ARTÍCULO DE INVESTIGACIÓN

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

Katharina H. Menn1,2,3,4,5

, Emma K. Ward2

Carlijn van den Boomen6
Sabine Hunnius2

, and Tineke M. Snijders1,2,8

, Ricarda Braukmann2
,

, Jan Buitelaar2,7

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1Max Planck Institute for Psycholinguistics, Nimega, Los países bajos
2Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nimega, Los países bajos
3Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Alemania
4Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Alemania
5International Max Planck Research School on Neuroscience of Communication: Function, Estructura,
and Plasticity, Leipzig, Alemania
6Department of Experimental Psychology, Helmholtz Institute, Universidad de Utrecht, Utrecht, Los países bajos
7Department of Cognitive Neuroscience, Radboud University Medical Center, Nimega, Los países bajos
8Cognitive Neuropsychology Department, Tilburg University

Palabras clave: autism, neural oscillations, speech segmentation, word learning, speech entrainment,
speech processing

ABSTRACTO

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. En el estudio actual, we focused on children
with a family history of autism, who often show a delay in first language acquisition. Nosotros
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 o 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 meses. 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. En tono rimbombante, 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.
De este modo, early tracking of sung nursery rhymes is related to language development in childhood.

INTRODUCCIÓN

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. Este
process is referred to as neural tracking and is directly linked to language comprehension

un acceso abierto

diario

Citación: Menn, k. h., Ward, mi. K.,
Braukmann, r., van den Boomen, C.,
Buitelaar, J., Hunnius, S., & Snijders,
t. METRO. (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

Recibió: 16 Septiembre 2021
Aceptado: 16 Puede 2022

Conflicto de intereses: Los autores tienen
declaró que no hay intereses en competencia
existir.

Autor correspondiente:
Katharina H. Menn
menn@cbs.mpg.de

Editor de manejo:
Marcela Peña Garay

Derechos de autor: © 2022
Instituto de Tecnología de Massachusetts
Publicado bajo Creative Commons
Atribución 4.0 Internacional
(CC POR 4.0) licencia

La prensa del MIT

Neural tracking in infancy predicts language development

(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
desarrollo (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
Asociación, 2013). Our research focuses on the communication aspect, which is often char-
acterized by differences in expressive language as well as language comprehension difficul-
corbatas. 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, ver: Eigsti et al., 2011; Groen et al.,
2008). Sin embargo, 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 (saldo E/I) 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, cognitivo, 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)
fuerza (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 (abajo 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
y 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. Además, Jochaut et al. (2015) ana-
lyzed individual differences between participants and found a positive correlation between

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Neural tracking in infancy predicts language development

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
niños. 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
comprensión. Adults rely mostly on linguistic knowledge for speech segmentation (Marslen-
wilson & galés, 1978), but infants who still lack the required knowledge need to rely on other
señales. 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. Fundamentalmente, the impor-
tance of acoustic cues for speech segmentation has been shown to decrease with age, como
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 y 14 meses, 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. De este modo, 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, sin embargo, 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 a 7 años (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-
hermana, 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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Neural tracking in infancy predicts language development

behavioral scores of vocabulary at 24 months and autism symptoms at 36 meses. 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 meses. 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.

MATERIALES Y MÉTODOS

Participantes

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. Tal como, the visual environment was similar for all children
(ver, p.ej., 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. Cifra 1 displays the final sample of infants after exclu-
sión, as well as the number and reasons for exclusions per age point. Since only 9 infantes
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), y 31 had autism measures at 36 meses (18 HL, 13 LL). Mesa 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

Estímulos

The stimuli consisted of five sung nursery rhymes that are highly familiar to Dutch infants
(Jones et al., 2019): “Dit zijn mijn wangetjes” (traducción: These are my cheeks; duración:
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-
turas. The total duration of the video recordings was 69 artículos de segunda clase. To identify the most impor-
tant amplitude modulation frequencies in the speech envelope in our stimuli, we transcribed

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Cifra 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) infantes. Not all infants provided follow-up measures for vocab-
ulary size (CDI) or autism symptoms (ADOS).

the duration of all stressed syllables, syllables and phonemes using Praat (campesinos, 2001). En
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 y 15 Hz. Además, we also looked at infants’ tracking in the
frequency rate from 3 a 5 Hz, which mostly captures the syllables. Tenga en cuenta que 85% de todo

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Mesa 1. Demographics of the children included in the final analysis per testing

Likelihood
HL

LL

norte
8

10

EEG: 10 meses
Age (Dakota del Sur)
10,26 (0, 72)

Sex (F:metro)
5:3

10 (0, 6)

4:6

EEG: 14 meses
Age
14,06 (0, 5)

14,45 (0, 6)

norte
14

9

Sex
8:6

4:5

norte
20

14

CDI: 24 meses
Age
24,7 (1)

Sex
13:7

ADOS: 36 meses
Age
38,8 (5)

Sex
13:5

norte
18

24,8 (1, 3)

6:8

13

38,4 (3)

6:7

Nota. HL: High-likelihood infants. LL: Low-likelihood infants. SDCDI: Vocabulary size. ADOS: Autism symptoms.

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Neural tracking in infancy predicts language development

syllables in the stimuli occurred within 1.7–6 Hz, but we limited the syllable rate to 3–5 Hz to
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 meses. It is a parent report measure of both
receptive and productive vocabulary with high reliability (zinc & Lejaegere, 2002). El
CDI was filled in by one of the child’s caregivers when the child was approximately 24 meses
viejo. 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
niño, Module 1 or Module 2 of the test was administered by a trained psychologist. For our
analiza, we used the comparison scores, which allow a reliable comparison of performance
on the different modules. The scores range from 1 a 10, with scores from 4 a 7 sugerencia
medium indication for autism and scores of 8 or more suggesting high indications for autism.

Procedimiento

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
píxeles) on which the stimuli were presented. The nursery rhymes were presented three times
during a session, leading to a total duration of 207 artículos de segunda clase. They were shown as part of a
larger experiment intermixed with other experimental conditions. The total experiment took
acerca de 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, a saber: 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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Neural tracking in infancy predicts language development

análisis (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
en 45 Hz. Próximo, 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). El 28 final electrodes were rereferenced to the common average of all elec-
trodes. Finalmente, 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, nosotros
included the data set with more artifact-free epochs, either from 10 meses (norte = 18) or from
14 meses (norte = 23), in our final analysis. De término medio, infants contributed 98 artifact-free
epochs to the analysis.

