Neural Tracking in Infancy Predicts Language Development in Children With and Without Family History of Autism

Abstract

During speech processing, neural activity in non-autistic adults and infants tracks the speech envelope. Recent research in adults indicates that this neural tracking relates to linguistic knowledge and may be reduced in autism. Such reduced tracking, if present already in infancy, could impede language development. In the current study, we focused on children with a family history of autism, who often show a delay in first language acquisition. We investigated whether differences in tracking of sung nursery rhymes during infancy relate to language development and autism symptoms in childhood. We assessed speech-brain coherence at either 10 or 14 months of age in a total of 22 infants with high likelihood of autism due to family history and 19 infants without family history of autism. We analyzed the relationship between speech-brain coherence in these infants and their vocabulary at 24 months as well as autism symptoms at 36 months. Our results showed significant speech-brain coherence in the 10- and 14-month-old infants. We found no evidence for a relationship between speech-brain coherence and later autism symptoms. Importantly, speech-brain coherence in the stressed syllable rate (1–3 Hz) predicted later vocabulary. Follow-up analyses showed evidence for a relationship between tracking and vocabulary only in 10-month-olds but not 14-month-olds and indicated possible differences between the likelihood groups. Thus, early tracking of sung nursery rhymes is related to language development in childhood.

Full text

RESEARCH ARTICLE
Neural Tracking in Infancy Predicts Language
Development in Children With and Without
Family History of Autism
Katharina H. Menn
1,2,3,4,5
, Emma K. Ward
2
, Ricarda Braukmann
2
,
Carlijn van den Boomen
6
, Jan Buitelaar
2,7
,
Sabine Hunnius
2
, and Tineke M. Snijders
1,2,8
1
Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
2
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
3
Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
4
Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
5
International Max Planck Research School on Neuroscience of Communication: Function, Structure,
and Plasticity, Leipzig, Germany
6
Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
7
Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
8
Cognitive Neuropsychology Department, Tilburg University
Keywords: autism, neural oscillations, speech segmentation, word learning, speech entrainment,
speech processing
ABSTRACT
During speech processing, neural activity in non-autistic adults and infants tracks the speech
envelope. Recent research in adults indicates that this neural tracking relates to linguistic
knowledge and may be reduced in autism. Such reduced tracking, if present already in
infancy, could impede language development. In the current study, we focused on children
with a family history of autism, who often show a delay in first language acquisition. We
investigated whether differences in tracking of sung nursery rhymes during infancy relate to
language development and autism symptoms in childhood. We assessed speech-brain
coherence at either 10 or 14 months of age in a total of 22 infants with high likelihood of
autism due to family history and 19 infants without family history of autism. We analyzed the
relationship between speech-brain coherence in these infants and their vocabulary at 24
months as well as autism symptoms at 36 months. Our results showed significant speech-brain
coherence in the 10- and 14-month-old infants. We found no evidence for a relationship
between speech-brain coherence and later autism symptoms. Importantly, speech-brain
coherence in the stressed syllable rate (1–3 Hz) predicted later vocabulary. Follow-up analyses
showed evidence for a relationship between tracking and vocabulary only in 10-month-olds
but not in 14-month-olds and indicated possible differences between the likelihood groups.
Thus, early tracking of sung nursery rhymes is related to language development in childhood.
INTRODUCTION
Autistic individuals often experience language difficulties (Eigsti et al., 2011), which usually
emerge early in life, with autistic children often showing delays in language acquisition
(Howlin, 2003). In non-autistic adults, brain activity synchronizes with incoming speech. This
processisreferredtoasneural tracking and is directly linked to language comprehension
an open access journal
Citation: Menn, K. H., Ward, E. K.,
Braukmann, R., van den Boomen, C.,
Buitelaar, J., Hunnius, S., & Snijders,
T. M. (2022). Neural tracking in infancy
predicts language development in
children with and without family history
of autism. Neurobiology of Language,
3(3), 495–514. https://doi.org/10.1162
/nol_a_00074
DOI:
https://doi.org/10.1162/nol_a_00074
Supporting Information:
https://doi.org/10.1162/nol_a_00074
Received: 16 September 2021
Accepted: 16 May 2022
Competing Interests: The authors have
declared that no competing interests
exist.
Corresponding Author:
Katharina H. Menn
menn@cbs.mpg.de
Handling Editor:
Marcela Peña Garay
Copyright: © 2022
Massachusetts Institute of Technology
Published under a Creative Commons
Attribution 4.0 International
(CC BY 4.0) license
The MIT Press
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(Peelle et al., 2013). There are indications that tracking of speech in the theta band is reduced
in autistic adults (Jochaut et al., 2015). Reduced tracking may also impact early language
development (Goswami, 2019). The current article investigates whether tracking in infancy
predicts language acquisition and the development of autism symptoms in children with high
and low likelihood for autism.
Autism spectrum disorder is a common neurodevelopmental condition characterized by
social communicative differences and restricted repetitive behaviours (American Psychiatric
Association, 2013). Our research focuses on the communication aspect, which is often char-
acterized by differences in expressive language as well as language comprehension difficul-
ties. Research suggests that autistic children differ from their non-autistic peers across a broad
range of linguistic skills (Kwok et al., 2015), ranging from differences in low-level acoustic
speech processing (Cardy et al., 2005;Kasai et al., 2005) to high-level linguistic abstraction
such as semantics, syntax, and pragmatics (for reviews, see: Eigsti et al., 2011;Groen et al.,
2008). However, the precise nature of these differences varies widely between individuals
(Anderson et al., 2007;Groen et al., 2008). Parents often experience a delay or regression
of language development as a first sign that their child is not developing typically (Kurita,
1985;Rogers, 2004;Thurm et al., 2014). Howlin (2003) showed that autistic children produce
their first word at an average age of 15–38 months, compared to 8–14 months in typically
developing children, who were matched for nonverbal IQ.
