Abnormal pattern of cortical speech feature discrimination in 6-year-old children at risk for dyslexia

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Research Report

Abnormal pattern of cortical speech feature discrimination in 6-year-old children at risk for dyslexia Riikka Lovio a,⁎, Risto Näätänen a,b,c , Teija Kujala a a

Cognitive Brain Research Unit, Faculty of Behavioural Sciences, University of Helsinki, Helsinki, Finland Department of Psychology, University of Tartu, Tartu, Estonia c Centre of Functional Neuroscience (CFIN), University of Aarhus, Aarhus, Denmark b

A R T I C LE I N FO

AB S T R A C T

Article history:

The present study aimed to determine whether speech sound encoding and discrimination

Accepted 26 March 2010

are affected in 6-year-old children having an elevated risk for dyslexia. Their event-related

Available online 8 April 2010

potentials (ERPs) for syllables and syllable changes critical in speech perception and language development (vowel, vowel-duration, consonant, frequency (F0), and intensity changes)

Keywords:

were compared with those of children without a dyslexia risk. ERPs were recorded with a new

Dyslexia

linguistic multi-feature paradigm which enables one to assess the discrimination of five

Mismatch negativity (MMN)

features in 20 min. Also, an oddball condition with vowel and vowel-duration deviants was

Event-related potential (ERP)

included. The amplitudes of the P1 response elicited by the standard stimuli were smaller in

Central auditory processing

the at-risk group. Furthermore, the amplitudes of the mismatch negativity (MMN) were

Sound discrimination

smaller for the vowel, vowel-duration, consonant, and intensity deviants in children at risk

Language development

for dyslexia. These results are consistent with earlier studies reporting auditory processing difficulties in children at risk for dyslexia and diagnosed dyslexia. However, the current study, enabling the recording of MMN for multiple sound features, suggests the presence of wide-spread auditory difficulties in children at risk for dyslexia. © 2010 Elsevier B.V. All rights reserved.

1.

Introduction

Dyslexia is a specific reading disorder with problems in reading-skill acquisition, spelling and writing despite adequate intellectual capacity and socio-economic resources. Problems with phonological awareness are widely acknowledged as a core feature of dyslexia. However, there are several theories of the underlying causes of the phonological processing deficit in dyslexia (for a review, see e.g. Vellutino et al., 2004). The phonological model postulates that dyslexic individuals have a specific phoneme awareness deficit, a deficit in the ability to manipulate sound constituents of the

oral language (Ramus, 2003; Mody et al., 1998). The rapidauditory processing deficit model in turn suggests that the phonological deficit is secondary to a more basic auditory processing deficit of short or rapidly varying sounds (Tallal et al., 1993; Farmer and Klein, 1995). The magnocellular model has attempted to integrate all the dyslexic symptoms and suggests that dyslexia results from a neurodevelopmental abnormality of the magnocellular system (Galaburda et al., 1994; Stein and Walsh, 1997). The attentional sluggishness hypothesis, in turn, suggests that the impaired processing of rapid stimulus sequences in dyslexia depends on attentionrelated prolongation of input chunks (Hari and Renvall, 2001).

⁎ Corresponding author. Cognitive Brain Research Unit, Faculty of Behavioural Sciences, P.O. Box 9, FIN-00014, University of Helsinki, Helsinki, Finland. Fax: +358 9 191 29450. E-mail address: [email protected] (R. Lovio). 0006-8993/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2010.03.097