Análisis

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. Entonces, we took the
Fourier transform of both the speech envelope and the EEG data from 1 a 15 Hz (con un
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 x and speech signal y, normalized by the power spectra of these signals (Rosenberg
et al., 1989).

Cohxy ¼

(cid:4)
(cid:4)

(cid:4)
(cid:1)
(cid:3)
(cid:4)
Sxy
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
q
(cid:3)
(cid:1)
i (cid:2) Syy
h
Sxx

The coherence values reflect the consistency of the phase difference between the two signals
at a given frequency. En tono rimbombante, 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 a
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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Neural tracking in infancy predicts language development

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-
ción, we used the following formula:

Coherencenormalized ¼ Coherenceobserved − Coherencesurrogate
Coherenceobserved þ Coherencesurrogate

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/
alto) 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. Desde
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, nosotros
added: Coherence in the stressed syllable rate, the interaction between coherence and age
grupo, and the interaction between coherence and likelihood group (only for the language
modelos). 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-
mento (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. Previo
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. Además, we used the caret package (Kuhn, 2008) to perform Monte Carlo cross-
validation (con 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.

RESULTADOS

Speech-Brain Coherence

Speech-brain coherence was significantly higher for the observed data than for the surrogate
datos (pag < 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. Neurobiology of Language 502 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u n o / l / l a r t i c e - p d f / / / / 3 3 4 9 5 2 0 3 9 9 9 4 n o _ a _ 0 0 0 7 4 p d / . l f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Neural tracking in infancy predicts language development 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 t tests 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 2 = 0.41, RMSECV = 28.84. Further exam- significant model fit, F(3, 30) = 4.6, p = 0.0091, RCV 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–C present 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. Neurobiology of Language 503 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u n o / l / l a r t i c e - p d f / / / / 3 3 4 9 5 2 0 3 9 9 9 4 n o _ a _ 0 0 0 7 4 p d / . l f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Neural tracking in infancy predicts language development 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. 2 = 0.349, RMSECV = 29.22, but not the 14-month-olds, r(21) = 0.05, p = 0.834. The RCV 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, 2 = 0.28, RMSECV = 33.12. Coherence in the p = 0.5333, and had lower generalizability, RCV 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, 2 = 0.27, RMSECV = 32.65. Inspection of p = 0.6288, and decreased model generalizability, RCV 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 (MLL = 57.79, SD = 34.35) than in the HL group (MHL = 27, SD = 29.44), t(32) = 2.35, p = 0.0253. The 2 = first step of the regression showed a significant model fit, F(3, 30) = 3.6, p = 0.0247, RCV Neurobiology of Language 504 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u n o / l / l a r t i c e - p d f / / / / 3 3 4 9 5 2 0 3 9 9 9 4 n o _ a _ 0 0 0 7 4 p d / . l f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Neural tracking in infancy predicts language development 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. 0.292, RMSECV = 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) = 2 = 0.02, RMSECV = 28.45, and was 0.50, p = 0.0235, but had a low generalizability RCV not significant for the low-likelihood group, r(12) = −0.06, p = 0.8276. The correlation 2 = 0.2, RMSECV = approached significance for the 10-month-olds, r(9) = 0.59, p = 0.058, RCV 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, 2 = 0.27, RMSECV = 34.16. Inspection of the new predictors in the second step showed that RCV 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- 2 = pared to the first model, F(3, 27) = 1.02, p = 0.4004, and led to a lower generalizability, RCV 0.23, RMSECV = 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 Neurobiology of Language 505 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u n o / l / l a r t i c e - p d f / / / / 3 3 4 9 5 2 0 3 9 9 9 4 n o _ a _ 0 0 0 7 4 p d / . l f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Neural tracking in infancy predicts language development 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. 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, pag = 0.9394.
Inspection of the individual predictors showed no significant main effect of coherence in the
stressed syllable rate, t = −0.01, pag = 0.9891, and no interaction between coherence in the
stressed syllable rate and age group, t = −0.08, pag = 0.9402. The inclusion of phonological
coherencia, t= 0.22, pag = 0.8298, and its interaction with age group, t = −0.206, pag =
0.8398, did not significantly improve the model fit, F(2, 13) = 0.02, pag = 0.9759. In the third
step, adding coherence in the syllable rate, t= 1.3, pag = 0.2165, and its interaction with age
grupo, t = −1.32, pag = 0.2107, did not improve model fit compared to the first step, F(2, 13) =
0.91, pag = 0.4253. The relationship between coherence in the different frequency rates and
ADOS scores is depicted in Figure 5B–D.

DISCUSIÓN

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. Además, we expected that increased speech-brain

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Neural tracking in infancy predicts language development

coherence in infancy would be related to better receptive and productive vocabulary at
24 months and fewer autism symptoms at 36 meses.

We identified speech-brain coherence to sung nursery rhymes in infants. En general, infantes
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. En tono rimbombante, 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 & Carretero, 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
estudiar, 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 (Col & Jakimik, 1980; Marslen-
wilson & galés, 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-
En g, at least for artificial language learning. Por ejemplo, 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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Neural tracking in infancy predicts language development

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. Sobre el
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.

Sin embargo, 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-
ción. 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-
ción, 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 (Broncearse
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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Neural tracking in infancy predicts language development

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.

Sin embargo, 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-
ejemplo de 13 autistic adults. This discrepancy could be explained in different ways. First of all, el
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, especialmente-
cially if the relationship shows a similar age-related modulation as we observed for language
desarrollo. 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, pero
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
oscilaciones (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
adultos, 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
adquisición, 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. Además, 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.

Conclusión

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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Neural tracking in infancy predicts language development

with high- and low-likelihood for autism. With this study, we replicate earlier studies indicating
that infants’ neural activity tracks speech. Más importante, 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.

EXPRESIONES DE GRATITUD

The authors would like to thank all the families who participated in this research, así como
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 y 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, y
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, analiza, 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.

INFORMACIÓN DE FINANCIACIÓN

Jan Buitelaar, Innovative Medicines Initiative Joint Undertaking, Award ID: 115300. Ene
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.