The exact causes behind language delays in autism remain unknown, but recent evidence
indicates they may be related to differences in neural development (Lombardo et al., 2015;
Van Rooij et al., 2018;Verly et al., 2014). One hypothesis states that the balance of neural
excitation and inhibition (E/I balance) is altered in autistic individuals (Bruining et al., 2020;
Dickinson et al., 2016;Rubenstein & Merzenich, 2003;Snijders et al., 2013). This E/I balance
is crucial for regulating the flow of information in the brain (Haider et al., 2013;Shew et al.,
2011) and also gives rise to neural oscillations (Poil et al., 2012), which underlie a broad range
of behavioral, cognitive, and perceptual processes, including language processing (see Meyer,
2018, for an overview). Different development of neural oscillations may thus also affect lan-
guage development in autistic children. In line with this, recent studies indicate that autistic
children show different development in resting-state spectral electroencephalography (EEG)
power (Tierney et al., 2012) and that these differences relate to different language development
between autistic and non-autistic children (Romeo et al., 2021;Wilkinson et al., 2020).
For assessing neural processing of continuous speech directly, one of the most influential
findings in the last years is that adults’oscillations synchronize with external signals such as
speech (Giraud & Poeppel, 2012). The amplitude envelope of speech contains amplitude
modulations at different timescales, which to a certain extent correspond to the occurrences
of phonemes (30–40 Hz, gamma range), syllables (4–8 Hz, theta range), and intonational
phrases (below 4 Hz, delta range). Adults’neural activity tracks the amplitude modulations
of speech in these different frequency bands (Di Liberto et al., 2015;Doelling et al., 2014;
Peelle & Davis, 2012), and tracking was shown to be related to language comprehension
(Riecke et al., 2018;Vanthornhout et al., 2018). Atypicalities in tracking have been found
for language-related neurodevelopmental conditions (Molinaro et al., 2016;Power et al.,
2013). To our knowledge, there is currently only one study that focused on speech tracking
in autism. Jochaut et al. (2015) examined tracking of continuous speech in 13 autistic adults
and 13 non-autistic adults. They found decreased speech tracking for the autistic group com-
pared to the non-autistic group in the theta range (4–7 Hz), which is assumed to synchronize
with the typical syllable rate in adult-directed speech. In addition, Jochaut et al. (2015) ana-
lyzed individual differences between participants and found a positive correlation between
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speech tracking and participants’verbal abilities along with a negative correlation between
speech tracking and general autism symptoms. This suggests tracking of speech is related to
language processing and possibly also general autism symptoms, but note that this relatively
low-sampled study still needs to be replicated.
Atypical tracking may be related to the delay in language acquisition reported for autistic
children. One of the first challenges infants need to overcome during language development is
segmenting continuous speech into smaller linguistic units, such as words, for language
comprehension. Adults rely mostly on linguistic knowledge for speech segmentation (Marslen-
Wilson & Welsh, 1978), but infants who still lack the required knowledge need to rely on other
cues. To a certain extent, the boundaries of linguistic units are cued by speech acoustics.
Leong and Goswami (2015) analyzed the amplitude modulation structure of nursery rhymes,
a particularly rhythmic form of infant-directed speech. They found that amplitude modulations
were centered around three frequency rates, which match the occurrence rates of stressed syl-
lables (∼2 Hz), syllables (∼5 Hz), and phonemes (∼20 Hz). This means that even infants who
still lack linguistic knowledge may be able to extract linguistic units from continuous speech
by tracking amplitude modulations (see also Goswami, 2019). Infants with better tracking
would thus be at advantage for their initial language acquisition, as they are able to extract
and learn the meaning of linguistic units from continuous speech faster. Crucially, the impor-
tance of acoustic cues for speech segmentation has been shown to decrease with age, as
infants start to use more linguistic knowledge for speech segmentation (Bortfeld et al., 2005;
Kidd et al., 2018;Männel & Friederici, 2013). It is unclear when the shift from acoustic to
linguistic speech segmentation happens, but both Dutch and English infants have been shown
to still rely on prosodic cues for word segmentation at least until 10 months of age (Johnson &
Seidl, 2009;Kooijman et al., 2009). Possibly, tracking may be more advantageous for infants
earlier in their language development, before they shift towards top-down segmentation strat-
egies. In the current study we compared 10-month-old infants to 14-month-old infants.
Between 10 and 14 months, infants show on average a fourfold increase in their receptive
vocabulary size (see Frank et al., 2017), indicating the speech segmentation of the
14-month-olds could rely more on linguistic cues. Thus, we assessed whether the importance
of tracking specific frequency bands might depend on the infants’developmental stage.
Studies investigating tracking in infants have been rare, but recent results indicate that typically
developing infants track the amplitude modulations in speech (Attaheri et al., 2022;Jessen
et al., 2019;Kalashnikova et al., 2018;Menn et al., 2022;Ortiz Barajas et al., 2021). It remains
unclear, however, how infants’tracking relates to language development.
The current study investigated the relationship between tracking in infancy, language devel-
opment, and later autism symptoms. Since autism cannot be reliably diagnosed before the age
of three (Charman & Baird, 2002) and the average age of diagnosis is 5 to 7 years (Szatmari
et al., 2016), this study employed a prospective longitudinal approach (Bölte et al., 2013;Jones
et al., 2019;Loth et al., 2017). We followed younger siblings of autistic children, referred to as
high-likelihood siblings as they have a 10–20% likelihood of receiving a later autism diagno-
sis, compared to a 1% likelihood in the general population (Constantino et al., 2010;Ozonoff
et al., 2011). In additon, we also followed a group of infants with an older non-autistic sibling,
referred to as low-likelihood group.
We obtained EEG recordings of 10- and 14-month-old infants listening to sung nursery
rhymes. Speech-brain coherence to sung nursery rhymes was taken as a measure of tracking.
We analyzed tracking of stressed syllables, syllables, and phonemes, since the amplitude
modulations of nursery rhymes are particularly pronounced in the corresponding frequency
bands (Leong & Goswami, 2015). We then examined the relationship between tracking and
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behavioral scores of vocabulary at 24 months and autism symptoms at 36 months. Based on
findings from autistic adults (Jochaut et al., 2015), we expected a relationship between track-
ing and both language abilities and autism symptoms. The exact hypotheses for the current
experiment were as follows: We expected 10- and 14-month-old infants in the high-likelihood
group to show decreased speech-brain coherence compared to the low-likelihood group. On
an individual level, we expected speech-brain coherence to correlate with higher vocabulary
at age 24 months and lower autism symptoms at age 36 months. Since the importance of
acoustic information in the different frequency bands may vary with language development,
we also explored the interaction between speech-brain coherence and age for predicting
vocabulary development.