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This prolongation might then distort proper development on cortical representations that are essential for reading acquisition (Hari and Renvall, 2001; Lallier et al., 2009). There are at least two more theories about the nature of problems present in dyslexia: According to the double deficit hypothesis, naming speed problems in dyslexics represent a second core deficit independent of a phonological deficit in dyslexia (Bowers and Wolf, 1993; Wolf, 1997; Wolf and Bowers, 1999). The Cerebellar deficit hypothesis, in turn, postulates that dyslexic children have a general impairment in skill automatisation, such as reading and writing, an ability thought to be dependent upon the cerebellum (Nicolson et al., 2001). Despite the growing knowledge of the causes and manifestations of dyslexia, children affected are not diagnosed until they have struggled with problems in learning to read. However, these children show symptoms that predict reading difficulties even before school age. Behavioral tests in rapid naming, working memory, phonological awareness, and letter knowledge predict children's later reading acquisition (Leppänen et al., 2008; Lyytinen et al., 2007). Early auditory responses reflecting cortical encoding and discrimination of sounds were also associated with children's later reading skills (Leppänen et al., 2002; Espy et al., 2004; Lyytinen et al., 2005). Recent results by Banai et al. (2009) show a correlation even between the timing of subcortical auditory processing and phonological decoding skills. More knowledge on the pattern of the underlying deficits in dyslexia is needed for detecting it early enough in order to prevent or diminish reading deficits, however. In the present study, the cortical encoding and discrimination of speech sounds was studied in children at risk for dyslexia by recording auditory event-related potentials (ERPs). The early components of the auditory ERP reflect the neural correlates of reception and encoding of a stimulus. In children, the P1 response usually peaks at 100 ms followed by a broad negativity at about 200 ms (N2) (Sharma et al., 1997; Čeponienė et al., 2001, 2002). The P1 and N2 components were suggested to reflect auditory sensory processing in the perception of tones in 4- to 9-year-olds by Čeponienė et al., 2002. Some studies reported abnormalities of the auditory P1 in children at familial risk for dyslexia and older children with diagnosed dyslexia. At-risk children tended to have delayed P1 peaks for standard word stimuli at the age of 17 months (Van Herten et al., 2008). Furthermore, at the age of 8–15 years, dyslexic children showed altered hemispheric asymmetry of the P1 component elicited by word stimuli (Heim et al., 2003). Cortical auditory discrimination can be studied with the mismatch negativity (MMN) ERP. The MMN reflects preattentive memory-based comparison process where each incoming sound is compared with the memory trace based on the regularities of the preceding auditory context (Näätänen and Winkler, 1999; Näätänen et al., 2005). The MMN is elicited if the change violating the regular sound pattern is discriminable. Further, the larger the deviance magnitude, the larger is the MMN amplitude and the shorter is the MMN latency (Sams et al., 1985; Tiitinen et al., 1994; Kujala and Näätänen, 2001; Rinne et al., 2006; Pakarinen et al., 2007). The MMN is well suited for studies addressing central auditory processing in clinical groups and children because it is elicited even without the subject's behavioral response or attention towards the sounds (Näätänen, 1979, 1985; Näätänen et al., 1978). The

advantage of the MMN is that it is considerably less affected by attention and task-related artifacts than behavioral measures. Several studies suggest that the problems in dyslexia are already expressed at the early auditory sensory memory stage of information processing (for reviews, see Bishop, 2007; Kujala, 2007). Studies in adults with dyslexia reported impaired cortical discrimination of sound frequencies (Baldeweg et al., 1999; Kujala et al., 2003, 2006b; Renvall and Hari, 2003) but normal MMNs for duration changes (Baldeweg et al., 1999; Kujala et al., 2006a) and even enhanced responses for location changes (Kujala et al., 2006b). Children with dyslexia have diminished MMN amplitudes for consonant changes in syllables, e.g. /da/ vs. /ga/ (Schulte-Körne et al., 1998; Sharma et al., 2006), and for duration changes (Corbera et al., 2006). Moreover, Maurer et al. (2003) showed that pre-school children at familial risk for dyslexia differ from their peers without such risk in their MMNs to frequency and phoneme changes. As early as in 6-month-old infants, children at familial risk for dyslexia show reduced MMN responses to varying /t/ durations in pseudoword /ata/ (Leppänen et al., 2002). The MMN has usually been recorded with the so-called oddball paradigm requiring long recording sessions. However, since vigilance and motivational factors affect the signal-tonoise ratio, especially in children, paradigm improvements are welcome (Kujala et al., 2007). To overcome these problems, Näätänen et al. (2004) developed a new multi-feature paradigm, “Optimum-1,” with which the MMN can be efficiently recorded in about 15 min for several different types of deviant sounds. This multi-feature paradigm was used for the first time in a study addressing central auditory processing in adults with dyslexia (Kujala et al., 2006b). In controls, there were no differences in the MMNs obtained with the multi-feature and oddball paradigms whereas in adults with dyslexia, impaired frequency and duration discrimination were more pronounced in the multi-feature than in oddball paradigm. The authors suggested that the multi-feature paradigm might be more sensitive than the traditional oddball paradigm in detecting problems in central auditory processing in dyslexia because it involves more variation in the sound stream as in normal listening environment. Recently, a new variant (Pakarinen et al., 2009) of the multifeature paradigm was developed for studies addressing speech sound discrimination. In this variant, semi-synthetic consonant-vowel syllables serve as standards and deviant stimuli include speech-specific changes, such as vowel, vowel-duration, consonant, and syllable frequency (F0) and intensity changes. In adults (Pakarinen et al., 2009) as well as in 6-yearold normally developing children (Lovio et al., 2009), the MMNs recorded with the multi-feature paradigm were quite similar to those obtained with the oddball paradigm. The authors concluded that this time-efficient paradigm is well suited for studies on the neural basis of auditory discrimination both in adults and children. In order to gain a comprehensive view on the possible markers associated with dyslexia already before school age, the P1, N2, N4 and MMNs were recorded with the multi-feature paradigm with speech stimuli from 6-year-old children at risk for dyslexia. The risk was defined by a family history of dyslexia and poor results in reading-related tests. Sound encoding was determined from early ERP components elicited by the standard

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stimuli and sound discrimination from MMNs elicited by vowel, vowel-duration, consonant, syllable frequency (F0), and syllable intensity changes. MMNs for vowel and vowel-duration changes were also recorded with the oddball paradigm in order to determine whether the new paradigm yields consistent results with the oddball paradigm (Kujala et al., 2006b).