CONTRIBUCIONES DE AUTOR

Katharina H. Menn: Conceptualización: Equal; Análisis formal: Lead; Software: Equal;
Visualización: Lead; Escritura – borrador original: Lead; Escritura – revisión & edición: Lead. Emma
k. Ward: Curación de datos: Equal; Investigación: Equal; Administración de proyecto: Equal; Software:
Secundario; Escritura – revisión & edición: Equal. Ricarda Braukmann: Investigación: Equal; Proyecto
administración: Equal; Software: Equal. Carlijn van den Boomen: Curación de datos: Equal; Inves-
tigation: Equal; Administración de proyecto: Equal; Recursos: Equal; Software: Equal; Writing –
revisar & edición: Equal. Jan Buitelaar: Conceptualización: Secundario; Adquisición de financiación:
Lead; Recursos: Equal; Supervisión: Secundario; Escritura – revisión & edición: Equal. Sabine
Hunnius: Conceptualización: Secundario; Adquisición de financiación: Equal; Recursos: Equal; Super-
visión: Secundario; Escritura – revisión & edición: Equal. Tineke M. Snijders: Conceptualización:
Lead; Análisis formal: Equal; Recursos: Equal; Software: Equal; Supervisión: Lead; Writing –
original draft: Equal; Escritura – revisión & edición: Lead.

REFERENCIAS

American Psychiatric Association. (2013). Diagnostic and statistical
manual of mental disorders (5ª edición). https://doi.org/10.1176/appi
.books.9780890425596

anderson, D. K., Lord, C., Risi, S., DiLavore, PAG. S., Shulman, C.,
Torre, A., Welch, K., & Pickles, A. (2007). Patterns of growth
in verbal abilities among children with autism spectrum

disorder. Journal of Consulting and Clinical Psychology, 75(4),
594–604. https://doi.org/10.1037/0022-006X.75.4.594,
PubMed: 17663613

Arnal, l. h., & Giraud, A.-L. (2012). Cortical oscillations and sen-
sory predictions. Tendencias en Ciencias Cognitivas, 16(7), 390–398.
https://doi.org/10.1016/j.tics.2012.05.003, PubMed: 22682813

Neurobiology of Language

510

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
norte
oh

/

yo
/

yo

a
r
t
i
C
mi
–
pag
d

F
/

/

/

/

3
3
4
9
5
2
0
3
9
9
9
4
norte
oh
_
a
_
0
0
0
7
4
pag
d

/

.

yo

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Neural tracking in infancy predicts language development

Attaheri, A., Choisdealbha, Á. NORTE., Di Liberto, GRAMO. METRO., Rocha, S.,
Brusini, PAG., Mead, NORTE., Olawole-Scott, h., Boutris, PAG., Gibbon,
S., williams, I., Grey, C., Flanagan, S., & Goswami, Ud.. (2022).
Delta-and theta-band cortical tracking and phase-amplitude cou-
pling to sung speech by infants. NeuroImagen, 247, Article
118698. https://doi.org/10.1016/j.neuroimage.2021.118698,
PubMed: 34798233

Audibert, NORTE., & Falk, S. (2018). Vowel space and f0 characteristics
of infant-directed singing and speech. In Proceedings of the 19th
international conference on speech prosody (páginas. 153–157). ISCA.
https://doi.org/10.21437/SpeechProsody.2018-31

Bastos, A. METRO., & Schoffelen, J.-M. METRO. (2016). A tutorial review of
functional connectivity analysis methods and their interpreta-
tional pitfalls. Frontiers in Systems Neuroscience, 9, Article 175.
https://doi.org/10.3389/fnsys.2015.00175, PubMed: 26778976
Belteki, Z., Lumbreras, r., Fico, K., Haman, MI., & Junge, C. (2022).
The vocabulary of infants with an elevated likelihood and diag-
nosis of autism spectrum disorder: A systematic review and
meta-analysis of infant language studies using the CDI and MSEL.
International Journal of Environmental Research and Public
H e a l t h , 1 9 ( 3 ) , A r t i c l e 1 4 6 9 . h t t p s : / / d o i . o rg / 1 0 . 3 3 9 0
/ijerph19031469, PubMed: 35162492

campesinos, PAG. (2001). Praat, a system for doing phonetics by com-
puter. Glot International, 5(9–10), 341–345. https://hdl.handle
.net/11245/1.200596

Bölte, S., Marschik, PAG. B., Falck-Ytter, T., Charman, T., Roeyers, h.,
& Elsabbagh, METRO. (2013). Infants at risk for autism: A European
perspective on current status, challenges and opportunities. Euro-
pean Child & Adolescent Psychiatry, 22(6), 341–348. https://doi
.org/10.1007/s00787-012-0368-4, PubMed: 23536211

Bölte, S., & Poustka, F. (2004). Diagnostische Beobachtungsskala
für Autistische Störungen (ADOS): Erste Ergebnisse zur Zuverläs-
sigkeit und Gültigkeit [Diagnostic observational scale for autistic
disorders (ADOS): Preliminary results on reliability and validity].
Zeitschrift für Kinder- und Jugendpsychiatrie und Psychotherapie
[Journal of Child and Adolescent Psychiatry and Psychotherapy],
32(1), 45–50. https://doi.org/10.1024/1422-4917.32.1.45,
PubMed: 14992047

Bortfeld, h., morgan, j. l., Golinkoff, R. METRO., & Rathbun, k. (2005).
Mommy and me: Familiar names help launch babies into
speech-stream segmentation. ciencia psicológica, 16(4),
298–304. https://doi.org/10.1111/j.0956-7976.2005.01531.X,
PubMed: 15828977

Bourguignon, METRO., Baart, METRO., Kapnoula, mi. C., & Molinaro, norte.
(2020). Lip-reading enables the brain to synthesize auditory fea-
tures of unknown silent speech. Revista de neurociencia, 40(5),
1053–1065. https://doi.org/10.1523/JNEUROSCI.1101-19.2019,
PubMed: 31889007

Bruining, h., Piedra dura, r., Juarez-Martinez, mi. l., Sprengers, J.,
Avramiea, A.-E., Simpraga, S., Houtman, S. J., Cabello, S.-S.,
Dallares, MI., Palva, S., Oranje, B., Palva, j. METRO., Mansvelder,
h. D., & Linkenkaer-Hansen, k. (2020). Measurement of
excitation-inhibition ratio in autism spectrum disorder using crit-
ical brain dynamics. Informes Científicos, 10(1), Article 9195.
https://doi.org/10.1038/s41598-020-65500-4, PubMed:
32513931