MATERIALS AND METHODS
Participants
All participants of this study were tested within a broader project investigating the early devel-
opment of autism (Jones et al., 2019). For this study, we obtained the data of 74 Dutch infants:
45 high-likelihood infants and 29 low-likelihood infants. High-likelihood infants (HL) had an
older autistic sibling, and low-likelihood infants (LL) had an older non-autistic sibling and no
family history of autism, psychiatric, or genetic conditions. All infants were raised in the
Netherlands and tested at one of two testing sites. Forty-seven of the infants (30 HL,
17 LL) were tested in the infant laboratory at site 1, the other 27 (15 HL, 12 LL) were tested
at their homes by researchers from site 2. For the at-home tests, experimenters took care to
create a homogeneous and non-distracting environment by placing a tent on the table that
surrounded the child and screen. As such, the visual environment was similar for all children
(see, e.g., Di Lorenzo et al., 2019). Infants were included in the final analysis if they pro-
vided one usable EEG data set. Exclusion criteria were excessive movement during testing,
more than four noisy channels, neighboring bad channels, or failure to reach the minimum
trial criterion after artifact rejection. Figure 1 displays the final sample of infants after exclu-
sion, as well as the number and reasons for exclusions per age point. Since only 9 infants
provided usable EEG data for both age points, we decided to use only one EEG data set per
infant. The final sample included a total of 41 infants with one usable EEG data set (22 HL,
19 LL). Thirty-four of these infants also had vocabulary scores at 24 months available (20 HL,
14 LL), and 31 had autism measures at 36 months (18 HL, 13 LL). Table 1 summarizes the
descriptive statistics per testing. The experimental procedure was approved by the relevant
ethics committee at each site and was conducted in accordance with the Declaration of
Helsinki.
Materials
Stimuli
The stimuli consisted of five sung nursery rhymes that are highly familiar to Dutch infants
(Jones et al., 2019): “Dit zijn mijn wangetjes”(translation: These are my cheeks; duration:
16.4 s), “De wielen van de bus”(Wheels on the bus; 12.5 s), “Hansje pansje kevertje”(Hansje
pansje beetle; 10.6 s), “Twinkel twinkel kleine ster”(Twinkle twinkle little star; 13 s), “Papegaaitje
leef je nog?”(Parrot are you still alive?; 17 s). Video recordings were made of two female
native Dutch speakers, alternately singing the nursery rhymes. Speakers were instructed to
present the nursery rhymes in an infant-directed manner, while making accompanying ges-
tures. The total duration of the video recordings was 69 seconds. To identify the most impor-
tant amplitude modulation frequencies in the speech envelope in our stimuli, we transcribed
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the duration of all stressed syllables, syllables and phonemes using Praat (Boersma, 2001). In
our stimuli, 85% of all stressed syllables occurred at a rate of 1–3 Hz and 85% of all phonemes
occurred at a rate between 5 and 15 Hz. In addition, we also looked at infants’tracking in the
frequency rate from 3 to 5 Hz, which mostly captures the syllables. Note that 85% of all
Figure 1. Numbers of infants included in the final analysis. Infants were included if they contrib-
uted one usable EEG data set. Our final sample for the first analysis included 22 high-likelihood (HL)
infants and 19 low-likelihood (LL) infants. Not all infants provided follow-up measures for vocab-
ulary size (CDI) or autism symptoms (ADOS).
Table 1. Demographics of the children included in the final analysis per testing
Likelihood
EEG: 10 months EEG: 14 months CDI: 24 months ADOS: 36 months
NAge (SD) Sex (f:m) NAge Sex NAge Sex NAge Sex
HL 8 10,26 (0, 72) 5:3 14 14,06 (0, 5) 8:6 20 24,7 (1) 13:7 18 38,8 (5) 13:5
LL 10 10 (0, 6) 4:6 9 14,45 (0, 6) 4:5 14 24,8 (1, 3) 6:8 13 38,4 (3) 6:7
Note. HL: High-likelihood infants. LL: Low-likelihood infants. SDCDI: Vocabulary size. ADOS: Autism symptoms.
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syllables in the stimuli occurred within 1.7–6 Hz, but we limited the syllable rate to 3–5Hzto
avoid overlap with the stressed syllable and phonological rate. We put more emphasis on
stressed syllables and phonemes, as these acoustic-phonological cues are thought to be espe-
cially relevant for infant language acquisition (Gervain & Mehler, 2010). These frequency rates
used in this study are slower than the frequency rates typically analyzed in adult studies,
including the study by Jochaut et al. (2015), but are similar to the modulation rate previously
reported for infant-directed speech (Leong et al., 2017), nursery rhymes (Leong & Goswami,
2015), and songs (Ding et al., 2017).
Behavioral tests
The vocabulary knowledge of the children was tested using the Dutch version of the
MacArthur-Bates Communicative Development Inventories (CDI), a standardized vocabulary
test for children between 10 months and 36 months. It is a parent report measure of both
receptive and productive vocabulary with high reliability (Zink & Lejaegere, 2002). The
CDI was filled in by one of the child’s caregivers when the child was approximately 24 months
old. To account for variability in children’s age at administration, the test scores of receptive
and productive vocabulary were transformed to age-normed percentile scores.
Autism symptoms were measured using the Autism Diagnostic Observation Schedule-
Second Edition (ADOS-2; Lord et al., 2000). The ADOS-2 is a highly reliable and valid mea-
sure for autistic symptoms (Bölte & Poustka, 2004). Depending on the linguistic ability of the
child, Module 1 or Module 2 of the test was administered by a trained psychologist. For our
analyses, we used the comparison scores, which allow a reliable comparison of performance
on the different modules. The scores range from 1 to 10, with scores from 4 to 7 suggesting
medium indication for autism and scores of 8 or more suggesting high indications for autism.
