Amplitude envelope onsets and developmental
dyslexia: A new hypothesis
, Jennifer Thomson*, Ulla Richardson*, Rhona Stainthorp
, Diana Hughes
, Stuart Rosen
and Sophie K. Scott
*Institute of Child Health, University College London, London WC1N 1EH, United Kingdom;
Institute of Education, University of London, London
WC1H 0AA, United Kingdom;
Department of Psychology, Royal Holloway and Bedford New College, University of London, Egham, Surrey
TW20 0EX, United Kingdom; and Departments of
Phonetics and Linguistics and
Psychology, University College London, London
WC1E 6BT, United Kingdom
Communicated by James L. McClelland, Carnegie Mellon University, Pittsburgh, PA, June 20, 2002 (received for review January 25, 2002)
A core difﬁculty in developmental dyslexia is the accurate speci-
ﬁcation and neural representation of speech. We argue that a likely
perceptual cause of this difﬁculty is a deﬁcit in the perceptual
experience of rhythmic timing. Speech rhythm is one of the earliest
cues used by infants to discriminate syllables and is determined
principally by the acoustic structure of amplitude modulation at
relatively low rates in the signal. We show signiﬁcant differences
between dyslexic and normally reading children, and between
young early readers and normal developers, in amplitude envelope
onset detection. We further show that individual differences in
sensitivity to the shape of amplitude modulation account for 25%
of the variance in reading and spelling acquisition even after
controlling for individual differences in age, nonverbal IQ, and
vocabulary. A possible causal explanation dependent on percep-
tual-center detection and the onset-rime representation of sylla-
bles is discussed.
hildren with developmental dyslexia have specific problems
with reading and spelling that cannot be accounted for by
low intelligence, poor educational opportunities, or obvious
sensory兾neurological damage. The accepted core problem
across languages is a deficit in phonological representation (1, 2),
although this can be accompanied by other deficits (3, 4). In
developmental theories of language acquisition, phonological or
speech-based representations in children no longer are thought
to be organized around phonemic segments from the outset (5).
The explicit phonemic representation of speech is thought to
depend on being taught to read an alphabetic script (6). If it is
accepted that phonemic representation is a product of literacy
and constitutes a psychological process that is not logically
necessary for speech perception and production (7), then the
phonological deficit in dyslexia must arise at a developmentally
earlier level of phonological representation than the phoneme.
Obvious candidate levels are those of the syllable (pop-si-cle,
gar-den) and onset rime (s-eat, sw-eet, str-eet). Phonological
awareness of the syllables, onsets, and rimes in words develops
before literacy across languages. For example, prereaders learn-
ing to speak English, Chinese, and German all perform well in
‘‘oddity’’ tasks in which they must select the odd word out that
does not rhyme (e.g., pat, hat, man; refs. 8–10). Onset-rime
awareness tasks (but not usually syllabic-awareness tasks) also
are good predictors of literacy acquisition across languages
(11–13). A perceptual deficit in the mechanisms used to extract
the suprasegmental attributes of the speech stream thus may
help to cause the phonological awareness and literacy problems
characteristic of developmental dyslexia across orthographies.
Rhythm in speech is a property of the slow amplitude mod-
ulation (AM) of the waveform (14), corresponding roughly to
the AM associated with syllables. Previous studies have shown
that dyslexic children have difficulty in detection of AM and
frequency modulation at rates (2–10 Hz) similar to those seen at
the syllable level in speech (15, 16). To investigate the impact that
these difficulties might have on the perception of rhythm in
acoustic signals, we designed a perceptual task in which AM was
varied to affect the perception of distinct, discrete ‘‘beats’’ in the
auditory stream. Potential differences in the psychometric func-
tion for beat detection between dyslexic and matched control
children, and between precocious readers and their matched
controls, then were investigated.
Our psychophysical task was based on a rate of AM change
task, which takes advantage of the relationship between beat
perception and the shape of AM change (17). The task was based
on a sinusoid that was modulated in amplitude to a depth of 50%.