Cardy, j. mi. o., Flagg, mi. J., Roberts, w., & Roberts, t. PAG. (2005).
Delayed mismatch field for speech and non-speech sounds in
children with autism. NeuroReport, 16(5), 521–525. https://doi
.o rg/10 . 109 7/00 00 17 56-2 00 504 04 0-0 002 1 , Pu bM ed:
15770164

Charman, T., & Baird, GRAMO. (2002). Practitioner review: Diagnosis of
autism spectrum disorder in 2- and 3-year-old children. Diario

of Child Psychology and Psychiatry, 43(3), 289–305. https://doi
.org/10.1111/1469-7610.00022, PubMed: 11944873

Choi, D., Batterink, l. J., Negro, A. K., Paller, k. A., & Werker, j. F.
(2020). Preverbal infants discover statistical word patterns at
similar rates as adults: Evidence from neural entrainment. Psy-
chological Science, 31(9), 1161–1173. https://doi.org/10.1177
/0956797620933237, PubMed: 32865487

Col, R. A., & Jakimik, j. (1980). A model of speech perception. En R. A.
Col (Ed.), Perception and production of fluent speech (páginas. 133–163).
Routledge. https://doi.org/10.4324/9781315638935

Constantino, j. NORTE., zhang, y., Frazier, T., Abbacchi, A. METRO., & Law, PAG.
(2010). Sibling recurrence and the genetic epidemiology of
autism. Revista americana de psiquiatría, 167(11), 1349–1356.
https://doi.org/10.1176/appi.ajp.2010.09101470, PubMed:
20889652

Courchesne, MI., Pierce, K., Schumann, C. METRO., Redcay, MI., Buckwalter,
j. A., Kennedy, D. PAG., & morgan, j. (2007). Mapping early brain
development in autism. Neurona, 56(2), 399–413. https://doi.org
/10.1016/j.neuron.2007.10.016, PubMed: 17964254

Crosse, METRO. J., mayordomo, j. S., & Lalor, mi. C. (2015). Congruent visual
speech enhances cortical entrainment to continuous auditory
speech in noise-free conditions. Revista de neurociencia,
35(42), 14195–14204. https://doi.org/10.1523/ JNEUROSCI
.1829-15.2015, PubMed: 26490860

Cutler, A., & Carretero, D. (1987). The predominance of strong initial syl-
lables in the English vocabulary. Habla y lenguaje informático,
2(3–4), 133–142. https://doi.org/10.1016/0885-2308(87)90004-0
de Rooij, METRO., & Weeda, W.. (2020). Cross-validation: A method
every psychologist should know. Advances in Methods and Prac-
tices in Psychological Science, 3(2), 248–263. https://doi.org/10
.1177/2515245919898466

Dickinson, A., jones, METRO., & milne, mi. METRO. (2016). Measuring neural
excitation and inhibition in autism: Different approaches, diferente
findings and different interpretations. Brain Research, 1648(punto. A),
277–289. https://doi.org/10.1016/j.brainres.2016.07.011,
PubMed: 27421181

Di Liberto, GRAMO. METRO., O'Sullivan, j. A., & Lalor, mi. C. (2015). Bajo-
frequency cortical entrainment to speech reflects phoneme-level
Procesando. Biología actual, 25(19), 2457–2465. https://doi.org
/10.1016/j.cub.2015.08.030, PubMed: 26412129

Di Lorenzo, r., Blasi, A., Junge, C., van den Boomen, C., Van
Rooijen, r., & Kemner, C. (2019). Brain responses to faces and
facial expressions in 5-month-olds: An fNIRS study. Fronteras en
Psicología, 10, Article 1240. https://doi.org/10.3389/fpsyg
.2019.01240, PubMed: 31191416

Ding, NORTE., patel, A. D., Chen, l., mayordomo, h., luo, C., & Poeppel, D.
(2017). Temporal modulations in speech and music. neurociencias-
ence & Revisiones de biocomportamiento, 81(punto. B), 181–187. https://doi
.org/10.1016/j.neubiorev.2017.02.011, PubMed: 28212857

Doelling, k. B., Arnal, l. h., Ghitza, o., & Poeppel, D. (2014).
Acoustic landmarks drive delta–theta oscillations to enable
speech comprehension by facilitating perceptual parsing. Neuro-
Image, 85(punto. 2), 761–768. https://doi.org/10.1016/j.neuroimage
.2013.06.035, PubMed: 23791839

Eigsti, I.-M., de Marchena, A. B., Schuh, j. METRO., & kelly, mi. (2011).
Language acquisition in autism spectrum disorders: A develop-
mental review. Research in Autism Spectrum Disorders, 5(2),
681–691. https://doi.org/10.1016/j.rasd.2010.09.001

Ference, J., & Curtin, S. (2013). Attention to lexical stress and early
vocabulary growth in 5-month-olds at risk for autism spectrum
disorder. Journal of Experimental Child Psychology, 116(4),
891–903. https://doi.org/10.1016/j.jecp.2013.08.006, PubMed:
24077464

Neurobiology of Language

511

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
norte
oh

/

yo
/

yo

a
r
t
i
C
mi
–
pag
d

F
/

/

/

/

3
3
4
9
5
2
0
3
9
9
9
4
norte
oh
_
a
_
0
0
0
7
4
pag
d

/

.

yo

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Neural tracking in infancy predicts language development

Franco, METRO. C., Braginsky, METRO., Yurovsky, D., & Marchman, V. A.
(2017). Wordbank: An open repository for developmental vocab-
ulary data. Journal of Child Language, 44(3), 677–694. https://doi
.org/10.1017/S0305000916000209, PubMed: 27189114

Fuglsang, S. A., Dau, T., & Hjortkjær, j. (2017). Noise-robust corti-
cal tracking of attended speech in real-world acoustic scenes.
NeuroImagen, 156, 435–444. https://doi.org/10.1016/j
.neuroimage.2017.04.026, PubMed: 28412441

Gervain, J., & Mehler, j. (2010). Speech perception and language
acquisition in the first year of life. Annual Review of Psychology,
61(1), 191–218. https://doi.org/10.1146/annurev.psych.093008
.100408, PubMed: 19575623

Giraud, A.-L., & Poeppel, D. (2012). Cortical oscillations and
speech processing: Emerging computational principles and oper-
ations. Neurociencia de la naturaleza, 15(4), 511–517. https://doi.org/10
.1038/nn.3063, PubMed: 22426255