Procedure
During the EEG recordings, infants sat either on their parent’s lap or in a highchair in front of a
computer screen with approximately 1 m distance to the screen (24 inch, 16:9, 1920 × 1080
pixels) on which the stimuli were presented. The nursery rhymes were presented three times
during a session, leading to a total duration of 207 seconds. They were shown as part of a
larger experiment intermixed with other experimental conditions. The total experiment took
about 20 minutes during which EEG was recorded continuously.
EEG Recordings
At site 1 a 32-channel actiCAP system by Brain Products was used. Site 2 made use of a
32-channel active electrode set by Biosemi. The main differences between the recordings of
the two systems are: different placement for four electrodes (Biosemi: AF3, AF4, PO3, PO4 vs.
actiCAP: TP9, TP10, PO9, PO10), a different sampling rate (Biosemi: 2048 Hz, actiCAP:
500 Hz), and different online reference electrodes (Biosmi: CMS and DLR electrodes, actiCAP:
AFz). The final analysis included only electrodes measured on both sites, namely: FP1/2,
Fz, F3/4, F7/8, FC1/2, FC5/6, Cz, C3/4, T7/8, CP1/2, CP5/6, Pz, P3/4, P7/8, Oz, O1/2.
EEG pre-processing
The EEG analysis was performed using the Fieldtrip toolbox (Oostenveld et al., 2011) in Matlab
R2016a. To accommodate for the differences in recording systems, Biosemi data were first
down-sampled to 500 Hz and re-referenced to Cz. To improve the independent component
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analysis (ICA) and channel interpolation, we reduced the electrodes to the final subset only
after preprocessing.
As a first pre-processing step, data were high-pass filtered at 0.1 Hz and low-pass filtered
at 45 Hz. Next, we performed ICA on the whole data set to remove noise by ocular move-
ments or noisy electrodes. We identified on average 1.8 (range: 0–6) noise components per
data set. Afterwards, the electrophysiological data corresponding to the presentation of nurs-
ery rhymes were extracted from the data set and divided into 3 s epochs using a sliding
window with two thirds overlap. This led to a maximum of 201 epochs per infant. ICA
components capturing noise were removed from the epochs and a maximum of four
non-neighbouring channels per infant were repaired using a spline interpolation (Perrin
et al., 1989). The 28 final electrodes were rereferenced to the common average of all elec-
trodes. Finally, epochs were demeaned and all EEG epochs containing fluctuations ±150 μV
were excluded using automatic artifact rejection. Only infants with at least 30 artifact-free
epochs were included in the final analysis. Since only 9 infants provided usable EEG data
for both age points, we decided to use only one EEG data set per infant. Per infant, we
included the data set with more artifact-free epochs, either from 10 months (n= 18) or from
14 months (n= 23), in our final analysis. On average, infants contributed 98 artifact-free
epochs to the analysis.
Analysis
Speech-brain coherence
Speech-brain coherence was established by first computing the speech envelope of the
stimuli using a Hilbert transform with a 4th-order Butterworth filter. Then, we took the
Fourier transform of both the speech envelope and the EEG data from 1 to 15 Hz (with a
frequency resolution of 0.33 Hz), which corresponds to the most important linguistic prop-
erties in our stimuli. Coherence was computed as the cross-spectrum between EEG electrode
signal xand speech signal y, normalized by the power spectra of these signals (Rosenberg
et al., 1989).
Cohxy ¼Sxy




ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Sxx
hi
Syy

q
The coherence values reflect the consistency of the phase difference between the two signals
at a given frequency. Importantly, this means that we directly look at the synchronization
between speech and brain activity (a similar approach has been used in Peelle et al., 2013).
To analyze the presence of speech-brain coherence, we compared the observed speech-
brain coherence to surrogate data. This was computed by shuffling the speech envelope across
epochs and computing the average coherence over 100 pairings of a random speech envelope
with the EEG data. We then used a cluster-based permutation test to analyze the coherence
difference between the observed and the surrogate data in the frequency range from 1 to
15 Hz, allowing us to assess all frequencies within one single test (Maris & Oostenveld, 2007).
Relationship speech-brain coherence with behavior
The relationship between speech-brain coherence and the behavioral measures was analyzed
in R 3.5.1 (R Core Team, 2018) with RStudio 1.1.456 (RStudio Team, 2016). All graphs were
created using the ggplot (Wickham, 2016) and the gghalves (Tiedemann, 2020) packages.
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For the analysis, we first normalized the coherence values to ensure that different numbers
of trials per child did not influence our result (see Bastos & Schoffelen, 2016). For normaliza-
tion, we used the following formula:
Coherencenormalized ¼Coherenceobserved −Coherencesurrogate
Coherenceobserved þCoherencesurrogate
We then averaged the normalized coherence values across all electrodes within the three
frequency bands of interest: The stressed syllable rate (1–3 Hz), the syllable rate (3–5 Hz),
and the phonological rate (5–15 Hz), leading to one coherence value per frequency band
per infant.
To test for a group difference between HL and LL infants, we first ran a repeated-measures
analysis of variance (ANOVA) using coherence as dependent variable, frequency band
(stressed syllable/syllable/phonological) as within-subjects factor, and likelihood group (low/
high) and age group (10 m/14 m) as between-subject factors.
To test for a relationship between coherence and behavior, we ran separate linear regres-
sion models using the receptive vocabulary percentile on the CDI, the productive vocabulary
percentile on the CDI, and the comparison scores of the ADOS as dependent variables. Since
the range of autism symptoms in the LL group was very low (see Figure 5A), the last model was
only run in the HL group. Because the coherence measures across the different frequency
bands are correlated, we entered the predictors in three steps for each regression model. Given
the limited research on speech tracking in infancy, we entered the coherence rates in order of
the importance of the different acoustic cues for language development. In the first step, we
added: Coherence in the stressed syllable rate, the interaction between coherence and age
group, and the interaction between coherence and likelihood group (only for the language
models). We first entered coherence in the stressed syllable rate, since prior research estab-
lished a relationship between word segmentation of trochaic words and vocabulary develop-
ment (Junge et al., 2012;Jusczyk, 1999). In the second step, we added coherence in the
phonological rate, and its interactions with both age group and likelihood group. Prior
research established a relationship between phonetic perception and language development
(Kuhl et al., 2008). In the third step, coherence in the syllable rate as well as its interactions
with age group and likelihood group were added to the model. Models were compared using
the ANOVA function and new predictors were only retained if they significantly improved the
model fit. In addition, we used the caret package (Kuhn, 2008) to perform Monte Carlo cross-
validation (with 200 repetitions, each holding back 20% of the sample) and assess the gener-
alizability of the regression models (de Rooij & Weeda, 2020;Song et al., 2021). For follow-up
analyses yielding significant effects on the group level we used leave-one-out cross-validation
to account for the small group sizes.