Within this, the rate of amplitude change only was varied by
varying the rise time of the modulation, while the overall rate of
modulation was held constant at 0.7 Hz (see Fig. 1). Very slow
rise times (⬎250 ms) give the percept of a continuous sound that
varies in loudness. When the rise time is shortened sufficiently,
however (e.g., to 120 ms), the percept changes to that of a
continuous sound with a loud beat occurring rhythmically at the
same rate as the modulation (18). Given that aspects of syllable
processing (i.e., onset-rime awareness) are poorer in dyslexic
children, we predicted that they would have poorer sensitivity to
the perceptual consequences of AM than control children. As
rise time is varied, dyslexic children should evidence less change
in AM-related experiences of beat perception than their con-
trols. Two studies of beat detection were carried out. In the first,
dyslexic children were compared with reading and chronological
age-matched control children in a cross-sectional design. In the
second, young early readers participating in a longitudinal study
were compared with their matched controls from the same study.
We predicted that precocious readers should be significantly
more sensitive to variations of rise time in the amplitude-
modulated sequences than their controls, as indexed by their
perception of beats.
The ‘‘phonological deficit’’ in developmental dyslexia is in-
dexed typically by behavioral difficulties in three related areas of
phonological processing, all of which we expected to be related
to beat detection. We therefore measured phonological aware-
ness (using the rhyme oddity task), rapid ‘‘automatized’’ naming
(or RAN) of letters and pictures, and phonological short-term
memory (PSTM, repetition of triples of nonwords) in our
dyslexic children and their controls. We also measured spelling
as well as reading. In languages other than English, develop-
mental dyslexia is diagnosed on the basis of a severe spelling
deficit accompanied by extremely slow performance in phono-
logical processing tasks (because orthographic transparency
makes decoding very accurate). To contribute to developmental
dyslexia across languages, therefore, beat detection should be
related to spelling as well as to reading.
Abbreviations: AM, amplitude modulation; RAN, rapid automatized naming; PSTM, pho-
nological short-term memory; RFD, rapid-frequency discrimination; TOJ, temporal order
judgement; CA, chronological age; RL, reading level; WISC, Wechsler Intelligence Scale for
Children; P center, perceptual center.
To whom reprint requests should be addressed at: Institute of Child Health, 30 Guilford
Street, London WC1N 1EH, United Kingdom. E-mail: firstname.lastname@example.org.
August 6, 2002
On the basis of the prior basic auditory processing literature
with dyslexic children, we also included two rapid temporal-
processing tasks in our study. One was a version of the rapid-
frequency discrimination (RFD) task pioneered by Tallal and
coworkers (19). The other was a temporal order-judgement
(TOJ) task based on easily labeled environmental sounds (dog兾
car horn). We expected dyslexic children to show deficits in both
tasks, because both require rapid spectrotemporal integration
(suggested to be the basis of the phonological deficit in dyslexia
by Tallal et al. in refs. 20 and 21). However, deficits in rapid
spectrotemporal integration (and processing related acoustic
cues such as place of articulation and voice-onset time) have not
always been found in dyslexic children (22).
Subjects. One hundred and one children were tested, of whom 24
had a statement of dyslexia from their local education authority.
In the United Kingdom, ‘‘statements of dyslexia’’ depend on
extensive testing by educational psychologists and are the basis
for service provision. Twenty of the dyslexic children were at
special schools with curricula focused on remediating the pho-
nological deficit. Because of this remediation, single-word de-
coding for this group was in the normal range. Subject charac-
teristics are shown in Table 1. None of the dyslexic children had
additional difficulties (e.g., dyspraxia, attention deficit兾
hyperactivity disorder, autistic spectrum disorder, or specific
language impairment) according to their specialist assessments.
The control children for the dyslexics (n ⫽ 49) were drawn from
local schools and comprised those in the right age兾reading age
range whose parents returned consent forms. Twenty-eight
children who were participating in a longitudinal study of
precocious readers were tested also with the beat-detection task.
All of these participants had been followed from 4 years of age
and were aged 11 at the time of testing. Of these children, 14 were
young early readers, and 14 were young early controls who had
been individually matched to the young early readers on the basis
of socioeconomic status and vocabulary ability at age 4 (23).
