European Journal of Neuroscience, Vol. 24, pp. 2420–2427, 2006
doi:10.1111/j.1460-9568.2006.05100.x
Speech- and sound-segmentation in dyslexia: evidence
for a multiple-level cortical impairment
T. Kujala,1,2,3,4 J. Halmetoja,1 R. Näätänen,1,3 P. Alku,5 H. Lyytinen6 and E. Sussman1,7
1
Cognitive Brain Research Unit, Department of Psychology, University of Helsinki, PO Box 9, 00014 Helsinki, Finland
Helsinki Collegium for Advanced Studies, University of Helsinki, PO Box 4, 00014, Helsinki, Finland
3
Helsinki Brain Research Centre, Helsinki, Finland
4
Department of Psychology, University of Turku, 20014 Turku, Finland
5
Laboratory of Acoustics and Audio Signal Processing, Department of Electrical Engineering and Communications, University of
Technology, Espoo, PO Box 3000, 02015 Helsinki, Finland
6
Department of Psychology, University of Jyväskylä, PO Box 35, 40351 Jyväskylä, Finland
7
Departments of Neuroscience and Otolaryngology, Albert Einstein College of Medicine, 1410 Pelham Parkway S, Bronx, NY
10461, USA
2
Keywords: attention, auditory, dyslexia, event-related brain potentials, word segmentation
Abstract
Developmental dyslexia involves deficits in the visual and auditory domains, but is primarily characterized by an inability to translate
the written linguistic code to the sound structure. Recent research has shown that auditory dysfunctions in dyslexia might originate
from impairments in early pre-attentive processes, which affect behavioral discrimination. Previous studies have shown that whereas
dyslexic individuals are deficient in discriminating sound distinctions involving consonants or simple pitch changes, discrimination of
other sound aspects, such as tone duration, is intact. We hypothesized that such contrasts that can be discriminated by dyslexic
individuals when heard in isolation are difficult to identify when occurring within words or structurally similar complex sound patterns.
In the current study, we addressed how segments of pseudo-words and their non-speech counterparts are processed in dyslexia. We
assessed the detection of long-duration differences in segments of these stimuli and identified the brain processes that could be
associated with the behavioral results. Consistent with previous studies, we found no early cortical sound-duration discrimination
deficit in dyslexia. However, differences between impaired and non-impaired readers were found in the brain processes associated
with sound-change recognition as well as in the behavioral performance. This suggests that even when the early, automatic, sound
discrimination processes are intact in dyslexic individuals, deficits in the later, attention-dependent processes may lead to impaired
perception of speech and other complex sounds.
Introduction
Dyslexia is a developmental disorder of reading that occurs in persons
with otherwise normal intelligence, sensory acuity and general
motivation (World Health Organization, 1993). Remarkably, approximately 5–18% of the population is affected by dyslexia (Shaywitz,
1998; Snowling, 2000). Individuals with dyslexia often have associated difficulties with writing, spelling, motor co-ordination and
attentional abilities, which vary across individuals, making it difficult
to specify the etiology (Habib, 2000; Snowling, 2001).
There are a number of theories on the behavioral manifestations of
the disorder. It has been suggested that reading difficulties are due to
(1) a general inability to process rapidly changing information (Tallal,
1980; Llinas, 1993; Farmer & Klein, 1995; Stein & Walsh, 1997;
Demp et al., 1998), (2) impairments in visual motion processing (Stein
& Walsh, 1997; Demp et al., 1998), (3) overall sensory processing
deficits affecting the ability to integrate visual and auditory processes
in reading (Laasonen et al., 2000; Kujala et al., 2001b) and (4)
impairments of phonological awareness, which is the most widely held
Correspondence: Dr T. Kujala, 1Cognitive Brain Research Unit, as above.