RESULTS
Speech-Brain Coherence
Speech-brain coherence was significantly higher for the observed data than for the surrogate
data (p< 0.001). In the cluster-based permutation analysis, one large cluster emerged that
included all electrodes in the frequencies from 1 to 15 Hz, covering the phonological, syllable,
and stressed syllable ranges. This indicates that across the groups, infants showed tracking of
sung nursery rhymes.
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Relationship Speech-Brain Coherence and Behavior
Group differences
Speech-brain coherence in the HL group did not significantly differ from speech-brain coher-
ence in the LL group. The repeated-measures ANOVA showed no significant main effect of
likelihood group, F(1, 37) = 0.22, p= 0.6385, and age group, F(1, 37) = 0.002, p=
0.9626, and no significant interactions, all Fs < 0.36. There was a significant main effect of
frequency rate, F(2, 74) = 26.36, p< 0.0001, indicating that mean coherence values differed
between the frequency rates. Follow-up ttests showed that normalized coherence in the
stressed syllable rate (M= 0.61, SD = 0.05) was significantly lower compared to the syllable
rate (M= 0.69, SD = 0.07), t(40) = −5.83, p< 0.0001, and the phonological rate (M= 0.66,
SD = 0.04), t(40) = −9.23, p< 0.0001. The syllable and the phonological rate did not signif-
icantly differ, t(40) = 1.31, p= 0.199. Figure 2 shows the distribution of coherence scores in
the frequencies of interest for both likelihood groups separately.
Vocabulary
Figure 3A shows the distribution of CDI percentile scores for receptive vocabulary for both
likelihood groups. Descriptively, the LL group had higher receptive vocabulary (M=55.5,
SD = 33.7) than the HL group (M= 33.85, SD = 34). This difference was not statistically sig-
nificant, t(32) = 1.83, p= 0.076. Results of the first step of the linear regression indicated a
significant model fit, F(3, 30) = 4.6, p= 0.0091, R
CV2
= 0.41, RMSE
CV
= 28.84. Further exam-
ination of the individual predictors showed that receptive vocabulary was significantly pre-
dicted by coherence in the stressed syllable rate, t= 3.65, p< 0.001, the interaction between
coherence in the stressed syllable rate and age group, t=−3.33, p= 0.0023, and the interac-
tion between coherence in the stressed syllable rate and likelihood group, t=−2.47, p=
0.0195. Figures 3B–Cpresent the data for the relationship between receptive vocabulary
and speech-brain coherence split by age group and likelihood group, respectively. Post hoc
analyses showed the correlation was significant for the 10-month-olds, r(9) = 0.71, p= 0.0134,
Figure 2. Coherence values for the HL and the LL group in (A) the stressed syllable rate (1–3 Hz), (B) the syllable rate (3–5 Hz), and (C) the
phonological rate (5–15 Hz). Dots depict individual data points.
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R
CV2
= 0.349, RMSE
CV
= 29.22, but not the 14-month-olds, r(21) = 0.05, p= 0.834. The
correlation for the likelihood groups were both non-significant (LL: r(12) = −0.16, p=
0.5783; HL: r(18) = 0.29, p= 0.2117). There was one outlier in the HL group. Removal of this
value did not change the pattern of results so we decided to include it in the analyses reported
here. In the second step of the model, inclusion of phonological coherence and its interactions
with age and likelihood group did not significantly improve the fit of the model, F(3, 27) = 0.75,
p= 0.5333, and had lower generalizability, R
CV2
= 0.28, RMSE
CV
= 33.12. Coherence in the
phonological rate was not predictive of receptive vocabulary, t= 1.03, p= 0.3108, nor was the
interaction between phonological rate and age group, t=−1.46, p= 0.1557, or likelihood
group, t= 0.15, p= 0.8785. Since the second model did not significantly improve the fit over
the first model, we compared the fit of the third model in the next step to the first model again.
Model comparisons showed that the addition of coherence in the syllable rate and its interac-
tions with age and likelihood group did not significantly improve the model fit, F(3, 24) = 0.59,
p= 0.6288, and decreased model generalizability, R
CV2
= 0.27, RMSE
CV
= 32.65. Inspection of
the individual predictor terms found no significant effect of coherence in the syllable rate on
receptive vocabulary, t=−0.05, p= 0.9627, nor of its interactions with age group, t=−0.42, p=
0.6756, or likelihood group, t=−0.37, p= 0.7145. The results indicate a relationship between
coherence specifically in the stressed syllable range (1–3 Hz) and the development of receptive
vocabulary. The interactions indicate that coherence in the stressed syllable rate was a predictor
for receptive vocabulary for 10-month-olds but possibly not for 14-month-olds (see Figure 3B).
In addition, the relationship between tracking in the stressed syllable rate and perceptive vocab-
ulary was possibly stronger in the high-likelihood group compared to the low-likelihood group
(see Figure 3C), but note that the post hoc tests were not significant in either group.
For productive vocabulary, the results were similar to those for receptive vocabulary, as
depicted in Figure 4. Productive vocabulary was significantly higher in the LL group (M
LL
=
57.79, SD = 34.35) than in the HL group (M
HL
= 27, SD = 29.44), t(32) = 2.35, p= 0.0253. The
first step of the regression showed a significant model fit, F(3, 30) = 3.6, p= 0.0247, R
CV2
=
Figure 3. Relationship between coherence in infancy and receptive vocabulary in childhood. (A) Distribution of CDI receptive vocabulary
percentiles for both likelihood groups. (B) Relationship between receptive vocabulary on the CDI at 24 months and speech-brain coherence in
the stressed syllable rate (1–3 Hz) by age group. (C) Relationship between speech-brain coherence in the stressed syllable rate and receptive
vocabulary by likelihood group.
