Project description:Phelan-McDermid syndrome (PMS) is a single-locus cause of developmental delay, autism spectrum disorder, and minimal verbal abilities. There is an urgent need to identify objective outcome measures of expressive language for use in this and other minimally verbal populations. One potential tool is an automated language processor called Language ENvironment Analysis (LENA). LENA was used to obtain over 542 h of audio in 18 children with PMS. LENA performance was adequate in a subset of children with PMS, specifically younger children and those with fewer stereotypic vocalizations. One LENA-derived language measure, Vocalization Ratio, had improved accuracy in this sample and may represent a novel expressive language measure for use in severely affected populations.
Project description:While stigma toward autistic individuals has been well documented, less is known about how autism is perceived relative to other stigmatized disabilities. As a highly stigmatized condition with similar social cognitive features to autism, schizophrenia may offer a useful comparison for stigma. Previous studies have found that autistic people may be perceived more favorably than those with schizophrenia, but little is known about the underlying volitional thoughts that contribute to differences in how these conditions are perceived. The present study utilizes a mixed-methods approach, allowing for a detailed understanding of how young adults perceive different diagnostic labels. 533 college undergraduates completed questionnaires reflecting their perceptions of one of eight diagnostic labels: four related to autism (autism, autistic, autism spectrum disorder, or Asperger's), two related to schizophrenia (schizophrenia or schizophrenic), and two related to an unspecified clinical condition (clinical diagnosis or clinical disorder). Participants also completed an open-ended question regarding their thoughts about, and exposure to, these labels. Responses were compared across broader diagnostic categories (autism, schizophrenia, general clinical condition), with thematic analysis used to assess the broader themes occurring within the open-ended text. While perceptions did not differ significantly for person-first and identity-first language within labels, several differences were apparent across labels. Specifically, quantitative results indicated greater prejudice towards autism and schizophrenia than the generic clinical condition, with schizophrenia associated with more perceived fear and danger, as well as an increased preference for social distance, compared to autism. Patterns in initial codes differed across diagnostic labels, with greater variation in responses about autism than responses about schizophrenia or the general clinical condition. While participants described a range of attitudes toward autism (patronizing, exclusionary, and accepting) and schizophrenia (fear, prejudice, and empathy), they refrained from describing their attitudes toward the general clinical label, highlighting the centrality of a cohesive group identity for the development of stigma. Finally, participants reported a number of misconceptions about autism and schizophrenia, with many believing features such as savant syndrome to be core characteristics of the conditions. These findings offer a more detailed account of how non-autistic individuals view autism and may therefore aid in the development of targeted programs to improve attitudes toward autism.
Project description:Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 participants (96 ASD, 28 TD, 45 ADHD) ages 7 to 17 were evaluated with the Autism Diagnostic Observation Schedule. Transcripts of one task were analyzed to generate seven ALMs: mean length of utterance in morphemes, number of different word roots (NDWR), um proportion, content maze proportion, unintelligible proportion, c-units per minute, and repetition proportion. With the exception of repetition proportion (p [Formula: see text]), nonparametric ANOVAs showed significant group differences (p[Formula: see text]). The TD and ADHD groups did not differ from each other in post-hoc analyses. With the exception of NDWR, the ASD group showed significantly (p[Formula: see text]) lower scores than both comparison groups. The ALMs were correlated with standardized clinical and language evaluations of ASD. In age- and IQ-adjusted logistic regression analyses, four ALMs significantly predicted ASD status with satisfactory accuracy (67.9-75.5%). When ALMs were combined together, accuracy improved to 82.4%. These ALMs offer a promising approach for generating novel outcome measures.
Project description:Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders involving a number of deficits to linguistic cognition. The gap between genetics and the pathophysiology of ASD remains open, in particular regarding its distinctive linguistic profile. The goal of this article is to attempt to bridge this gap, focusing on how the autistic brain processes language, particularly through the perspective of brain rhythms. Due to the phenomenon of pleiotropy, which may take some decades to overcome, we believe that studies of brain rhythms, which are not faced with problems of this scale, may constitute a more tractable route to interpreting language deficits in ASD and eventually other neurocognitive disorders. Building on recent attempts to link neural oscillations to certain computational primitives of language, we show that interpreting language deficits in ASD as oscillopathic traits is a potentially fruitful way to construct successful endophenotypes of this condition. Additionally, we will show that candidate genes for ASD are overrepresented among the genes that played a role in the evolution of language. These genes include (and are related to) genes involved in brain rhythmicity. We hope that the type of steps taken here will additionally lead to a better understanding of the comorbidity, heterogeneity, and variability of ASD, and may help achieve a better treatment of the affected populations.
