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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: [email protected] (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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