Newborn brain event-related potentials revealing atypical processing of sound frequency and the subsequent association with later literacy skills in children with familial dyslexia

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journal homepage: www.elsevier.com/locate/cortex

Special issue: Research report

Newborn brain event-related potentials revealing atypical processing of sound frequency and the subsequent association with later literacy skills in children with familial dyslexia Paavo H.T. Leppa¨nen a,*, Jarmo A. Ha¨ma¨la¨inen a, Hanne K. Salminen a, Kenneth M. Eklund a, Tomi K. Guttorm b, Kaisa Lohvansuu a, Anne Puolakanaho a and Heikki Lyytinen a a b

Department of Psychology, University of Jyva¨skyla¨, Finland Agora Center, University of Jyva¨skyla¨, Finland

article info

abstract

Article history:

The role played by an auditory-processing deficit in dyslexia has been debated for several

Received 2 October 2008

decades. In a longitudinal study using brain event-related potentials (ERPs) we investigated

Reviewed 16 June 2009

1) whether dyslexic children with familial risk background would show atypical pitch

Revised 16 July 2009

processing from birth and 2) how these newborn ERPs later relate to these same children’s

Accepted 25 September 2009

pre-reading cognitive skills and literacy outcomes. Auditory ERPs were measured at birth

Published online 25 June 2010

for tones varying in pitch and presented in an oddball paradigm (1100 Hz, 12%, and 1000 Hz, 88%). The brain responses of the typically reading control group children (TRC group,

Keywords:

N ¼ 25) showed clear differentiation between the frequencies, while those of the group of

Dyslexia

reading disability with familial risk (RDFR, 8 children) and the group of typical readers with

Cognitive skills

familial risk (TRFR, 14 children) did not differentiate between the tones. The ERPs of the

Auditory processing

latter two groups differed from those of the TRC group. However, the two risk groups also

Event-related potentials (ERPs)

showed a differential hemispheric ERP pattern. Furthermore, newborn ERPs reflecting

Infant

passive change detection were associated with phonological skills and letter knowledge prior to school age and with phoneme duration perception, reading speed (RS) and spelling accuracy in the 2nd grade of school. The early obligatory response was associated with more general pre-school language skills, as well as with RS and reading accuracy (RA). Results suggest that a proportion of dyslexic readers with familial risk background are affected by atypical auditory processing. This is already present at birth and also relates to pre-reading phonological processing and speech perception. These early differences in auditory processing could later affect phonological representations and reading development. However, atypical auditory processing is unlikely to suffice as a sole explanation for dyslexia but rather as one risk factor, dependent on the genetic profile of the child. ª 2010 Elsevier Srl. All rights reserved.

* Corresponding author. Department of Psychology, University of Jyva¨skyla¨, P.O. Box 35, 40014 Jyva¨skyla¨, Finland. E-mail address: [email protected] (P.H.T. Leppa¨nen). 0010-9452/$ e see front matter ª 2010 Elsevier Srl. All rights reserved. doi:10.1016/j.cortex.2010.06.003

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1.

Introduction

Debate as to the underlying causes of developmental dyslexia or specific reading disability (RD) has continued for decades. Specific impairment in the acquisition of literacy skills, despite average or above intelligence, normal peripheral sensory systems and educational opportunities, is assumed to result from a combination of complicated interwoven genetic and environmental factors (cf. Lyon et al., 2003; Vellutino et al., 2004;). Using epidemiological samples, dyslexia has been demonstrated to run in families (Gilger et al., 1991; Pennington and Lefly, 2001). According to Pennington (1995), the median relative increase in risk for a child with an affected parent is about eight times the population risk. This familial risk allowed us, in the Jyva¨skyla¨ longitudinal study of dyslexia (JLD; Lyytinen et al., 2004, 2006), to follow from as early as birth, a group of children (using a reasonable sample size for statistical analysis) at familial risk and their matched controls. In the present study, we investigated how auditory brain responses recorded shortly after birth are associated with later reading/writing measures and cognitive skills known to be important for the development of literacy skills. Evidence for low-level auditory-processing deficits in dyslexic individuals has been found, for example, in the processing of small sound frequency differences (e.g., Ahissar et al., 2006; Baldeweg et al., 1999; Banai and Ahissar, 2004; France et al., 2002; Halliday and Bishop, 2006; Kujala et al., 2006; Lachmann et al., 2005; for a review see, Ha¨ma¨la¨inen et al., in press). Most of these studies have used behavioural methods although brain event-related potential (ERP) measures have also been applied to both adults and school age children to investigate auditory processing in dyslexia. Furthermore, using both methodologies, kindergarten children at risk for familial dyslexia have, when compared to control children, been shown to differently process a change in sound frequency (Maurer et al., 2003). Deficits in the processing of other non-speech features have also been found; for example in temporal order judgement (Tallal, 1980), as well as amplitude and frequency modulation (FM) (McAnally and Stein, 1997; Witton et al., 2002). The earliest examples related to other language learning impairments come from 6-monthold infants at familial risk for specific language impairment (SLI). At-risk children show differences in auditory processing of frequency change in paired stimuli and infant auditory processing is predictive of later language skills at the ages of 2 and 3 years (Benasich and Tallal, 2002; Benasich et al., 2006; Choudhury et al., 2007). However, not all studies have found impaired auditory processing of non-speech stimuli in dyslexic readers (see e.g., Mody et al., 1997; Schulte-Ko¨rne et al., 1998; Ramus et al., 2003; White et al., 2006). The question asking how basic auditory-processing deficits could be related to reading problems is still open. It is likely that atypical auditory-processing abilities are not directly related to reading or writing but rather to cognitive processes important for reading development. The cognitive skills known to be related to reading skills and dyslexia include phonological sensitivity or awareness, verbal short term memory (vSTM), and automatic rapid naming (RAN), referred together as phonological processing skills (see e.g., Wagner

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and Torgesen, 1987, Pennington, 1991; Vellutino et al., 2004; Puolakanaho et al., 2007, 2008). There are several possibilities as to how low-level auditory-processing abilities could be related to reading and reading related skills. The relationship could be causal. It is well established that problems in phonological processing are predictive of and probably causal of many cases of reading difficulty (for a review, see Vellutino et al., 2004). A possible cause of poor phonological processing in dyslexia is weak phonological representations that could be attributed to difficulties in speech perception (Fowler, 1991; Schulte-Ko¨rne et al., 1998; Serniclaes et al., 2001; McBrideChang, 1995). Furthermore, these difficulties could stem in part from an underlying bottom-up deficit in the processing of basic sound features such as frequency and duration (e.g., Baldeweg et al., 1999; Corbera et al., 2006). For example, it has recently been shown that dynamic auditory processing, involving processing of FM at 2 Hz, in five-year-old pre-school children was related to speech perception and phonological processing (Boets et al., 2008). Both speech perception and phonological processing were then related to reading skills at the end of the first school grade at age of 6e7 years. However, it could also be that auditory deficits and RD are co-morbid without a causal connection. Some behavioural studies, as well as ERP-studies, have failed to show poorer discrimination of non-speech sounds in dyslexic individuals (Mody et al., 1997; Schulte-Ko¨rne et al., 1998; Ramus et al., 2003; White et al., 2006). This has been taken to suggest that the problems related to reading ability in dyslexic individuals are speech-specific or phonological, rather than auditory in general. It is possible, however, that the lower level sensory impairments diminish during development due to maturation and environmental factors. Studies of children with SLI have shown age inappropriate ERP responses (McArthur and Bishop, 2005) and different developmental progression in masking tasks (Wright and Zecker, 2004). Interestingly, juvenile rats with problems in auditory processing caused by cortical lesions at birth have shown improvements in their discrimination in adulthood of gaps in sounds (Peiffer et al., 2004). Therefore, it is important to investigate sensory processes of children at risk for dyslexia as early as possible in development. Furthermore, the relationship between auditory deficits and RD could be a more complex accumulation of different risk factors in different cognitive domains (Pennington, 2006; Barry et al., 2007; Snowling, 2008). In this view, basic auditory-processing problems could be one of the risk factors at the cognitive level. These could be in a causal relationship with dyslexia in a sub-group of individuals, or act as a moderator together with other risk factors making the dyslexia phenotype more severe; quite likely depending on the combination of genes associated with the development of dyslexia (see e.g., Bishop et al., 1999a; Pennington, 2006). Longitudinal studies could help to disentangle the relationship between auditory-processing deficits and dyslexia. In the JLD-project, children have been followed from birth to school age thus providing data at different developmental stages (see Lyytinen et al., 2004, 2006). The earlier in development a cognitive process is shown to differ between children with typical reading skills and children with reading problems and with associations to later reading related cognitive skills and reading/writing, the more likely it is that