E-mail: teija.m.kujala@helsinki.fi
Received 12 May 2006, revised 11 July 2006, accepted 8 August 2006
theory (Bradley & Bryant, 1983; Liberman & Shankweiler, 1985;
Studdert-Kennedy & Mody, 1995; Mody et al., 1998; Ramus et al.,
2003). Phonological impairments specifically affect the ability properly to map the written code of the language (the orthography) to the
sound structure of the language (the phonology). Within this
framework, some scholars have proposed that the deficits in phonological processing originate from a more general difficulty in
perceiving acoustic information (Baldeweg et al., 1999; Tallal,
1980; Reed, 1989; McGivern et al., 1991; Farmer & Klein, 1995;
Wright et al., 1997, 2000; Kujala et al., 2000, 2001b; Temple et al.,
2000; Kujala & Näätänen, 2001). The notion is that the more general
acoustic processing deficit affects the ability accurately to perceive
critical acoustic elements in the speech stream, thus disrupting the
development of a veridical phonological code. In addition to having
impairments in discriminating particular types of sounds, dyslexic
subjects have greater problems during complex than simple perceptual
tasks (Heiervang et al., 2002; Banai & Ahissar, in press). For example,
dyslexic subjects with additional learning problems were shown to
have normal ability to carry out tasks in which they had to judge
whether two sounds were the same or different, whereas they were
impaired in telling which one of the two sounds was higher (Banai &
Ahissar, in press).
ª The Authors (2006). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
Sound segmentation in dyslexia 2421
By and large, previous studies on dyslexia have assessed the neural
processing of sound contrasts in isolation (e.g. ⁄ ba ⁄ vs. ⁄ da ⁄ or ⁄ i ⁄
vs. ⁄ e ⁄ ), whereas in natural speech these minimal-difference pairs
seldom occur in isolation. Moreover, some studies have suggested that
stimulus complexity or masking effects might affect the ability of
dyslexic individuals to perceive sound differences (Schulte-Körne
et al., 1999; Kujala et al., 2000, 2003). This is more likely to be
evident for word-length acoustic information than isolated syllables.
Therefore, in the current study, we evaluated how dyslexic individuals
perceive and process speech-sound contrasts in their natural context,
embedded within words.
The critical contrast used in the present study was a relatively long
duration difference (200 vs. 100 ms). Although dyslexic subjects
(children) may have problems in discriminating duration differences of
relatively short sounds (100 vs. 33 ms; Corbera et al., 2006), the
discrimination of long-duration sounds (200 vs. 160, 120, 80 and 40 ms)
was suggested to be unimpaired in dyslexic adults (Baldeweg et al.,
1999). The results showed that when these long-duration sounds were
presented as single events (without surrounding sounds) in sequences,
dyslexic subjects had a normal cortical mismatch negativity (MMN)
response for the deviant stimuli. Furthermore, they behaviorally discriminated deviant sounds equally well as non-dyslexic control subjects.
We hypothesized that even such a large contrast that can be
discriminated by dyslexic individuals when heard in isolation would
be difficult to identify when occurring within complex word-length
information. Additionally, we used both speech (pseudo-words) and
similar non-speech (tone patterns) as stimuli in the current study to
address the issue of whether auditory impairments in dyslexia are
specific to speech sounds or may be more general to acoustic
processing mechanisms.
To distinguish different sensory-information processing stages
preceding the behavioral response, we recorded evoked neural
responses (event-related brain potentials), which provide high temporal resolution and are time-locked to stimulus events. Therefore,
they are highly conducive to determine the timing of cognitive
processes underlying, and leading to, perception.
Neural responses were recorded while subjects, in separate sessions,
watched a video and ignored sounds and while they attended to them
and responded with a button press to deviant segments. Early cortical
processes underlying sound discrimination were assessed with the
MMN brain response, which is elicited 100–250 ms after a change in
sound stimulation whether or not the sounds are attended to
(Näätänen, 1992). The MMN amplitude reflects the magnitude of
the physical difference between sounds and is associated with
behavioral measures of sound discrimination (Tiitinen et al., 1994;
Amenedo & Escera, 2000; Kujala et al., 2001a; Sussman et al., 2001).
Therefore, it has been suggested that MMN is a cortical correlate of
sound discrimination accuracy (Näätänen, 1992). However, MMN and
behavioral measures do not always reveal corroborating results (e.g.
Bradlow et al., 1999) as behavioral data are affected by a variety of
cognitive factors, such as attention, decision processes and motivation.
When sound sequences are attended, the N2b and P3b responses are
elicited after the MMN. These attention-related responses occur
successively (N2b, 200–300 ms; P3b, 300–800 ms). They are
associated with attentive deviant detection and recognition (Sutton
et al., 1965; Näätänen et al., 1982; Näätänen & Gaillard, 1983;
Donchin & Coles, 1988; Novak et al., 1992; Polich & Kok, 1995).
