Neurobiology of Language最新文献

Prenatal alcohol exposure (PAE) is associated with various neurological, behavioral and cognitive deficits, including reading and language. Previous studies have demonstrated altered white matter in children and adolescents with PAE and associations with reading and language performance in children aged 3 years and older. However, little research has focused on the toddler years, despite this being a critical period for behavioral and neural development. We aimed to determine associations between structural brain connectivity and early language skills in toddlers, in the context of PAE. Eighty-eight toddlers (2-3 yr, 56 males), 23 of whom had PAE, underwent a diffusion MRI scan in Cape Town, South Africa, with language skills assessed using the Expressive and Receptive Communication subtests from the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III). Diffusion scans were preprocessed to create a structural network of regions associated with language skills using graph theory analysis. Linear regression models were used to examine moderation effects of PAE on structural network properties and language skills. Toddlers with PAE had higher structural connectivity in language networks than unexposed children. PAE moderated the relationship between structural network properties and Expressive Communication scores. None of the effects survived correction for multiple comparisons. Our findings show weak moderation effects of PAE on structural language network properties and language skills. Our study sheds light on the structural connectivity correlates of early language skills in an understudied population during a critical neurodevelopmental period, laying the foundation for future research.

Our understanding of the neurobiological bases of stuttering remains limited, hampering development of effective treatments that are informed by basic science. Stuttering affects more than 5% of all preschool-age children and remains chronic in approximately 1% of adults worldwide. As a condition that affects a most fundamental human ability to engage in fluid and spontaneous verbal communication, stuttering can have substantial psychosocial, occupational, and educational impacts on those who are affected. This article summarizes invited talks and breakout sessions that were held in June 2023 as part of a 2-day workshop sponsored by the US National Institute on Deafness and Other Communication Disorders. The workshop encompassed topics including neurobiology, genetics, speech motor control, cognitive, social, and emotional impacts, and intervention. Updates on current research in these areas were summarized by each speaker, and critical gaps and priorities for future research were raised, and then discussed by participants. Research talks were followed by smaller, moderated breakout sessions intended to elicit diverse perspectives, including on the matter of defining therapeutic targets for stuttering. A major concern that emerged following participant discussion was whether priorities for treatment in older children and adults should focus on targeting core speech symptoms of stuttering, or on embracing effective communication regardless of whether the speaker exhibits overt stuttering. This article concludes with accumulated convergent points endorsed by most attendees on research and clinical priorities that may lead to breakthroughs with substantial potential to contribute to bettering the lives of those living with this complex speech disorder.

A growing body of evidence has shown that repetitive transcranial magnetic stimulation (rTMS) can enhance word-retrieval abilities in chronic aphasia. However, there remains significant variability in the efficacy of combined rTMS and language treatments. This study investigated how semantic and phonological characteristics of baseline word-retrieval impairments may influence the efficacy of rTMS on long-term naming improvements following language treatment in individuals with chronic aphasia. Thirty participants with post-stroke aphasia underwent 10 sessions of 1 Hz rTMS to right pars triangularis followed by a modified constraint-induced language treatment (mCILT). Nineteen participants were randomly assigned to active rTMS and 11 participants were assigned to sham rTMS. All participants completed the Philadelphia Naming Test (PNT) at baseline and at 3 and 6 months post-treatment. We coded PNT errors and fit data to the semantic-phonological (or SP) computational model (Foygel & Dell, 2000) to derive semantic and phonological parameter weights. We ran linear regressions for the proportional improvement in naming, with fixed effects for interactions between rTMS, time, and baseline parameter weights. While there was no immediate effect of rTMS post-treatment, rTMS combined with mCILT improved long-term naming more than language therapy alone. Furthermore, greater baseline semantic and phonological characteristics of word-retrieval abilities were each associated with increased rTMS-induced gains in proportional naming improvements. These patterns were maintained at both 3 and 6 months post-treatment. This study is among the first in a larger sample to demonstrate that individual differences in lexical retrieval contribute to variability in sustained rTMS and aphasia treatment outcomes.

