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Impaired non-speech auditory processing at a pre-reading ageis a risk-factor for dyslexia but not a predictor: An ERP study

Anna Plakas a,*, Titia van Zuijen a, Theo van Leeuwen b, Jennifer M. Thomson c andAryan van der Leij a

aDepartment of Child Development and Education, University of Amsterdam, The NetherlandsbDepartment of Instructional Technology, University of Twente, The NetherlandscHarvard Graduate School of Education, Cambridge, USA

a r t i c l e i n f o

Article history:

Received 20 January 2011

Reviewed 13 June 2011

Revised 27 September 2011

Accepted 27 February 2012

Action editor Roberto Cubelli

Published online 29 March 2012

Keywords:

Dyslexia

Event-related potentials (ERPs)

Auditory processing

Rise time

Longitudinal

* Corresponding author. Department of ChildAmsterdam, The Netherlands.

E-mail address: A.Plakas@uva.nl (A. Plaka0010-9452/$ e see front matter ª 2012 Elsevdoi:10.1016/j.cortex.2012.02.013

a b s t r a c t

Impaired auditory sensitivity to amplitude rise time (ART) has been suggested to be a primary

deficit in developmental dyslexia. The present study investigates whether impaired

ART-sensitivity at a pre-reading age precedes and predicts later emerging reading problems

in a sample of Dutch children. An oddball paradigm, with a deviant that differed from the

standard stimulus in ART, was administered to 41-month-old children (30 genetically at-risk

for developmental dyslexia and 14 controls) with concurrent EEG measurement. A second

deviant that differed from the standard stimulus in frequency served as a control deviant.

Grade two reading scores were used to divide the at-risks in a typical-reading and a dyslexic

subgroup. We found that both ART- and frequency processing were related to later reading

skill. We however also found that irrespective of reading level, the at-risks in general showed

impaired basic auditory processing when compared to controls and that it was impossible to

discriminate between the at-risk groups on basis of both auditory measures. A relatively

higher quality of early expressive syntactic skills in the typical-reading at-risk group might

indicate a protective factor against negative effects of impaired auditory processing on

reading development. Based on these results we argue that ART- and frequency-processing

measures, although they are related to reading skill, lack the power to be considered single-

cause predictors of developmental dyslexia. More likely, they are genetically driven risk

factors that may add to cumulative effects on processes that are critical for learning to read.

ª 2012 Elsevier Ltd. All rights reserved.

1. Introduction damage. At the level of cognitive processes, a deficit in

Developmental dyslexia is a hereditary disorder (Pennington

and Lefly, 2001) that is characterized by difficulties in reading

and/or spelling. These difficulties cannot be explained by low

intelligence, lack of education or sensory or neurological

Development and Educat

s).ier Ltd. All rights reserve

phonological processing is generally accepted as one of the

main causes of reading problems (e.g., Ramus et al., 2003;

Vellutino et al., 2004). Although alternative causal hypotheses

exist (e.g., McCloskey and Rapp, 2000) and the directionality of

the relationship between phonological difficulties and reading-

ion, University of Amsterdam, Nieuwe Prinsengracht 130, 1018 VZ

d.

c o r t e x 4 9 ( 2 0 1 3 ) 1 0 3 4e1 0 4 5 1035

problems is still debated (Castles and Coltheart, 2004), overall,

phonological measures show higher and more robust correla-

tions with reading skills than other cognitive or sensory

measures (Lyon et al., 2003; Wimmer and Schurz, 2010).

Phonological deficits are thought to stem from under-

specified phonological representations (Elbro, 1996; Fowler,

1991; Snowling and Hulme, 1994). In the course of normal

early language development, phonological representations

becomemore specified and accurate (Elbro, 1996; Fowler, 1991;

Swan and Goswami, 1997). When phonological representa-

tions are imprecise or remain too global, the acquisition of an

alphabetic writing system that relies heavily on the fine-

grained segmentation of spoken words into phonemes is

endangered (Swan and Goswami, 1997; Elbro et al., 1998).

It has been suggested that a phonological deficit has amore

basic origin in processing impairments of non-speech attri-

butes in the auditory signal (e.g., Tallal, 1980; Farmer and

Klein, 1995; Talcott et al., 1999; van Ingelghem et al., 2005).

A relatively new hypothesis is that dyslexics are particularly

impaired in tracking amplitude rise times (ARTs) in acoustic

signals and that this impairment leads to inaccurate phono-

logical representations at the syllable level (Goswami et al.,

2002; see for a recent extension of the theoretical framework

Goswami, 2011). Goswami et al. (2002) even suggest that

impaired tracking of ARTs is “the primary auditory-processing

deficit in dyslexia” (p. 10915). If this assumption is correct,

then impairments in ART-processing should be present in

early childhood when phonological representations are still

developing. The present study aims to investigate on

a neurophysiological level whether impaired ART processing

was already present at the pre-reading age of 41 months in

a Dutch sample of children who show reading disability in

grade two. When this is the case, then early impaired ART

processing might be regarded as an early biomarker for

developmental dyslexia. A biomarker is usually defined as an

objectively measurable physical correlate of an externally

observable event or process of medical interest (Kraemer

et al., 2002). Finding strong early biomarkers for develop-

mental dyslexia implies the possibility of identifying later

poor readers at an age before formal schooling begins and this

may open opportunities for prevention.

ART refers to the rate at which the amplitude (i.e., loud-

ness) of an acoustic signal, speech or non-speech, increases

from sound onset. A slower ART results in a delayed percep-

tual moment of occurrence (Howell, 1988; Scott, 1998). In

speech, ARTs correspond to the perception of syllables, with

the amplitude peak coinciding with vowel-onset. Stressed

syllables are marked by a relatively large amplitude change at

onset and therefore with a sharper rise time than non-

stressed syllables. Next to variations in duration, intensity,

and frequency, variations in ARTs at syllable-onsets deter-

mine in large part the rhythmical pattern in speech. Percep-

tion of prosodic features in a language has been proven to be

important for segmentation of the speech stream in early

language development (e.g., Mattys et al., 1999) and subse-

quently, the quality of phonological representations. There-

fore impairments in ART-sensitivity could result in fuzzy

phonological representations and consequently have a nega-

tive influence on literacy development, possibly even causing

dyslexia. To initially test their hypothesis Goswami et al.