Golumbic, mi. Z., Cogan, GRAMO. B., Schroeder, C. MI., & Poeppel, D.
(2013). Visual input enhances selective speech envelope tracking
in auditory cortex at a “cocktail party.” Journal of Neuroscience,
33(4), 1417–1426. https://doi.org/10.1523/JNEUROSCI.3675-12
.2013, PubMed: 23345218

Goswami, Ud.. (2019). Speech rhythm and language acquisition: Un
amplitude modulation phase hierarchy perspective. Annals of the
New York Academy of Sciences, 1453(1), 67–78. https://doi.org
/10.1111/nyas.14137, PubMed: 31237357

Groen, W.. B., Zwiers, METRO. PAG., van der Gaag, R.-J., & Buitelaar, j. k.
(2008). The phenotype and neural correlates of language in
autism: An integrative review. Neurociencia & Biobehavioral
Reseñas, 32(8), 1416–1425. https://doi.org/10.1016/j.neubiorev
.2008.05.008, PubMed: 18562003

Guiraud, j. A., Tomalski, PAG., Kushnerenko, MI., Ribeiro, h., Davies,
K., Charman, T., Elsabbagh, METRO., Johnson, METRO. h., & the BASIS
Equipo. (2012). Atypical audiovisual speech integration in infants
at risk for autism. MÁS UNO, 7(5), Article e36428. https://doi.org
/10.1371/diario.pone.0036428, PubMed: 22615768

Haider, B., Häusser, METRO., & Carandini, METRO. (2013). Inhibition domi-
nates sensory responses in the awake cortex. Naturaleza, 493(7430),
97–100. https://doi.org/10.1038/nature11665, PubMed:
23172139

Howlin, PAG. (2003). Outcome in high-functioning adults with autism
with and without early language delays: Implications for the dif-
ferentiation between autism and Asperger syndrome. Diario de
Autism and Developmental Disorders, 33(1), 3–13. https://doi
.org/10.1023/A:1022270118899, PubMed: 12708575

Jessen, S., Fiedler, l., monedas, t. F., & Obleser, j. (2019). Quantifying
the individual auditory and visual brain response in 7-month-old
infants watching a brief cartoon movie. NeuroImagen, 202, Article
116060. https://doi.org/10.1016/j.neuroimage.2019.116060,
PubMed: 31362048

Jochaut, D., Lehongre, K., Saitovitch, A., Devauchelle, A.-D.,
Olasagasti, I., Chabane, NORTE., Zilbovicius, METRO., & Giraud, A.-L.
(2015). Atypical coordination of cortical oscillations in response
to speech in autism. Frontiers in Human Neuroscience, 9, Article
171. https://doi.org/10.3389/fnhum.2015.00171, PubMed:
25870556

Johnson, mi. K., & Seidl, A. h. (2009). En 11 meses, prosody still out-
ranks statistics. Developmental Science, 12(1), 131–141. https://
doi.org/10.1111/j.1467-7687.2008.00740.x, PubMed: 19120421
jones, mi. J., Mason, l., Alí, j. B., van den Boomen, C., Braukmann,
r., Cauvet, MI., Demurie, MI., Hessels, r., Ward, MI., Hunnius, S.,
Bolte, S., Tomalski, PAG., Kemner, C., Warreyn, PAG., Roeyers, h.,
Buitelaar, J., Falck-Ytter, T., Charman, T., Johnson, METRO. h., & el
Eurosibs Team. (2019). Eurosibs: Towards robust measurement of

infant neurocognitive predictors of autism across Europe. Infant
Behavior and Development, 57, Article 101316. https://doi.org
/10.1016/j.infbeh.2019.03.007, PubMed: 31128517

Junge, C., Kooijman, v., Hagoort, PAG., & Cutler, A. (2012). Rapid rec-
ognition at 10 months as a predictor of language development.
Developmental Science, 15(4), 463–473. https://doi.org/10.1111
/j.1467-7687.2012.1144.x, PubMed: 22709396

Jusczyk, PAG. W.. (1999). How infants begin to extract words from
speech. Tendencias en Ciencias Cognitivas, 3(9), 323–328. https://doi
.org/10.1016/S1364-6613(99)01363-7, PubMed: 10461194

Kalashnikova, METRO., Peter, v., Di Liberto, GRAMO. METRO., Lalor, mi. C., &
Burnham, D. (2018). Infant-directed speech facilitates
seven-month-old infants’ cortical tracking of speech. Científico
Informes, 8(1), Article 13745. https://doi.org/10.1038/s41598
-018-32150-6, PubMed: 30214000

Kasai, K., Hashimoto, o., Kawakubo, y., Yumoto, METRO., Kamio, S.,
Itoh, K., Koshida, I., Iwanami, A., Nakagome, K., Fukuda, METRO.,
Yamasue, h., Yamada, h., Abe, o., Aoki, S., & Kato, norte.
(2005). Delayed automatic detection of change in speech sounds
in adults with autism: A magnetoencephalographic study. Clini-
cal Neurophysiology, 116(7), 1655–1664. https://doi.org/10.1016
/j.clinph.2005.03.007, PubMed: 15899591

Kidd, MI., Junge, C., Spokes, T., Morrison, l., & Cutler, A. (2018).
Individual differences in infant speech segmentation: Achieving
the lexical shift. Infancy, 23(6), 770–794. https://doi.org/10.1111
/infa.12256

Kooijman, v., Hagoort, PAG., & Cutler, A. (2009). Prosodic structure
in early word segmentation: ERP evidence from Dutch
ten-month-olds. Infancy, 14(6), 591–612. https://doi.org/10
.1080/15250000903263957, PubMed: 32693518

Kooijman, v., Junge, C., Johnson, mi. K., Hagoort, PAG., & Cutler, A.
(2013). Predictive brain signals of linguistic development. Fron-
tiers in Psychology, 4, Article 25. https://doi.org/10.3389/fpsyg
.2013.00025, PubMed: 23404161

kühl, PAG. K., Conboy, B. T., Coffey-Corina, S., Padden, D., Rivera-
Gaxiola, METRO., & nelson, t. (2008). Phonetic learning as a pathway
to language: New data and native language magnet theory
expanded (NLM-e). Philosophical Transactions of the Royal
Society B: Ciencias Biologicas, 363(1493), 979–1000. https://
doi.org/10.1098/rstb.2007.2154, PubMed: 17846016