2.

Results

2.1.

Standard-sound ERPs

The grand-mean waves for the standard stimuli presented in the multi-feature and oddball paradigms are shown in Fig. 1. Standard stimuli elicited P1 responses that significantly differed from zero (P > 0.001–0.05) in the control and at-risk groups in both conditions at all electrodes tested (F3, Fz, F4, C3, Cz, and C4) (see Table 1 listing amplitudes, latencies, and p-values at F3). The N2 and N4 responses elicited by the standard stimuli also significantly differed from zero (P > 0.001– 0.05) in the control and at-risk groups in both conditions at all electrodes tested (F3, Fz, F4, C3, Cz, and C4) except for the N2 response in the controls in the oddball condition (see Table 2 listing amplitudes, latencies, and p-values at F3). The four-way ANOVA (Group × Condition × Frontocentral electrode location × Lateral electrode location) revealed a significant Group × Lateral electrode location interaction for the P1 responses (F(2,34) = 3.73, P < 0.05). An LSD post hoc indicated that the P1 amplitudes were generally smaller in the at-risk than control children (P < 0.001). Furthermore, the responses of the controls were the smallest at the Fz–Cz loci, whereas in the at-risk children they were smallest at the F4–C4 loci. The group main effect was marginally significant (F(1,17) = 3.52, P < 0.08). The comparisons of the N2 and N4 components revealed no significant amplitude differences between the two groups. In addition, no latency differences of the P1, N2, or N4 components were found between the groups.

2.2.

The MMN

The grand-mean difference waves (the response elicited by the standard stimulus subtracted from that elicited by the

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deviant stimulus) for the deviants presented in the multifeature paradigm are shown in Fig. 2 and those for the deviants presented in the oddball paradigm in Fig. 3. All deviant stimuli elicited MMNs that significantly differed from zero (P > 0.001–0.05) at all electrodes tested (F3, Fz, F4, C3, Cz, and C4) in both groups except for the vowel deviant in the oddball paradigm in the at-risk group (see Table 2 listing amplitudes, latencies, and p-values at F3). The five-way ANOVA (Group × Condition × Stimulus × Frontocentral electrode location × Lateral electrode location) revealed a significant stimulus main effect (F(1, 17) = 15.92, P < 0.001). The vowelduration deviant elicited larger MMN amplitudes than those for the other deviants in both groups. In the multi-feature condition, the four-way ANOVA (Group × Stimulus × Frontocentral electrode location × Lateral electrode location) revealed a significant Group × Stimulus × Lateral electrode location interaction (F(8,136) = 2.02, P < 0.05). An LSD post hoc indicated that the MMN amplitudes were smaller in the at-risk children than in the controls for the vowel deviant in all electrode locations (P < 0.001–0.05), for the vowel-duration and intensity deviants over the left (F3 and C3) and right (F4 and C4) hemispheres (P < 0.001–0.05), and for the consonant deviant over the right hemisphere (F4 and C4) (P < 0.001). In addition, there was a significant stimulus main effect (F(4,68) = 7.73, P < 0.001), resulting from a larger MMN for the vowel-duration deviant than for the other deviants in both groups. Also, in the oddball condition the MMN for the duration deviant was larger than for the vowel deviant (F (1,17) = 5.94, P < 0.05). The four-way ANOVA revealed no other significant effects in the oddball condition. The MMN latency comparisons revealed no significant effects.

3.

Discussion

Standard stimuli elicited similar P1, N2 and N4 waveforms as typically obtained in children at this age (Čeponienė et al., 2001). No latency differences in the standard-sound ERPs were found between the groups, indicating that sound feature encoding was as fast in the children at risk for dyslexia as in their controls. This contradicts Van Herten et al. (2008) results showing delayed P1 peaks for standard stimuli in 17-month-

Fig. 1 – ERPs to standard stimuli. Grand mean-waves for the standard sounds at the Fz, F4, C3, Cz, and C4 scalp location in the multi-feature and oddball paradigm in control children and in children at risk for dyslexia.