(2002) designed a perceptual paradigm with amplitude

modulated (AM) tones that varied in ARTs (15e300 msec). The

short ARTs resulted in perceiving ‘beats’ in the signal,

whereas tones with longer ARTs were perceived as contin-

uous sounds that gradually got louder while the amplitude

was rising. The participating children had to decide whether

they heard a beat in the signal or not. It turned out that

dyslexic children had more trouble in perceiving beats in the

signals and were less sensitive to ART differences than same

age typically reading children. Additionally Goswami et al.

(2002) found that ART-sensitivity predicted both reading and

spelling ability with 25% unique explained variance after

controlling for age, non-verbal intelligence quotient (IQ) and

vocabulary. Subsequently Richardson et al. (2004) used two

ART-paradigms in which the participants had to compare

sound stimuli that differed from one other in ART. As

Goswami et al. (2002) did, Richardson et al. (2004) also found

that dyslexic children were less sensitive to ART-differences

than controls were, although the explained variance of their

ART-measures after controlling for age, IQ and vocabulary, on

reading (8%) and spelling (up to 11%) was lower than the

explained variance that was found by Goswami et al. (2002).

The results of both of these studies suggest that ART-

sensitivity is indeed impaired in dyslexics. Additionally,

several studies found impaired ART-sensitivity in dyslexics in

other languages than English as well (French: Muneaux et al.,

2004; Finnish: Hamalainen et al., 2005, 2007, 2008; Hungarian:

Suranyi et al., 2009). Two studies however failed to find group

differences between dyslexics and non-dyslexics on ART-

sensitivity, although both of these studies did find links

between ART-sensitivity and some literacy measures in

dyslexics only (Finnish: Hamalainen et al., 2009; Greek:

Georgiou et al., 2010). Taken together, these studies suggest

that the link between ART-processing and reading-

development is universal, but that the degree or manner in

which ART-sensitivity influences literacy development may

vary across languages and orthographies.

Goswami (2009) suggests that ART-sensitivity may be

a good candidate to be developed into a robust biomarker at

a pre-reading age to predict the risk for dyslexia later on.

However, since all of the above mentioned ART-studies had

a cross-sectional design and focused on older children (age

7e11) or adults, no evidence directly tests this hypothesis.

Therefore longitudinal studies are needed to investigate links

betweenART processing at a pre-reading age and later reading

skill. When ART-processing is to be considered a universal

core deficit for dyslexia, as phonological processing itself is,

then it should be predictive for reading development in any

language. To our knowledge, the present study is the first to

investigate links between ART-processing at a pre-reading age

and reading development in a sample of children with Dutch

as their native language. It should be noted that Dutch is

comparable to English in syllabic complexity but has a more

regular orthography (e.g., Seymour et al., 2003).

1.1. Longitudinal designs in research concerned withdevelopmental dyslexia

Since developmental dyslexia is known to run in families, it is

possible to identify children with a familial risk of developing

c o r t e x 4 9 ( 2 0 1 3 ) 1 0 3 4e1 0 4 51036

dyslexia even before they are born. In such samples children

who develop dyslexia will be overrepresented because their

chance of developing manifest dyslexia is about 4e8 times

higher in comparison to children born in families without

known genetic risk for dyslexia (e.g., Scarborough, 1990;

Gallagher et al., 2000; see for a recent Dutch example van

Bergen et al., 2011). At a young age, atypical early perceptual,

language and literacy development can be investigated by

comparing childrenwith andwithout genetic risk. In addition,

by linking literacy outcomes at a later age retrospectively to

early perceptual and language measures, early markers of

problematic literacy-development may be identified. In

particular, the comparison within the at-risk group between

children who develop dyslexia (dyslexic at-risks) and children

who do not (typical-reading at-risks) is a promisingmethod to

detect causal markers. The advantage of this design is that

causality is directly linked to genetic risk. Because of the high

heritability of reading-problems, most poor readers that

participate in cross-sectional studies will also be genetically

at-risk for dyslexia, while typical-reading controls will not.

Therefore, in cross-sectional designs in which groups are

selected on basis of reading ability only, it is impossible to

distinguish effects that are linked to genetic risk from effects

that are linked to reading ability itself. To make this distinc-

tion is important because a biological marker that is linked to

a genetic risk for dyslexia may not affect reading ability at all,

only up to some degree, or only in some individuals. For

example, when at-risk children who have no reading prob-

lems show sensory processing deficits in comparison to

unaffected controls, such processing deficits are indicative for

the genetic risk but not for dyslexia and subsequently do not

qualify as biomarkers for dyslexia. The critical issue is

whether at risks with and without reading problems are

differentiated by the biological marker.

1.2. Neurophysiological methods

There are at least two studies that found evidence for early

sensory processing deficits that differentiate between typical-

reading at-risks, dyslexic at-risks and unaffected controls, one

concerns visual processing (Regtvoort et al., 2006), and the other

auditory processing (Leppanen et al., 2010). Of interest for the

present study, Leppanen et al. (2010) found that change detec-

tion responses to pitch differences at birth explained 10.7% of

reading speedat the age of nine. Both of these studies haveused

neurophysiological methods. To investigate perceptual pro-

cessing supplementary to behavioural methods, event-related

potential (ERP) techniques are particularly useful because they

offer the possibility to gain insight in the neurophysiological

mechanisms underlying behaviourally overt perceptual

processes. A frequently used design in ERP studies is a so-called

oddball design. With this design it is possible to investigate

sensitivity for perceptual differences without the need for

attention. An oddball paradigm consists of a repeatedly pre-

sented stimulus (the standard) that is occasionally interrupted

by a stimulus that differs from the standard in one specific

aspect (thedeviant). Subtracting themeanERP-responseelicited

by thestandardstimulus fromthemeanresponseelicitedby the

deviant stimulusresults in themismatchresponse (MMR) that is

a measure for auditory change detection (e.g., Naatanen, 1995).