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the deficit in this process is causally related to reading level outcome, albeit as one of the several risk factors. At the behavioural level, in the JLD-project we have found several cognitive skills tested before school from ages 3.5 to 5.5 years to be related to reading speed (RS), as well as reading accuracy (RA)/writing accuracy at the end of 2nd grade in school (age 9 years) (Puolakanaho et al., 2007, 2008). In the present study, we investigated the associations between newborn ERPs to a selected set of these cognitive skills and later reading and spelling skills and compared newborn ERPs between children in different reading level groups. ERPs have been widely applied to the study of infant cognition. ERPs are based on electroencephalography (EEG) and reflect brain responses to presented stimuli without active behaviour from the participant, even during sleep. This provides a valuable means of measuring newborns’ processing of sounds (Cheour et al., 2000; Taylor and Baldeweg, 2002). In previous studies, an occasional change in a stream of sounds has been shown to elicit a change-detection response, even in infants (Alho et al., 1990; Cheour-Luhtanen et al., 1995; Leppa¨nen et al., 1997; Cheour et al., 2000; Na¨a¨ta¨nen, 1992; Taylor and Baldeweg, 2002). In the JLD-project, ERPs have been used successfully to study the brain activity in newborns and 6-month-olds with and without familial risk for dyslexia. For example, there are early hemispheric differences in speech sound processing between these risk groups (Leppa¨nen et al., 1999, 2002; Pihko et al., 1999; Guttorm et al., 2001, 2003). In addition, associations have also been found between newborn ERPs for consonant-vowel (CV)-syllables and later language and vSTM skills as well as pre-reading skills, such as phonological skills, RAN, and letter knowledge (LK) (Guttorm et al., 2005, 2010). In the present study, we aimed to elaborate upon the findings of speech processing anomalies in infants at risk for familial dyslexia to non-speech tones, particularly to the processing of pitch change, in order to test whether we could find a connection between early basic auditory processing and dyslexia. The oddball paradigm used in the present study has previously been used in typically developing children. Their auditory ERPs show that, already soon after birth, the newborns can discriminate between frequently and rarely occurring tones that differ in pitch (Leppa¨nen et al., 1997, 2004). In this study, we investigated 1) whether dyslexic readers with familial risk differ at birth in their brain responses from those at-risk children who became normal readers and from typically reading control (TRC) children and 2) how non-speech sound processing in newborns is associated with later reading related cognitive skills and reading outcomes measured at the 2nd grade in school. Here we also extended earlier findings to early latency obligatory responses to see at what level of processing any possible differences related to dyslexia are found.

2.

Methods

2.1.

Participants

ERPs from 54 healthy newborns were included in this study, 23 of whom belonged to the group with familial risk for dyslexia

(with at least one parent with dyslexia and one close relative with reported reading difficulties; see below), and 31 to the control group from matched families with no incidence of familial dyslexia. For comparisons between newborn ERPs and later cognitive, language and reading measures in the longitudinal study, data were available for all included measures (see below) for 51 (22 at-risk and 29 control children) of the 54 children. None of the children had neurological problems (excepting one at-risk group participant who underwent surgery for medullo blastoma vermidis at the age of 4 years, but was assessed one year later by a hospital neuropsychologist to have normal IQ and language development. As this child’s data did not alter the results in the confirmatory analyses, the participant was included in the study). The hearing levels were reported as normal in all children in the analyses (at birth, a reaction to a 100 dB sound to each ear was recorded; for one whom no right ear response was observed, the audiometry showed normal hearing at the 3-year check-up at family guidance centre, and for one with missing information at birth, audiometry at 5 years showed normal hearing). The families were screened and recruited according to institutional informed-consent procedures and were participants in the JLD (Lyytinen et al., 2004). The inclusion criteria for the at-risk group were multiple diagnostic test results indicative of dyslexia for at least one parent and reported reading difficulties in at least one of his/her close relative. Each parent’s IQ was required to be 80 or above (assessed with the Ravens B, C and D matrices; Raven et al., 1992; for details see Leinonen et al., 2001). The 51 children participating in the brain-behavioural measure comparisons were further divided into four groups based on their reading skills at the 2nd grade at the age of 9 years. Eight (2 boys, 6 girls) belonged to the at-risk group and had RD (reading disability with familial risk, RDFR); 14 (8 boys, 6 girls) belonged to the at-risk group and had typical reading skills (typical readers with familial risk, TRFR); 4 (all boys) had RD but belonged to the control group (RDC); 25 (12 boys, 13 girls) had typical reading skills and belonged to the control group (TRC). The division of children into those with typical or disabled reading was based on the performance of the whole JLD sample. The criterion for determining the children as dyslexic readers entailed performance at or below the 10th percentile in reference to the whole control group’s (n ¼ 89) performance on at least 3 from 4 measures separately in RS or in RA/spelling accuracy (SA) at the end of 2nd grade (see below). In addition, those children who fell below the criterion on a combination of two speed measures AND two accuracy measures were considered to be dyslexic readers. As an exclusionary criterion, all children were required at the 2nd grade to have a verbal IQ (vIQ) or performance IQ (pIQ) of at least 80, as measured with five and four sub-tests of the Wechsler Intelligence Scale for Children-Third Edition, respectively (WISCIII; Wechsler, 1991; see below). One child had no 2nd grade IQ measures available, but had both vIQ and pIQ above 80 at the age of five years in Wechsler Preschool and Primary Scale of Intelligence-R (WPPSI-R; Wechsler, 1989). For further details on the dyslexia criteria, see Puolakanaho et al. (2007) and Ha¨ma¨la¨inen et al. (2007).

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For ERPs, the infants were tested within 1e7 days of birth, except for 5 participants (1 at-risk child and 4 control children), whose gestational age (GA) was below 38 weeks. These 5 participants were tested at approximately 40 weeks postconceptional age (i.e., within 16e23 days from birth). There were no statistically significant differences between the RDFR, TRFR and TRC groups in GA, birth weight or Apgar scores (see Table 1).

2.2.

ERP measures for newborns

2.2.1.