During the COVID-19 pandemic, children worldwide experienced school closures. Several studies have detected a negative impact on reading-related skills in children who experienced these closures during the early stages of reading instruction, but the impact on the reading network in the brain has not been investigated. In the current longitudinal study in a sample of 162 Dutch-speaking children, we found a short-term effect in the growth of phonological awareness in children with COVID-19 school closures compared to children without school closures, but no long-term effects one year later. Similarly, we did not find a long-term effect on the longitudinal development of white matter connectivity in tracts implicated during early reading development. Together, these findings indicate that one year after school closures no effects on the development of phonological awareness and white matter are found, yet it remains an open question whether short-term effects on the reading network could have been present and/or whether other networks (e.g., psychosocial related networks) are potentially more affected.

Research over the past two decades has documented the importance of sleep to language learning. Sleep has been suggested to play a role in establishing new speech representations as well; however, the neural mechanisms corresponding to sleep-mediated effects on speech perception behavior are unknown. In this study, we trained monolingual English-speaking adults to perceive differences between the Hindi dental vs. retroflex speech contrast in the evening. We examined the blood oxygen level dependent signal using functional magnetic resonance imaging during perceptual tasks on both the trained talker and on an untrained talker shortly after training, and again the next morning. We also employed diffusion tensor imaging to determine if individual differences in white matter structure could predict variability in overnight consolidation. We found greater activity in cortical regions associated with language processing (e.g., left insula) on the second day. Fractional anisotropy values in the anterior thalamic radiation and the uncinate fasciculus were associated with the magnitude of overnight change in perceptual behavior on the generalization (untrained) talker, after controlling for differences in sleep duration and initial learning. Our findings suggest that speech-perceptual information is subject to an overnight transfer of information to the cortex. Moreover, neural structure appears to be linked to individual differences in efficiency of overnight consolidation.

Tool use and language are highly refined human abilities which may show neural commonalities due to their potential reciprocal interaction during evolution. Recent work provided evidence for shared neural resources between tool use and syntax. However, whether activity within the tool-use network also contributes to semantic neural representations of tool nouns remains untested. To this aim, we identified the tool-use planning network with functional magnetic resonance imaging while participants used pliers. The very same participants underwent a semantic priming task including two categories, tool nouns and animal nouns, to highlight the respective underlying networks. With multivariate analyses of the activation neural patterns, we tested whether activity in tool-use brain clusters takes part in the neural representation of tool nouns as compared with animal nouns. The results revealed that word semantic categories were decoded within the left occipito-temporal cortex activated by preparing to use a tool, with similar patterns of brain activity for words within the same category. In addition, in the same area, neural activations for tool nouns were found to be higher than those for animal nouns. These findings suggest that activity in tool-use related brain areas encodes semantic information separately for tool nouns and animal nouns, thus supporting the embodiment of tool-noun processing in the tool-use sensorimotor network.

Statistical learning (SL) is the ability to rapidly track statistical regularities and learn patterns in the environment. Recent studies show that SL is constrained by domain-specific features, rather than being a uniform learning mechanism across domains and modalities. This domain-specificity has been reflected at the neural level, as SL occurs in regions primarily involved in processing of specific modalities or domains of input. However, our understanding of how SL is constrained by domain-specific features in the developing brain is severely lacking. The present study aims to identify the functional neural profiles of auditory SL of linguistic and nonlinguistic regularities among children. Thirty children between 5 and 7 years old completed an auditory fMRI SL task containing interwoven sequences of structured and random syllable/tone sequences. Using traditional group univariate analyses and a group-constrained subject-specific analysis, frontal and temporal cortices showed significant activation when processing structured versus random sequences across both linguistic and nonlinguistic domains. However, conjunction analyses failed to identify overlapping neural indices across domains. These findings are the first to compare brain regions supporting SL of linguistic and nonlinguistic regularities in the developing brain and indicate that auditory SL among developing children may be constrained by domain-specific features.