The N2b is elicited when the subject becomes aware that a change has
occurred in the stimulus sequence. P3b, in turn, indicates identification
of a target stimulus.
The purpose of the current study was to determine how dyslexic
adults discriminate duration contrasts within speech and complex
sounds by using a combination of behavioral and electrophysiological
measures and to determine the stages of cognitive processing (e.g.
early, automatic sound discrimination, attentive deviant identification)
that are impaired in dyslexia.
Materials and methods
Subjects
Data were collected from nine dyslexic adults (aged 19–40 years,
mean 27 years; five males) and nine non-dyslexic (control) adults
(aged 18–39 years, mean 25 years; four males). Informed consent was
obtained from the subjects, and the study conforms with The Code of
Ethics of the World Medical Association (Declaration of Helsinki).
The dyslexic and control groups were matched for educational
background. Three subjects in the dyslexic group were university
students, two had a senior high school education, one was a senior
high school student (having finished a vocational school) and three
had vocational education. Four subjects in the control group were
university students, one was a senior high school student and four had
vocational education. The subjects had normal hearing and normal (or
corrected to normal) vision, they were healthy and without medication
affecting the central nervous system.
Diagnosis of dyslexia was made in elementary school. Current level
of reading function was also evaluated prior to the experiment using a
reading-skill test designed and created for Finnish adults (Leinonen
et al., 2001). The test is based on normative data involving 20–
40 year-old individuals. The subtests included in the present study
were accuracy and speed in text reading and accuracy in reading
pseudo-words. This test battery also includes an interview with which
the reading problems both in childhood and in adulthood were
determined. Additionally, the subjects completed a questionnaire
describing their current problems related to reading and writing.
The interview revealed a reading impairment in all subjects
included in the dyslexic group. Furthermore, all but one dyslexic
subject performed below 1 standard deviation of the reference
population in at least one of the three variables in the reading test
(Table 1). According to the interview, this subject clearly had dyslexia
in her childhood, and was considered a ‘compensated dyslexic’ in
adulthood. Therefore, the data were not excluded from the study. In
the answers to the questionnaire, all dyslexic subjects reported that
they have letter omissions or reversals in writing and confusion
between certain phonemes such as b-d, b-p, t-d, k-g, i-e, nk-ng, and ⁄ or
problems with speech-sound duration perception.
Stimulation and procedure
The stimuli were three-syllable Finnish pseudo-words and their nonphonetic counterparts (complex tone patterns with three segments)
presented in separate conditions (Speech Condition and Tone Condition, respectively). The stimulus sequences were composed of
repetitive standard stimuli (p. 0.79) and occasional deviant stimuli
(p. 0.07 for each deviant-stimulus type), presented pseudo-randomly
(at least one standard pseudo-word ⁄ tone pattern preceding the stimuli
that included a deviant segment). In each deviant pseudo-word and
tone pattern, there was only one deviant segment.
The Finnish pseudo-word [tatata] and its non-phonetic complex
sound counterpart, which approximated the acoustic complexity of
the pseudo-word in the spectral domain (see also Sussman et al.,
2004), served as standard stimuli. The sounds of the pseudo-words
(total length 390 ms) were synthesized by concatenating a wave
form of the plosive ⁄ t ⁄ cut from a word produced by a real speaker
ª The Authors (2006). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 24, 2420–2427
2422 T. Kujala et al.
Table 1. Results from the dyslexia tests and questionnaire on current problems in reading and writing
Dyslexia tests*
Normative data
Dyslexic
Dyslexic
Z-scores
Questionnaire Reading scores
Accuracy (errors)
Speed (s)
10.8 ± 11.8
120.4 ± 23.9
31.7 ± 16.2
165.1 ± 31.4
)1.8 ± 1.4
)1.9 ± 1.3
–
–
Pseudo-word naming (errors)
Problems with phoneme duration
Letter omissions ⁄ reversals
Letter confusions
Word parts missing
Slow reading
Lines get confused
11.4 ± 6.7
–
–
–
–
–
–
)1.5 ± 1.4
–
–
–
–
–
–
–
7⁄9
9⁄9
8⁄9
7⁄9
5⁄9
5⁄9
4.0 ± 5.0
–
–
–
–
–
–
*Means ± SD of the raw data values for reading accuracy (number of errors), speed and pseudo-word naming (number of errors) in normative data and in
dyslexic subjects. On the right, Means ± SD of the Z-scores. These were obtained by subtracting the number of errors (accuracy) and the time in seconds used for
reading the text passage (speed) from the corresponding values of the normative data. Thereafter, the values obtained were divided by the standard deviation of the
normative data. Results from the questionnaire. The number indicates how many of the dyslexic subjects (n ¼ 9) responded having such a problem. Phoneme
duration: single vs. double consonants and vowels (common in the Finnish language). Letter omissions ⁄ reversals: letters are missing or they are in wrong parts of the
word in written text. Letter confusions: letter pairs such as b-d, b-p, t-d, k-g, i-e may be confused. Word parts missing: when reading or writing, parts of words may
be missing.