Stimuli and procedure

The sinusoidal tone stimuli were presented in an oddballsequence, where an 1100 Hz deviant stimulus (probability 12%) was embedded among 1000 Hz standard stimuli (probability 88%). There were at least 5e10 standard tones between any subsequent deviant stimuli. The auditory stimuli were delivered through a loudspeaker located at the foot of the crib, 39 cm above the bed level and 60 cm from the estimated head position of the infant (the angle between the loudspeaker and the infants crib was 41 ). The intensity of the stimuli was 75 dB

SPL measured from the estimated head position of the infant. The duration of the stimuli was 74 msec, with 24 msec rise and fall times, and the stimulus onset asynchrony (SOA) for all stimuli was 425 msec. For further details, see Leppa¨nen et al. (1997).

2.2.2.

EEG recording and ERP averaging

EEG was recorded using Ag/AgCl-electrodes from F3, F4, C3, C4, P3, P4 scalp sites, according to the 10e20 system (and from bi-polar derivations F3eF4, C3eC4, P3eP4, and T3eT4, not reported here). The electrodes were referred to the ipsilateral mastoid. The electro-oculogram (EOG) was recorded with two electrodes, one slightly below and lateral to the right eye and the other above and lateral to the left eye. The EOG electrodes were referred to the left mastoid. A ground electrode was placed on the participant’s forehead. The EEG was stored at a sampling rate of 200 Hz, and a band pass filter of .5e35 Hz. The ERPs were calculated by averaging EEG-epochs from 50 to 425 msec (with a 50 msec pre-stimulus baseline) for each stimulus type separately. EEG-epochs with artefacts or

Table 1 e Means (standard deviations) of the background measures at birth and later pre-reading, speech perception and literacy measures with group comparisons. Means (standard deviations)

Group comparisons

RDFR (n ¼ 8)

TRFR (n ¼ 14)

TRC (n ¼ 25)

39.88 (.74) 3701.25 (440.31) 9.00 (.00)

40.24 (1.27) 3575.71 (415.34) 9.07 (.27)

39.71 (1.43) 3719.20 (584.44) 8.92 (.28)

RDFR ¼ TRFR ¼ TRC RDFR ¼ TRFR ¼ TRC RDFR ¼ TRFR ¼ TRC

Phonology, 3.5 years Word-level segment identification Syllable-level segment identification Synthesis of phonological units Continuation of phonological units

.44 (.82)

.38 (.59)

.06 (.48)

RDFR ¼ TRFR ¼ TRC

vSTM, 5 years Digit Span Memory for Names (NEPSY)

.66 (.92)

.31 (.97)

.01 (.71)

RDFR ¼ TRFR ¼ TRC

RAN, 5.5 years Rapid Serial Naming (RAN) of objects Rapid Serial Naming (RAN) of colours

.91 (.92)

.18 (.80)

.29 (.66)

RDFR < TRFR**, RDFR < TRC**

At-birth measures GA Birth weight (g) 5-min Apgar scores

(3.70) (2.96) (.79) (9.30) (11.00)

34.29 11.38 2.12 101.64 107.64

(6.16) (8.28) (.54) (12.01) (13.63)

35.68 13.16 2.13 101.96 103.28

(5.34) (7.73) (.58) (12.52) (12.85)

RDFR ¼ TRFR ¼ TRC RDFR < TRFR*, RDFR < TRC** RDFR < TRFR**, RDFR < TRC** RDFR < TRC* RDFR ¼ TRFR ¼ TRC

EV, 5.5 years (BNT) LK, 5 years PDP, 9 years (D-prime) vIQ, 9 years pIQ, 9 years

37.00 3.25 1.25 89.75 94.88

RS, 9 years Oral text reading Oral pseudo-word text reading Single word and non-word reading Single word reading, Lukilasse

1.61 (.37)

.06 (.75)

.29 (.74)

RDFR < TRFR**, RDFR < TRC**

RA, 9 years Oral text reading Oral pseudo-word text reading Single word and non-word reading

1.20 (.85)

.25 (.73)

.22 (.58)

RDFR < TRFR**, RDFR < TRC**

SA, 9 years Word spelling Non-word spelling

1.37 (1.18)

.18 (.68)

.19 (.62)

RDFR < TRFR***, RDFR < TRC***

Note. The group differences were based on post-hoc paired comparisons (Tukey), *p < .05, **p < .01, ***p < .001 (2-tailed).

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deflections exceeding 150 microvolts (mV) at EOG (excessive eye movements) and 200 mV at EEG channels (muscle activity or other extra-cerebral artefacts) were excluded from averaging. For the ERPs to the standard stimuli, the responses to the standard stimuli preceding the deviant stimuli were included, because these are assumed to represent the most firm memory trace of the repeated stimulus, as well as to obtain a similar signal-to-noise ratio for both stimulus types. The minimum number of acceptable EEG-epochs was 70 for each stimulus type. There were no statistically significant between-group (RDFR, TRFR and TRC) differences in the number of accepted epochs.

2.2.3.

The sleep-state classification of EEG

EEG was mainly recorded while the infants were asleep. Following the sleep-state scoring manual by Anders and colleagues, the EEG-epochs were classified into four categories: quiet sleep, active sleep, indeterminate sleep, or wakefulness (Anders et al., 1971). The behaviour of the infant was observed and coded during the experiment and eye movements were monitored from the EOG-channels. For a more detailed description of the sleep-state classification, see Leppa¨nen et al. (1997). Only the data recorded during quiet sleep are reported in this study.

2.3.

Behavioural and outcome measures

The behavioural measures described below have been reported in more detail in Puolakanaho et al. (2007, 2008). A summary of the variables, the means, and group differences are displayed in Table 1.

2.3.1.

Pre-school age measures

child, each separated by 750 msec. The child was required to blend the segments to produce the resulting word (e.g., per-ho-nen (butterfly) or m-u-n-a (egg)). Only a response containing the right assembled form was coded as correct. 4) Continuation of phonological units (8 items at the age of 3.5). The child was presented with the onset of a ‘secret’ word and asked to guess how the word would continue (e.g., ‘mu-?’). Only continuations that were meaningful words were coded as correct. vSTM. The composite was calculated using the following two variables: 1) The Digit Span subtest was used at 5 years of age as described in the literature (e.g., Gathercole and Adams, 1993). The child repeated lists of two to six digits presented by computer. The score obtained was the number of correctly repeated lists. 2) The Memory for Names e task was administered at the age of 5.5 years in association with the Developmental Neuropsychological Assessment (NEPSY; Korkman et al., 1998). In this task, the examiner read names aloud and the child was asked to recall them. The measure obtained was the number of correctly repeated items. RAN. Rapid Serial Naming (RAN) of objects and colours e tasks were assessed at the age of 5.5 years (see Denckla and Rudel, 1976) within a reduced (30-item; 5 stimuli by 6 times random presentation) matrix. The composite score for the naming time was an average of the z-scores for these measures (see above). Expressive vocabulary (EV). The Boston Naming Test (BNT; Kaplan et al., 1983) was used for the measure of productive vocabulary at 5.5 years. The score was a sum of the items (maximum of 60) named correctly, both spontaneously and after a semantic stimulus cue. Letter knowledge (LK). At the age of 5 years, the child was asked to name 23 capital letters. The test was initialized by presenting the child with the first letter of his or her own name. The sum of the correct answers was used as a measure of LK. Use of a phoneme or a letter name was accepted as a correct response.

For all composite measures described below, the mean z-score was calculated based on the mean and standard deviation of the whole control group (n ¼ 92) of the JLD sample. The sign of z-scores for RAN (see below) was reversed to be in line with other z-values; the higher positive z-value means better performance, i.e., shorter naming time. Phonological processing skills. The composite score for this measure was calculated from the four measures intended to represent a measure of early phase of phonological awareness (or phonological sensitivity; in the present study, the terms phonological processing and phonology are used interchangeably).

