Precursors to infant sensorimotor synchronization to speech and non-speech rhythms: A longitudinal study.

Impaired sensorimotor synchronization (SMS) to acoustic rhythm may be a marker of atypical language development. Here, Motion Capture was used to assess gross motor rhythmic movement at six time points between 5- and 11 months of age. Infants were recorded drumming to acoustic stimuli of varying linguistic and temporal complexity: drumbeats, repeated syllables and nursery rhymes. Here we show, for the first time, developmental change in infants' movement timing in response to auditory stimuli over the first year of life. Longitudinal analyses revealed that whilst infants could not yet reliably synchronize their movement to auditory rhythms, infant spontaneous motor tempo became faster with age, and by 11 months, a subset of infants decelerate from their spontaneous motor tempo, which better accords with the incoming tempo. Further, infants became more regular drummers with age, with marked decreases in the variability of spontaneous motor tempo and variability in response to drumbeats. This latter effect was subdued in response to linguistic stimuli. The current work lays the foundation for using individual differences in precursors of SMS in infancy to predict later language outcomes. RESEARCH HIGHLIGHT: We present the first longitudinal investigation of infant rhythmic movement over the first year of life Whilst infants generally move more quickly and with higher regularity over their first year, by 11 months infants begin to counter this pattern when hearing slower infant-directed song Infant movement is more variable to speech than non-speech stimuli In the context of the larger Cambridge UK BabyRhythm Project, we lay the foundation for rhythmic movement in infancy to predict later language outcomes.

Decoding speech information from EEG data with 4-, 7- and 11-month-old infants: Using convolutional neural network, mutual information-based and backward linear models.

BACKGROUND: Computational models that successfully decode neural activity into speech are increasing in the adult literature, with convolutional neural networks (CNNs), backward linear models, and mutual information (MI) models all being applied to neural data in relation to speech input. This is not the case in the infant literature. NEW METHOD: Three different computational models, two novel for infants, were applied to decode low-frequency speech envelope information. Previously-employed backward linear models were compared to novel CNN and MI-based models. Fifty infants provided EEG recordings when aged 4, 7, and 11 months, while listening passively to natural speech (sung or chanted nursery rhymes) presented by video with a female singer. RESULTS: Each model computed speech information for these nursery rhymes in two different low-frequency bands, delta and theta, thought to provide different types of linguistic information. All three models demonstrated significant levels of performance for delta-band neural activity from 4 months of age, with two of three models also showing significant performance for theta-band activity. All models also demonstrated higher accuracy for the delta-band neural responses. None of the models showed developmental (age-related) effects. COMPARISONS WITH EXISTING METHODS: The data demonstrate that the choice of algorithm used to decode speech envelope information from neural activity in the infant brain determines the developmental conclusions that can be drawn. CONCLUSIONS: The modelling shows that better understanding of the strengths and weaknesses of each modelling approach is fundamental to improving our understanding of how the human brain builds a language system.

Neural detection of changes in amplitude rise time in infancy.

Amplitude rise times play a crucial role in the perception of rhythm in speech, and reduced perceptual sensitivity to differences in rise time is related to developmental language difficulties. Amplitude rise times also play a mechanistic role in neural entrainment to the speech amplitude envelope. Using an ERP paradigm, here we examined for the first time whether infants at the ages of seven and eleven months exhibit an auditory mismatch response to changes in the rise times of simple repeating auditory stimuli. We found that infants exhibited a mismatch response (MMR) to all of the oddball rise times used for the study. The MMR was more positive at seven than eleven months of age. At eleven months, there was a shift to a mismatch negativity (MMN) that was more pronounced over left fronto-central electrodes. The MMR over right fronto-central electrodes was sensitive to the size of the difference in rise time. The results indicate that neural processing of changes in rise time is present at seven months, supporting the possibility that early speech processing is facilitated by neural sensitivity to these important acoustic cues.

Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.

Currently there are no reliable means of identifying infants at-risk for later language disorders. Infant neural responses to rhythmic stimuli may offer a solution, as neural tracking of rhythm is atypical in children with developmental language disorders. However, infant brain recordings are noisy. As a first step to developing accurate neural biomarkers, we investigate whether infant brain responses to rhythmic stimuli can be classified reliably using EEG from 95 eight-week-old infants listening to natural stimuli (repeated syllables or drumbeats). Both Convolutional Neural Network (CNN) and Support Vector Machine (SVM) approaches were employed. Applied to one infant at a time, the CNN discriminated syllables from drumbeats with a mean AUC of 0.87, against two levels of noise. The SVM classified with AUC 0.95 and 0.86 respectively, showing reduced performance as noise increased. Our proof-of-concept modelling opens the way to the development of clinical biomarkers for language disorders related to rhythmic entrainment.