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0.292, RMSE
CV
= 30.51. Inspection of the individual predictors showed that coherence in the
stressed syllable rate was a significant predictor of productive vocabulary, t= 2.97, p= 0.0059.
In addition, we found a significant interaction between coherence in the stressed syllable rate
and age group, t=−2.36, p= 0.0248, and the interaction between coherence in the stressed
syllable rate and likelihood group trended toward significance, t=−1.98, p= 0.0568. Post hoc
analyses showed that the correlation was significant for the high-likelihood group, r(18) =
0.50, p= 0.0235, but had a low generalizability R
CV2
= 0.02, RMSE
CV
= 28.45, and was
not significant for the low-likelihood group, r(12) = −0.06, p= 0.8276. The correlation
approached significance for the 10-month-olds, r(9) = 0.59, p= 0.058, R
CV2
= 0.2, RMSE
CV
=
33.44, and was not significant for the 14-month-olds, r(21) = 0.26, p= 0.2298. Inclusion of
coherence in the phonological rate and its interactions with age and likelihood group did not
significantly improve model fit, F(3, 27) = 0.88, p= 0.4623, and decreased generalizability,
R
CV2
= 0.27, RMSE
CV
= 34.16. Inspection of the new predictors in the second step showed that
neither coherence in the phonological rate, t= 0.83, p= 0.4114, nor its interactions with age,
t=−0.74, p= 0.4643, or likelihood group, t=−1.31, p= 0.2016, significantly predicted pro-
ductive vocabulary. The inclusion of coherence in the syllable rate and its interactions with
age group and likelihood group in the third step did not significantly improve model fit com-
pared to the first model, F(3, 27) = 1.02, p= 0.4004, and led to a lower generalizability, R
CV2
=
0.23, RMSE
CV
= 35.1. Inspection of the individual new predictors did not show a significant
effect of coherence in the syllable rate on productive vocabulary, t=−1.29, p= 0.2097, nor a
significant interaction of coherence in the syllable rate with age group, t= 0.33, p= 0.7427, or
likelihood group, t= 1.13, p= 0.2696.
Note we always assessed the average of the speech-brain-coherence across electrodes to
increase power. For exploratory purposes, topographic maps displaying the correlations
between stressed syllable speech-brain coherence and vocabulary are shown in Figure S1
(Supporting Information can be found at https://doi.org/10.1162/nol_a_00074). As we
included stressed syllable rate first, it might be that the other rates are explaining the same
Figure 4. Relationship between coherence in infancy and productive vocabulary in childhood. (A) Distribution of CDI productive vocabulary
percentiles for both likelihood groups. (B) Relationship between speech-brain coherence and productive vocabulary by age group. (C) Rela-
tionship between speech-brain coherence and productive vocabulary by likelihood group.
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variance, but no additional variance, and because of that they turned out to be non-significant
predictors. To check for this possibility, we ran models predicting receptive and productive
vocabulary including only phonological rate or only syllable rate and their respective interac-
tions with age and likelihood group as predictors. The models did not reach significance, all
ps > 0.157, suggesting that the identified relationships with vocabulary were indeed specific to
the stressed syllable rate.
Autism symptoms
Figure 5A depicts the distribution of ADOS scores for both likelihood groups. We only tested
the relation between ADOS scores and speech-brain coherence in the HL group. The model fit
for the first model predicting ADOS scores was not significant, F(2, 15) = 0.06, p= 0.9394.
Inspection of the individual predictors showed no significant main effect of coherence in the
stressed syllable rate, t=−0.01, p= 0.9891, and no interaction between coherence in the
stressed syllable rate and age group, t=−0.08, p= 0.9402. The inclusion of phonological
coherence, t= 0.22, p= 0.8298, and its interaction with age group, t=−0.206, p=
0.8398, did not significantly improve the model fit, F(2, 13) = 0.02, p= 0.9759. In the third
step, adding coherence in the syllable rate, t= 1.3, p= 0.2165, and its interaction with age
group, t=−1.32, p= 0.2107, did not improve model fit compared to the first step, F(2, 13) =
0.91, p= 0.4253. The relationship between coherence in the different frequency rates and
ADOS scores is depicted in Figure 5B–D.
DISCUSSION
The current study investigated the relationship between neural tracking in infancy and devel-
opment of vocabulary and autism symptoms in early childhood. We expected that infants with
a high likelihood for autism would show decreased speech-brain coherence compared to a
low-likelihood comparison group. In addition, we expected that increased speech-brain
Figure 5. Relationship between coherence in infancy and autism symptoms in childhood. (A) presents the distribution of ADOS scores in the
HL and the LL groups. (B) shows the data for the relationship between speech-brain coherence in the stressed syllable rate (1–3 Hz) and the
ADOS score for the HL group. (C) shows the relationship between speech-brain coherence in the syllable rate (3–5 Hz) and the ADOS score.
(D) shows the relationship between speech-brain coherence in the phonological rate (5–15 Hz) and the ADOS score.
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coherence in infancy would be related to better receptive and productive vocabulary at
24 months and fewer autism symptoms at 36 months.
We identified speech-brain coherence to sung nursery rhymes in infants. Overall, infants
showed more coherence between the speech envelope and EEG data than expected by
chance across all tested frequencies (1–15 Hz) and electrodes. Speech-brain coherence to
our sung nursery rhymes might be larger than if we had used spoken stimuli, as results from
Vanden Bosch der Nederlanden et al. (2020) suggest that the regular rhythm of songs can aid
phase-locking compared to speech.
We found no evidence for a difference in speech-brain coherence between the HL and LL
groups and no support for a relationship between speech-brain coherence and the later ADOS
score in the HL group. Importantly, we did observe a significant relationship between speech-
brain coherence and later vocabulary development. Infants with higher speech-brain coherence
in the stressed syllable rate showed higher receptive and productive vocabulary. Follow-up
correlation analyses only showed evidence for this effect in the 10-month-old group but no
evidence for such an effect in the 14-month-old group. The relationship between coherence
and vocabulary also seemed to be stronger for the high-likelihood group compared to the
low-likelihood group, but this should be interpreted with care, as follow-up correlations were
non-significant for both groups.