2.3.2. Reading and spelling tests for participant selection and outcome measures at the 2nd grade

1) Word-level segment identification (8 items at the age of 3.5 years). In this task, the child was presented with 3 pictures of objects on the screen, immediately followed by the name of each object (all compound words) and asked to identify the picture containing a specified part of the compound (e.g., ‘lentokone’ (aeroplane); ‘soutuvene’ (rowing boat); ‘polkupyo¨ra¨’ (bicycle) e in which picture can you hear the sound ‘kone’ (plane)?). 2) Syllable-level segment identification (8 items at the age of 3.5). The task was the same as above but with the requirement to identify sub-word-level units (syllables or phonemes) within the target (e.g., the ‘koi’ in the word ‘koira’ (dog)). 3) Synthesis of phonological units (12 items at the age of 3.5 years). Segments (syllables or phonemes) were presented to the

Participants’ reading and writing skills were tested during the summer following their 2nd school year when they averaged 9 years of age (range: 8 years, 5 months to 9 years, 10 months). IQ-tests (see below) were carried out during an assessment visit in November or March of the second school year. For participant selection criteria, performance in the separate tests below was used, while for the reading outcome measures, composite variables were created (see below). In both cases, z-scores based on the mean and standard deviation of the whole control group (n ¼ 89) of the JLD sample were used (for more details, see Puolakanaho et al., 2008). Oral text reading. Participants read five passages with a total of 124 words of Finnish text appropriate for age. Reading performance was audio-recorded and the number of words read per 1 min (speed), as well as the percentage of

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correctly read words were measured from the recording (accuracy). Oral pseudo-word text reading. Participants read a short text constructed of pseudo-words that resemble real Finnish words but are devoid of meaning (a total of 19 words). This performance was also audio-recorded and the number of pseudo-words read in 1 min (speed) as well as the percentage of correctly read pseudo-words were measured from the recording (accuracy). Single word and non-word reading. Tests of word and nonword RA and fluency were administered by computer (Cognitive Workshop program, developed in cooperation between the Universities of Dundee and Jyva¨skyla¨). Separate sets of 10 items were presented in a fixed order on the screen with the task requirement to read aloud the target item as quickly and accurately as possible. Altogether, 4 sets (40 items in total) were used: 2 of words and 2 of non-words (both with 3- and 4-syllables). The mean of the sum for the reaction time (the time between presentation of the target on the screen and initiation of the child’s vocalisation) and the production time (the time taken to articulate the target) was used as a measure of RS. The number of correctly read items was used as a measure of RA. Word and non-word spelling. Participants were asked to write (with paper and pencil) six 4-syllable words which were presented via headphones by a computer. In two additional separate 6-item sets, the items were 4-syllable non-words. The sum of the scores from these 18 items was used as a measure of SA. Lukilasse. In this national standardized reading task (Ha¨yrinen et al., 1999), participants were allocated 2 min to read aloud as many words as possible from a 90-item list. The standard score was based on the correctly read words and was used as a measure of RS. Composite scores of the above described measures were used in the brain-behaviour analyses as outcome measures according to Table 1. IQ-tests. vIQ and pIQ were measured using the WISC-III (Wechsler, 1991), and were calculated from 5 sub-tests for vIQ (Arithmetic, Comprehension, Digit Span, Similarities and Vocabulary) and 4 sub-tests for pIQ (Picture Completion, Block Design, Object Assembly and Coding). Phoneme duration perception (PDP). The task, administered at 9 years (2nd grade in school, see below), consisted of 22 pseudo-word and non-word pairs with two, three or four syllables. In twelve of the pseudo- and non-word pairs the members differed from each other only by one element e phoneme duration. The members of the other 10 pairs were identical. The duration differences of the pseudo- and nonword phonemes were 70, 110 and 130 msec. In the discrimination task, the child heard the pairs through Sony headphones with the task requirement to say whether the two stimuli were exactly identical or not. All pairs were played in the same pseudorandom order for each child. The interstimulus interval (ISI) between the two pseudo- or non-word pairs was 1000 msec. D-prime (d0 ) values, which are used to eliminate possible response biases of different participants by taking into account both hits and false alarms, were used in this study. D-primes were calculated by subtracting the percentage of false alarms (transformed into z-scores of

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normal distribution) from the percentage of hits transformed into z-scores (of normal distribution).

2.4.

Statistical analyses

2.4.1. Analyses for ERPs: temporal principal component analysis (tPCA) and group difference analyses In order to locate the latencies of interest, the averaged ERPs were used as input for a tPCA using a covariance matrix. PCA extracts a small number of components from the total variance in the original data, thus summarizing the data into a more manageable form (see Molfese et al., 1991; Dien et al., 2003, 2005; Kayser and Tenke, 2003). To reduce the number of variables for tPCA, every other time point (i.e., one in every 10 msec) of each averaged ERP was used, producing 48 dependent variables over a 50 to 420 msec latency range (see Guttorm et al., 2003). The ERPs recorded from the different electrode locations, stimuli and participants were treated as the cases (6  2  54 ¼ 648 cases; including the data from 54 newborns). The principal components were rotated using the promax rotation (see Dien et al., 2005; cf. Kayser and Tenke, 2003). Further analyses were carried out on the original averaged ERPs using the mean amplitude values over the latency range of interest as indicated by tPCA. Multivariate analysis of variance (MANOVA) and analysis of variance (ANOVA) were used to examine ERP differences between the three reading groups (RDFR, TRFR and TRC); no statistics were calculated for the RDC group due to only four participants in this group. GA was controlled for in these analyses, because it was previously shown to correlate with the ERP amplitude using the same paradigm in the control children (Leppa¨nen et al., 1997). A paired samples t-test was used to examine differences between the responses to the standard and deviant stimuli within these groups. Effect sizes for significant group differences in different electrode sites were calculated using Cohen’s d (with pooled standard deviation).

2.4.2.

Analyses for ERP e later outcome associations

Pearson correlation coefficients were used to study the connections between early brain responses and later cognitive skills, RA/RS, and SA. Hierarchical regression analyses were used to examine how much of the variance in reading and spelling measures could be explained by newborn ERP measures and additionally by selected pre-school age cognitive measures after controlling for GA and pIQ. Outlier values were assessed with the box-plot function in SPSS 15.1. All outliers (values of at least 1.5 interquartile range below the 25th or above 75th percentile) in both ERP- and behavioural measures were corrected and moved next to a non-outlier value in the tail. An alpha level of .05 was used for all statistical tests.

3.

Results

3.1.

Outcome measure and IQ group differences

Table 1 displays means and group differences for IQ, prereading and reading skills. The group of children with RDFR had lower scores compared to the group of TRFR and the TRC

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group in RAN, LK, and phoneme duration perception (PDP). The RDFR group also had poorer performance on all reading and spelling measures compared to the TRFR and TRC groups. The TRC and TRFR groups did not differ in their reading or spelling measures. Similarly, for vIQ, the RDFR group had lower scores compared to the TRC group, while the TRFR group did not differ from the other two groups. No group differences were found for pIQ.

3.2.