Fig. 1. Spectra of the stimuli illustrating the vowel segment of the pseudowords (upper panel) and the corresponding tone-composite of the non-speech
tone-pattern stimulus (lower panel).
to the waveform of the vowel ⁄ a ⁄ generated by the SSG
(Semisynthetic Speech Generation) method (Alku et al., 1999;
Fig. 1). With an SSG, vowel ⁄ a ⁄ of two different segment
durations, 100 ms for standard and 200 ms for deviant stimuli, were
produced, which were then used together with the plosive ⁄ t ⁄ in
synthesizing the pseudo-words. The SSG was used as it yields
naturally sounding vowels with formants (i.e. resonances of the
vocal tract that determine the phoneme) that can be adjusted as
desired. In addition, the modification of the vowel duration is
straightforward in the SSG. The segments of the non-phonetic
complex sound patterns were composed of four sinusoidals, the
frequencies and intensity levels of which matched the strongest
harmonics in the vicinity of the lowest four formants of the vowel
⁄ a ⁄ : 490, 1195, 2175 and 3725 Hz (Fig. 1). Stop gaps occurred in
the beginning of each non-phonetic segment. Finally, the energies
of the pseudo-words and their non-phonetic counterparts were
normalized.
The stimuli were binaurally presented with intensity of 50 dB (SPL)
above the individual hearing threshold with a stimulus-onset asynchrony of 860 ms in three blocks of pseudo-word and three blocks of
tone pattern stimuli. Half of the subjects were presented with tone
patterns (Tone Condition) first and the other half with pseudo-words
(Speech Condition) first. These conditions were administered in two
sessions: ignore and attend. In the ignore session, subjects watched a
silent movie and ignored auditory stimuli. In the attend session,
subjects listened to the sounds and were instructed to press one of the
three response keys corresponding to the position of the detected
deviant segment. Every time the subjects recognized a deviant
stimulus, they pressed button #1 (with their index finger) if they
identified the deviant as occurring in the first segment of the pseudoword (Speech Condition) or of the tone pattern (Tone Condition);
button #2 (with their middle finger) for a deviant in the second
segment, and button #3 (with their ring finger) for a deviant in the
third segment. Subjects used their preferred hand for the behavioral
task. Before this session, a short practice block was run to familiarize
subjects with the task. Each stimulus block included 750 stimuli in the
ignore session and 550 stimuli in the attend session. The ignore
session was always presented before the attend session in order to
avoid possible carry-over effects of attention.
Brain-activity recording and data analysis
An electroencephalogram (EEG) was recorded (DC recording, 40-Hz
low pass, sampling rate 500 Hz) with the NeuroScan system and
SYNAMPS amplifiers from electrodes placed at the F3, C3, P3, Fz,
Cz, Pz, F4, C4 and P4 locations (according to the international 10–20
system) on the scalp, and on the left and right mastoids. The nose was
used as a reference. Horizontal eye movements were monitored with
ª The Authors (2006). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 24, 2420–2427
Sound segmentation in dyslexia 2423
Fig. 2. Behavioral target detection. Mean hit rate, false alarm rate, discrimination accuracy rate and reaction time (with standard errors of mean). The reaction time
was calculated from the beginning of the stimulus. Segments 1, 2, and 3 refer to the corresponding position (first, middle and last, respectively) in the stimulus.
Significant (P < 0.01–0.05) group differences in the performance are marked with asterisks.
