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.

BackgroundComputational 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 methodThree 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.ResultsEach 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 methodsThe 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.ConclusionsThe 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

Bilingualism and cognitive reserve: It’s a matter of top-down or bottom-up process

Cognitive performance declines with age following different trajectories. The cognitive trade-off, however, between age and cognitive reserve is still not clear. In addition, bilingualism has been thought to play a role in delaying cognitive decline by affecting cognitive control outside the scope of language. However, the effect has been unreliably reproduced and without exploring sufficiently the differences between cognitive functions that govern language control. In the current study 112 adults varying in age, level of bilingualism and cognitive reserve, completed a modified version of the Simon task, which engaged the mechanisms of interference suppression and shifting. Using ex-Gaussian analysis, the Simon effect was replicated in the normal component and the shifting effect was found in the exponential. Additional linear mixed-effects model analysis showed a significant “negative” effect of bilingualism on inhibition and a “positive” effect of cognitive reserve on shifting, both independent of age. Age affected similarly the speed of engagement of both executive functions irrespectively of language or cognitive background. Implications of a bilingual disadvantage and a beneficial effect of cognitive reserve during ageing are discussed

Variations of the NodB Architecture Are Attuned to Functional Specificities into and beyond the Carbohydrate Esterase Family 4

Enzymes of the carbohydrate esterase family 4 (CE4) deacetylate a broad range of substrates, including linear, branched and mesh-like polysaccharides. Although they are enzymes of variable amino acid sequence length, they all comprise the conserved catalytic domain NodB. NodB carries the metal binding and active site residues and is characterized by a set of conserved sequence motifs, which are linked to the deacetylation activity. Besides a non-structured, flexible peptide of variable length that precedes NodB, several members of the CE4 family contain additional domains whose function or contribution to substrate specificity are not efficiently characterized. Evidence suggests that CE4 family members comprising solely the NodB domain have developed features linked to a variety of substrate specificities. To understand the NodB-based substrate diversity within the CE4 family, we perform a comparative analysis of all NodB domains structurally characterized so far. We show that amino acid sequence variations, topology diversities and excursions away from the framework structure give rise to different NodB domain classes associated with different substrate specificities and particular functions within and beyond the CE4 family. Our work reveals a link between specific NodB domain characteristics and substrate recognition. Thus, the details of the fold are clarified, and the structural basis of its variations is deciphered and associated with function. The conclusions of this work are also used to make predictions and propose specific functions for biochemically/enzymatically uncharacterized NodB-containing proteins, which have generally been considered as putative CE4 deacetylases. We show that some of them probably belong to different enzymatic families

Milk Adulteration: Detection of bovine milk in caprine dairy products by Real Time PCR

status: Published onlin

Language Acquisition in the Longitudinal Cambridge UK BabyRhythm Cohort

The Cambridge UK BabyRhythm project is a study of 122 infants as they age from 2 – 30 months, investigating cortical tracking and sensorimotor synchronisation to acoustic and visual rhythm in relation to language acquisition. As there are few standardised language tasks appropriate for this age range, the BabyRhythm project adapted a range of parent-report and infant-led experimental measures that could be used within a home testing environment. Here we present a rich description of infant performance on tasks intended to sample 5 linguistic domains: semantics, phonology, grammar, rhythmic timing and gesture. For each task we describe infant performance (mean, median, range), and we also report performance by sex (N female = 57) and by monolingual (N = 91) versus multilingual (N = 31) home environments. We report relations between measures. We share our unique longitudinal database (all data available on OSF), and ‘lessons learned’ on adapting language assessments for very young children. Critically, we identify the language tasks that will be utilised in our longitudinal brain-behaviour analyses, providing the benchmark upon which future neural and behavioural markers will be measured.</jats:p