In several studies MMR has demonstrated to bear clear links to

basic auditoryperception. Earlier onsets, higher amplitudesand

longerMMR-durations have been found to correlate with larger

perceived differences between standards and deviants, and

therefore sensitivity to differences in auditory cues (e.g., de

Baene et al., 2004; Dehaene-Lambertz et al., 2005; Bauer et al.,

2009). Since MMR’s are involuntarily generated responses they

do not require conscious or behavioural responses from

subjects and this makes the technique very sui to investigate

infants andyoungchildren (see alsoMorr etal., 2002;Glass etal.,

2008; Leppanen et al., 2010).

There are two cross-sectional studies concerned with

investigating neurophysiological correlates for impaired ART-

processing in dyslexics (Hamalainen et al., 2007, 2008). Both of

these studies found some differences in ART-processing

between dyslexic and typical-reading school-aged children but

not systematically since group-differences seem to be caused

not only by ART-processing but also by other aspects of the

sound stimuli. Hamalainen et al. (2007) presented their partici-

pants with tone-pairs in which the second tone in the pair

differed from the first in either frequency or ART. They found

that dyslexic children did not differ from controls in processing

tone frequency differences, while they did differ in ART-

processing. However, this group difference was only present

for tone-pairs in which the stimuli followed each other rapidly

and not for tone-pairs inwhich the stimuli were far apart. Since

an ART-processing difference between dyslexics and controls

onlyappeared inasituation inwhichstimulihadtobeprocessed

fast, Hamalainen et al. (2007) suggested that ART-sensitivity is

impaired in dyslexics but that impaired ART-sensitivity alone

does not cause reading disabled children to process auditory

signals differently. Contrary to their earlier study Hamalainen

et al. (2008) only found signs of atypical ART-processing when

the stimuli in the tone-pairwere far apart andnotwhen tones in

the tone pair followedeachother rapidly. Althoughboth studies

offer some support for the idea that dyslexics are impaired in

ART-processing, the evidence is not conclusive.

1.3. Present study

This is the first longitudinal ERP-study that is concerned with

investigating links between early ART-processing and later

reading skill. The present study is part of the Dutch Dyslexia

Program (DDP; van der Leij et al., 2001). This program is

implemented nationwide in The Netherlands and follows the

development of children with a genetic risk of developing

dyslexia and children without such risk from infancy until the

age of 10. One of the main goals of DDP is to find early

neurophysiological biomarkers of later reading problems. As

a part of DDP we investigated whether ART-sensitivity at

a pre-reading age qualifies as an early biomarker for dyslexia.

Genetic risk is controlled for by retrospectively comparing

ART-sensitivity at a pre-reading age in typical-reading at-

risks, dyslexic at-risks and typical-reading controls without

genetic risk. If ART-sensitivity differentiates between typical-

reading and dyslexic children with a genetic risk, then

impaired ART-sensitivity qualifies as a reliable biomarker for

developmental dyslexia.

To investigate the research question, an odd-ball design

with concurrent EEG-measurement was administered. This

c o r t e x 4 9 ( 2 0 1 3 ) 1 0 3 4e1 0 4 5 1037

paradigm contained two deviants. There was an ART deviant

(ARTD) with a longer rise time than the standard stimulus,

and a frequency deviant (FD) that differed from the standard

in frequency to investigate whether there was a more general

auditory problem. It is hypothesized that, if impaired ART-

sensitivity is the primary underlying cause of developmental

dyslexia, it will show a strong connection to differences in

reading skill of the at-risk groups. In addition, it will outper-

form frequency-sensitivity in predictive power. The children’s

degree of sensitivity to the ART and frequency difference

included in the present studywasmeasured by calculating the

MMR to both deviants.

2. Methods

2.1. Participants

The ART-paradigm was presented to a sample of 48 children

(32 at-risk and 16 controls) of the participants of DDP at an age

of 41 months � 2 weeks. All children came from monolingual

Dutch speaking families. Based on the quality of the data

(inclusion criteria are described below), 44 children were

included. There were 30 children (18 boys) with a genetic risk

of developing dyslexia and 14 control children (six boys).

The childrenwith a genetic risk for dyslexiahad at least one

dyslexic parent and an additional dyslexic first-degree family

member as reported by the parent. The dyslexic parents and

family members were tested for dyslexia with standardized

tests for word- and pseudoword reading fluency and a test for

verbal comprehension.Word readingfluency (WRF)was tested

with the Een Minuut Test [One Minute Test] (Brus and Voeten,

1995). This is a standardized single-word reading fluency test

with 150 words that increase in difficulty. The score was the

total number of words read correctly during 1 min. Pseudo-

word reading fluency was tested with the Klepel, a standard-

ized pseudoword-reading test (van den Bos et al., 1994). This

test consists of a list with 150 pseudowords that increase in

difficulty and have to be read aloudwithin 2min. The score on

this task was the total number of words read correctly within

2min. Verbal comprehensionwasmeasured using the subtest

for verbal comprehension from the Wechsler Adult Intelli-

gence Scale (WAIS; Wechsler, Dutch adaptation, 1970). All

scores were converted to percentile scores based on norm

percentile scores that were obtained by Kuijpers et al. (2003).