Newborn ERPs and group differences

As can be seen from Fig. 1, the newborn ERP response to the 1000 Hz standard stimuli was close to the baseline in all groups. In contrast, the response to the 1100 Hz deviant stimuli in the TRC group showed a large positive rather slow wave reaching maximum at about 300 msec and lasting to the end of the epoch. The responses to the deviant stimuli were

largest at fronto-central recording sites. In the TRFR and RDFR groups, the responses to the standard and deviant stimuli were largely overlapping. In the TRFR group, a small difference was observed at the right frontal and central electrode sites. However, unlike in the TRC group, the deviant-response returned close to baseline towards the end of the epoch. The PCA resulted in 3 factors that accounted for 93.3% of the total variance. The latency ranges showing factor loadings (after rotation) greater than .8 were identified as the region of significant variability on each of the factors and were chosen for further analyses. The first temporal principal component (PC1-320e420) accounted for most (79.3%) of the variance in the ERP waveform. It rose above .8 at 320 msec, reached its maximum value at 400 msec and remained above the limit until 420 msec, the end of the analysis window. PC2-160e250 explained 9.9% of the variance and was above .8 at 160e250 msec with maximum value at 200 msec. PC3-30e80

Fig. 1 e Newborn ERPs to the rarely presented deviant 1100 Hz tone (thick line) and to the frequent standard 1000 Hz tone (thin line) in three groups of children classified according to their later reading performance: A (upper left), the at-risk children with RD (RDFR, n [ 8); B (lower left), the at-risk children with typical reading skills (TRFR, n [ 14); C (upper right), the control children with typical reading skills (TRC, n [ 25); D (lower right), the control children with RD (RDC, n [ 4). The boxes in panel A mark the windows for PC1 at 320e420 msec, PC2 at 160e250 msec, and PC3 at 30e80 msec (for details, see the text). The vertical line marks the stimulus onset. The time window is -50e420 msec (negativity up).

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explained 4.1% of the variance remaining above .8 at 30e80 msec latency with a maximum at 50 msec. The mean amplitude scores (mV) from the original averaged ERPs across these latency ranges were used for further analyses. Within-group deviantestandard response differences. At the PC1-320e420 latency, TRC children had somewhat larger deviant-responses at the right hemisphere compared to the corresponding responses at the left electrode sites (see Figs. 1 and 2). The TRFR children showed a similar pattern but with lower amplitude values. However, the RDFR children seemed to have an opposite effect for the hemispheric pattern of amplitudes with the left hemispheric preponderance. The ERPs at the PC2-160e250 latency had a similar pattern for the deviant-responses e being largest for TRC children with a right hemispheric preponderance. Again, the TRFR children showed a similar pattern but their responses were lower overall while the RDFR children had an opposite hemispheric pattern. A similar hemispheric pattern with small amplitudes was observed for the deviant-responses at the PC3-30e80 latency in the TRC group, only the right electrode sites showing a positive response. The pattern was similar with somewhat smaller responses in the TRFR group with the exception of the left frontal site now having the most positive response. In the RDFR group, the hemispheric pattern was again opposite to that of the TRC group. The within-group paired samples t-tests for ERPs at PC1320e420 showed that, in the TRC group, the newborn ERPs to the standard and deviant stimuli were significantly different at all electrode sites except for the left parietal (P3) site [t(24) ¼ 2.29e3.47, .030 < ps < .003; see Fig. 1]. The TRFR and RDFR groups showed no differences between the responses to the standard and deviant stimuli at any of the scalp sites. For the PC2-160e250 latency range in the TRC group, the responses to the standard and deviant stimuli were significantly different at all electrode sites except for the left central (C3) and parietal

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(P3) sites [t(24) ¼ 2.57e4.63, .018 < ps < .001; see Fig. 1]. Again, the TRFR and RDFR groups showed no differences between the deviant and standard responses at any electrode site. As expected, for PC3-30e80, no deviant versus standard response differences were found in any group. Between reading group differences. For ERPs at PC1320e420, repeated measures MANOVA [stimulus (deviant, standard)  hemisphere (left, right)  anterioreposterior (frontal, central, parietal)  group (RDFR, TRFR, and TRC)] using GA as a covariate showed a stimulus by group interaction [F(2,43) ¼ 4.59, p < .017, L ¼ .82; see Figs. 1 and 2]. A stimulus by GA interaction was also found [F(1,43) ¼ 8.18, p < .008, L ¼ .84]. A corresponding MANOVA only for the deviantresponse showed a group main effect [F(2,43) ¼ 3.41, p < .043], which was not found for the standard response ( p > .05). Posthoc univariate F-tests between different groups for the deviant-responses (controlling for GA) showed that the TRFR group had smaller amplitudes than the TRC group at both frontal electrode sites and the left central site [F3: F(1,36) ¼ 6.05, p < .020; F4: F(1,36) ¼ 6.98, p < .013; C3: F(1,36) ¼ 4.42, p < .044]. Effect sizes were .8, .9, and .7 (non-overlap of the distributions 47%, 52%, 43%) for the F3, F4, and C3 electrodes, respectively. The RDFR group’s responses to the deviant stimuli were smaller than those of the TRC group at the frontal and central electrode sites, but only at the right hemisphere [F4: F(1,30) ¼ 5.48, p < .027; C4: F(1,30) ¼ 5.65, p < .025]. The effect size for both electrode sites was 1.0 (indicating 55% nonoverlap of the distributions of the responses to the deviant stimulus between the RDFR and TRC groups). In contrast, the two risk groups (RDFR and TRFR) did not show any significant group differences (Fs < 1.50, all ps > .24, Cohen’s d < .5 for all electrode sites). Corresponding MANOVA analyses for ERPs at PC2-160e250 showed a nearly significant stimulus by group interaction [F (2,43) ¼ 2.90, p < .067, L ¼ .88; see Figs. 1 and 2] and

Fig. 2 e Mean amplitudes of newborn ERPs to the rarely presented deviant 1100 Hz tone at the 3 PC time windows at the frontal (F3, F4) and central (C3, C4) electrode sites in the 3 reading groups: the at-risk children with RD (RDFR, n [ 8), the at-risk children with typical reading skills (TRFR, n [ 14), and the control children with typical reading skills (TRC, n [ 25). A (left panel), at PC3-30e80; B (middle panel), PC2-160e250; C (right panel), PC1-320e420. The error bars (thin lines on the bars) represent 1 standard error (SE) from the mean.

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a significant stimulus by GA interaction [F(1,43) ¼ 6.91, p < .013, L ¼ .86]. In a separate MANOVA for the deviantresponse only, a group main effect was found [F(2,43) ¼ 3.32, p < .046], which was not observed for the standard response ( p > .05). Post-hoc univariate F-tests showed, that the TRFR group had a smaller response than the TRC group at the left central site [C3: F(1,36) ¼ 7.57, p < .01]. The effect size was .9 (non-overlap of the distributions 52%). In contrast, the RDFR group’s amplitude was smaller than in the TRC group at the right central site [C4: F(1,30) ¼ 4.75, p < .038] with a .9 effect size (52% non-overlap of the distributions). No significant differences were found between the two risk groups (RDFR and TRFR) at any scalp site. For ERPs at PC3-30e80, no group differences were found, except in univariate F-tests at P4 between the TRC and RDFR groups [F(1,30) ¼ 5.36, p < .029]. Hemispheric differences between groups, suggested by Fig. 2, were tested with separate repeated measures ANOVA analyses (L vs R) for ERPs to the deviant stimuli at the frontal and central electrode sites. The TRFR and TRC groups did not differ significantly from each other at any of the studied latency ranges. A hemisphere by group effect was found for the RDFR and TRC groups for ERPs at PC3-30e80, showing larger right than left hemispheric responses in the TRC group with an opposite pattern in the RDFR group across the frontal and central sites [F(1,30) ¼ 5.182, p < .031]. A similar effect for the RDFR and TRFR groups was observed for ERPs at PC2160e250 separately for the central sites [F(1,19) ¼ 4.84,

p < .041], the TRFR group showing the right hemispheric preponderance and the RDFR group showing an opposite pattern.