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Table 1 – Mean P1, N2 and N4 amplitudes (μV) and latencies (ms) at F3. Paradigm

Response

Control group Amplitude

Multi-feature

Oddball

P1 N2 N4 P1 N2 N4

5.6 −2.1 −3.0 5.4 −1.9 −3.2

At-risk group Latency

(1.6)*** (2.4)* (1.9)** (1.2)*** (2.6) ns (1.9)***

104 (14) 238 (24) 361 (23) 106 (11) 239 (25) 356 (17)

Amplitude 4.7 −1.2 −2.2 4.3 −1.5 −2.4

(2.0)*** (1.5)* (1.6)** (2.4)*** (2.2)* (1.4)***

Latency 106 229 360 109 237 356

(11) (22) (25) (7.5) (23) (26)

Standard deviation in brackets. The amplitudes significantly differing from the baseline are marked with asterisks: *P < 0.05, **P < 0.01, and ***P < 0.001.

old children at risk for dyslexia. However, in the present study, the amplitude of the P1 waveform was smaller in the at-risk group particularly over the right hemisphere. This might reflect difficulties in establishing sound representations in the at-risk group. Previously, Heim et al. (2003) also found an altered hemispheric asymmetry of the auditory P100m to consonant-vowel syllables in children and adolescents with dyslexia. The authors suggested that the results reflect an atypical organization of the right hemisphere in dyslexia. Furthermore, the group waveforms (Fig. 1) in the present study suggest amplitude differences of the N2 and N4 responses between the groups but these differences were not statistically significant. Since the exact functional correlates of the childhood P1, N2 and N4 responses are still poorly understood, further studies in normally developing children and in children at- risk for dyslexia in different ages are needed before these findings can be used as markers of dyslexia risk. The cortical discrimination of vowel, vowel-duration, consonant, and intensity changes was altered in children at risk for dyslexia, as indicated by their diminished MMN responses over both hemispheres. These results are in agreement with previous MMN studies reporting difficulties in phoneme (Maurer et al., 2003; Schulte-Körne et al., 1998) and duration processing (Corbera et al., 2006; Leppänen et al., 2002; Maurer et al., 2003) in children with dyslexia and those at risk for dyslexia. The MMN amplitudes were diminished in the at-risk children for the vowel deviant in both paradigms (see also Figs. 2 and 3) and all over the scalp compared to the controls. This result indicates deficient pre-attentive vowel processing and is compatible with the contention that dyslexia is

associated with a difficulty in establishing accurate phonological representations (Snowling et al., 2000). The MMN amplitudes were also diminished in the at-risk group for the vowel-duration deviant at electrodes over the left and right loci of the scalp. The MMNs for the consonant deviant, in turn, were diminished on the right side of the scalp whereas there were no effects over the left side of the scalp. Interestingly, MMNs for intensity changes, which to our knowledge were not earlier investigated in dyslexic children, were also diminished in the at-risk group. This suggests that even the discrimination of non-linguistic changes and changes involving no rapid transitions is impaired in dyslexia. The present results showing MMN topography differences for sound feature processing between the at-risk and control children are in agreement with previous studies suggesting altered lateralization of speech sound processing in dyslexia. There are several studies reporting left-hemispheric (Shaywitz et al., 1998; Galaburda, 1999, Renvall and Hari, 2003, Temple et al., 2003) deficits in dyslexia, and the left-hemispheric dysfunction is suggested to underlie the impairments of the language system (Shaywitz et al., 1998; for a review, see Caylac, 2009). There are also imaging studies showing differences between dyslexic subjects and normal readers in both hemispheres (Eden et al., 1996; Klingberg et al., 2000). Differences in the experimental design, stimuli and the age of the participants make it difficult to compare the results. Even though the present study shows group differences in the MMN topography for several sound features, the results are not indicating a special left-hemisphere dysfunction in at-risk children. Since only a limited number of electrodes were used, it is difficult to interpret exactly which brain areas contributed to the results.

Table 2 – Mean MMN amplitudes (μV) and latencies (ms) at F3. Paradigm

Deviant

Control group Amplitude

Multi-feature

Oddball

Vowel Vowel-duration Consonant Frequency (F0) Intensity Vowel Vowel-duration

−1.6 −3.4 −1.2 −1.3 −1.7 −1.6 −2.1

(1.6)* (1.1)*** (1.6)* (1.4)* (1.9)* (1.1)** (1.8)**

At-risk group Latency 325 282 335 287 319 310 270

(42) (20) (74) (72) (54) (69) (15)

Amplitude −1.4 −2.6 −1.7 −1.7 −1.4 −1.3 −1.4

(1.8)* (1.6)*** (1.4)** (1.8)* (1.5)* (2.4) ns (1.8)*

Standard deviation in brackets. The amplitudes significantly differing from the baseline are marked with asterisks: *P < 0.05, **P < 0.01, and ***P < 0.001.