Criteria for dyslexia and subsequent inclusion in our genetic

risk group were (1) a score in the bottom 20% range on both

reading tests or (2) a score in the bottom 10% range on one of

both reading tests or (3) a discrepancy of a more than 60%

difference between the WAIS percentile and the percentile of

one of the reading tests to identify individuals who scored

unexpectedly lowon reading (Kuijpers et al., 2003; Koster et al.,

2005). Control infants had no familial history of dyslexia and

both parents had at least average scores (>40%) on the tests

described above.

All participating families were non-paid volunteers, but

each child received a small present after every test session.

The parents signed an informed consent. A Dutch medical

ethics committee approved the procedure. Koster et al. (2005)

give a more detailed description of this program.

2.2. Child characteristics

Several developmental measures of intelligence, language

development, phonological and reading skills were obtained

during the children’s development and will be described in

detail below. The children were divided into reading-level

groups after 2 years of reading instruction at the end of grade

two. It iswell established that reading impairments ina regular

orthography such as Dutch, are mainly characterised by

fluency- and not so much by accuracy problems (e.g., de Jong

and van der Leij, 2003; Share, 2008; Wimmer and Schurz,

2010). Therefore WRF is considered to be the most important

diagnostic criterion for dyslexia in The Netherlands. In the

present study several word and non-word reading fluency

measures were used to test the children’s reading skills.

Analysis of variance (ANOVAs) were carried out to investigate

whether there were group-differences on these measures.

2.2.1. Reading skillsAt the end of grade two, WRF was measured using the Drie-

Minuten-Toets [Three-Minute-Test] (Verhoeven, 1995; see

also Verhoeven and van Leeuwe, 2009). This test consists of

three lists of single words. The first list, WRF-1, consists of 150

monosyllabic words with a CV, VC, or CVC pattern. The

second list, WRF-2, consists of 150monosyllabic wordswith at

least one consonant cluster. The third list, WRF-3, consists of

120 polysyllabic and polymorphemic words. The score per list

was the total number of words read aloud correctly during

1 min. Pseudoword reading fluency was measured using the

Klepel (van den Bos et al., 1994). Thismeasurewas also used to

test the parents and is described above.

Based on the results of the above mentioned reading tests,

children were divided into a group of typical-reading controls,

typical-reading at-risks, and dyslexic at-risks. Participants

were regarded as dyslexic when their test-scores fell below

the 10th percentile according to norms on at least three out of

four reading fluency measures and no measure exceeded the

25th percentile according to norms. Childrenwere regarded as

typical readers when not more than one of their test-scores

fell below the 10th percentile according to norms. Reading-

fluency scores from one participant in the control-group

were missing. Since this participant scored above mean-

level on reading fluency when measured at the start of grade

two as well as in grade three, this participant was included in

further analyses. The scores of another participant in the

control group fell below the 10th percentile on all reading

fluency tests and were therefore left out of analyses. The

reading fluency scores of five at-risks did not meet the above

mentioned criteria for inclusion in the typical-reading or

dyslexic at-risk groups andwere therefore left out of analyses.

In the end, 13 children were included in the control group, 15

children were included in the typical-reading at-risk group

and 10 children were included in the dyslexic at-risk group.

Since both the WRF and Klepel are read-aloud tests, the

children were also tested with a silent word reading test Door-

streepleestoets (DLT) [cross out reading test] (van Bon, 2007) to

exclude the possibility that slow reading speed resulted from

speech production problems. With the DLT the participants

werepresentedwitha list inwhich two syllable polymorphemic

words and pseudowords were mixed. The children were asked

c o r t e x 4 9 ( 2 0 1 3 ) 1 0 3 4e1 0 4 51038

to read silently for 1 min and to cross out all the pseudowords

they encountered, marking the last word they had read after

1 min. The score was the total number of words read, sub-

tracting the number of words that were incorrectly crossed out

ornot. After inspectionof individualDLT-data, itwas found that

there were no discrepancies between individual WRF and DLT

scores.All of thechildrenthat scoredbelowaverageontheWRF-

lists also scored below average on the DLT. Statistical analyses

showed very high correlations between the DLT and the three

WRF-scores (WRF-1: r ¼ .89, p < .001; WRF-2: r ¼ .95, p < .001;

WRF-3: r ¼ .94, p < .001).

2.2.2. Phonological skillsIt is hypothesized that basic auditory perceptual impairments

exert their detrimental influence on reading skill through

phonological impairments (e.g., Tallal, 1980; Goswami et al.,

2002). For this reason and to test for homogeneity on phono-

logical skillswithin groups, a task for phonemedeletion that the

childrenwere testedwith at the end of grade two, was included

in thepresent study aswell (de Jongandvander Leij, 2003).With

this task children were asked to delete one phoneme from

a pseudoword (e.g.,/ston/without/t/). This test consisted of nine

one-syllable items,nine twosyllable itemsandnine twosyllable

items inwhich the phoneme that had to be deleted appeared in

the pseudo-word twice. The test was aborted after six consec-

utiveerrors in thefirst 18 itemsor three consecutiveerrors in the

last nine items. The score was the total amount of items in

which the phoneme was deleted correctly from the pseudo-

word. Eight out of 10 dyslexic at-risk children scored below one

standard deviation from the mean according to norms. The

remaining two at-risks scored at the low end of the average

range. This result is in line with results from other studies with

Dutch children finding that up to 80% of dyslexic children show

a phonological deficit (Bekebrede et al., 2009; van Bergen et al.,

2011). In contrast, none of the controls and four out of 15

typical-reading at-risks scored below one standard deviation

from the mean on the phoneme deletion task. An ANOVA and

post hoc analyses (TukeyHSD) showed that the dyslexic at risks

group scored significantly lower on both measures than the

other two groups while both typical-reading groups did not

differ significantly from one-another. Summarizing, it can be

concluded that the dyslexic group was quite homogeneous in

terms of a phonological impairment.