Fig. 3. Brain responses elicited by standard pseudo-word and tone-pattern
stimuli. The onsets of the sound segments of the standard stimuli elicit N1
responses. Arrows point to a significant N1 amplitude (the second segment in
speech and tone patterns, ignore session) and latency (the second segment in
pseudo-words, attend session) difference between the groups.
electrodes placed on the left and right canthi, and vertical eye
movements with electrodes placed above and below the right eye. The
EEG was digitally filtered with a bandpass of 1.5–15 Hz (24 dB per
octave). Neural responses were obtained by averaging EEG epochs
separately for the three different deviant stimulus types and for the
standard stimuli. The length of the analysis epoch was 1100 ms,
beginning 100 ms before and terminating 1000 ms after stimulus
onset. The epochs contaminated by eye movements or artifacts of nonbiological origin producing a voltage of ±100 lV at any electrode
were removed. In addition, the responses to the first ten stimuli of each
block were omitted from averaging.
The N1, response to sound onset (Näätänen & Picton, 1987), was
identified from the responses elicited by the standard stimuli. The
MMN, N2b and P3b, reflecting stimulus-change discrimination and
identification (Näätänen, 1992), were identified from the deviantminus-standard difference waves.
The amplitudes of the neural responses were measured by first
determining the peak latency from the grand-mean difference
waveforms at Fz for the MMN, at Cz for the N2b and at Pz for the
P3b, and from the grand-mean standard responses at Cz for the N1,
and then measuring the amplitude using a 40-ms window centered on
the grand-mean peak latency. Because the MMN and N2b may
sometimes overlap, the data at mastoid leads were taken into account
in determining the latency of the MMN, which inverts its polarity at
mastoids unlike the N2b (e.g. Näätänen, 1992).
Data recorded from all nine scalp electrodes were included in the
amplitude and scalp-distribution analyses. Group differences were
assessed with mixed-mode anova, including factors of Group
(control vs. dyslexic), Front-to-back electrode location (frontal vs.
central vs. parietal) and Lateral electrode location (left vs. central vs.
right). Planned comparisons were carried out by running an anova
separately for each response elicited by the three stimulus positions in
the speech and non-speech stimuli.
ª The Authors (2006). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 24, 2420–2427
2424 T. Kujala et al.
Fig. 4. Brain responses elicited by deviant pseudo-word and tone-pattern stimuli in the ignore (at frontal Fz electrode, at which the MMN is largest) and attend
sessions (at central Cz electrode, at which the N2b is largest and at parietal Pz electrode, at which the P3b is largest). The responses are deviant-minus-standard
difference waves. Segments 1, 2, and 3 refer to the corresponding position (first, middle and last, respectively) of the deviant sound in the pseudo-word ⁄ tone pattern.
In the ignore session, an MMN is elicited by each deviant segment with a similar amplitude in both groups. In the attend session, the deviant segments elicit a
prominent N2b and P3b. Significant amplitude differences were found between the groups for the N2bs elicited by the 3rd deviant speech segment and by all deviant
tone pattern segments (marked with arrows).
Behavioral measures and data analysis
Results
Button presses were classified as hits when a correct button (#1 for a
deviant in the first segment, #2 for the second and #3 for the third) was
pressed within 150–1500 ms from deviant-stimulus onset. Button
presses outside that window and incorrect responses (e.g. pressing
button #1 when a deviant occurred in the second segment) were
regarded as a false alarms. Hit rates (HR) and false alarm (FA) rates
were calculated for each token type (speech and non-speech) and
segment. Then, the probability that a response was correct (‘discrimination-accuracy rate’) was calculated by dividing the number of hits
by the number of hits plus FAs for each subject, token type and
segment position, individually. A secondary analysis was done on the
FAs to determine the type of errors both groups made. When
participants pressed the key erroneously, we analysed the percentage
of errors of misidentifying the position of the deviant within the
stimulus (i.e. indicating that the wrong segment was deviant).
Reaction time (RT) was measured from stimulus onset. Group
differences in HR, FA, accuracy rate and RT were analysed with
separate one-way anovas for each token type and deviant stimulus
segment.