Latency 308 290 331 280 320 299 283

(78) (39) (57) (60) (47) (67) (44)

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Fig. 2 – MMNs in multi-feature condition. Deviant-minus-standard difference waveforms for vowel, vowel-duration, consonant, frequency (F0) and intensity deviants at the F3, Fz, F4, C3, Cz, and C4 scalp location in control children and in children at risk for dyslexia.

Anatomical and neurophysiological data indicate a maturation of the auditory, language, and interhemispheric pathways from the childhood to the early adulthood (Salamy, 1978; Paus et al., 1999). For example, Ressel et al. (2008), using the MEG, found a clearly lateralized and distinct activation pattern only in children older than 11 years whereas younger children showed a more symmetrical distribution of activity during verbgeneration and vowel identification tasks. The left-hemispheric deficit linked to dyslexia might be more pronounced in older subjects than children at the age of 6. It is also noteworthy that in the present study, the children had an increased risk for dyslexia but the diagnosis could not be confirmed since readingskill acquisition formally starts at the age of 7 in Finland. Surprisingly, there were no significant differences between the groups in their MMNs for the frequency (F0) deviant. This is not in agreement with previous studies showing altered

frequency processing in dyslexic adults (e.g. Baldeweg et al., 1999; Kujala et al., 2003, 2006b; Renvall and Hari, 2003), and in children at risk for dyslexia (Maurer et al., 2003). However, there are also studies reporting similar MMN amplitudes for frequency deviants in children with dyslexia and controls (Schulte-Körne et al., 1998; Corbera et al., 2006). Different stimulus types and deviants of different magnitudes might be some of factors contributing to these inconsistencies. In Baldeweg et al.'s (1999) study, dyslexic adults did show attenuated MMN responses when the change was smaller than 6% but normal-like responses to the 9% change. In the present study, the deviance was of either+ or − 8% in frequency. This difference might have been too large and above the critical level in order to differentiate the two groups. The great heterogeneity of the symptoms in dyslexics could also explain the varying findings. It has been suggested

Fig. 3 – MMNs in oddball condition. Deviant-minus-standard difference waveforms for vowel and vowel-duration deviants at the F3, Fz, F4, C3, Cz, and C4 loci of the scalp in control children and in children at risk for dyslexia.

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that only a sub-group of dyslexic individuals has a pitch discrimination deficit (Bailey and Snowling, 2002; Banai and Ahissar, 2004). There might be several ways to become dyslexic, depending on which subset of the network underlying phonological skills is affected (Ramus, 2004). It has been discussed that the different components of the phonological deficit vary partly independently from each other and provide additive contributions to the reading disability (Wolf et al., 2002). Children in the present study perhaps followed a different developmental path not having problems in frequency discrimination. In the future, studies using both linguistic and well-matched non-linguistic material in the same subjects are needed in order to shed light on the long-standing question whether the auditory processing deficits in dyslexia are specific for linguistic material or whether they are more general. Thanks to its timeefficiency, the multi-feature paradigm is well suited for these future studies. In order to learn more about the heterogeneity in dyslexia and the link between central auditory processing and conscious auditory processing, it would also be important to include behavioural measures of auditory discrimination to these studies (Bishop, 2007). Additional information on these matters will make it easier to develop tools that could be used for early dyslexia detection and interventions.

4.

Conclusions

The results of the present study showed significantly diminished P1 responses for the standard syllables and significantly diminished MMNs for the vowel, vowel-duration, consonant, and intensity deviants in children at risk for dyslexia but insignificant MMN differences for frequency (F0) deviants in the two groups. Thus, children at risk for dyslexia have a widespread pattern of impaired cortical basis for detecting differences between different speech sound features. Furthermore, the present results are encouraging by suggesting that the new effective multi-feature MMN paradigm with changes in speech sounds could be used for identifying sound discrimination impairments even before the school age in children at risk for dyslexia.

5.

Experimental procedures

5.1.