Kuhn, METRO. (2008). Building predictive models in R using the caret
package. Journal of Statistical Software, 28(1), 1–26. https://doi
.org/10.18637/jss.v028.i05

Kurita, h. (1985). Infantile autism with speech loss before the age of
thirty months. Journal of the American Academy of Child Psychi-
atry, 24(2), 191–196. https://doi.org/10.1016/S0002-7138(09)
60447-7, PubMed: 3989162

Kushnerenko, mi. v., Tomalski, PAG., Ballieux, h., Potton, A., Birtles,
D., Frostick, C., & moore, D. GRAMO. (2013). Brain responses and
looking behavior during audiovisual speech integration in infants
predict auditory speech comprehension in the second year of
vida. Fronteras en psicología, 4, Article 432. https://doi.org/10
.3389/fpsyg.2013.00432, PubMed: 23882240

Kwok, mi. y., Marrón, h. METRO., Smyth, R. MI., & Cardy, j. oh. (2015).
Meta-analysis of receptive and expressive language skills in
autism spectrum disorder. Research in Autism Spectrum Disor-
ders, 9, 202–222. https://doi.org/10.1016/j.rasd.2014.10.008
Leong, v., & Goswami, Ud.. (2015). Acoustic-emergent phonology in
the amplitude envelope of child-directed speech. MÁS UNO,
10(12), Article e0144411. https://doi.org/10.1371/journal.pone
.0144411, PubMed: 26641472

Leong, v., Kalashnikova, METRO., Burnham, D., & Goswami, Ud..
(2017). The temporal modulation structure of infant-directed

Neurobiology of Language

512

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
norte
oh

/

yo
/

yo

a
r
t
i
C
mi
–
pag
d

F
/

/

/

/

3
3
4
9
5
2
0
3
9
9
9
4
norte
oh
_
a
_
0
0
0
7
4
pag
d

.

/

yo

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Neural tracking in infancy predicts language development

speech. Mente abierta, 1(2), 78–90. https://doi.org/10.1162/OPMI
_a_00008

Lombardo, METRO. v., Pierce, K., Eyler, l. T., Barnes, C. C., Ahrens-Barbeau,
C., Solso, S., Campbell, K., & Courchesne, mi. (2015). Different
functional neural substrates for good and poor language outcome
in autism. Neurona, 86(2), 567–577. https://doi.org/10.1016/j
.neuron.2015.03.023, PubMed: 25864635

Lord, C., Risi, S., Lambrecht, l., Cocinar, mi. h., Leventhal, B. l., DiLavore,
PAG. C., Pickles, A., & Rutter, METRO. (2000). The Autism Diagnostic Obser-
vation Schedule–Generic: A standard measure of social and com-
munication deficits associated with the spectrum of autism. Diario
of Autism and Developmental Disorders, 30(3), 205–223. https://
doi.org/10.1023/A:1005592401947, PubMed: 11055457

Loth, MI., Charman, T., Mason, l., Tillmann, J., jones, mi. J., lanaridge,
C., Ahmad, J., Auyeung, B., Brogna, C., Ambrosino, S.,
Banaschewski, T., Baron-Cohen, S., Baumeister, S., beckman,
C., Brammer, METRO., Brandeis, D., Bölte, S., Bourgeron, T., Bours,
C., … Buitelaar, j. k. (2017). The EU-AIMS longitudinal European
autism project (LEAP): Design and methodologies to identify and
validate stratification biomarkers for autism spectrum disorders.
Molecular Autism, 8(1), Article 24. https://doi.org/10.1186
/s13229-017-0146-8, PubMed: 28649312

Maguire, METRO. J., & Abel, A. D. (2013). What changes in neural oscil-
lations can reveal about developmental cognitive neuroscience:
Language development as a case in point. Developmental Cog-
nitive Neuroscience, 6, 125–136. https://doi.org/10.1016/j.dcn
.2013.08.002, PubMed: 24060670

Männel, C., & Friederici, A. D. (2013). Accentuate or repeat? Cerebro
signatures of developmental periods in infant word recognition.
Corteza, 49(10), 2788–2798. https://doi.org/10.1016/j.cortex
.2013.09.003, PubMed: 24120263

Maris, MI., & Oostenveld, R. (2007). Nonparametric statistical testing
of EEG- and MEG-data. Journal of Neuroscience Methods, 164(1),
177–190. https://doi.org/10.1016/j.jneumeth.2007.03.024,
PubMed: 17517438

Marslen-Wilson, W.. D., & galés, A. (1978). Processing interactions
and lexical access during word recognition in continuous
speech. Psicología cognitiva, 10(1), 29–63. https://doi.org/10
.1016/0010-0285(78)90018-X

Menn, k. h., Michel, C., Meyer, l., Hoehl, S., & Männel, C. (2022).
Natural infant-directed speech facilitates neural tracking of pros-
ody. NeuroImagen, 251, Article 118991. https://doi.org/10.1016/j
.neuroimage.2022.118991, PubMed: 35158023

Meyer, l. (2018). The neural oscillations of speech processing and
language comprehension: State of the art and emerging mecha-
nisms. European Journal of Neuroscience, 48(7), 2609–2621.
https://doi.org/10.1111/ejn.13748, PubMed: 29055058

Molinaro, NORTE., Lizarazu, METRO., Lallier, METRO., Bourguignon, METRO., & Carreiras,
METRO. (2016). Out-of-synchrony speech entrainment in developmental
dyslexia. Mapeo del cerebro humano, 37(8), 2767–2783. https://doi.org
/10.1002/hbm.23206, PubMed: 27061643

Obleser, J., & Kayser, C. (2019). Neural entrainment and attentional
selection in the listening brain. Tendencias en Ciencias Cognitivas, 23(11),
913–926. https://doi.org/10.1016/j.tics.2019.08.004, PubMed:
31606386

Oostenveld, r., Fries, PAG., Maris, MI., & Schoffelen, J.-M. (2011). Field-
Trip: Open source software for advanced analysis of MEG, EEG,
and invasive electrophysiological data. Computational Intelli-
gence and Neuroscience, 2011, Article 156869. https://doi.org
/10.1155/2011/156869, PubMed: 21253357

Ortiz Barajas, METRO. C., Guevara, r., & Gervain, j. (2021). The origins
and development of speech envelope tracking during the first
months of life. Developmental Cognitive Neuroscience, 48,

