Kristin McNealy

Cracking the Language Code: Neural Mechanisms Underlying Speech Parsing

Word segmentation, detecting word boundaries in continuous speech, is a critical aspect of language learning. Previous research in infants and adults demonstrated that a stream of speech can be readily segmented based solely on the statistical and speech cues afforded by the input. Using functional magnetic resonance imaging (fMRI), the neural substrate of word segmentation was examined on-line as participants listened to three streams of concatenated syllables, containing either statistical regularities alone, statistical regularities and speech cues, or no cues. Despite the participants’ inability to explicitly detect differences between the speech streams, neural activity differed significantly across conditions, with left-lateralized signal increases in temporal cortices observed only when participants listened to streams containing statistical regularities, particularly the stream containing speech cues. In a second fMRI study, designed to verify that word segmentation had implicitly taken place, participants listened to trisyllabic combinations that occurred with different frequencies in the streams of speech they just heard (“words,” 45 times; “partwords,” 15 times; “nonwords,” once). Reliably greater activity in left inferior and middle frontal gyri was observed when comparing words with partwords and, to a lesser extent, when comparing partwords with nonwords. Activity in these regions, taken to index the implicit detection of word boundaries, was positively correlated with participants’ rapid auditory processing skills. These findings provide a neural signature of on-line word segmentation in the mature brain and an initial model with which to study developmental changes in the neural architecture involved in processing speech cues during language learning.

Development of the Default Mode and Central Executive Networks Across Early Adolescence: a Longitudinal Study

Highlights • We examined functional connectivity in Default Mode and Central Executive Networks.• We examined the development of these functional networks in a longitudinal sample.• Each network developed stronger internal connectivity from age 10 to 13.• The networks also became increasingly anticorrelated with one another over time.• IQ related to level of within-network connectivity and between-network segregation.

No Neural Evidence of Statistical Learning During Exposure to Artificial Languages in Children with Autism Spectrum Disorders

BACKGROUND Language delay is a hallmark feature of autism spectrum disorders (ASD). The identification of word boundaries in continuous speech is a critical first step in language acquisition that can be accomplished via statistical learning and reliance on speech cues. Importantly, early word segmentation skills have been shown to predict later language development in typically developing (TD) children. METHODS Here we investigated the neural correlates of online word segmentation in children with and without ASD with a well-established behavioral paradigm previously validated for functional magnetic resonance imaging. Eighteen high-functioning boys with ASD and 18 age- and IQ-matched TD boys underwent functional magnetic resonance imaging while listening to two artificial languages (containing statistical or statistical + prosodic cues to word boundaries) and a random speech stream. RESULTS Consistent with prior findings, in TD control subjects, activity in fronto-temporal-parietal networks decreased as the number of cues to word boundaries increased. The ASD children, however, did not show this facilitatory effect. Furthermore, statistical contrasts modeling changes in activity over time identified significant learning-related signal increases for both artificial languages in basal ganglia and left temporo-parietal cortex only in TD children. Finally, the level of communicative impairment in ASD children was inversely correlated with signal increases in these same regions during exposure to the artificial languages. CONCLUSIONS This is the first study to demonstrate significant abnormalities in the neural architecture subserving language-related learning in ASD children and to link the communicative impairments observed in this population to decreased sensitivity to the statistical and speech cues available in the language input.

Altered integration of speech and gesture in children with autism spectrum disorders

The presence of gesture during speech has been shown to impact perception, comprehension, learning, and memory in normal adults and typically developing children. In neurotypical individuals, the impact of viewing co‐speech gestures representing an object and/or action (i.e., iconic gesture) or speech rhythm (i.e., beat gesture) has also been observed at the neural level. Yet, despite growing evidence of delayed gesture development in children with autism spectrum disorders (ASD), few studies have examined how the brain processes multimodal communicative cues occurring during everyday communication in individuals with ASD. Here, we used a previously validated functional magnetic resonance imaging (fMRI) paradigm to examine the neural processing of co‐speech beat gesture in children with ASD and matched controls. Consistent with prior observations in adults, typically developing children showed increased responses in right superior temporal gyrus and sulcus while listening to speech accompanied by beat gesture. Children with ASD, however, exhibited no significant modulatory effects in secondary auditory cortices for the presence of co‐speech beat gesture. Rather, relative to their typically developing counterparts, children with ASD showed significantly greater activity in visual cortex while listening to speech accompanied by beat gesture. Importantly, the severity of their socio‐communicative impairments correlated with activity in this region, such that the more impaired children demonstrated the greatest activity in visual areas while viewing co‐speech beat gesture. These findings suggest that although the typically developing brain recognizes beat gesture as communicative and successfully integrates it with co‐occurring speech, information from multiple sensory modalities is not effectively integrated during social communication in the autistic brain.