Subjects

Fifty-seven children participated in the experiments which were a part of a larger project. Of these children, 12 met our criteria of having a risk for dyslexia (a mother or a father with dyslexia and at least one additional close relative with dyslexia, and a poor performance in reading-related tests). To the control group, we chose 12 children who had no relatives with known language related problems and whose results were within the normal range in tests assessing reading-related skills. The data of 2 children in the at-risk group and 3 from the control group had to be rejected, however. The 2 children in the at-risk group were excluded because the subjects possibly filled the criteria for ADD, as found out later. The 3 control children were rejected because of the poor signal-to-noise ratio due to a

low number of trials (below 100) after artifact rejection. The mean age of the remaining 10 children in the at-risk group was 6.7 years (range 6.1–7.3 years; 3 girls; one left-handed) and that of the 9 control children 6.5 years (range 6.2–7.2 years; 3 girls; one left-handed). The mean PIQ was 107 (6) in the at-risk group and 114 (16) in the control group, as assessed with the Wechsler Intelligence Scale for Children III (WISC-III, Wechsler, 1991). There were no significant group differences in the ages or PIQ values (t test for independent samples). All children were monolingual Finnish speakers from working and middle-class families in the Helsinki region. According to the parental reports, the children had normal hearing and vision and no history of neurological diseases, head injuries, or continuous medication. The children gave a verbal informed consent and the parents signed an informed consent prior to the experiment, which was carried out according to the Declaration of Helsinki. The study was approved by the Ethical Committee of the Department of Psychology, the University of Helsinki. The history of reading problems in the families was studied with a structured questionnaire combined with an interview in which the reading and writing difficulties of the child's close relatives (parents, siblings, grandparents, and parents’ siblings) as well as success in school attendance, together with motor, attentional, visuospatial, and language development, were addressed. In addition, skills related to the developing reading skills were assessed in all children in one session with the following tests (the same tests were used in our previous study; see Lovio et al., 2009 for more detailed descriptions of the tests): Phonological Processing (NEPSY; Korkman et al., 1997), Phonological Processing (Diagnostic tests 1; Poskiparta et al., 1994), Repetition of Non-words (NEPSY; Korkman et al., 1997), Reading Fluency (Lukilasse; Häyrinen et al., 1999), Reading Syllables and Non-words, Letter Knowledge, Writing Words and Non-words, Writing Syllables and Non-words, Rapid Alternating Naming (RAN; Ahonen et al., 2003) test and Digit Span (WISC-III, Wechsler, 1991). The raw scores and normative means for tests which have normative means are presented in Table 3. The criteria for the control group were not to have relatives with reported history of motor, attentional, visuospatial deficits, or dyslexia or other language development problems and to have no more than one test result one standard deviation or more below the normative mean. In the tests with no normative data (Letter Knowledge, Reading Syllables and Non-words, Writing Words and Non-words, Writing Syllables and Non-words), the criteria were to be able to name more than 17 letters and to be able to write one's own name. Finnish children, whose reading acquisition at the age of 7–8 years is normal, know 17 letters or more at the age of 6 (Lyytinen et al., 2007). Therefore, we decided to use the knowledge of at least 17 letters as our criterion. Children were not expected to be able to read or write syllables, non-words or words, even though many of the control group children did. The criteria for the dyslexia risk definition was to have at least the mother or the father and one additional close by relative with a history of reading-related problems and a performance worse than 1 SD below the mean of the age-matched normative data in at least two of the following tests: Phonological processing (NEPSY; Korkman et al., 1997), Rapid Alternating Naming (time or the amount of errors) (RAN; Ahonen et al., 2003), Repetition of

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Table 3 – The means from the reading-skill tests. Test

Control group

At-risk group

Mean (SD)

Mean (SD)

Phonological processing and reading Phonological processing, NEPSY Repetition of non-words, NEPSY Phonological processing, Diagnostic tests 1 (20) Reading syllables and non-words (6) Reading fluency (100)

11.2 (3.3) 9.7 (1) 17 (4.4)

7.7 (1.8)** 7.9 (1.7)* 2.7 (2.9)***

5.1 (2) 32.7 (30.1)

0.2 (0.6)*** 0**

Letter knowledge and writing Letter knowledge (29) Writing words and non-words (9) Writing syllables and non-words (18)

28.4 (0.7) 5.7 (2.7) 13.4 (5.8)

20.8 (6.4)** 1.2 (5.7)*** 0.1 (0.3)***

Rapid naming (RAN) RAN colors, time (66) RAN numbers, time (66) RAN letters, time (64) RAN objects, time (73) RAN numbers and letters, time (83) RAN colors, numbers and letters, time (91) RAN colors, errors (0–5) RAN numbers, errors (0–16) RAN letters, errors (0–7) RAN objects, errors (0–6) RAN numbers and letters, errors (0–14) RAN colors, numbers and letters, errors (0–10) Working memory Digit Span, WISC-III

66 52 59 69 60 68 1 0.7 2.4 3.2 1.8 3.9

(12) (10) (14) (12) (11) (13) (0.9) (1) (3.2) (4.6) (1.6) (4.8)

12.4 (2.3)

83 (19)* 70 (20)* 79 (22)* 95 (21)** 105 (23)*** 108 (26)*** 4.7 (3.9)* 4.9 (4.8)* 14.1 (30.4)* 5.7 (3.3) 15.7 (29.8) 16.3 (29.6)

10.8 (2)

Group comparisons with a one-way ANOVA. *P < 0.05, **P < 0.01, ***P < 0.001. The maximum possible test score is marked in brackets after each test without normative data. The normative mean time and range of errors for RAN are marked in brackets after each subtest. SD = standard deviation.