Article 100915. https://doi.org/10.1016/j.dcn.2021.100915,
PubMed: 33515956

Ozonoff, S., Joven, GRAMO. S., Carretero, A., Messinger, D., Yirmiya, NORTE.,
Zwaigenbaum, l., Bryson, S., Carver, l. J., Constantino, j. NORTE.,
Dobkins, K., et al. (2011). Recurrence risk for autism spectrum
disorders: A baby siblings research consortium study. Pediatrics,
128(3), e488–e495. https://doi.org/10.1542/peds.2010-2825,
PubMed: 21844053

Parque, h., Ince, R. A., Schyns, PAG. GRAMO., Thut, GRAMO., & Bruto, j. (2018).
Representational interactions during audiovisual speech entrain-
mento: Redundancy in left posterior superior temporal gyrus and
synergy in left motor cortex. PLOS Biology, 16(8), Article
e2006558. https://doi.org/10.1371/journal.pbio.2006558,
PubMed: 30080855

Parque, h., Kayser, C., Thut, GRAMO., & Bruto, j. (2016). Lip movements
entrain the observers’ low-frequency brain oscillations to facili-
tate speech intelligibility. eVida, 5, Article e14521. https://doi
.org/10.7554/eLife.14521, PubMed: 27146891

Peelle, j. MI., & davis, METRO. h. (2012). Neural oscillations carry speech
rhythm through to comprehension. Fronteras en psicología, 3,
Article 320. https://doi.org/10.3389/fpsyg.2012.00320,
PubMed: 22973251

Peelle, j. MI., Bruto, J., & davis, METRO. h. (2013). Phase-locked
responses to speech in human auditory cortex are enhanced dur-
ing comprehension. Corteza cerebral, 23(6), 1378–1387. https://
doi.org/10.1093/cercor/bhs118, PubMed: 22610394

Perrin, F., Pernier, J., Bertrand, o., & Echallier, j. (1989). Spherical
splines for scalp potential and current density mapping. Electro-
encephalography and Clinical Neurophysiology, 72(2), 184–187.
https://doi.org/10.1016/0013-4694(89)90180-6, PubMed:
2464490

Cabello, S.-S., Piedra dura, r., Mansvelder, h. D., & Linkenkaer-Hansen,
k. (2012). Critical-state dynamics of avalanches and oscillations
jointly emerge from balanced excitation/inhibition in neuronal
redes. Revista de neurociencia, 32(29), 9817–9823. https://
doi.org/10.1523/ JNEUROSCI.5990-11.2012, PubMed:
22815496

Pomper, r., Weismer, S. MI., Saffran, J., & Edwards, j. (2021). Co-
articulation facilitates lexical processing for toddlers with autism.
Cognición, 214, Article 104799. https://doi.org/10.1016/j
.cognition.2021.104799, PubMed: 34139478

Fuerza, A. J., Mead, NORTE., Barnes, l., & Goswami, Ud.. (2013). Neural
entrainment to rhythmic speech in children with developmental
dyslexia. Frontiers in Human Neuroscience, 7, Article 777.
https://doi.org/10.3389/fnhum.2013.00777, PubMed: 24376407
R Core Team. (2018). R: A language and environment for statistical
computing [Computer software]. R Foundation for Statistical
Informática. https://www.R-project.org/

Riecke, l., Formisano, MI., Sorger, B., Baskent, D., & Gaudrain, mi.
(2018). Neural entrainment to speech modulates speech intellig-
ibilidad. Biología actual, 28(2), 161–169. https://doi.org/10.1016/j
.cub.2017.11.033, PubMed: 29290557

Rogers, S. j. (2004). Developmental regression in autism spectrum
disorders. Mental Retardation and Developmental Disabilities
Research Reviews, 10(2), 139–143. https://doi.org/10.1002
/mrdd.20027, PubMed: 15362172

Romeo, R. r., Choi, B., Gabard-Durnam, l. J., Wilkinson, C. l.,
Levin, A. r., Rowe, METRO. l., Tager-Flusberg, h., & nelson, C. A.
(2021). Parental language input predicts neuroscillatory patterns
associated with language development in toddlers at risk of
autism. Journal of Autism and Developmental Disorders, 52,
2717–2731. https://doi.org/10.1007/s10803-021-05024-6,
PubMed: 34185234

Neurobiology of Language

513

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
norte
oh

/

yo
/

yo

a
r
t
i
C
mi
–
pag
d

F
/

/

/

/

3
3
4
9
5
2
0
3
9
9
9
4
norte
oh
_
a
_
0
0
0
7
4
pag
d

.

/

yo

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Neural tracking in infancy predicts language development

Rosenberg, J., Amjad, A., Breeze, PAG., Brillinger, D., & Halliday, D.
(1989). The Fourier approach to the identification of functional
coupling between neuronal spike trains. Progress in Biophysics
and Molecular Biology, 53(1), 1–31. https://doi.org/10.1016
/0079-6107(89)90004-7, PubMed: 2682781

Rosenblum, l. D., Schmuckler, METRO. A., & Johnson, j. A. (1997). El
McGurk effect in infants. Percepción & Psicofísica, 59(3),
347–357. https://doi.org/10.3758/ BF03211902, PubMed:
9136265

RStudio Team. (2016). RStudio: Integrated development environment
for R [Computer software]. RStudio, Cª. https://www.rstudio.com/
Rubenstein, j. l. r., & Merzenich, METRO. METRO. (2003). Model of autism:
Increased ratio of excitation/inhibition in key neural systems: Modelo
of autism. Genes, Brain and Behavior, 2(5), 255–267. https://doi.org
/10.1034/j.1601-183X.2003.00037.x, PubMed: 14606691

Schroeder, C. MI., & Lakatos, PAG. (2009). Low-frequency neuronal
oscillations as instruments of sensory selection. Trends in Neuro-
sciences, 32(1), 9–18. https://doi.org/10.1016/j.tins.2008.09.012,
PubMed: 19012975

mostrar, W.. l., Cual, h., Yu, S., roy, r., & Plenz, D. (2011). Informa-
tion capacity and transmission are maximized in balanced corti-
cal networks with neuronal avalanches. Revista de neurociencia,
31(1), 55–63. https://doi.org/10.1523/ JNEUROSCI.4637-10
.2011, PubMed: 21209189

Snijders, t. METRO. (2020). Getting the rhythm for infant language
aprendiendo: Infants’ cortical tracking of speech rhythm relates to
their word segmentation performance [Paper presentation].
The 45th Annual Boston University Conference on Language
Desarrollo (BUCLD 45), Bostón, MAMÁ, United States.