Tentatively, the relationship between tracking of stressed syllables and vocabulary might be
based on individual differences in infants’word segmentation skills, which then predict later
vocabulary development (Junge et al., 2012;Kooijman et al., 2013). In stress-based languages
like English or Dutch, stressed syllables can provide a valuable cue for segmenting words from
continuous speech (Jusczyk, 1999), as the majority of content words in these languages have
word-initial stress (Cutler & Carter, 1987;Stärk et al., 2021). This effect may be even stronger in
infant-directed speech, as caregivers increase amplitude modulations in the prosodic stress
rate when addressing infants (Leong et al., 2017) and it was shown that infants’tracking is
sensitive to this adaptation (Menn et al., 2022). High speech-brain coherence indicates an
alignment between peaks in neural activity and relevant input (Schroeder & Lakatos, 2009)
such as stressed syllables and may thus aid or reflect word segmentation. This idea is sup-
ported by a recent study showing a relation between infants’speech-brain coherence at the
stressed syllable rate and word-segmentation performance (Snijders, 2020). In the current
study, we provide evidence for a long-term relationship between higher tracking in infancy
and vocabulary development.
While acoustic cues may be initially beneficial for speech segmentation, listeners must also
use different cues for word segmentation, as there is no perfect relationship between acoustic
and linguistic units. Research has shown that adults employ linguistic knowledge, most impor-
tantly lexical knowledge, for top-down word segmentation (Cole & Jakimik, 1980;Marslen-
Wilson & Welsh, 1978). This indicates that there is a transition from bottom-up to top-down
word segmentation during language development, as linguistic knowledge increases (Kidd
et al., 2018). There are some indications that lexical knowledge can top-down influence track-
ing, at least for artificial language learning. For example, Choi et al. (2020) tested infants in a
statistical learning paradigm in which they presented 6-month-olds with trisyllabic pseudo-
words concatenated to syllable strings. While infants initially phase-locked to the syllable rate,
they progressed to phase-locking to the trisyllabic word rate over the course of the familiari-
zation phase. A transition from bottom-up to top-down word segmentation could explain the
interaction between age and speech-brain coherence in the stressed syllable rate for predicting
vocabulary development, as observed in the current study. Bottom-up word segmentation
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based on acoustic cues may still be beneficial for 10-month-olds, who do not yet have much
lexical knowledge, and stronger tracking at this age predicts larger later vocabulary. On the
other hand, 14-month-olds have acquired more lexical knowledge and may thus shift from
bottom-up to top-down word segmentation of continuous speech. Higher speech-brain coher-
ence would therefore indicate better word segmentation and later vocabulary development in
the younger age group, but not in the older age group. Note that at this point this interpretation
is rather speculative and needs to be corroborated in the future. Also keep in mind that
the final included sample to assess the relationship with vocabulary was rather small (11
10-month-olds), so replication is necessary.
However, following this explanation, it may be the case that infants who are delayed in
their language development also transition later from bottom-up to top-down word segmenta-
tion. Such a delay could explain the interaction between likelihood group and tracking in the
stressed syllable rate for predicting vocabulary knowledge. If the low-likelihood group transi-
tions from bottom-up to top-down speech segmentation earlier, tracking of the stressed syllable
rate could be more predictive of their vocabulary development at 10 months and less predic-
tive at 14 months of age. For the high-likelihood group, a later transition would mean that
tracking in the stressed syllable rate stays predictive for their vocabulary development longer.
It is also possible that autistic children focus more on acoustic cues in general. In line with this,
Pomper et al. (2021) showed that autistic toddlers rely more on coarticulation cues during
lexical processing than non-autistic toddlers. Both of these explanations are rather speculative
at this moment, as our sample size did not allow us to test for a three-way interaction between
likelihood group, age group, and speech-brain coherence. It is also possible that the interac-
tion between likelihood group and speech-brain coherence in the stressed syllable rate is
based on higher heterogeneity in vocabulary scores in the high-likelihood group.
The relationship between tracking in the stressed syllable rate and vocabulary development
may also be explained by other factors than differential use of acoustic cues, such as differences
in audiovisual speech processing or selective attention. Infants start to integrate visual informa-
tion concurrent with speech at an early age (Rosenblum et al., 1997), and better audiovisual
integration in infancy predicts better language development (Kushnerenko et al., 2013). In addi-
tion, infants with an older autistic sibling show decreased audiovisual integration (Guiraud
et al., 2012). Such differences in audiovisual integration of speech information may also affect
neural tracking of speech. Past research has shown that visual information increases speech
tracking (Crosse et al., 2015;Golumbic et al., 2013;Power et al., 2013), either by enhancing
acoustic processing itself or by providing additional information the brain tracks such as the
rhythm of lip movements (Bourguignon et al., 2020;Park et al., 2016,2018). The facilitation
of tracking by visual information was shown to be especially strong in preverbal infants (Tan
et al., 2022). Since the current study presented the nursery rhymes as videos, which included
gestures and other facial information of the speaker during the presentation, we cannot exclude
the possibility that differences in audiovisual integration between infants may have contributed
to our findings. Another possibility is that we measured differences in attentional resources.
Neural tracking is affected by attention (Fuglsang et al., 2017) and reflects the selection of rel-
evant attended information (Obleser & Kayser, 2019). It is thus possible that the relationship
between tracking in the stressed syllable rate and later vocabulary reflects individual differences
in general attention abilities between the infants. Tentative evidence for this comes from the fact
that infants’attention to speech as well as specifically to lexical stress predicts later vocabulary
(Ference & Curtin, 2013;Vouloumanos & Curtin, 2014). Future research should specify how the
use of video affects infants’speech-brain coherence compared to audio-only stimuli and how
speech-brain coherence in infants is affected by selective attention.