Auditory Processing Tasks. AM兾beat-perception task. The children
were presented with 7.857-s sound sequences, all of which were
sinusoidal carriers at 500 Hz, were amplitude-modulated at a
rate of 0.7 Hz, and had a depth of 50%. The underlying
modulation envelope was based on a square wave, but the fall
time was fixed at 350 ms, and the rise time could be varied from
15 to 300 ms (logarithmically spaced over a continuum of 40
stimuli). Before testing, the children were trained by using the
two extremes of the continuum. The 15-ms stimulus (which
yielded a clear beat) was presented as the sound of two toys
(Tigger and Eeyore) swinging on a double-toy swing. The
back-and-forth rhythm of their swing coincided with the beat in
the signal. The 300-ms stimulus was presented as the sound of
Winnie the Pooh sliding down a solid plastic straw in the form
of a spiral (he got nearer to the child or further away as the
training sound got louder and quieter, respectively). The chil-
dren then were asked to decide whether subsequent stimuli
(given by computer through headphones) belonged to Winnie
the Pooh or to Tigger and Eeyore. Performance on this task was
measured by using Levitt’s adaptive procedure (24) with mod-
ifications to increase efficiency (25). Two independent adaptive
tracks were used to estimate the points on the rise-time contin-
uum at which the stimuli were labeled as Winnie the Pooh 29 and
71% of the time, with a maximum of 40 trials. Tracks started at
the endpoints of the continuum, with rise times of 15 and 300 ms.
The categorization function was derived from all trials in a
particular test, and summary statistics for slope and category
boundary estimated by Probit analysis (26). Shallower slopes
Fig. 1. Examples of the stimulus wave form for rise times of 15 (a) and 300
Fig. 2. Bubble plots of the psychometric functions from the dyslexic (a), chronological age (CA) control (b), and reading level (RL) control (c) groups for the
beat-detection task. The size of the bubbles represents the number of trials.
www.pnas.org兾cgi兾doi兾10.1073兾pnas.122368599 Goswami et al.
indicate less sensitivity to variations in the acoustic feature
varied across the continuum (here, rise time).
RFD task. The nonspeech same兾different task was similar to
that described in ref. 19. The basic stimuli were two vowel-like
50-ms complex periodic tones (rise and fall times of 5 ms) with
fundamental frequencies of 100 and 305 Hz. Every trial con-
sisted of two stimuli presented sequentially with an interstimulus
interval (ISI) of 0, 10, 50, 100, or 400 ms. All four possible
stimulus orders were presented (low-low, low-high, high-low, and
high-high), and listeners responded by indicating ‘‘same’’ or
‘‘different.’’ Trials were presented in a random order, with one
occurrence of each ISI and stimulus order, making 20 trials
(5 ISIs ⫻ 4 orders).
Dog兾car-horn TOJ task. This task used two stimuli that were
readily identifiable without prior training as a dog bark and a car
horn. The dog bark was aperiodic, whereas the car horn was
periodic with a fundamental frequency of ⬇400 Hz. Starting
from sounds accompanying a children’s computer game, various
manipulations of amplitude envelope and duration were used to
create stimuli with a total duration of 115 ms each, with rise and
fall times of 5 ms. The two stimuli then were normalized to have
the same rms level. The continuum of sounds consisted of 204
stimuli in which the stimulus onset asynchrony varied from ⫹405
ms (horn leading dog) to ⫺405 ms (dog leading horn) in 4-ms
steps. Stimuli were allowed to overlap to the degree necessary to
create the specified stimulus onset asynchronys. For testing, the
same adaptive procedure was used as for the beat-detection task,
but the children indicated simply which sound they heard first.
If dyslexic children are poorer at TOJs, then their psychometric
functions in this task also should be flatter than those of controls.
Phonological Processing Tasks. Oddity task. The child listened to
sets of three words and had to select the nonrhyme (e.g., gap,
PSTM. The child listened to sets of three nonwords and had to
repeat them (e.g., loff, bup, heg). Both tasks were presented via
headphones by using digitized speech.
RAN task. The child had to name familiar pictures and letters
under timed conditions.
Standardized Psychometric Tests. The children received four sub-
sets of the Wechsler Intelligence Scale for Children (WISC):
blocks, picture arrangement, similarities, and vocabulary. The
British Ability Scales reading, spelling, and mathematics sub-
scales also were administered, along with the Graded Test of
Nonword Reading (27).