Behavioral sound discrimination
Dyslexic subjects performed worse than control subjects in the target
identification task (Fig. 2). HR was lower (F1,16 ¼ 6.83–15.91,
P < 0.02) and FA rate was higher (F1,16 ¼ 9.70–13.89, P < 0.01) in
dyslexic than in control subjects for all other deviant segments except
for the second segment in pseudo words. The discrimination-accuracy
rate was significantly lower in the dyslexic than control subjects for
each deviant pseudo-word and tone-pattern segment (F1,16 ¼ 6.15–
17.57, P < 0.03). The RT was significantly longer in the dyslexic than
control subjects for the middle segment of the pseudo-words
(F1,16 ¼ 6.99, P < 0.02), whereas for the tone-pattern segments and
for the first and the third pseudo-word segments, the difference did not
reach significance (F1,16 ¼ 3.34–3.62, P < 0.09).
Analysis of the types of false button presses showed that false
alarms never occurred to standard stimuli for either group. Thus, the
FA rate fully reflected detection of a deviant but misidentification of
the position within the stimulus where the deviant occurred. The rate
of misidentification of the deviant position within the stimulus was
significantly higher for the dyslexic group than the control group
ª The Authors (2006). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 24, 2420–2427
Sound segmentation in dyslexia 2425
Table 2. The amplitudes and latencies of MMN (Ignore Condition, Fz), N2b (Attend Condition, Cz) and P3b (Attend Condition, Pz)
Speech condition
Tone condition
Control
Dyslexic
Control
Dyslexic
)2.9 ± 0.3
)3.4 ± 0.5
)2.1 ± 0.5
)2.7 ± 0.4
)2.9 ± 0.5
)1.6 ± 0.3
)3.1 ± 0.5
)2.6 ± 0.3
)2.5 ± 0.4
)2.2 ± 0.3
)2.4 ± 0.5
)1.8 ± 0.4
MMN latency (ms)
Segment 1
Segment 2
Segment 3
237 ± 5
385 ± 5
532 ± 8*
235 ± 6
381 ± 4
509 ± 5*
246 ± 3
388 ± 4
534 ± 9
244 ± 1
378 ± 5
520 ± 6
N2b amplitude (lV)
Segment 1
Segment 2
Segment 3
)4.0 ± 1.0
)5.1 ± 1.4
)9.2 ± 2.1*
)1.6 ± 1.1
)1.8 ± 0.9
)2.0 ± 1.3*
)4.1 ± 1.0*
)4.4 ± 1.7*
)5.9 ± 1.6*
)0.4 ± 0.7*
)0.0 ± 0.6*
)1.8 ± 0.7*
MMN amplitude (lV)
Segment 1
Segment 2
Segment 3
N2b latency (ms)
Segment 1
Segment 2
Segment 3
306 ± 7
447 ± 8
584 ± 4
328 ± 12
435 ± 12
583 ± 10
321 ± 11
433 ± 10
591 ± 12
326 ± 17
486 ± 15
571 ± 13
P3b amplitude (lV)
Segment 1
Segment 2
Segment 3
4.2 ± 1.0
4.8 ± 1.3
5.3 ± 1.0
3.9 ± 0.9
3.0 ± 1.0
2.0 ± 0.9
5.0 ± 0.9
3.5 ± 0.9
3.9 ± 0.8
4.5 ± 0.9
2.9 ± 0.9
2.4 ± 0.8
P3b latency (ms)
Segment 1
Segment 2
Segment 3
453 ± 19
575 ± 14
723 ± 11
455 ± 11
583 ± 14
696 ± 21
453 ± 11
599 ± 11
734 ± 14
446 ± 8
584 ± 8
716 ± 19
Data are given as means ± SEM. The latencies were determined from the onset of the deviant pseudo-word or complex-tone. *Significant group differences (main
effects).
(F1,16 ¼ 9.70–13.89, P < 0.01) for all segments except for the second
speech segment for which it was marginally significant (F1,16 ¼ 3.86,
P < 0.07).
Cortical standard-sound processing
The N1 response to standard-stimulus onsets or onsets of the third
segment showed no significant differences between dyslexic and
control subjects. However, in response to the second segment onset in
the Ignore Condition, the N1 amplitude was significantly higher in
dyslexic than control subjects for both speech (F1,16 ¼ 6.35,
P < 0.03) and non-speech (F1,16 ¼ 5.15, P < 0.04) stimuli. In the
attend session, the second segment of the pseudo-words elicited an N1
with a shorter latency in control than dyslexic subjects (F1,16 ¼ 5.26,
P < 0.04; Fig. 3). No other significant differences in standard-sound
processing were observed.
