different interpretation of these divergences, namely, that implicit learning deficits as well as phonological, visual-spatial, attention, and executive function disorders may be present in some cases of DD but not in others. The different levels of impairment showed by dyslexics in implicit learning as well as in other neuropsychological abilities support the idea that DD is a multifactorial disorder with neurocognitive deficits in different domains. The hierarchical regression analyses conducted in our study sustain this hypothesis, showing that, in addition to phonological measures, in dyslexic children also individual differences in nonphonological tasks turn out to be good predictors of their reading

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difficulties, accounting for 23.3% of word reading unique variance and for 19.3% of non-word reading unique variance. These results demonstrate that reading and phonological decoding difficulties in dyslexic children are not only related to phonological processes, but they depend also on non-phonological cognitive impairments. As a consequence, to try to better understand possible factors linked to the peculiar reading pattern of children with DD, other components, beyond typically studied aspects (i.e. phonological abilities) have to be taken into account. Finally, we determined whether DD children showed impairments in single cognitive domain or whether multiple deficits were present. To this aim, we evaluated the number of tasks on which participants showed remarkable deficits (beyond the 5th NR percentile) and the percentage of children with DD which failed on a specific task (see Fig. 3 and Table 4). A large percentage of children with DD (41%) resulted impaired in multiple (four or five) neuropsychological tasks. In summary, results confirmed in our children with DD the presence of deficits on phonological awareness and processing tasks, as well as more diffused cognitive impairments on tasks assessing auditory sustained attention, executive function and category fluency, and visual-spatial abilities. Our findings are consistent with the hypothesis that individuals with DD may show multiple impairments in different cognitive domains, thus suggesting that DD may result from multifactorial impairments. To represent the complexity of developmental disorders such as DD, Pennington (2006) recently proposed a multiple cognitive deficit model in which the aetiology of complex behavioural disorders was not uni-causal and deterministic – as typically believed in the traditional neuropsychological approach – but multifactorial and probabilistic, involving the interaction of multiple risk and genetic or environmental factors. Studies conducted to investigate the possible genetic origin of DD support this view. In fact, a range of loci potentially linked to DD, in particular chromosome regions such as 1p34–p36, 6p21–p22, 15q21 and 18q11 was documented (Fisher & De Fries, 2002). These multiple genes could independently cause DD (presumably by hitting the same pathway at different places in different families). The candidate genes for DD (for a review see, Galaburda, LoTurco, Ramus, Fitch, & Rosen, 2006) that control neuronal migration are likely to have effects in many brain areas, thus producing a risk factor for cognitive deficits of different and independent types. A meta-analysis of the functional neuroanatomy of reading (Turkeltaub, Eden, Jones, & Zeffiro, 2002) suggests that the reading process involves many different brain regions including the occipital cortices, fusiform gyri, inferior parietal lobules, cerebellum, precentral gyri, inferior and middle frontal gyri, thalami, right cingulated gyrus, left parahippocampal gyrus and left lentiform nucleus, as well as the superior temporal gyrus. The functional development of these regions may affect the expression of DD. Extensive areas of cortical and subcortical brain regions were found to be significantly affected in several functional neuroimaging studies of individuals with DD. Indeed, adults with DD show an atypical pattern of activation in the brain regions usually involved in phonological and language processing (Paulesu et al., 1996, 2001; Shaywitz et al., 1998), in rapidly changing auditory stimuli (Temple et al., 2001), in visual processing linked to the transient or MD pathway (Eden & Zeffiro, 1998), or in procedural learning (Menghini et al., 2006; Nicolson et al., 1999). Moreover, from a structural point of view neuroanatomical studies in adults with DD document alterations in several different brain regions, supporting the hypothesis that DD is a multifocal disorder. Eckert’s (2004) review of structural imaging in DD studies shows anatomical abnormalities in a large number of brain areas such as the superior temporal gyrus, inferior parietal lobule, inferior frontal gyrus and cerebellum. Even in the first voxel-based morphometric study by Brown et al. (2001), neuroanatomical differences

between adults with DD and controls were evident in a large number of brain regions. Decreased grey matter volume was found in participants with DD, specifically in the left temporal lobe and bilaterally in the temporo-parieto-occipital juncture, as well as in the frontal lobe, caudate, thalamus and cerebellum. Thus, multifocal distributed morphologic and functional abnormalities, which are probably genetically determined and affect several brain regions, may contribute to the multifactorial neurocognitive aetiology of DD. One way to explore the complexity and heterogeneity of the cognitive profile of individuals with DD is to simultaneously test and analyse different cognitive competencies and their mutual relationship in the same group of people. To our knowledge, only a few studies have been conducted on this topic (Edwards, Walley, & Ball, 2003; Ramus et al., 2003; Reid, Szczerbinski, Iskierka-Kasperek, & Hansen, 2007; White et al., 2006). Specifically, two successive works by Ramus and co-workers (Ramus et al., 2003; White et al., 2006) examined several cognitive domains in adults and children with DD. Results showed a general phonological deficit in individuals with DD and, only occasionally, difficulty in other cognitive areas, such as magnocellular or cerebellar domains. The authors’ interpreted these data as showing that phonological disabilities are the core deficit in DD and that other cognitive impairments only account for some cases or only contribute to determining the severity of the disorder (Ramus, 2004). It should be noted that in these previous studies, which were based on univariate analyses, the relationships between the different neurocognitive performances of the same subject were not taken into account. Differently, in the present work multivariate procedures were used to compare not only single independent cognitive abilities, but also their interactions. Moreover, the phonological hypothesis of DD is not universally accepted (Bishop, 2006). It has been shown that reading improvement can increase phonological performances, supporting the idea that phonological abilities might be influenced by the level of literacy and reading. In fact, no evidence definitively supports the role of phonological awareness in determining proficient reading (for a review see, Castles & Coltheart, 2004). A review of the studies exploring multiple cognitive competencies in DD reveals two critical factors: children’s age and sample size. In fact, in agreement with Edwards et al. (2003), we believe it is relevant to investigate the trajectory and coexistence of cognitive and reading deficits throughout development, as the nature and severity of the impairment vary at different ages (for a review see, Goswami, Ziegler, Dalton, & Schneider, 2003). Furthermore, although DD is characterized by difficulty in acquiring adequate reading skills, many dyslexics compensate their disorder and reach an adequate reading level (Bruck, 1990; Brunswick, McCrory, Price, Frith, & Frith, 1999). Starting from these considerations, in the present study we controlled the analysis for chronological age. As regards sample size, unlike previous studies (e.g. Ramus et al., 2003; White et al., 2006) we tested a broad sample of individuals to represent the heterogeneity of cognitive profiles in DD. In conclusion, the present study documents DD as a composite disorder in which other competencies besides linguistic ones are compromised. In this perspective, a diagnostic system that collects only linguistic symptoms of dyslexics is not sufficient for understanding their reading difficulties, making a correct diagnosis and, consequently, developing a consistent program of treatment. Indeed, the importance of identifying the cognitive impairments underlying DD must be emphasized (Fawcett, 2007). Early diagnosis and intervention on all cognitive competencies that might be impaired in children with DD is crucial to avoid negative outcomes in adolescence. Indeed, to better understand the nature of this disability it is crucial to develop effective methods of diagnosis and remediation.

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