3.3.

Brain e outcome measure associations

Correlations between newborn ERPs, later cognitive, reading and spelling skills. Pearson correlation analyses (see Table 2) across the three reading groups (RDFR, TRFR, and TRC) revealed that newborn ERPs for the deviant stimulus were related to several later outcome measures. The most consistently significant correlations were observed for the mean amplitude at the latency of PC1-320e420 at right frontal electrode site (F4). The larger the positive response at this scalp site, the better the performance on the phonological tasks at 3.5 years (.397), in the LK task at 5 years (.304), in the task of PDP at 9 years (.331), and in the RS (.338). The deviantresponse at PC2-160e250 at the same scalp site correlated to phonology at 3.5 years (.403), PDP (.410) and again, to both RS (.2.95) and SA (.291). The response at the earliest latency (PC330e80) at the same F4 site was related to vSTM at 5.0 years (.337), vocabulary at 5.5 years (.382), PDP (.385), as well as to RS (.458) and RA (.353). This response was also correlated to RAN speed at 5.5 years (.311), indicating that a larger positive response was related to faster naming. The pre-school and school age outcome measures correlated with each other to a relatively high degree, up to .81 (see Table 2).

Table 2 e Correlations between newborn mean ERPs at the latencies of PC1-320e420, PC2-160e250, and PC3-30e80 components and reading related skills. GA

vIQ, 9y

pIQ, 9y

Phonology, 3.5 y

vSTM, 5y

RAN, 5.5 y

EV, 5.5 y

LK, 5y

PDP, 9y

RS, 9y

RA, 9y

SA, 9y

PC1

F3 F4 C3 C4

.184 .285 .218 .071

.046 .151 .087 .211

.103 .166 .155 .207

.230 .397** .222 .274

.008 .249 .209 .221

.104 .234 .177 .028

.024 .101 .008 .070

.281 .304* .228 .130

.035 .331* .067 .029

.083 .338* .102 .154

.170 .199 .119 .070

.080 .245 .040 .214

PC2

F3 F4 C3 C4

.147 .296* .302* .176

.196 .173 .081 .163

.005 .178 .151 .225

.238 .403** .164 .256

.054 .190 .075 .216

.170 .180 .127 .021

.078 .121 .143 .015

.214 .162 .153 .057

.174 .410** .002 .033

.020 .295* .032 .180

.181 .245 .217 .043

.046 .291* .141 .163

PC3

F3 F4 C3 C4

.113 .042 .290* .208 .160 .096 .254 .073

.105 .162 .152 .186

.147 .280 .008 .097

.077 .337* .110 .221

.203 .311* .064 .150

.157 .606** 1.000

.106 .300* .289 1.000

.086 .171 .257 .437** 1.000

.017 .454** .174 .192 .277 1.000

GA vIQ pIQ Phonology vSTM RAN EV LK PDP RS RA SA

1.000 9y 9y 3.5 y 5y 5.5 y 5.5 y 5y 9y 9y 9y 9y

.054 1.000

.111 .029 .382** .218 .089 .037 .229 .121 .284 .208 .215 .174 .303* .056 1.000

.112 .534** .374* .463** .375* .556** .359* 1.000

Note. PC1, 320e420 msec; PC2, 160e250 msec; PC3, 30e80 msec. *p < .05, **p < .01, ***p < .001 (2-tailed). Significant correlations between newborn mean ERPs and later reading related skills are marked in bold.

.021 .385* .051 .091

.050 .170 .458** .335* .160 .203 .267 .181

.168 .353* .239 .134

.030 .352* .232 .366* .181 .399** .075 .428** 1.000

.012 .530** .245 .297* .393** .663** .156 .571** .442** 1.000

.033 .507** .386** .524** .390** .446** .008 .501** .551** .700** .783** 1.000

.129 .534** .400** .450** .408** .563** .089 .567** .538** .814** 1.000

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Hierarchical regression analyses were carried out to examine the possible pathways through which the ERP measures were associated with each of the reading and spelling outcome measures. RS, RA and SA at 9 years (2nd grade) were used in three separate models as the dependent variables. The GA and pIQ (at the 2nd grade) were controlled for and entered in the 1st and 2nd steps. The mean amplitude measures at the right frontal site (F4) for the deviant stimuli from each latency range were entered next. Pre-school age cognitive measures were entered after newborn ERPs as the independent variables to see how much of the reading/ spelling skills after newborn ERPs these would explain (see below). The ERP measures entered into the model showed most consistent correlations to later outcome measures. The ERPs at the latest time window were entered first, followed by the ERPs at the middle and early time windows, to see whether early level responses would explain any additional variance to that explained by the change-detection response. Pre-school cognitive skills were entered next in separate steps (6e10) in the order shown in Table 3; early phonological processing at 3.5 years (to explain variance after ERPs related to sensitivity to phonetic information not yet affected by literacy skills; Puolakanaho et al., 2008), vSTM (to explain an independent verbal memory component), RAN (to see how much of reading/spelling is uniquely explained by it) and vocabulary (as a higher level measure of language skills after lower level processes). LK was entered at the last step because of its proximity to reading itself. The change-detection newborn ERP at PC1-320e420 explained 10.7% of the variance of RS. The ERPs at the early PC3-30e80 explained an additional 19.5% of the variance. All the entered newborn ERP measures explained 30.7% of RS after GA and pIQ. Of the cognitive measures, only RAN had a unique contribution in explaining 18.7% of the variance after ERPs. pIQ at the 2nd grade explained 18.1% of the variance of RA after GA (see Table 3). Of the ERPs, only the early ERP at PC330e80 had a unique contribution of its own, the unique contribution of all ERP measures being 13.3% in total. Of the

cognitive measures, phonology at 3.5 years contributed with 7% of the variance. After phonology, verbal memory explained only 1.6% (ns) and RAN 13.6% of the variance. For SA, pIQ explained 14.9% of the variance after GA. None of the ERP measures explained any further significant variance of their own even though ERPs at PC2-160e250 and PC330e80 were significantly correlated with SA. For ERPs the change in explained variance was 11.8% in total. Phonology at 3.5 years explained an additional 11.9% of the variance in spelling. After this, only RAN contributed with additional significant variance (6.8%). It should be noted that the majority of the explanatory power of newborn right frontal ERP at PC1-320e420 for RS seems to emerge from the variance in the at-risk group (both TRFR and RDFR). This is shown in the scatter plot in Fig. 3B (note that the values presented in the figure were not controlled for GA or pIQ). Interestingly, for ERP at PC3-30e80, most of the explanatory power for RS came from the TRC group, which had a larger correlation than both risk groups combined (Fig. 3C; r ¼ .76 vs r ¼ .19, respectively; Z ¼ 2.55, p < .006). This indicates that the late and early ERPs and their associations to later outcome measures have a somewhat different pattern in the TRC group as compared to the at-risk groups. On the other hand, the scatter plot for the relationship between newborn ERP at PC1-320e420 and 3.5 year phonology showed a more similar pattern in all the groups (Fig. 3A).

4.