Beat Detection in Dyslexic and Normally Reading Children. A signif-
icant difference was found between the group of dyslexic chil-
dren and their chronological age controls in the slope of the
categorization function (see Fig. 2), with the dyslexics showing
flatter slopes as predicted [mean slope ⫽⫺0.03 for dyslexics
(SD ⫽ 0.04) and ⫺0.12 for controls (SD ⫽ 0.08), P ⬍ 0.000]. The
reading age match controls showed intermediate slopes (mean
slope ⫽⫺0.06, SD ⫽ 0.05). Detection of beats in AM signals
Fig. 3. Bubble plots of the psychometric functions from the dyslexic (a), CA control (b), and RL control (c) groups for the dog兾car-horn TOJ task. The stimulus
onset asynchrony (SOA) values refer to the stimulus onset asynchrony of the dog in relation to the car horn (e.g., ⫺400 ms means the dog barked 400 ms before
the horn sounded).
Table 1. Participant characteristics
Dyslexic CA match RL match
readers Non-early readers
N 24 25 24 14 14
Age in years and months 9, 0 (11) 9, 0 (8) 7, 11 (4) 11, 4 (4) 11, 4 (4)
Reading standard score* 101.1 (11.7) 142.5 (14.7) 108.3 (13.0) 117.4 (3.6) 110.9 (5.5)
Spelling standard score* 69.0 (12.1) 107.8 (16.2) 85.5 (12.1) 124.4 (8.3) 109.7 (6.7)
Nonword reading兾20 7.4 (5.5) 15.7 (4.0) 11.3 (5.1) ——
109.1 (11.4) 111.9 (11.0) 105.7 (10.6) 50.4 (3.3) 47.8 (5.4)
Standard deviations are shown in parentheses.
*Dyslexics and CA and RL controls: British Ability Scales. Young early readers, non-early readers, WORD.
Dyslexics and CA and RL controls: WISC. Young early readers, non-early readers, Ravens raw score.
Goswami et al. PNAS
August 6, 2002
thus was poorer in the dyslexic children than in their peers and
seemed to vary with reading level.
To explore the relationship between beat detection and
phonological processing, reading and spelling, partial correla-
tions controlling for age, and WISC IQ (WISC short form) were
calculated. Group performance in the behavioral tasks is shown
in Table 2, and the partial correlations are shown in Table 3. As
predicted, there were highly significant relationships between
beat detection and RAN, phonological memory, phonological
awareness, reading, spelling, and nonword reading. On the rapid
spectrotemporal integration hypothesis, significant relationships
with phonological processing and literacy would be expected also
for the RFD and the dog兾car-horn TOJ tasks (group perfor-
mance shown in Figs. 3 and 4); this was the case, but the
relationships found were not as strong as those for the beat-
detection task (see Table 3). Both the RFD task and the
beat-detection task showed a significant relationship with math-
ematical ability, which was not predicted. This result could
reflect the short-term memory demands of the mental arithmetic
tasks in the standardized mathematical assessment used here.
If basic auditory processing is important in causing the pho-
nological deficit that characterizes developmental dyslexia, then
measures of basic auditory processing should predict reading,
spelling, and phonological ability even when age, nonverbal IQ,
and vocabulary are controlled. To determine predictive rela-
tionships, a series of four-step fixed-entry multiple regression
equations were computed on the data set (73 children). The
dependent variables were reading ability, spelling ability, non-
word reading, rime oddity, RAN, and PSTM. The independent
variables were (in a fixed order) (i) age, (ii) nonverbal IQ,
(iii) vocabulary, and (iv) an auditory-processing measure (beat
detection, RFD, or dog兾car-horn TOJ). The beat-detection
measure accounted for an additional 25% of the variance in
reading and spelling in these stringent analyses (see Table 4).
The RFD measure did not predict spelling, but it did predict
reading and nonword reading, accounting for an additional 10
and 12% of the variance, respectively. The TOJ measure was less
sensitive, predicting a significant proportion of the variance in
reading (6%) only. All three measures predicted phonological
awareness and PSTM, but only the beat-detection measure
predicted RAN performance (see Table 4).
Whereas the RFD and TOJ tasks are thought to tap the ability
to detect rapid acoustic change (at a time scale of ⬍40 ms), the
rise times that yield the perceptual experience of beats are
considerably longer in duration (up to 150 ms or more). To
determine whether there was overlap in the variance in reading
accounted for by the beat-detection and RFD tasks, a pair of
five-step multiple regression equations were computed, both
entering (i) age, (ii) nonverbal IQ, and (iii) vocabulary followed
by the two auditory measures in either order. When entered last,
the beat-detection measure accounted for an additional 19% of
the variance in reading (P ⬍ 0.001). The RFD measure entered
last accounted for an additional 4% (P ⬍ 0.02). A large
proportion of the variance in reading predicted by the RFD task
was clearly shared with the beat-detection task but not vice versa.