Non-words (NEPSY; Korkman et al., 1997), Working memory (Digit Span, WISC-III; Wechsler, 1991) or to have a score lower than 17 in letter knowledge. The comparison of the performance of the two groups revealed significant differences in all the tests mentioned above (Table 3) except for Digit span (WISC-III; Wechsler, 1991).

5.2.

Stimuli and experimental design

The stimuli were semi-synthetic consonant-vowel (CV) syllables (see also Pakarinen et al., 2009; Lovio et al., 2009, using identical stimuli). The standard stimuli were syllables /te:/ in a half of the blocks and /pi:/ in the other half of the blocks. The duration of the syllable was 170 ms and the fundamental frequency (F0) 101 Hz. The deviant syllables differed from the standards by the 1) consonant (/pe:/ and /ti:/), 2) or vowel identity (/ti:/ and /pe:/), 3) vowel-duration (−70 ms; 100 ms /te/ and /pi/), or by 4) syllable frequency (±8%); 93/109) or intensity (±7 dB).

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The Stimuli were created using the Semisynthetic Speech Generation method (SSG) (for details, see Alku et al., 1999). First, the glottal waveform was extracted from the natural utterance of long isolated vowels (/e:/ and /i:/) and short words (/pito/ and /pe:ti/), produced by a Finnish male speaker. Then, a semi-synthetic speech sound was created by using the estimated glottal flow, or its modified version, as an input to the artificial vocaltract model. The lowest 4 formant frequencies of the vocal tract models were 410, 2045, 2260, and 3320 Hz for /e:/ and 320, 2240, 2690 and 3275 Hz for /i:/. Thereafter, the estimated glottal flow was modified to obtain semi-synthetic vowels with 3 F0 values (93, 101 and 109 Hz) and 4 durations (/e:/, /e/, /i:/ and /i/). The unvoiced plosives /p/ and /t/ were then added to the beginning of each of the semi-synthetic vowel sounds (/pe/, /pe:/, /pi/, /pi:/, /te/, /te:/, /ti/, /ti:/). Finally, the intensity levels of the stimuli were normalised. Additionally, for the /te:/ and /pi:/ syllables, two intensity variants (±7 dB) were generated.

5.2.1.

The multi-feature and oddball conditions

There were 8 stimulus blocks of which 4 were multi-feature paradigm sequences (“Optimum-1,” Näätänen et al., 2004) and four were oddball sequences. There were 465 stimuli in each block and each block lasted for about 5 min. All stimulus blocks started with 5 standards and the stimulus onset asynchrony (SOA) always was 500 ms. The order of the stimulus blocks was counterbalanced and the total EEG recording net time was about 40 min. In the multi-feature blocks, the MMN was recorded for all the 5 deviant types (vowel, vowel- duration, consonant, frequency (F0), and intensity) which alternated with the standard stimulus (Fig. 4). In the oddball blocks, the MMN was recorded for 2 deviant types (vowel and vowel-duration) and the rest of the deviant stimuli were replaced with a standard stimulus. The probability of both deviant types thus was the same (0.1) as that in the multi-feature paradigm. The experiment was carried out in a video-monitored, electrically shielded and sound-attenuated chamber. The stimuli were presented through two loudspeakers which were located behind the child on the left and right sides 60 cm apart at 20 cm from the child's head. The child heard the stimuli as coming from the back midline space at an intensity of 60 dB (SPL). During the experiment, the child was watching a silent cartoon movie and there was at least one break during which the child received juice.

5.3.