Sleep quality and neural circuit function supporting emotion regulation

BackgroundRecent laboratory studies employing an extended sleep deprivation model have mapped sleep-related changes in behavior onto functional alterations in specific brain regions supporting emotion, suggesting possible biological mechanisms for an association between sleep difficulties and deficits in emotion regulation. However, it is not yet known if similar behavioral and neural changes are associated with the more modest variability in sleep observed in daily life.MethodsWe examined relationships between sleep and neural circuitry of emotion using the Pittsburgh Sleep Quality Index and fMRI data from a widely used emotion regulation task focusing on cognitive reappraisal of negative emotional stimuli in an unselected sample of 97 adult volunteers (48 women; mean age 42.78±7.37 years, range 30–54 years old).ResultsEmotion regulation was associated with greater activation in clusters located in the dorsomedial prefrontal cortex (dmPFC), left dorsolateral prefrontal cortex (dlPFC), and inferior parietal cortex. Only one subscale from the Pittsburgh Sleep Quality Index, use of sleep medications, was related to BOLD responses in the dmPFC and dlPFC during cognitive reappraisal. Use of sleep medications predicted lesser BOLD responses during reappraisal, but other aspects of sleep, including sleep duration and subjective sleep quality, were not related to neural activation in this paradigm.ConclusionsThe relatively modest variability in sleep that is common in the general community is unlikely to cause significant disruption in neural circuits supporting reactivity or regulation by cognitive reappraisal of negative emotion. Use of sleep medication however, may influence emotion regulation circuitry, but additional studies are necessary to determine if such use plays a causal role in altering emotional responses.

Age and Experience Shape Developmental Changes in the Neural Basis of Language-Related Learning.

Very little is known about the neural underpinnings of language learning across the lifespan and how these might be modified by maturational and experiential factors. Building on behavioral research highlighting the importance of early word segmentation (i.e. the detection of word boundaries in continuous speech) for subsequent language learning, here we characterize developmental changes in brain activity as this process occurs online, using data collected in a mixed cross-sectional and longitudinal design. One hundred and fifty-six participants, ranging from age 5 to adulthood, underwent functional magnetic resonance imaging (fMRI) while listening to three novel streams of continuous speech, which contained either strong statistical regularities, strong statistical regularities and speech cues, or weak statistical regularities providing minimal cues to word boundaries. All age groups displayed significant signal increases over time in temporal cortices for the streams with high statistical regularities; however, we observed a significant right-to-left shift in the laterality of these learning-related increases with age. Interestingly, only the 5- to 10-year-old children displayed significant signal increases for the stream with low statistical regularities, suggesting an age-related decrease in sensitivity to more subtle statistical cues. Further, in a sample of 78 10-year-olds, we examined the impact of proficiency in a second language and level of pubertal development on learning-related signal increases, showing that the brain regions involved in language learning are influenced by both experiential and maturational factors.

Semantic Processing and Thought Disorder in Childhood-Onset Schizophrenia: Insights from fMRI.

Impairments in language processing and thought disorder are core symptoms of schizophrenia. Here we used fMRI to investigate functional abnormalities in the neural networks subserving sentence-level language processing in childhood-onset schizophrenia (COS). Fourteen children with COS (mean age: 13.34; IQ: 95) and 14 healthy controls (HC; mean age: 12.37; IQ: 104) underwent fMRI while performing a semantic judgment task previously shown to differentially engage semantic and syntactic processes. We report four main results. First, different patterns of functional specialization for semantic and syntactic processing were observed within each group, despite similar level of task performance. Second, after regressing out IQ, significant between-group differences were observed in the neural correlates of semantic and, to a lesser extent, syntactic processing, with HC children showing overall greater activity than COS children. Third, while these group differences were not related to effects of medications, a significant negative correlation was observed in the COS group between neuroleptic dosage and activity in the left inferior frontal gyrus for the semantic condition. Finally, COS childrens level of thought disorder was significantly correlated with task-related activity in language-relevant networks. Taken together, these findings suggest that children with COS exhibit aberrant patterns of neural activity during semantic, and to a lesser extent syntactic, processing and that these functional abnormalities in language-relevant networks are significantly related to severity of thought disorder.

Using confirmatory factor analysis to measure contemporaneous activation of defined neuronal networks in functional magnetic resonance imaging

Functional neuroimaging often generates large amounts of data on regions of interest. Such data can be addressed effectively with a widely-used statistical technique based on measurement theory that has not yet been applied to neuroimaging. Confirmatory factor analysis is a convenient hypothesis-driven modeling environment that can be used to conduct formal statistical tests comparing alternative hypotheses regarding the elements of putative neuronal networks. In such models, measures of each activated region of interest are treated as indicators of an underlying latent construct that represents the contemporaneous activation of the elements in the network. As such, confirmatory factor analysis focuses analyses on the activation of hypothesized networks as a whole, improves statistical power by modeling measurement error, and provides a theory-based approach to data reduction with a robust statistical basis. This approach is illustrated using data on seven regions of interest in a hypothesized mesocorticostriatal reward system in a sample of 262 adult volunteers assessed during a card-guessing reward task. A latent construct reflecting contemporaneous activation of the reward system was found to be significantly associated with a latent construct measuring impulsivity, particularly in males.