Discussion

To our knowledge, this is the first study to investigate the processing of non-speech pitch change in sounds at birth in relation to familial risk for dyslexia and later reading outcome measures. Children were divided into groups based on their later reading skills at 9 years during the 2nd grade in school: the groups of reading disabled with familial risk and typical readers with familial risk (RDFR and TRFR, respectively) and typically reading control group (TRC). At birth, both of the familial dyslexia risk groups showed smaller

Table 3 e Regression analyses using reading speed, reading accuracy and spelling accuracy as outcome measures and newborn ERP measures and pre-reading cognitive skills as explaining variables. Dependent variable

Reading speed, 9 years 2

Predictors

DR

Step Step Step Step Step Step Step Step Step Step

.000 1,43 .006 .012 .062 1,42 2.794 .253 .107 1,41 5.260* .343* .005 1,40 .225 .103 .195 1,39 12.065** .586** .005 1,38 .326 .084 .014 1,37 .842 .142 .187 1,36 15.833** .483*** .000 1,35 .021 .019 .033 1,34 2.880 .278 Model R2 ¼ .609, Adj. R2 ¼ .493, F(10,34) ¼ 5.286***

1: GA 2: pIQ, 9 years 3: F4 ERP at PC1-320e420 4: F4 ERP at PC2-160e250 5: F4 ERP at PC3-30e80 6: Phonology, 3.5 years 7: vSTM, 5 years 8: RAN speed, 5.5 years 9: EV, 5.5 years 10: LK, 5 years

df

Note. *p < .05, **p < .01, ***p < .001 (2-tailed). a p ¼ .087 (2-tailed).

F change

b

Reading accuracy, 9 years 2

DR

df

F change

b

.017 1,43 .726 .129 .181 1,42 9.481** .431** .037 1,41 2.005 .203 .021 1,40 1.137 .219 .075 1,39 4.363* .363* .070 1,38 4.460* .304* .016 1,37 1.040 .154 .136 1,36 10.933** .411** .002 1,35 .179 .056 .031 1,34 2.553 .269 Model R2 ¼ .587, Adj. R2 ¼ .465, F(10,34) ¼ 4.826***

Spelling accuracy, 9 years DR2

df

F change

b

.001 1,43 .046 .33 .149 1,42 7.359* .391* .039 1,41 1.987 .209 .021 1,40 1.070 .219 .319a .058 1,39 3.089a .119 1,38 7.378* .396** .007 1,37 .436 .102 .068 1,36 4.526* .290* .036 1,35 2.497 .221 .027 1,34 1.898 .249 Model R2 ¼ .524, Adj. R2 ¼ .385, F(10,34) ¼ 3.749**

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Fig. 3 e Scatter plots of newborn ERPs and later outcome measures in the 3 reading groups: at-risk children with RD (RDFR, n [ 8), at-risk children with typical reading skills (TRFR, n [ 14) and control children with typical reading skills (TRC, n [ 25): A (left), the right frontal (F4) deviant-response at PC1-320e420 with 3.5-year phonology; B (middle) the deviantresponse at F4 at PC1-320e420 with RS; C (right), the deviant-response at F4 at PC3-30e80 with RS.

change-detection ERP amplitudes at 320e420 msec compared to the TRC group. The earlier obligatory and stimulus driven response at 160e250 msec were also smaller in both at-risk groups than in the TRC group. The two at-risk groups also differed from each other in the ERP hemispheric pattern at the central electrode sites at 160e250 msec, the TRFR group having a more comparable pattern to that of the TRC group. Further, the brain responses at birth were associated with later phonological processing at 3.5 years, as well as the reading outcome at school age. The present results are in line with our earlier findings from partly overlapping sub-samples of children participating in the JLD-project showing that infants at familial risk for dyslexia have atypical brain responses to speech sounds at birth and at six months (Leppa¨nen et al., 1999, 2002; Pihko et al., 1999; Guttorm et al., 2001, 2003). The novel aspect of the present study is that compromised auditory processing is also seen for non-speech sounds and that it is related to the later 2nd grade reading outcome. These early differences in the brain responses to pure tone frequencies at birth and their association to pre-reading and later literacy skills can be taken to indicate that one of the developmental pathways leading to dyslexia involves compromised low-level auditory-processing skills. This and other differences in sensory processing could also be one of the risk factors of dyslexia at the neural substrate level, with cascading effects on the development of important skills which are a prerequisite of learning to read. An auditory-processing deficit as an underlying factor in dyslexia has generated much discussion. According to some studies, dyslexia is related to a general auditory deficit (Tallal, 1980; Baldeweg et al., 1999; Maurer et al., 2003), whereas other studies suggest that the processing deficits related to dyslexia are speech-specific or only phonological in nature (StuddertKennedy and Mody, 1995; Mody et al., 1997; White et al., 2006). The finding that the RDFR and TRC groups differ in ERPs to pitch changes in tones at birth is in line with earlier behavioural and ERP-studies that show differences between

adults and older children with and without dyslexia in the processing of tone frequency differences (e.g., de Weirdt, 1988; Baldeweg et al., 1999; France et al., 2002; Renvall and Hari, 2003; Ahissar et al., 2006; Lachmann et al., 2005; Banai and Ahissar, 2004; Halliday and Bishop, 2006; Kujala et al., 2006). Nonetheless, the group differences were also found between the TRFR and TRC groups. The smaller responses in both of the at-risk groups compared to the control group could suggest a genetically driven difference in the auditory system of at least a sub-group of at-risk children. Fig. 1 shows that typical control group readers (TRC group) and reading disabled control children (RDC group) seem to have a more similar waveform structure compared to the at-risk groups. The RDFR and TRFR group waveforms also resemble each other, albeit with some differences, when compared to the control groups. It is possible that the TRFR children were able to compensate their compromised auditory processing and other factors contributing to dyslexia. However, a more plausible explanation could be that, based on the correlations between pre-school measures and reading outcome (see also Puolakanaho et al., 2008), more risk factors are accumulating in the group of dyslexic readers and poor auditory perception increases their problems. This could be related to multiple gene profiles affecting reading related skills (e.g., Galaburda et al., 2006; Pennington, 2006). The results showed effect sizes of .7e1.0 corresponding to 43e55% non-overlap of the distributions of the TRC and RDFR groups for the late change detection and earlier stimulus feature driven responses. In the previous studies with older children and adults, the prevalence of auditory-processing deficits in dyslexia has been estimated to be approximately 39% (e.g., Ramus, 2003). A recent review by Ha¨ma¨la¨inen et al. (in press) showed that average effect size for frequency discrimination differences between dyslexic and typical readers is .7, corresponding to a 43% non-overlap. The present findings are in line with this observation. The observation that only a proportion of children with dyslexia have auditory-processing problems is assumed to