Snijders, t. METRO., Milivojevic, B., & Kemner, C. (2013). Atypical
excitation–inhibition balance in autism captured by the gamma
response to contextual modulation. NeuroImagen: Clinical, 3, 65–72.
https://doi.org/10.1016/j.nicl.2013.06.015, PubMed: 24179850

Song, q. C., Espiga, C., & Wee, S. (2021). Making sense of model
generalizability: A tutorial on cross-validation in R and Shiny.
Advances in Methods and Practices in Psychological Science,
4(1). https://doi.org/10.1177/2515245920947067

Stärk, K., Kidd, MI., & Frost, R. l. (2021). Word segmentation cues in
German child-directed speech: A corpus analysis. Language and
Discurso, 65(91), 3–27. https://doi.org/10.1177/0023830920979016,
PubMed: 33517856

Szatmari, PAG., Chawarska, K., Dawson, GRAMO., Georgiades, S., Landa, r.,
Lord, C., Messinger, D. S., Torre, A., & Halladay, A. (2016). Pro-
spective longitudinal studies of infant siblings of children with
autism: Lessons learned and future directions. Journal of the Amer-
ican Academy of Child & Adolescent Psychiatry, 55(3), 179–187.
https://doi.org/10.1016/j.jaac.2015.12.014, PubMed: 26903251
Broncearse, S. h. J., Kalashnikova, METRO., Di Liberto, GRAMO. METRO., Crosse, METRO. J., &
Burnham, D. (2022). Seeing a talking face matters: The relationship
between cortical tracking of continuous auditory-visual speech and
gaze behaviour in infants, children and adults. NeuroImagen, 256,
Article 119217. https://doi.org/10.1016/j.neuroimage.2022
.119217, PubMed: 35436614

Torre, A., Manwaring, S. S., Luckenbaugh, D. A., Lord, C., &
Swedo, S. mi. (2014). Patterns of skill attainment and loss in young
children with autism. Development and Psychopathology, 26(1),
203–214. https://doi.org/10.1017/S0954579413000874,
PubMed: 24274034

Tiedemann, F. (2020). Gghalves: Compose half-half plots using
your favourite geoms (R package version 0.1.1) [Computer soft-
mercancía]. https://CRAN.R-project.org/package=gghalves

Tierney, A. l., Gabard-Durnam, l., Vogel-Farley, v., Tager-Flusberg,
h., & nelson, C. A. (2012). Developmental trajectories of resting
EEG power: An endophenotype of autism spectrum disorder.
MÁS UNO, 7(6), Article e39127. https://doi.org/10.1371
/diario.pone.0039127, PubMed: 22745707

Tort, A. B. l., Komorowski, r., Eichenbaum, h., & Kopell, norte. (2010).
Measuring phase-amplitude coupling between neuronal oscilla-
tions of different frequencies. Revista de neurofisiología, 104(2),
1195–1210. https://doi.org/10.1152/jn.00106.2010, PubMed:
20463205

Van Rooij, D., Anagnostou, MI., Arango, C., Auzias, GRAMO., behrman,
METRO., Busatto, GRAMO. F., Calderoni, S., Daly, MI., Deruelle, C., Di
Martino, A., Dinstein, I., Duran, F. l. S, Durston, S., Ecker, C.,
Fair, D., Fedor, J., Fitzgerald, J., Freitag, C. METRO., Gallagher, l., …
Buitelaar, j. k. (2018). Cortical and subcortical brain morphom-
etry differences between patients with autism spectrum disorder
and healthy individuals across the lifespan: Results from the
ENIGMA ASD working group. Revista americana de psiquiatría,
175(4), 359–369. https://doi.org/10.1176/appi.ajp.2017
.17010100, PubMed: 29145754

Vanden Bosch der Nederlanden, C., Joanisse, METRO., & Grahn, j. A.
(2020). Music as a scaffold for listening to speech: Better neural
phase-locking to song than speech. NeuroImagen, 214, Article
116767. https://doi.org/10.1016/j.neuroimage.2020.116767,
PubMed: 32217165

Vanthornhout, J., Decruy, l., Wouters, J., Simón, j. Z., & Francart, t.
(2018). Speech intelligibility predicted from neural entrainment
of the speech envelope. Journal of the Association for Research
in Otolaryngology, 19(2), 181–191. https://doi.org/10.1007
/s10162-018-0654-z, PubMed: 29464412

Verly, METRO., Verhoeven, J., zinc, I., Mantini, D., Oudenhove, l. v.,
Lagae, l., Sunaert, S., & Rommel, norte. (2014). Structural and func-
tional underconnectivity as a negative predictor for language in
autism. Mapeo del cerebro humano, 35(8), 3602–3615. https://doi.org
/10.1002/hbm.22424, PubMed: 24375710

Vouloumanos, A., & Curtin, S. (2014). Foundational tuning: Cómo
infants’ attention to speech predicts language development. Cog-
nitive Science, 38(8), 1675–1686. https://doi.org/10.1111/cogs
.12128, PubMed: 25098703

Wickham, h. (2016). ggplot2: Elegant graphics for data analysis.

Saltador. https://doi.org/10.1007/978-3-319-24277-4

Wilkinson, C. l., Gabard-Durnam, l. J., Kapur, K., Tager-Flusberg,
h., Levin, A. r., & nelson, C. A. (2020). Use of longitudinal EEG
measures in estimating language development in infants with
and without familial risk for autism spectrum disorder. Neurobi-
ology of Language, 1(1), 33–53. https://doi.org/10.1162/nol_a
_00002, PubMed: 32656537

zinc, I., & Lejaegere, METRO. (2002). N-CDIs lijsten voor communica-
tieve ontwikkeling [N-CDIs lists for communicative development
(Adaptation and restandardization of the MacArthur CDIs of
Fenson et al., 1993)]. Acco. https://www.hanze.nl/nld
/onderzoek/kenniscentra/ hanzehogeschool-centre-of-expertise
-healthy-ageing/lectoraten/lectoraten/lahc/producten/producten
/ t a a l e x p e r t / l o g o p e d i e / d i a g n o st i c – t o o l s / norte – c d i -s – l i j s t e n
-communicatieve-ontwikkeling

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