Pre-attentive cortical sound-change discrimination
In the ignore session, significant MMNs were elicited by all deviant
segments in both groups (t8 ¼ 5.20–9.84, P < 0.004; Fig. 4, Table 2).
There were no other group differences in the MMN latencies or
amplitudes either for the pseudo-words or tone patterns except for a
shorter MMN latency in the dyslexic than control subjects for the lastsegment deviant of the pseudo-words (F1,16 ¼ 6.12, P < 0.03).
However, there was a significant difference in MMN scalp topography
between the groups for the tone deviance at the end of the stimulus
(F4,46 ¼ 2.74, P < 0.04, Group · Front-back laterality interaction).
This was due to diminished parietal and left-hemisphere MMNs in the
dyslexic subjects.
Attentive cortical deviant-sound identification
The most striking finding was the absence of the N2b in dyslexic
subjects for attended stimuli (Fig. 4). N2b was elicited by all deviant
segments of attended stimuli in control subjects (t8 ¼ 2.58–4.43,
P < 0.04), whereas the response in the expected latency range of N2b
was not significantly different from zero in dyslexic subjects.
Furthermore, the difference waveforms showed a significantly stronger
neural activity at the N2b latency range for the control than dyslexic
subjects for each tone-segment change (F1,16 ¼ 4.75–7.75, P < 0.05).
Group differences were also found for the speech sounds but they were
significant only when the deviant was in the last segment, which
elicited an N2b with a lower amplitude in dyslexic than control subjects
(F1,16 ¼ 8.40, P < 0.02). This result can also be accounted for by the
absence of N2b in the dyslexic group.
The P3b was elicited by deviant segments in all three positions and
both stimulus types for the control group (t8 ¼ 2.93–5.38, P < 0.02).
In dyslexic subjects, the P3b was elicited by deviant segments in all
positions of non-speech sounds and for the beginning and middle
segments of the speech sounds. For the last segment speech deviant,
the P3b was marginally significant (t8 ¼ )2.28, P < 0.06). No
significant group differences (group main effects) were found in the
P3b latencies or amplitudes. There were significant Group · Frontback interactions, revealing more prominent P3b responses in parietal
scalp regions in control than dyslexic subjects for the second and third
deviant-tone segment (F2,32 ¼ 3.37–13.90, P < 0.04).
Discussion
Our results show that dyslexic individuals are severely impaired in
identifying relatively long-duration contrasts (100 vs. 200 ms)
ª The Authors (2006). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 24, 2420–2427
2426 T. Kujala et al.
embedded within speech and complex sounds. A previous study
showed no differences between dyslexic and control subjects in
discriminating tones with durations of 200 ms vs. 160, 120, 8, and
40 ms presented in isolation (Baldeweg et al., 1999). Thus, it can be
suggested that sounds that are easily discriminated by dyslexic adults
when presented in isolation are hard to identify when occurring within
words or other complex sounds. It has been proposed that the key
difficulty in speech perception in dyslexia is the perception of brief or
rapidly occurring elements (Tallal, 1980; Reed, 1989; Farmer & Klein,
1995; Temple et al., 2000). Our results show that the perceptual
impairment in dyslexia is more intricate than one confined to
discriminating brief or rapidly presented sounds. When appearing in
a natural context, within speech or complex sounds, even large
differences of long-duration sounds were difficult for dyslexic
individuals to discriminate.
Although there was some indication on abnormal neural generators
of the MMN in dyslexia (a group difference of MMN topography for
the last tone segment), no statistically significant group differences
(main effects) were found in MMN amplitudes. This result is
consistent with previous findings on duration discrimination of
relatively long sounds (standard sound being 200 ms) presented in
isolation (Baldeweg et al., 1999). The unaffected MMN amplitude
suggests that the early cortical discrimination of the duration
difference was not impaired in dyslexic individuals. However, even
though the duration contrasts were automatically discriminated in the
cortex, they were not attentively detected, as shown by the absence of
the later N2b response and impaired behavioral discrimination in
dyslexic subjects. Thus, the deficit of the dyslexic subjects was in the
attentive identification of the duration contrasts. These results illustrate
the complexity of the disorder. Whereas the discrimination of some
sound features (e.g. stimulus frequency) is impaired at the early,
automatic level in information processing (e.g. Baldeweg et al., 1999;
Kujala et al., 2003; Renvall & Hari, 2003), the processing of some
other features (e.g. durations of long stimuli) is intact at this level but
becomes compromised when the sounds are embedded in speech or
sound patterns.
