Neural sampling of the speech signal at different timescales by children with dyslexia.

Phonological difficulties characterize individuals with dyslexia across languages. Currently debated is whether these difficulties arise from atypical neural sampling of (or entrainment to) auditory information in speech at slow rates (<10 Hz, related to speech rhythm), faster rates, or neither. MEG studies with adults suggest that atypical sampling in dyslexia affects faster modulations in the neurophysiological gamma band, related to phoneme-level representation. However, dyslexic adults have had years of reduced experience in converting graphemes to phonemes, which could itself cause atypical gamma-band activity. The present study was designed to identify specific linguistic timescales at which English children with dyslexia may show atypical entrainment. Adopting a developmental focus, we hypothesized that children with dyslexia would show atypical entrainment to the prosodic and syllable-level information that is exaggerated in infant-directed speech and carried primarily by amplitude modulations <10 Hz. MEG was recorded in a naturalistic story-listening paradigm. The modulation bands related to different types of linguistic information were derived directly from the speech materials, and lagged coherence at multiple temporal rates spanning 0.9-40 Hz was computed. Group differences in lagged speech-brain coherence between children with dyslexia and control children were most marked in neurophysiological bands corresponding to stress and syllable-level information (<5 Hz in our materials), and phoneme-level information (12-40 Hz). Functional connectivity analyses showed network differences between groups in both hemispheres, with dyslexic children showing significantly reduced global network efficiency. Global network efficiency correlated with dyslexic children's oral language development and with control children's reading development. These developmental data suggest that dyslexia is characterized by atypical neural sampling of auditory information at slower rates. They also throw new light on the nature of the gamma band temporal sampling differences reported in MEG dyslexia studies with adults.

Predicting time-resolved electrophysiological brain networks from structural eigenmodes.

How temporal modulations in functional interactions are shaped by the underlying anatomical connections remains an open question. Here, we analyse the role of structural eigenmodes, in the formation and dissolution of temporally evolving functional brain networks using resting-state magnetoencephalography and diffusion magnetic resonance imaging data at the individual subject level. Our results show that even at short timescales, phase and amplitude connectivity can partly be expressed by structural eigenmodes, but hardly by direct structural connections. Albeit a stronger relationship was found between structural eigenmodes and time-resolved amplitude connectivity. Time-resolved connectivity for both phase and amplitude was mostly characterised by a stationary process, superimposed with very brief periods that showed deviations from this stationary process. For these brief periods, dynamic network states were extracted that showed different expressions of eigenmodes. Furthermore, the eigenmode expression was related to overall cognitive performance and co-occurred with fluctuations in community structure of functional networks. These results implicate that ongoing time-resolved resting-state networks, even at short timescales, can to some extent be understood in terms of activation and deactivation of structural eigenmodes and that these eigenmodes play a role in the dynamic integration and segregation of information across the cortex, subserving cognitive functions.

Development of binaural temporal fine structure sensitivity in children.

The highest frequency for which the temporal fine structure (TFS) of a sinewave can be compared across ears varies between listeners with an upper limit of about 1400 Hz for young normal-hearing adults (YNHA). In this study, binaural TFS sensitivity was investigated for 63 typically developing children, aged 5 years, 6 months to 9 years, 4 months using the temporal fine structure-adaptive frequency (TFS-AF) test of Füllgrabe, Harland, Sek, and Moore [Int. J. Audiol. 56, 926-935 (2017)]. The test assesses the highest frequency at which an interaural phase difference (IPD) of ϕ° can be distinguished from an IPD of 0°. The values of ϕ were 30° and 180°. The starting frequency was 200 Hz. The thresholds for the children were significantly lower (worse) than the thresholds reported by Füllgrabe, Harland, Sek, and Moore [Int. J. Audiol. 56, 926-935 (2017)] for YNHA. For both values of ϕ, the median age at which children performed above chance level was significantly higher (p < 0.001) than for those who performed at chance. For the subgroup of 40 children who performed above chance for ϕ = 180°, the linear regression analyses showed that the thresholds for ϕ = 180° increased (improved) significantly with increasing age (p < 0.001) with adult-like thresholds predicted to be reached at 10 years, 2 months of age. The implications for spatial release from masking are discussed.

Comparing multilayer brain networks between groups: Introducing graph metrics and recommendations.