EEG recording and averaging

The EEG (amplified by SynAmps, band pass 0.1–100 Hz, sampling rate 500 Hz) was recorded with electrodes placed at the F3, Fz, F4, C3, Cz, and C4 scalp loci according to the International 10–20 system. In addition, electrodes were placed on the right and left mastoids and at the tip of the nose, which served as a reference electrode. Electrodes placed below and at the outer corner of the right eye monitored vertical and horizontal eye-movements, respectively. The ERPs were averaged from EEG epochs separately for the standard and for each deviant-stimulus type. In order to control for possible increment/decrement effects, the ±8% changes in frequency (F0) deviants were averaged together as was done with the ±7 dB

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Fig. 4 – Schematic illustration of the multi-feature (a) and oddball (b) conditions. S denotes standard tone and D1–5 different deviant tone types; D1 and D2 were vowel and vowel-duration deviants, used in both conditions, and D3–D5 consonant, frequency (F0) and intensity deviants, used in the multi-feature condition only (Adapted from Näätänen et al., 2004).

changes in intensity deviants as well. In order to control for possible vowel/consonant specificity effects, the different vowel deviants in the blocks with the /te:/ and the /pi:/ standards were averaged together as were the different consonant deviants in the blocks with the /te:/ and the /pi:/ standards. The analysis epoch began 100 ms before and terminated 600 ms after stimulus onset. The responses to the first 5 stimuli of each block were omitted as well as the epochs contaminated by eye-movements or artifacts producing voltage changes of ±75 µV at any electrode. The epochs were digitally filtered with 1–20 Hz band-pass filter and baselinecorrected with respect to the 100-ms pre-stimulus period. The final data set consisted of, on the average, 165 accepted deviant trials/deviant type for controls (range 131–184), and 160 for children at risk for dyslexia (range 110–183).

5.4.

Data-analysis

The grand-mean peak P1, N2 and N4 latencies were identified from the waveforms for the standards at the F3. The MMN latencies were identified from the difference waveforms which were obtained by subtracting ERPs elicited by the standard stimuli from those elicited by the deviant stimuli. The F3 electrode was chosen for the identification of the ERPs because the visual inspection of the difference waveforms showed the best identifiable MMN responses at this locus of the scalp in both groups. The windows for the latency identification were at 50–150 ms (P1), 150–300 (N2) and 300–400 ms (N4) from standard stimulus onset and at 200–400 ms (MMN) from deviant-stimulus onset. The individual mean amplitudes were integrated over 50 ms around these grand-mean peak latencies. The statistical presence of the P1, N2 and N4 for standard tones and the MMN for each deviant was determined at the F3, Fz, F4, C3, Cz, and C4 electrode by comparing mean amplitudes with the zero baseline using two-tailed t-tests. First, the analyses for the ERPs P1, N2, and N4 elicited by the standard stimuli were carried out separately for each component with a four-way ANOVA [including group (at-risk, control), condition (multi-feature, oddball), frontocentral electrode location (F3–Fz–F4, C3–Cz–C4), and lateral electrode location (F3–C3, Fz–Cz, F4–C4)]. The P1, N2 and N4 latencies in the oddball and multi-feature conditions were each compared at F3 between the groups with two-way ANOVAs [including group (at-risk, control) and condition (multi-feature, oddball)]. Next, analyses for the MMN including both conditions were carried out with a five-way ANOVA [including group (at-risk, control), condition (multi-feature, oddball), stimulus (vowel, vowel-duration), frontocentral electrode location (F3–Fz–F4, C3–

Cz–C4), and lateral electrode location (F3–C3, Fz–Cz, F4–C4)]. Then, the MMN mean amplitudes for the different deviants in the multi-feature condition were compared between the groups by using a four-way ANOVA [including group (at-risk, control), stimulus (vowel, vowel-duration, consonant, frequency (F0), intensity), frontocentral electrode location (F3–Fz–F4, C3–Cz– C4), and lateral electrode location (F3–C3, Fz–Cz, F4–C4)]. The MMN mean amplitudes for the vowel and vowel-duration deviants in the oddball condition were also compared between the groups with a four-way ANOVA [including group (at-risk, control), stimulus (vowel, vowel-duration), frontocentral electrode location (F3–Fz–F4, C3–Cz–C4), and lateral electrode location (F3–C3, Fz–Cz, F4–C4)]. The MMN latencies of the vowel and vowel-duration deviants in the oddball and multi-feature conditions were compared at F3 between the groups with a three-way ANOVA [including group (at-risk, control), condition (multi-feature, oddball) and stimulus (vowel, vowel-duration)]. The latencies of the deviants in the multi-feature paradigm were compared at F3 between the groups with a two-way ANOVA [including group (at-risk, control), stimulus (vowel, vowel-duration, consonant, frequency (F0), intensity)]. The latencies of the deviants in the oddball paradigm were also compared between the groups at F3 with a two-way ANOVA [including group (at-risk, control), stimulus (vowel, vowel-duration, consonant, frequency (F0), intensity)]. The Greenhouse–Geisser correction was applied when appropriate. The sources of the significant effects were calculated with Fisher's LSD post-hoc test.

Acknowledgments This study was supported by The Finnish Cultural Foundation and The Academy of Finland (grant numbers 128840 and 122745).

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