2.2.3. Non-verbal intelligenceNon-verbal intelligence was measured with the Snijders-

Oomen Niet-Verbale Intelligentietest (SON-R 2½-7) [Snijders-

Oomen Non-Verbal Intelligence test] (Tellegen et al., 1998)

when the children were 47 months old. This test consists of

six subtests. Three subtests combine to create a performance

subscale and the other three subtests combine to create a logic

reasoning subscale. Three standardized IQ scores (M ¼ 100

and st ¼ 15) were derived from the scores on the subtests for

an overall intelligence quotient, a performance IQ quotient

and a logic reasoning quotient. An ANOVA showed that all

groups were comparable in terms of non-verbal IQ.

2.2.4. Language developmentAt 53 months, language comprehension was tested with the

Reynell Test voor Taalbegrip [Reynell Test for Language

Comprehension] (van Eldik et al., 2001). In this test the child’s

understanding of prepositions, qualities of objects, such as

colour, size and number, and the use of verbs in active and

passive voice were tested. To this end the child was presented

with different kinds of toys, afterwhich the test-assistant read

aloud a sentence. The child had to show understanding of the

meaning of sentence by carrying out the appropriate action

with the toys (e.g., ‘put the cube in the box’ or ‘the horse is

bitten by the dog’). All test components of the Reynell Test

voor Taalbegrip add up to one standard score (M ¼ 100 and

st ¼ 15) for language comprehension.

Expressive syntax, vocabulary and verbal short-term

memory were measured with the Schlichting Test voor

Taalproductie [Schlichting Test for Expressive Language]

(Schlichting et al., 2003). Expressive syntax was tested by

eliciting syntactical structures from the child that

increased in length and level of difficulty. For this purpose

the test-assistant presented the child with toys or pictures

that were used to perform actions while the test-

assistant concurrently said aloud what he or she was

doing. The child was then invited to do the same (e.g., ‘I’m

picking up a pencil. Look, the pencil that I’m holding is blue.

Now it is your turn’). Vocabulary was tested by presenting the

child with pictures in which objects, verbs, adjectives or

adverbs were depicted that the child had to name. Auditory

memory was being tested by presenting the child with an

increasing number of words (one to six) that the child had to

repeat.

AnANOVAand post hoc analyses (TukeyHSD) showed that

the dyslexic at-risks had lower scores than the two other

groups on expressive syntax. Additionally, the typical-reading

at-risks scored significantly higher than the dyslexic at-risks

on vocabulary and verbal short-term memory. Table 1

summarizes the mean scores on the behavioural measures

and the ANOVA results.

2.3. Procedure

The ART- and frequency oddball paradigmwas employed with

concurrent 64-channel EEG recording. Electrode caps were

attachedwhile the childrenweredistractedwith toys or a video.

The childrenwere seated in a video-controlled recording room,

100 cm from a computer screen. Loudspeakers were positioned

left and right of the screen in front of the subjects. The children

were watching a silent video during the measurement. They

were instructed not to talk and to sit quietly.

2.4. Stimuli

An auditory oddball paradigm, which consisted of 2500 tone

stimuli, was presented to the children. The duration of each

tone was 155 msec. There were 2260 standard stimuli at

500 Hz with a 15 msec rise time, 90 msec maximum steady

state and 50 msec fall time. There were 120 (4.8%) amplitude

rise-time deviants (ARTDs) at 500 Hz with a 90 msec rise time,

15msecmaximum steady and 50msec fall time and 120 (4.8%)

frequency deviants (FD) at 550 Hz with a 15 msec rise time,

90 msec maximum steady state and 50 msec fall time. Inter

Stimulus Interval was 450 msec. Deviants were randomly

presented. Sound volume was set at 75 dB(A) and the

Table 1 e Cognitive, language development and reading fluency scores in controls, typical-reading at-risks and dyslexicat-risks.

Controls (N ¼ 13)mean (SD)

Typical-reading at-risks(N ¼ 15) mean (SD)

Dyslexic at-risks(N ¼ 10) mean (SD)

F-values

SON-R IQ 114.1 (13.7) 110.9 (15.0) 106.6 (9.4) F2,35 ¼ 0.9

SON-R performance scale 115.9 (10.7) 112.8 (15.9) 104.6 (11.4) F2,35 ¼ 2.2

SON-R logic reasoning scale 115.7 (17.9) 112.3 (16.4) 114.0 (12.2) F2,35 ¼ 0.2

Language comprehension 110.0 (12.2) 112.9 (9.2)a 100.7 (11.9)b F2,35 ¼ 3.7*

Expressive syntax 114.0 (10.0)a 115.7 (10.0)a 99.9 (12.4)b F2,35 ¼ 7.4**

Vocabulary 109.0 (9.8) 112.5 (13.2) 101.7 (16.1) F2,35 ¼ 2.1

Verbal short term memory 110.9 (11.8) 114.0 (11.6)a 99.1 (16.9)b F2,35 ¼ 4.0***

Phoneme deletion 19.3 (4.2)a 16.1 (7.1)a 8.1 (5.0)b F2,33 ¼ 11.1

Note: Different letters indicate significant group differences (Tukey HSD). *p < .05, **p < .01, ***p ¼ .000.

c o r t e x 4 9 ( 2 0 1 3 ) 1 0 3 4e1 0 4 5 1039

recording lasted 25 min. The ART contrast used in the present

study was based on behavioural data from other studies that

had found that typically reading children can usually distin-

guish the contrast selected here while dyslexic children

cannot (see Richardson et al., 2004). Fig. 1 shows a schematic

diagram of the standard stimulus and the rise time deviant.