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Contrary to our predictions, we did not find evidence for a relationship between tracking of
sung nursery rhymes in infancy and autism symptoms. This is surprising, given that autistic
children often have language impairments (Belteki et al., 2022) and we find a relationship
between tracking and language development. One reason could be, that speech-brain coher-
ence only captures the language component of autism symptoms, whereas the ADOS captures
a broad range of autism symptoms. Tracking of speech might be more sensitive to the devel-
opment of language specific impairments than to general autism symptoms.
Nevertheless, the data of this developmental study is not in line with the findings by Jochaut
et al. (2015), who find a relationship between speech tracking and ADOS scores in their sam-
ple of 13 autistic adults. This discrepancy could be explained in different ways. First of all, the
null effect could be caused by low power. Despite large variability in ADOS scores, our final
analysis included only six children with indications of autism and two who met the diagnostic
criterion of autism on the ADOS. This sample might be too small to find a relationship, espe-
cially if the relationship shows a similar age-related modulation as we observed for language
development. The relationship between tracking and autism symptoms might emerge in a big-
ger data set with more children who meet the diagnostic criteria for autism. A second possible
explanation is that the two groups may have differed in their tracking of spoken stimuli, but
that the song modality used in the current study provides additional prosodic cues that make it
easier for the HL group to track (Audibert & Falk, 2018;Vanden Bosch der Nederlanden et al.,
2020). Thirdly, it is possible that the difference in tracking in autistic individuals only emerges
after infancy. During childhood, there are still many developmental changes that affect neural
oscillations (Maguire & Abel, 2013), and autism has been linked to differences in the devel-
opment of key brain structures and neurotransmitters during childhood and adolescence
(Courchesne et al., 2007;Van Rooij et al., 2018). Changes in tracking could thus still emerge
after infancy. A fourth possible explanation for the difference with the findings by Jochaut et al.
(2015) is that the ADOS score might primarily be related to the interactions between different
oscillatory frequencies (Arnal & Giraud, 2012). During oscillatory nesting, lower-frequency
oscillations influence the amplitude of higher-frequency oscillations. While Jochaut et al.
(2015) found a difference for tracking in the theta band between autistic and non-autistic
adults, individual measures of autism symptoms were related to an atypical interaction
between theta and gamma oscillations. The limited data available in our study did not allow
us to precisely replicate this analysis (Tort et al., 2010).
While we saw a developmental pattern in the relationship between tracking and language
acquisition, our cross-sectional analysis makes it difficult to draw conclusions about the tem-
poral development of tracking during infancy. Future studies should focus on the individual
development of tracking, both in younger age groups (while bottom-up segmentation strategies
are still developing) and as children acquire more linguistic knowledge. Furthermore, it would
be very interesting to investigate how within-subject changes in tracking during infancy pre-
dict later language development. Such research could further test the theory that infants tran-
sition from using bottom-up cues to top-down cues for word segmentation from continuous
speech. The current study contributes an empirical foundation for such future investigations,
by relating tracking in infancy to language development in early childhood but also showing
that this relationship might depend on age and linguistic ability.
Conclusion
This study focused on neural tracking of sung nursery rhymes in infancy and its relationship to the
development of vocabulary and autism symptoms in childhood. We analyzed a data set of infants
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with high- and low-likelihood for autism. With this study, we replicate earlier studies indicating
that infants’neural activity tracks speech. Most importantly, we show that tracking of nursery
rhymes during infancy is predictive for later vocabulary development. This finding sheds new
light on the importance of oscillatory brain activity in infancy for first language acquisition.
ACKNOWLEDGMENTS
The authors would like to thank all the families who participated in this research, as well as
Loes Vinkenvleugel and Yvette De Bruijn for their assistance with running the project, and Lars
Meyer for his valuable feedback on an earlier version of this manuscript. This work has been
supported by the EU-AIMS (European Autism Interventions) and AIMS-2-TRIALS programmes,
which receive support from Innovative Medicines Initiative Joint Undertaking Grant No.
115300 and 777394, the resources of which are composed of financial contributions from
the European Union’s FP7 and Horizon 2020 Programmes, from the European Federation of
Pharmaceutical Industries and Associations (EFPIA) companies’in-kind contributions, and
from AUTISM SPEAKS, Autistica and SFARI; by the Horizon 2020 supported programme
CANDY Grant No. 847818; and by the Horizon 2020 Marie Sklodowska-Curie Innovative
Training Network 642996, BRAINVIEW. The funders had no role in the design of the study;
in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the
decision to publish the results. Any views expressed are those of the author(s) and not neces-
sarily those of the funders.
FUNDING INFORMATION
Jan Buitelaar, Innovative Medicines Initiative Joint Undertaking, Award ID: 115300. Jan
Buitelaar and Sabine Hunnius, Innovative Medicines Initiative Joint Undertaking, Award ID:
77394. Jan Buitelaar and Sabine Hunnius, Horizon 2020 Marie Sklodowska-Curie Innovative
Training Network, Award ID: 642996. Jan Buitelaar and Sabine Hunnius, Horizon 2020
CANDY, Award ID: 847818.
AUTHOR CONTRIBUTIONS
Katharina H. Menn: Conceptualization: Equal; Formal analysis: Lead; Software: Equal;
Visualization: Lead; Writing –original draft: Lead; Writing –review & editing: Lead. Emma
K. Ward: Data curation: Equal; Investigation: Equal; Project administration: Equal; Software:
Supporting; Writing –review & editing: Equal. Ricarda Braukmann: Investigation: Equal; Project
administration: Equal; Software: Equal. Carlijn van den Boomen: Data curation: Equal; Inves-
tigation: Equal; Project administration: Equal; Resources: Equal; Software: Equal; Writing –
review & editing: Equal. Jan Buitelaar: Conceptualization: Supporting; Funding acquisition:
Lead; Resources: Equal; Supervision: Supporting; Writing –review & editing: Equal. Sabine
Hunnius: Conceptualization: Supporting; Funding acquisition: Equal; Resources: Equal; Super-
vision: Supporting; Writing –review & editing: Equal. Tineke M. Snijders: Conceptualization:
Lead; Formal analysis: Equal; Resources: Equal; Software: Equal; Supervision: Lead; Writing –
original draft: Equal; Writing –review & editing: Lead.
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Neural tracking in infancy predicts language development
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