Dyslexic subjects were impaired in identifying the position of the
segment within the stimulus in which the deviant occurred. This was
evident in the significantly lower hit rate and significantly higher false
alarm rate in dyslexic than control subjects. The false alarms that were
recorded were button presses to misidentified deviant-segment
positions, whereas there were no false alarms made to standard
stimuli. Thus, dyslexic subjects were able to perceive which pseudowords or complex sounds included a deviant but they were unable to
identify its position within the stimulus. These results are in agreement
with previous behavioral studies showing that task complexity may
affect sound-discrimination performance of dyslexic subjects (Heiervang et al., 2002; Banai & Ahissar, in press). The present results
suggest that the neural correlate of the impaired sound identification is
the N2b response, which was absent in dyslexic subjects.
By including speech stimuli and their non-phonetic counterparts we
wished to determine whether the group differences could be specifically attributed to speech processing or reflected a deficiency of
acoustic processing in general. The finding of group differences in
response to deviants embedded both in speech- and in non-speech
stimulus tokens (indicated by both behavioral and brain measures)
suggests that dyslexic individuals have problems in both speech and
non-speech sound processing. Our results suggest that dyslexic
subjects might be even more impaired in non-speech than speech
processing, as the N2b was significantly reduced in dyslexic than
control subjects for all tone-pattern segments but only for the last
segment of speech sounds. Thus, our results, consistent with a number
of previous studies (e.g. Tallal, 1980; Reed, 1989; McGivern et al.,
1991; Farmer & Klein, 1995; Baldeweg et al., 1999; Kujala et al.,
2000, 2001b; Temple et al., 2000; Wright et al., 2000; Kujala &
Näätänen, 2001) support the theory that auditory impairments in
dyslexia are not specific to speech. However, in order to determine
whether impaired non-speech processing is causally related to
dyslexia, further studies, preferably those showing ameliorating
effects of intervention with non-speech stimuli, should be carried
out (e.g. Kujala et al., 2001b).
We found several statistically significant group differences in the
scalp-topography of the neural responses, suggesting abnormal loci
of activated brain areas in dyslexia. For example, the MMN
amplitude was diminished over the left hemisphere and parietal
areas (for the final tone-pattern segment) and the P3b amplitude
over the parietal areas (the second and third tone-pattern segments)
in dyslexic subjects. These findings corroborate those of previous
studies suggesting left-hemispheric (Shaywitz et al., 1998; Galaburda, 1999; Renvall & Hari, 2003; Temple et al., 2003) and
parietal (Vidyasagar, 1999; Hari et al., 2001) deficits in dyslexia.
The left-hemispheric dysfunction is well documented in dyslexia,
and is proposed to underlie the impairments of the language system
(Shaywitz et al., 1998; Temple et al., 2003), whereas the parietal
dysfunction might relate to problems in attentional regulation
(Vidyasagar, 1999; Hari et al., 2001).
In conclusion, our results demonstrate that dyslexic individuals
are severely impaired in identifying long sound differences within
speech and non-speech complex sounds that, according to previous
studies, are normally discriminated by dyslexic individuals when
they are presented in isolation. On the basis of these and previous
results we suggest that auditory perception in dyslexia is impaired
in a specific way, reflected in the MMN and N2b responses. The
MMN, associated with discrimination accuracy, is impaired in
dyslexia for some sound deviances such as pitch and stop
consonant changes (Schulte-Körne et al., 1998; Baldeweg et al.,
1999; Kujala et al., 2003; Renvall & Hari, 2003) whereas MMN is
normally elicited by certain deviants, such as duration changes of
long sounds (Baldeweg et al., 1999). When embedded in a natural
context (words, complex sounds), the position of the deviant
sounds is misidentified, as suggested by an absent N2b, even when
the deviant sounds elicit an intact MMN.
Acknowledgements
This study was supported by the Academy of Finland (grant 200522) and the
National Institute of Health (grant R01 DC06003). We thank Ms Seija
Leinonen for help with dyslexia tests.
Abbreviations
EEG, electroencephalogram; FA, false alarm; HR, hit rate; MMN, mismatch
negativity; RT, reaction time.
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