When we understand language, we recognize words and combine them into sentences. In this article, we explore the hypothesis that listeners use probabilistic information about words to build syntactic structure. Recent work has shown that lexical probability and syntactic structure both modulate the delta-band (<4 Hz) neural signal. Here, we investigated whether the neural encoding of syntactic structure changes as a function of the distributional properties of a word. To this end, we analyzed MEG data of 24 native speakers of Dutch who listened to three fairytales with a total duration of 49 min. Using temporal response functions and a cumulative model-comparison approach, we evaluated the contributions of syntactic and distributional features to the variance in the delta-band neural signal. This revealed that lexical surprisal values (a distributional feature), as well as bottom-up node counts (a syntactic feature) positively contributed to the model of the delta-band neural signal. Subsequently, we compared responses to the syntactic feature between words with high- and low-surprisal values. This revealed a delay in the response to the syntactic feature as a consequence of the surprisal value of the word: high-surprisal values were associated with a delayed response to the syntactic feature by 150-190 ms. The delay was not affected by word duration, and did not have a lexical origin. These findings suggest that the brain uses probabilistic information to infer syntactic structure, and highlight an importance for the role of time in this process.

It is well-established from fMRI experiments employing gradient echo echo-planar imaging (EPI) sequences that overt speech production introduces signal artefacts compromising accurate detection of task-related responses. Both design and post-processing (denoising) techniques have been proposed and implemented over the years to mitigate the various noise sources. Recently, fMRI studies of speech production have begun to adopt multiband EPI sequences that offer better signal-to-noise ratio (SNR) and temporal resolution allowing adequate sampling of physiological noise sources (e.g., respiration, cardiovascular effects) and reduced scanner acoustic noise. However, these new sequences may also introduce additional noise sources. In this study, we demonstrate the impact of applying several noise-estimation and removal approaches to continuous multiband fMRI data acquired during a naming-to-definition task, including rigid body motion regression and outlier censoring, principal component analysis for removal of cerebrospinal fluid (CSF)/edge-related noise components, and global fMRI signal regression (using two different approaches) compared to a baseline of realignment and unwarping alone. Our results show the strongest and most spatially extensive sources of physiological noise are the global signal fluctuations arising from respiration and muscle action and CSF/edge-related noise components, with residual rigid body motion contributing relatively little variance. Interestingly, denoising approaches tended to reduce and enhance task-related BOLD signal increases and decreases, respectively. Global signal regression using a voxel-wise linear model of the global signal estimated from unmasked data resulted in dramatic improvements in temporal SNR. Overall, these findings show the benefits of combining continuous multiband EPI sequences and denoising approaches to investigate the neurobiology of speech production.

We examined neural mechanisms associated with the learning of novel morphologically derived words in native Hebrew speakers within the Complementary Learning Systems (CLS) framework. Across four sessions, 28 participants were trained on an artificial language, which included two types of morphologically complex words: linear (root + suffix) with a salient structure, and non-linear (root interleaved with template), with a prominent derivational structure in participants' first language (L1). A third simple monomorphemic condition, which served as baseline, was also included. On the first and fourth sessions, training was followed by testing in an fMRI scanner. Our behavioural results showed decomposition of both types of complex words, with the linear structure more easily learned than the non-linear structure. Our fMRI results showed involvement of frontal areas, associated with decomposition, only for the non-linear condition, after just the first session. We also observed training-related increases in activation in temporal areas specifically for the non-linear condition, which was correlated with participants' L1 morphological awareness. These results demonstrate that morphological decomposition of derived words occurs in the very early stages of word learning, is influenced by L1 experience, and can facilitate word learning. However, in contrast to the CLS framework, we found no support for a shift from reliance on hippocampus to reliance on cortical areas in any of our conditions. Instead, our findings align more closely with recent theories showing a positive correlation between changes in hippocampus and cortical areas, suggesting that these representations co-exist and continue to interact with one another beyond initial learning.