To determine whether individual differences in these basic
processing abilities still would be predictive of reading even when
Fig. 4. Performance on the RFD task by group. ISI, interstimulus interval.
Table 3. Partial correlations between the basic
auditory-processing measures and the experimental variables
controlling for age and WISC IQ
P-center slope Dog兾car TOJ RFD
Reading ⫺0.59* 0.27
Spelling ⫺0.56* 0.25
Nonword reading ⫺0.43* 0.20 0.42*
Oddity ⫺0.43* 0.28
P center ⫺0.25
Dog兾car-horn TOJ ⫺0.25
*P ⬍ 0.0001.
P ⬍ 0.05.
P ⬍ 0.01.
Table 2. Mean performance for the dyslexics and CA and RL
controls on the behavioral tasks
Dyslexics* CA match RL match
Oddity, % correct 46.9 (16.7) 74.2 (12.2) 53.5 (14.3)
PSTM, % phonemes
79.0 (8.8) 86.5 (5.1) 79.9 (10.0)
RAN mean speed, sec 36.7 (7.5) 29.1 (3.7) 34.6 (5.6)
92.7 (20.3) 114.6 (16.0) 94.4 (14.0)
Beat detection: slope ⫺0.03 (⫺0.04) ⫺0.12 (⫺0.08) ⫺0.06 (⫺0.05)
RFD task, % correct 75.7 (13.3) 88.6 (10.7) 72.3 (18.2)
⫺0.03 (⫺0.02) ⫺0.04 (⫺0.03) ⫺0.03 (⫺0.02)
Standard deviations are shown in parentheses.
*Dyslexics ⬍ CA at P ⬍ 0.05.
RL ⬍ CA at P ⬍ 0.05.
Table 4. Percentage of variance in reading, spelling, nonword
reading, phonological awareness (oddity), PSTM, and RAN
explained by the different independent variables in separate
ﬁxed-entry multiple-regression equations
(columns show separate equations), R
Reading Spelling Nonword R Oddity PSTM RAN
Step 1: age 0.09* 0.03 0.01 0.00 0.00 0.11*
Step 2: blocks 0.05
0.04 0.04 0.13* 0.00 0.05
Step 3: vocabulary 0.11* 0.07 0.02 0.04 0.05 0.00
Step 4: P center 0.25
Step 4: RFD 0.10* 0.04 0.12* 0.09* 0.13* 0.01
Step 4: Dog兾car
0.05 0.03 0.06
Steps 1–3 were always the same (age, nonverbal IQ, and vocabulary). Step
4 was a basic auditory-processing variable (P centers, RFD, or dog兾car TOJ).
*P ⬍ 0.01.
P ⬍ 0.05.
P ⬍ 0.001.
www.pnas.org兾cgi兾doi兾10.1073兾pnas.122368599 Goswami et al.
phonological awareness was controlled, a second pair of five-step
multiple regression equations were computed, entering (i) age,
(ii) nonverbal IQ, (iii) vocabulary, (iv) oddity, and (v) beat
detection or RFD. Here only beat detection remained a signif-
icant predictor of reading, accounting for an additional 9% of the
variance (P ⬍ 0.001, the oddity measure at step four accounted
for 31% of the variance in reading).
Beat Detection in Young Early Readers and Normally Developing
As a further test of the hypothesis that AM-driven beat
detection is associated with the phonological determinants of
reading ability, we also assessed beat detection in a group of
young early readers who had learned to read without parental
instruction before entering school (see ref. 23). These children,
now aged 11, had taught themselves to read on the basis of their
superior phonological skills at age 4. Theoretically, these supe-
rior phonological skills may have developed at least partly
because of excellent rhythm perception (i.e., enhanced ability to
perceive beats in amplitude-modulated sequences). Compared
with control children from the same longitudinal study, the
young early readers showed greater sensitivity to beats, with
significantly sharper psychometric functions [young early read-
ers, mean slope ⫽⫺0.14 (SD 0.06), matched controls ⫽⫺0.10
(SD 0.04), P ⬍ 0.04]. Sensitivity to AM was also significantly
related to reading progress in this cohort, both in terms of
reading comprehension (r ⫽⫺0.42) and development of the
orthographic lexicon (word chains test, r ⫽⫺0.43).