There is an increasing awareness of the advantages of multi-modal neuroimaging. Networks obtained from different modalities are usually treated in isolation, which is however contradictory to accumulating evidence that these networks show non-trivial interdependencies. Even networks obtained from a single modality, such as frequency-band specific functional networks measured from magnetoencephalography (MEG) are often treated independently. Here, we discuss how a multilayer network framework allows for integration of multiple networks into a single network description and how graph metrics can be applied to quantify multilayer network organisation for group comparison. We analyse how well-known biases for single layer networks, such as effects of group differences in link density and/or average connectivity, influence multilayer networks, and we compare four schemes that aim to correct for such biases: the minimum spanning tree (MST), effective graph resistance cost minimisation, efficiency cost optimisation (ECO) and a normalisation scheme based on singular value decomposition (SVD). These schemes can be applied to the layers independently or to the multilayer network as a whole. For correction applied to whole multilayer networks, only the SVD showed sufficient bias correction. For correction applied to individual layers, three schemes (ECO, MST, SVD) could correct for biases. By using generative models as well as empirical MEG and functional magnetic resonance imaging (fMRI) data, we further demonstrated that all schemes were sensitive to identify network topology when the original networks were perturbed. In conclusion, uncorrected multilayer network analysis leads to biases. These biases may differ between centres and studies and could consequently lead to unreproducible results in a similar manner as for single layer networks. We therefore recommend using correction schemes prior to multilayer network analysis for group comparisons.

Amplitude rise time sensitivity in children with and without dyslexia: differential task effects and longitudinal relations to phonology and literacy.

The speech amplitude envelope carries important acoustic information required for speech intelligibility and contains sensory cues (amplitude rise times, ARTs) that play a key role in both sensory rhythm perception and neural speech encoding. Individual differences in children's sensitivity to ARTs have been related to the development of children's phonological processing skills across languages by the Temporal Sampling theory. Impaired processing of ARTs also characterises children with dyslexia. However, different ART tasks have been employed in different studies, in different languages, and at different ages. Here, we compare the sensitivity of three frequently used ART tasks (based on synthetic syllables, sine tones, and speech-shaped noise) in a longitudinal study of English-speaking children with and without dyslexia. Children's ability to discriminate rising frequency, duration, and intensity was also tested. ART discrimination in all 3 tasks was significantly inter-related, but different relations to phonology and literacy were found for different ART tasks at different ages. In particular, the often-used sine tone and speech-shaped noise ART tasks showed greater sensitivity in older children, while the synthetic syllable task (/ba/ rise) showed greater sensitivity in younger children. Sensitivity to rising frequency was also related to phonology and literacy across ages. The data are interpreted with respect to the Temporal Sampling theory of developmental dyslexia.

Atypical beta-band effects in children with dyslexia in response to rhythmic audio-visual speech.

OBJECTIVE: Previous studies have reported atypical delta phase in children with dyslexia, and that delta phase modulates the amplitude of the beta-band response via delta-beta phase-amplitude coupling (PAC). Accordingly, the atypical delta-band effects in children with dyslexia may imply related atypical beta-band effects, particularly regarding delta-beta PAC. Our primary objective was to explore beta-band oscillations in children with and without dyslexia, to explore potentially atypical effects in the beta band in dyslexic children. METHODS: We collected EEG data during a rhythmic speech paradigm from 51 children (21 control; 30 dyslexia). We then assessed beta-band phase entrainment, beta-band angular velocity, beta-band power responses and delta-beta PAC. RESULTS: We found significant beta-band phase entrainment for control children but not for dyslexic children. Furthermore, children with dyslexia exhibited significantly faster beta-band angular velocity and significantly greater beta-band power. Delta-beta PAC was comparable in both groups. CONCLUSION: Atypical beta-band effects were observed in children with dyslexia. However, delta-beta PAC was comparable in both dyslexic and control children. SIGNIFICANCE: These findings offer further insights into the neurophysiological basis of atypical rhythmic speech processing by children with dyslexia, suggesting the involvement of a wide range of frequency bands.

Atypical low-frequency cortical encoding of speech identifies children with developmental dyslexia.