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suggest that these problems only co-occur with but do not cause dyslexia (see Ramus et al., 2003). It could be argued that the deviant auditory processing found in the at-risk group dyslexic readers relates more to the familial risk status than to the later reading outcome. In this case, the auditory-processing deficit would be a co-occurring phenomenon with dyslexia risk. However, when the deviant-response amplitude values at the fronto-central scalp sites are examined more closely (see Figs. 1 and 2), it seems that the hemispheric response pattern differs between RDFR and TRFR children. TRFR children show a similar right larger than left hemispheric response as the TRC children, only smaller in amplitude. In fact, they did not differ from the TRC children in the response at the right central site at the late change-detection window. On the other hand, RDFR children showed an opposite hemispheric pattern, differing statistically significantly from the TRC group at 30e80 msec and from the TRFR group at 160e250 msec. Given that the hemispheric preponderance of brain responses in the TRFR group is more similar to that in the TRC group, the newborn ERP differences are not likely driven only by the risk status, but rather by genetic factors leading to dyslexia (cf. Galaburda et al., 2006; Pennington, 2006). In some studies where the existence of impaired auditory processing in dyslexics has been established, the association between a deficit in processing auditory stimuli and problems in reading has been questioned (Marshall et al., 2001; Heiervang et al., 2002). However, for example, Boets et al. (2008) demonstrated that processing of FM at the pre-school phase is related to speech perception and phonological processing. Both, in turn, predict later reading performance at the end of the first grade. Our results are in line with this finding and show association between newborn brain responses and later phonology as well as reading outcome itself. Previously, for example Espy et al. (2004) and Guttorm et al. (2005, 2010) have obtained similar evidence for the predictability for later language, phonological and reading development from ERPs to speech sounds measured at birth. The right hemispheric frontal response (at F4) at the late latency (320e420 msec) was related to later phonological processing at 3.5 years when phonological skills are as yet unaffected by emerging literacy skills to the same extent as performance in typical phonological awareness tasks later on (see e.g., Puolakanaho et al., 2008). This ERP response was also related to LK at 5 years, as well as speech perception and RS at 9 years, but not to RAN before school age. As shown by Leppa¨nen et al. (1997; see also Leppa¨nen et al., 2004) using the same paradigm, this response is related to detection of change in a sound stream and adaptation to the context of repeated sounds. The brain response at an earlier latency of 160e250 msec, likely representing afferent activation to the deviant stimulus features per se, was also correlated to phonology at 3.5 years, as well as speech perception, RS and SA at 9 years. The correlation and regression patterns suggest that the association of passive processing of frequency change with RS/spelling is due, in part, to shared variance with phonology and speech perception. The change-detection response at 320e420 msec also engulfed the effect of vSTM for reading and spelling, suggesting a close connection. Other factors beyond infant auditory processing also explain the impact of phonological processes for reading/spelling skills, as

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phonological processing explained additional variance in RA/ SA after ERP measures. RAN, on the other hand, seems to develop independently and regardless of infant auditory processing. Efficient processing of auditory stimuli could thus be important for the development of phonological representations. This, in turn, would impact on other phonological processing skills and on reading and spelling. The early latency response at 30e80 msec did not differentiate between the deviant and standard frequencies representing an obligatory or exogenous response to any sound in newborns. This ERP explained a unique variance of its own to both RS and RA after the later change-detection ERP. It also correlated with several pre-school cognitive measures, including vSTM, RAN, and EV, but not with phonology or letter naming. This suggests that the early ERP measure is related more generally to several cognitive skills that are also important for reading and spelling. Interestingly, the relationship between the infant change-detection ERP at the late latency and RS seemed mainly to be due to the variance in both at-risk groups (see Fig. 3). In contrast, the association of the early obligatory ERP was mainly due to the control group variance. This suggests that at least partially different processes are related to later reading outcomes in the control and at-risk groups. This lends further support to the idea of different genetic profiles related to reading skills. It could be argued that the observed group differences could be due to factors other than differences in auditoryprocessing abilities, for example, general novelty detection and/or other factors related to attention. Supporting evidence for differences in auditory change detection comes from a bulk of developmental ERP literature. Several studies have reported a mismatch response in newborns and infants to various stimulus features, including both non-speech and speech sounds (for reviews, see Cheour et al., 2000; Kujala et al., 2006). The reported infant mismatch responses are generally thought to reflect detection of change in the auditory stream and to be an infant ‘version’ of the adult preattentive change-detection response, mismatch negativity (MMN; Na¨a¨ta¨nen, 1992; Na¨a¨ta¨nen and Alho, 1997). The results of the current study with positive responses to the deviant stimulus in the control group with a fronto-central distribution and the maximum amplitude around 300 msec are in line with previous studies using both non-speech and speech sounds in similar MMN paradigms (Alho et al., 1990; DehaeneLambertz and Dehaene, 1994; Kurtzberg et al., 1995; Leppa¨nen et al., 1997, 2004; Dehaene-Lambertz and Baillet, 1998: Pihko et al., 1999; Dehaene-Lambertz, 2000; Dehaene-Lambertz and Pena, 2001; Friederici et al., 2002; Morr et al., 2002; Ruusuvirta et al., 2003; Trainor et al., 2003). In an oddball experiment, Leppa¨nen and colleagues (Leppa¨nen et al., 1997) have also shown that, when the same deviant stimulus is presented alone without intervening standard stimuli, the response returns faster to the baseline than the deviantresponse. Further, the processing of novelty in infants is typically seen in the Nc component, peaking at a later time window of around 500e800 msec (for a review, see Courchesne, 1990). These observations suggest that the larger response to the deviant stimulus in the TRC group was not due to processes related to the perception of novelty or salience.

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Recently, differences in MMN-response between dyslexic and typical readers have been speculatively linked to a deficit in ‘anchoring’ to the repeated stimuli. Ahissar (2007) suggests that typical readers can quickly and automatically tune around the incoming stimuli, ‘anchor to them’ and therefore also perform faster and more accurately when these stimuli are subsequently repeated. Dyslexic individuals, on the other hand, fail to benefit from stimulus-specific repetitions and implicitly represent the repeated reference (but cf. Georgiou et al., 2010, this issue). Some support for this idea is seen in Fig. 1, where the response to the repeated standard stimuli is somewhat larger in at-risk dyslexic readers. However, this possibility requires further studies. Given that not all children with dyslexia had the infant brain response in the range of the lowest amplitude (as shown in the scatter plots in Fig. 3) and that phonology and RAN explained a part of reading and spelling variance after ERPs suggests that early auditory processing is one of several risk factors for dyslexia. Whether a causal path from auditory processing to dyslexia could apply to a sub-group of children with dyslexia remains unanswered due to the small size of the dyslexic risk group in this study. The results give, however, quite clear support to the idea that the unaffected children with familial background of dyslexia can share deficits in some area with affected individuals. On the other hand, one cognitive deficit, such as an early auditory deficit may be insufficient in itself to cause impairment in reading or spelling (see e.g., Pennington and Lefly, 2001; Pennington, 2006; Barry et al., 2007; Snowling, 2008; see also Georgiou et al., 2010, this issue; Willcutt et al., 2010, this issue). Nevertheless, connections between infant deviancy related responses and RS/SA in 2nd grade imply that the early discrimination ability and problems in atypical basic auditory processing may, together with other risk factors, affect the severity of difficulties in dyslexia (see e.g., Bishop et al., 1999a, 1999b; Barry et al., 2007). In summary, the brain responses of the reading disabled children with familial risk (RDFR group) and those of the typical readers with familial risk (TRFR group) showed no significant discrimination between the deviant and standard stimuli at the birth. Furthermore, the responses of both groups to the deviant stimulus differed from the response of the typically reading control group children (TRC) children, though somewhat differently. Of note here is that we were able to demonstrate the existence of group differences already at birth, well before any significant post-natal environmental effects. Thus, our results support the idea of a general auditory-processing deficit that affects the processing of both non-speech and speech sounds in children with dyslexia, at least in a sub-group. However, the role of this deficit in dyslexia, for example as a causal factor or a moderator that makes dyslexia more severe still remains open as both the at-risk groups showed reduced changedetection responses. It is unlikely that atypical non-speech sound processing alone is sufficient to lead to dyslexia but rather is a risk factor leading to cumulative effects on those processes that are critical for learning to read. Future studies would need to be carried out with larger sample sizes to determine the role of auditory and speech deficits on different dyslexia sub-types.

Acknowledgements This study was funded by Academy of Finland via the Finnish Centre of Excellence program (44858 and 213486). We would like to thank all families participating in the JLD study and Dr. Jane Erskine for corrections and comments on language.

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