2.5. ERP recording and data analysis

The 64-channel ERPs were recorded at 500 Hz/channel with

0.1e100 Hz (Synampsmodel 5083, Neuroscan Inc., El Paso, TX,

USA). We used caps (Easy Cap, FMS, Munich, Germany) which

included 62 electrodes including all 10e20 locations. The

reference electrodes were attached to themastoids. Eye blinks

and eye-movements were measured with four electro-

oculogram (EOG) electrodes (above and below the left eye

and at the outer canthus of each eye). Impedance was brought

below 20 kU (Ferree et al., 2001).

The data were analysed in Brain Vision Analyser (Brain

Vision Analyser software, Brain Products, GmbH, Munich).

Fig. 1 e Physical features of standard stimulus and rise

time deviant. (a) Standard stimulus; 500 Hz; 15 msec rise

time, 90 msec steady state and 50 msec fall time. (b) Rise

time deviant; 500 Hz; 90 msec rise time, 15 msec steady

state and 50 msec fall time.

The continuous EEG was digitally filtered (low cutoff: 1 Hz,

12 dB/oct; high cutoff: 30 Hz, 48 dB/oct) after which an inde-

pendent component analysis (ICA) was used to remove eye

artefacts after which the signal was segmented (�100 msec

prior and 500 msec relative to stimulus onset) and baseline

corrected. Trials with artefacts exceeding �125 mV in any

channel were left out of further analysis. All children had at

least 60 useful trials. Averages were calculated for the stan-

dard stimulus and both deviants. Difference waves (deviant

minus standard) were calculated for each deviant. Based on

the existing literature of ERP studies that included 3 year old

subjects (Cheour et al., 2002; Morr et al., 2002; �Ceponien _e et al.,

2003) and the subtle acoustic differences between standard

and both deviants, MMRs were expected at latencies approx-

imately between 200 and 350 msec at the centrofrontal elec-

trodes. Therefore the centrofrontal electrodes (FC4, FCz and

FC3) were selected for further analysis. An area (þ/�20 msec)

around the peaks in the grand-average differencewaves of the

selected channels in the control group was used to compare

ART and frequency sensitivity.

2.6. Statistical analyses

To calculate whether peaks in the difference curves indicated

significant MMRs, one-sample t-tests for a pool of the selected

electrodes (FC4, FCz, FC3) were carried out for each group in

a 40 msec window around peak latencies. ANOVA’s for

repeated measurements with channels (FC4, FCz, FC3) as

within-subject factor, were carried out for each mismatch

component to check for hemispheric differences within each

group. Group differences for each individual mismatch

component were tested with (3) � 3 ANOVA’s for repeated

measures with selected channels (FC4, FCz, FC3) as within

subject factor and group (controls, typical-reading at-risks,

dyslexic at-risks) as between subject factor. Greenhouse-

Geisser correction was performed as needed. Additionally

Pearson correlations were calculated to investigate links

between the basic-auditory and behavioural measures.

3. Results

Fig. 2 shows the ERP waveforms of the responses to the

standard and both deviants for the controls and both at-risk

Fig. 2 e ERP’s following the standard, FD and ARTD for

controls, typically reading at-risks and reading disabled at-

risks.

c o r t e x 4 9 ( 2 0 1 3 ) 1 0 3 4e1 0 4 51040

groups. In response to the standard and both deviants, all

groups show a positive deflection around 100 msec, followed

by a negative deflection around 200 and 400 msec.

3.1. Within-group standard-deviant responsedifferences

Fig. 3 shows the difference-curves of both deviants for FC3,

FCz and FC4. In the control group, the ARTD grand-average

difference curve showed a negative MMR peak (MMRARTD),

maximum at FC4 (t12 ¼ �2.740, p ¼ .018) at 346 msec. The

controls’ grand-average frequency deviant difference curve

showed a negative MMR peak (MMRFD), maximum at FC3

(t12 ¼ �3.001, p ¼ .014) at 314 msec.

The dyslexics and typical-reading at-risks did not show

any significant peaks in the difference curves of both deviants

but there was a trend in FC3 (.05 < p < .10) for MMRFD in the

typical-reading at-risks. Table 2 gives an overview of the one-

sample t-test results for FC4, FCz and FC3.

The ANOVA’s did not indicate hemispheric differences in

magnitude of MMRs in individual groups as no significant

effects of hemisphere or interaction between hemisphere and

area were obtained.

3.2. Between-group differences

The ANOVA’s that were carried out to investigate between-

group differences in a pool of FC3, FCz, FC4 showed a signifi-

cant between-group effect for MMRFD (F2,35 ¼ 4.265, p ¼ .022).

Since variances within groups were not equal for MMRFD,

GameseHowell tests were used post-hoc to compare the

three experimental groups to each other. These tests showed

that the difference in response between standard and

frequency-deviant was larger in the controls than in the

dyslexic at-risks ( p ¼ .034).

For MMRARTD (F2,35 ¼ 2.883, p ¼ .069) a trend was found for

which post-hoc analyses (Tukey HSD) showed a larger MMR

effect for controls than the typical-reading at risks ( p ¼ .056).

3.3. Controls versus at-risks

Our results showed thatbasic auditoryprocessingskills inboth

at-risk groups did not differ. To investigate whether basic

auditory processing is impaired in children genetically at risk

for dyslexia, regardless of their reading-level, controls (N¼ 13)

were compared to the combined group of at-risk children

(N ¼ 25). One-sample t-tests showed that there were no indi-

cations for changedetection responses in the combinedat-risk

group. A (3) � 2 ANOVA for repeated measures with channels

(FC4, FCz, FC3) as within subject factor and group (controls,

at-risks) as between subject factor was carried out to investi-

gate processing-differences between controls and the

combined at-risk group in the time-windows in which MMR-

peaks could be identified in the controls. Significant between-

group differences were found for both MMRFD (F1,36 ¼ 7.968,

p¼ .008) andMMRARTD (F1,35¼ 4.373, p¼ .044). The difference in

response between standard and deviants was significantly

larger in the controls for both MMR components.