Our hypothesis was that the potential deficits in AM and
frequency-modulation detection in dyslexic individuals reported
by other groups (see refs. 15 and 16) might relate to deficits in
the processing of acoustic structure at the level of the syllable.
This processing is best described as rhythm detection. If this
version of a syllabic hypothesis is correct, then children with
phonological developmental dyslexia should be characterized by
poorer AM beat detection. This hypothesis was supported.
Dyslexic children showed significantly inferior detection of AM
beats compared with controls, and children with superior liter-
acy acquisition showed significantly superior detection of AM
beats. This report demonstrates a developmental continuum in
a basic auditory-processing ability (beat detection) from dyslexic
to exceptional child readers.
Theoretically, the detection of beats in AM sequences such as
those used here corresponds to the detection of ‘‘perceptual
centers’’ (P centers) in acoustic signals. P centers are the
perceptual moments of occurrence in speech (28) and musical
(29) sounds. Determined by the onsets of signals (30), P centers
are associated in speech with rapid increases of midband spectral
energy, typically occurring around the onset of a vowel (31).
From a speech-development perspective therefore, they consti-
tute a nonspeech-specific mechanism for segregating syllable
onsets and rhymes. Their accurate detection should be important
for the quality of phonological representation. In line with this
hypothesis, beat detection was shown to be related to individual
differences in phonological processing, although the strongest
relationships found were for reading and spelling progress. Beat
detection was a significant predictor of literacy even when
phonological processing was controlled, which could reflect
developmental factors. Stronger relationships between beat de-
tection and phonological processing might be found in younger
children who are just beginning to read. Note that because
beats兾P centers are a consequence of the processing of complex
sound, both speech and nonspeech, it is difficult to argue that
differences in such sensitivity are a product of reading acquisi-
tion. Nevertheless, this possibility cannot be ruled out on the
basis of the current data.
Working from the association of beats in a perceptual se-
quence and P centers, our hypothesis is that the primary
auditory-processing deficit in dyslexia is related to P-center
processing of speech and nonspeech sounds. AM rise time
contributes to this perceptual primitive, and thus other observed
auditory deficits (e.g., auditory-stream segregation and back-
ward masking; refs. 32–34) may arise in part because the stimuli
used in these judgement tasks of necessity have P centers. A
P-center hypothesis also can explain dyslexic children’s difficul-
ties in producing speech in time with a metronome and finger-
tapping in time with a metronome or an internally generated
rhythm (35, 36). It further explains why a focus on rhyme and
rhythm in preschool (e.g., clapping out nursery rhymes, which in
effect gives children practice in coordinating a manual rhythm
with the P centers of certain syllables) is important for later
literacy development across languages (37, 38). Note, however,
that the potential P-center deficit in dyslexia is a subtle one. The
deficit is not sufficient to interfere markedly with the acquisition
of spoken language, although spoken-language processing in
metalinguistic tasks remains effortful and slow. More serious
deficits in P-center perception theoretically should cause
spoken- as well as written-language impairments of the kind
found in specific language impairment.
In the current study, two measures of rapid spectrotemporal
integration (RSI) were also administered to the dyslexic children
and their controls. These two tasks were highly correlated and
were both performed poorly by dyslexic children. These tasks
measure the importance of rapid changes in the signal, which
should affect the child’s ability to detect changes in speech at the
segmental level (e.g., ‘‘p’’ vs. ‘‘b’’). The beat-detection task
measures the importance of the syllabic information given by
amplitude envelope onsets, which in speech affect suprasegmen-
tal attributes of the vowel onsets. Both aspects of auditory
processing seem to be poor in dyslexic children, but most of the
variance in reading accounted for by the RSI tasks is shared with
beat detection (although not vice versa). As children become
aware of onsets and rimes without being taught to read, we argue
that the ability to process amplitude envelope onsets accurately
may constitute the primary deficit in developmental dyslexia.
Detailed experiments, ideally across languages, are required to
test this hypothesis further.
We thank the children who participated in this research and their schools.
Research at the Institute of Child Health and Great Ormond Street
Hospital benefits from research and development funding received from
the National Health Service Executive.
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