Slow cortical oscillations play a crucial role in processing the speech amplitude envelope, which is perceived atypically by children with developmental dyslexia. Here we use electroencephalography (EEG) recorded during natural speech listening to identify neural processing patterns involving slow oscillations that may characterize children with dyslexia. In a story listening paradigm, we find that atypical power dynamics and phase-amplitude coupling between delta and theta oscillations characterize dyslexic versus other child control groups (typically-developing controls, other language disorder controls). We further isolate EEG common spatial patterns (CSP) during speech listening across delta and theta oscillations that identify dyslexic children. A linear classifier using four delta-band CSP variables predicted dyslexia status (0.77 AUC). Crucially, these spatial patterns also identified children with dyslexia when applied to EEG measured during a rhythmic syllable processing task. This transfer effect (i.e., the ability to use neural features derived from a story listening task as input features to a classifier based on a rhythmic syllable task) is consistent with a core developmental deficit in neural processing of speech rhythm. The findings are suggestive of distinct atypical neurocognitive speech encoding mechanisms underlying dyslexia, which could be targeted by novel interventions.

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 responses to natural and enhanced speech edges in children with and without dyslexia.

Sensory-neural studies indicate that children with developmental dyslexia show impairments in processing acoustic speech envelope information. Prior studies suggest that this arises in part from reduced sensory sensitivity to amplitude rise times (ARTs or speech "edges") in the envelope, accompanied by less accurate neural encoding of low-frequency envelope information. Accordingly, enhancing these characteristics of the speech envelope may enhance neural speech processing in children with dyslexia. Here we applied an envelope modulation enhancement (EME) algorithm to a 10-min story read in child-directed speech (CDS), enhancing ARTs and also enhancing low-frequency envelope information. We compared neural speech processing (as measured using MEG) for the EME story with the same story read in natural CDS for 9-year-old children with and without dyslexia. The EME story affected neural processing in the power domain for children with dyslexia, particularly in the delta band (0.5-4 Hz) in the superior temporal gyrus. This may suggest that prolonged experience with EME speech could ameliorate some of the impairments shown in natural speech processing by children with dyslexia.

Decoding of speech information using EEG in children with dyslexia: Less accurate low-frequency representations of speech, not "Noisy" representations.

The amplitude envelope of speech carries crucial low-frequency acoustic information that assists linguistic decoding. The sensory-neural Temporal Sampling (TS) theory of developmental dyslexia proposes atypical encoding of speech envelope information < 10 Hz, leading to atypical phonological representations. Here a backward linear TRF model and story listening were employed to estimate the speech information encoded in the electroencephalogram in the canonical delta, theta and alpha bands by 9-year-old children with and without dyslexia. TRF decoding accuracy provided an estimate of how faithfully the children's brains encoded low-frequency envelope information. Between-group analyses showed that the children with dyslexia exhibited impaired reconstruction of speech information in the delta band. However, when the quality of speech encoding for each child was estimated using child-by-child decoding models, then the dyslexic children did not differ from controls. This suggests that children with dyslexia encode neither "noisy" nor "normal" representations of the speech signal, but different representations.

Atypical delta-band phase consistency and atypical preferred phase in children with dyslexia during neural entrainment to rhythmic audio-visual speech.

According to the sensory-neural Temporal Sampling theory of developmental dyslexia, neural sampling of auditory information at slow rates (<10 Hz, related to speech rhythm) is atypical in dyslexic individuals, particularly in the delta band (0.5-4 Hz). Here we examine the underlying neural mechanisms related to atypical sampling using a simple repetitive speech paradigm. Fifty-one children (21 control children [15M, 6F] and 30 children with dyslexia [16M, 14F]) aged 9 years with or without developmental dyslexia watched and listened as a 'talking head' repeated the syllable "ba" every 500 ms, while EEG was recorded. Occasionally a syllable was "out of time", with a temporal delay calibrated individually and adaptively for each child so that it was detected around 79.4% of the time by a button press. Phase consistency in the delta (rate of stimulus delivery), theta (speech-related) and alpha (control) bands was evaluated for each child and each group. Significant phase consistency was found for both groups in the delta and theta bands, demonstrating neural entrainment, but not the alpha band. However, the children with dyslexia showed a different preferred phase and significantly reduced phase consistency compared to control children, in the delta band only. Analysis of pre- and post-stimulus angular velocity of group preferred phases revealed that the children in the dyslexic group showed an atypical response in the delta band only. The delta-band pre-stimulus angular velocity (-130 ms to 0 ms) for the dyslexic group appeared to be significantly faster compared to the control group. It is concluded that neural responding to simple beat-based stimuli may provide a unique neural marker of developmental dyslexia. The automatic nature of this neural response may enable new tools for diagnosis, as well as opening new avenues for remediation.

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.