Project description:The relation between the timing of language input and development of neural organization for language processing in adulthood has been difficult to tease apart because language is ubiquitous in the environment of nearly all infants. However, within the congenitally deaf population are individuals who do not experience language until after early childhood. Here, we investigated the neural underpinnings of American Sign Language (ASL) in 2 adolescents who had no sustained language input until they were approximately 14 years old. Using anatomically constrained magnetoencephalography, we found that recently learned signed words mainly activated right superior parietal, anterior occipital, and dorsolateral prefrontal areas in these 2 individuals. This spatiotemporal activity pattern was significantly different from the left fronto-temporal pattern observed in young deaf adults who acquired ASL from birth, and from that of hearing young adults learning ASL as a second language for a similar length of time as the cases. These results provide direct evidence that the timing of language experience over human development affects the organization of neural language processing.
Project description:Studies on bilingual word processing have demonstrated that the two languages in a mental lexicon can be parallelly activated. However, it is under discussion whether the activated, non-target language gets involved in the target language. The present study aimed to investigate the role of the first language (L1, the non-target one) translation in the second language (L2, the target one) word processing. The tasks of semantic relatedness judgment and lexical decision were both adopted, to explore the relation of the possible L1 involvement and the task demand. Besides, bilinguals with relatively higher and lower L2 proficiency were recruited, to clarify the potential influence of L2 proficiency. Results showed that the manipulation of L1 translation exerted an influence on bilinguals' task performances, indicating that L1 translation was involved, but did not just serve as a by-product when bilinguals were processing L2 words. And about the influence of L2 proficiency, the higher proficiency bilinguals performed better than the lower proficiency ones when the L1 translations could be taken advantage of, indicating a better access to L1 translation in L2 word processing, as bilinguals' L2 proficiency increased. As for the task demands, the L1 translation was partially involved in Experiment 1 while a full involvement was observed in Experiment 2, suggesting a differed depth of L1 translation involvement, if the task demands allowed. The present study supplemented the previous ones due to its participants (the intermediate bilinguals) and tasks (the tasks of semantic relatedness judgment and lexical decision); besides, it provided an interesting view into interpreting the "task schema" of the BIA+ model.
Project description:The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality). From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches. Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation.
Project description:Linguistic and cognitive abilities manifest huge heterogeneity in children with autism spectrum disorder (ASD). Some children present with commensurate language and cognitive abilities, while others show more variable patterns of development. Using spontaneous language samples, we investigate the presence and extent of grammatical language impairment in a heterogeneous sample of children with ASD. Findings from our sample suggest that children with ASD can be categorized into three meaningful subgroups: those with normal language, those with marked difficulty in grammatical production but relatively intact vocabulary, and those with more globally low language abilities. These findings support the use of sensitive assessment measures to evaluate language in autism, as well as the utility of within-disorder comparisons, in order to comprehensively define the various cognitive and linguistic phenotypes in this heterogeneous disorder.
Project description:Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with substantial clinical heterogeneity, especially in language and communication ability. There is a need for validated language outcome measures that show sensitivity to true change for this population. We used Natural Language Processing to analyze expressive language transcripts of 64 highly-verbal children and young adults (age: 6-23 years, mean 12.8 years; 78.1% male) with ASD to examine the validity across language sampling context and test-retest reliability of six previously validated Automated Language Measures (ALMs), including Mean Length of Utterance in Morphemes, Number of Distinct Word Roots, C-units per minute, unintelligible proportion, um rate, and repetition proportion. Three expressive language samples were collected at baseline and again 4 weeks later. These samples comprised interview tasks from the Autism Diagnostic Observation Schedule (ADOS-2) Modules 3 and 4, a conversation task, and a narration task. The influence of language sampling context on each ALM was estimated using either generalized linear mixed-effects models or generalized linear models, adjusted for age, sex, and IQ. The 4 weeks test-retest reliability was evaluated using Lin's Concordance Correlation Coefficient (CCC). The three different sampling contexts were associated with significantly (P < 0.001) different distributions for each ALM. With one exception (repetition proportion), ALMs also showed good test-retest reliability (median CCC: 0.73-0.88) when measured within the same context. Taken in conjunction with our previous work establishing their construct validity, this study demonstrates further critical psychometric properties of ALMs and their promising potential as language outcome measures for ASD research.