Fig. 3 e Difference curves for ARTD and FD in controls, typically reading at-risks and reading disabled at risks.

c o r t e x 4 9 ( 2 0 1 3 ) 1 0 3 4e1 0 4 5 1041

3.4. Additional ERP components

Additional early fronto-central MMR components were

observed in the difference curve of the controls and dyslexic at

risks. In the ARTD-difference curve an early positive MMR peak

component was present in the controls (maximum at FC4

(t12 ¼ 4.142, p ¼ .001) at 188 msec), and the dyslexic at-risks

(maximum at Fz (t10 ¼ 4.497, p ¼ .001) at 226 msec). In the

typical-reading at-risks a small peak comparable to the early

peak in the controls and dyslexic at-risks, could visually be

discerned maximum at FC3 at 194 msec. A t-test showed that

this peak did not significantly differ from zero at this electrode,

although there was a trend (t14 ¼ 2.120, p ¼ .052) but this peak

did significantly differ from zero at C3 (t14 ¼ 2.364, p ¼ .033).

Comparing these maximum mean amplitudes using an

ANOVA, it was found that there were no significant differences

in amplitude of this early component between groups.

Additionally, in the FD-difference curve there was a posi-

tive fronto-central peak in the controls (maximum at FC4

(t12 ¼ 2.721, p ¼ .019) at 106 msec) that was absent in both at-

risk groups. A one way ANOVA that was used to compare

group mean amplitudes on FC4, indicated that there were no

differences in amplitude between groups.

3.5. Correlations between MMR-measures, phonologicalawareness and reading fluency

In the combined group of at-risk and control children there

were small to moderate but significant correlations between

Table 2 e One-sample t-test results for the FC4, FCz, andFC3 difference-curves in controls, typical-readingat-risks and dyslexic at-risks.

Controls(N ¼ 13) t-values

(df ¼ 12)

Typical-readingat-risks (N ¼ 15)t-values (df ¼ 14)

Dyslexicat-risks (N ¼ 10)t-values (df ¼ 9)

MMRARTD

FC3 �1.139 1.641 �.346

FCz �2.264* 1.663 �.421

FC4 �2.740* .799 .014

MMRFD-2

FC3 �2.876* �1.917 1.701

FCz �2.675* �.716 �.283

FC4 �3.291** �1.299 �.592

*p < .05, **p < .01.

MMRARTD and silent WRF (r ¼ �.286, p ¼ .043) and between

MMRFD and the aloud and silent WRF lists (WRF-1: r ¼ �.302,

p ¼ .035; WRF-2: r ¼ �.353, p ¼ .016; WRF-3: r ¼ �.333, p ¼ .022;

DLT: �.415, p ¼ .005). Figs. 4 and 5 show the scatterplots

between MMRARTD, MMRFD and the silent word reading test.

There were no significant correlations between any of the

auditory measures and the participants results on the

phoneme deletion task, but therewere significant correlations

between the phoneme deletion task and reading in the

combined groups (WRF-1: r ¼ 691, p < .001; WRF-2: r ¼ 747,

p < .001; WRF-3: r ¼ 797, p < .001; DLT: .770, p < .001).

4. Discussion

To our knowledge the present study is the first to investigate

the question whether impaired ART-sensitivity on a neuro-

physiological level at a pre-reading age is a useful biomarker

for developmental dyslexia. A particular strength in the

design is the inclusion of a group of children with genetic risk

for dyslexia but without reading problems. Therefore,

contrary to previous ART-studies, we were able to distinguish

genetic risk from reading ability itself.

In response to both deviants, it was found that controls

showed a negative MMR-component in a time-window

Fig. 4 e Scatterplot correlation between MMRARTD and

silent word reading.

Fig. 5 e Scatterplot correlation between MMRFD and silent

word reading.

c o r t e x 4 9 ( 2 0 1 3 ) 1 0 3 4e1 0 4 51042

comparable to previous studies with children at a pre-

reading age (e.g., Cheour et al., 2002). This MMR-component

is similar to the classical MMN that is usually found in

older children and adults (e.g., Naatanen, 1995; de Baene

et al., 2004; Dehaene-Lambertz et al., 2005; Bauer et al.,

2009). We may therefore assume that it reflects auditory

discrimination. However, these MMR-components were

absent in both at-risk groups. In contrast, in response to the

ARTD, an early positive MMR-component was present in

response to the ARTD in controls, typical-reading at-risks

and dyslexic at-risks. In response to the frequency-deviant

an early positive MMR component was only present in the

controls.

When investigating group-differences, it was found that

in line with the above mentioned results, the controls

showed a larger processing difference between standard

and ARTD than the at-risks. The at-risk groups did not differ

from one another in ART-processing. Comparable results

were obtained for the frequency-deviant. The controls

showed a larger processing difference between standard

and the frequency-deviant than the at-risks, while both at-

risks groups did not differ from one another in this

respect. However, in contrast to the ARTD, for the

frequency-deviant there also was a significant group-

difference for reading-level groups between controls and

dyslexic at-risks. For both ART- and frequency-sensitivity

correlations with reading-fluency were found at the level

of the combined groups of at-risks and controls. With regard

to frequency processing, this result is in line with the

correlations between ERP-measurements in newborns and

their grade two reading fluency scores found by Leppanen

et al. (2010).

The present results show that both ART- and frequency

processing are related to reading development. However,

because it is not possible to discriminate between typical-

reading and dyslexic at-risks with these basic auditory

measures, they cannot be regarded as early biomarkers for

developmental dyslexia.

4.1. Genetic risk for dyslexia and the role of basicauditory processing skills in reading development

The finding that regardless of reading skill, subjects geneti-

cally at risk for developmental dyslexia are significantly

impaired in general basic auditory processing of non-speech

properties in the sound signal, raises the question as to how

basic auditory processing skills are related to reading devel-

opment. In all hypotheses that concern links between basic

auditory processing and reading skill, it is assumed that

impairments in basic auditory processing compromise

reading development through their detrimental effect on the

development of phonological skills (e.g., Tallal, 1980; Goswami

et al., 2002).We however did not find any correlations between

basic auditory processing and later phonological skills, while

we did find correlations between basic auditory processing

and WRF and also between phonological skills and WRF. In

addition, whereas both groups of at-risks showed diminished

sensitivity for subtle acoustic differences, only the dyslexic at-

risks also showed impaired phonological skills when

compared to controls. Therefore the link between early basic

auditory processing and later phonological skills might be

questioned and the mechanisms by which basic auditory

processing influences phonological and reading-development

remain a subject for further investigation. Regarding the

present results, it is also important to note that ART-

processing has not been investigated in the Dutch language

until now and behavioural ART-processing data in Dutch

native speaking subjects is lacking. Therefore precaution is

needed inmaking conclusions about ART-processing in Dutch

children. Additionally it should be noted that in the present

study only one ART-contrast was investigated and that more

research with more contrasts is needed to be able to make

inferences about the role and importance ART-processing in

literacy progress in the Dutch orthography.

4.2. Protective mechanisms?

It may be suggested that in the typical-reading at-risks,

protective mechanisms may have undone the detrimental

effects of auditory processing impairments on phonological

and reading development. In the present study some indica-

tions were found to support this view. Whereas all three

groups scored at or abovemean level on early intelligence and

general language development measures (see Table 1), at

a pre-reading age, the performance of the typical-reading at-

risks on language comprehension, sentence development,

verbal short term memory was significantly higher than that

of the dyslexic at-risks. These results support the suggestion

that early language and phonological skills are predictive for

reading skill within pre-reading children genetically at risk for

dyslexia (Scarborough, 1990; Snowling et al., 2003; Lyytinen

et al., 2004). The question, however, is how these protective

mechanisms work. One possibility is that better ability in

preceding subskills may give a better start in reading devel-

opment. According to the connectionist model of Plaut et al.

(1996), phonological skills, orthographic knowledge and

semantic knowledge are used for word recognition. Our

results suggest that the typical-reading at-risks already show

an advantage at a pre-reading age for two out of three of the

c o r t e x 4 9 ( 2 0 1 3 ) 1 0 3 4e1 0 4 5 1043

abovementioned subskills that are used for word recognition.

This advantage may have facilitated learning the connections

with orthographic knowledge.

An alternative interpretation is suggested by the study of

Scarborough (1990). She found that at a pre-reading age

reading-disabled children were particularly impaired in

syntax measures, which are also the main constituents in our

sentence development measure. She suggests that dyslexics

may suffer from rule-learning difficulties that impede

mastering structural language skills such as correctly

applying syntactic rules and reading. It is, however, important

to note that the dyslexic at-risks in the present study did not

score below average according to test-norms on any of the

language developmental measures. Therefore the findings

cannot be interpreted in terms of impairments. The difference

in language comprehension and expressive syntax between

typically reading and dyslexic at-risks arose from the fact that

the typically reading at-risks scored well above mean level on

these measures. Therefore, the present results suggest that

better ability in two components involved in learning relevant

connections or, alternatively, relatively stronger rule-learning

skills may overrule a possible compromising effect of poor

auditory processing skills. This idea of a ‘protective surplus’

deserves further investigation.

4.3. Searching for biomarkers, what way to proceed?

The results of the present study suggest that although there is

a relation to genetic risk, and there are links between basic

auditory processing measures and reading skill, the findings of

the present study do not support the view that these measures

qualify as biomarkers for dyslexia. In particular, since these

measures did not differentiate between typical-reading and

dyslexic at-risks. In line with the study of Leppanen et al. (2010)

it was found that early frequency-sensitivity predicted grade

two reading ability. The Finnish study also revealed a differen-

tiating effect between typical-reading and dyslexic at-risk

groups. It should be noted though that the authors suggest

that this effect was too small to support atypical auditory

processing as the sole explanation for developmental dyslexia.

They consider it to be “a risk factor leading to cumulative

effects on those processes that are critical for learning to read”

(p. 13). In the present study this differentiating effect for

frequency-sensitivity was not replicated. Possibly, at the age of

our participants (41 months) developmental dynamics may

have reduced the differences in automated auditory perception

within the at-risk group in comparison to the age of the

newborns of Leppanen et al. (2010).

Several ERP-studies suggest that speech-processing bears

links to literacy-development that may be stronger than links

between non-speech processing and literacy-development. For

example, one study found that speech-perception at a pre-

reading age explained more variance in later literacy-

outcomes than non-speech auditory perception did (Maurer

et al., 2009). Guttorm et al. (2010) have reported that early

speech processing predicts poorer pre-reading skills in at-risk

children. Early data of the DDP revealed differentiating effects

for automated speech processing between at-risk babies and

controls (vanLeeuwenet al., 2006; vanHertenet al., 2008). In the

near future early speech-processing data of DDP will be related

to readingdevelopment inGrade2and3 inorder to replicateand

extend the afore mentioned speech-processing studies.

At present, the question whether one single biomarker

such as ART-sensitivity predicts dyslexia still remains unan-

swered. Because opinions seem to converge to the idea that

developmental dyslexia is not caused by a single factor but

develops as a consequence of an interaction between stronger

risk and weaker protective factors (Pennington, 2006;

Snowling et al., 2003; Snowling, 2008), the balance seems to be

against a single cause.

Acknowledgements

This studywas funded by NWO [Nederlandse Organisatie voor

Wetenschappelijk Onderzoek (Netherlands Organisation for

Scientific Research)]. We would like to thank the participating

families for their time and effort and Usha Goswami for

providing the stimuli used in the present study and for her

valuable remarks.

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