Patterns of objectively measured motor activity among infants developing ASD and concerns for ADHD.

BACKGROUND: Heightened motor activity is a hallmark of attention-deficit/hyperactivity disorder (ADHD), yet high activity levels are also often reported in young children with autism spectrum disorder (ASD). It is currently unclear whether increased motor activity represents a distinct versus shared early predictor of ASD and ADHD; no prior studies have directly examined this prospectively. We investigated differences in longitudinal patterns of objectively measured motor activity during early development. METHODS: Participants included 113 infants at high and low risk for ASD or ADHD. Continuous motion-based activity was recorded using tri-axial accelerometers at 12, 18, 24, and 36 months of age. At 36 months, participants were categorized into one of three outcome groups: ASD (n = 19), ADHD Concerns (n = 17), and Typically Developing (TD; n = 77). Group differences in trajectories of motor activity were examined in structured and semistructured contexts. Associations with behaviors relevant to ASD, ADHD, and general development were also examined. RESULTS: In both structured and semistructured contexts, both the ASD and ADHD Concerns groups exhibited heightened activity relative to the TD group by 18 months; the ASD group exhibited higher activity than the ADHD Concerns group at 24-36 months in the structured context only. Attention/behavior regulation, nonverbal, and verbal development-but not social engagement-were differentially associated with objectively measured activity by outcome group across contexts. CONCLUSIONS: Overactivity may be a shared, rather than distinct, precursor of atypical development in infants/toddlers developing ASD and concerns for ADHD, emerging as early as 18 months. Group differences in overactivity may be context-specific and associated with different underlying mechanisms.

Applications of Pose Estimation in Human Health and Performance across the Lifespan.

The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively low-cost cameras (e.g., smartphones, tablets, laptop computers). In our view, these technologies offer clear and exciting potential to make measurement of human movement substantially more accessible; for example, a clinician could perform a quantitative motor assessment directly in a patient's home, a researcher without access to expensive motion capture equipment could analyze movement kinematics using a smartphone video, and a coach could evaluate player performance with video recordings directly from the field. In this review, we combine expertise and perspectives from physical therapy, speech-language pathology, movement science, and engineering to provide insight into applications of pose estimation in human health and performance. We focus specifically on applications in areas of human development, performance optimization, injury prevention, and motor assessment of persons with neurologic damage or disease. We review relevant literature, share interdisciplinary viewpoints on future applications of these technologies to improve human health and performance, and discuss perceived limitations.

Early behavioral profiles elucidating vulnerability and resiliency to later ASD outcomes.

Infant siblings of children with autism spectrum disorder (ASD) exhibit greater heterogeneity in behavioral presentation and outcomes relative to infants at low familial risk (LR), yet there is limited understanding of the diverse developmental profiles that characterize these infants. We applied a hierarchical agglomerative cluster analysis approach to parse developmental heterogeneity in 420 toddlers with heightened (HR) and low (LR) familial risk for ASD using measures of four dimensions of development: language, social, play, and restricted and repetitive behaviors (RRB). Results revealed a two-cluster solution. Comparisons of clusters revealed significantly lower language, social, and play performance, and higher levels of restricted and repetitive behaviors in Cluster 1 relative to Cluster 2. In Cluster 1, 25% of children were later diagnosed with ASD compared to 8% in Cluster 2. Comparisons within Cluster 1 between subgroups of toddlers having ASD+ versus ASD- 36-month outcomes revealed significantly lower functioning in the ASD+ subgroup across cognitive, motor, social, language, symbolic, and speech dimensions. Findings suggest profiles of early development associated with resiliency and vulnerability to later ASD diagnosis, with multidimensional developmental lags signaling vulnerability to ASD diagnosis.

Error patterns of native and non-native listeners' perception of speech in noise.

Speech perception in noise requires both bottom-up sampling of the stimulus and top-down reconstruction of the masked signal from a language model. Previous studies have provided mixed evidence about the exact role that linguistic knowledge plays in native and non-native listeners' perception of masked speech. This paper describes an analysis of whole utterance, content word, and morphosyntactic error patterns to test the prediction that non-native listeners are uniquely affected by energetic and informational masks because of limited information at multiple linguistic levels. The results reveal a consistent disadvantage for non-native listeners at all three levels in challenging listening environments.

Stability and plasticity in neural encoding of linguistically relevant pitch patterns.

While lifelong language experience modulates subcortical encoding of pitch patterns, there is emerging evidence that short-term training introduced in adulthood also shapes subcortical pitch encoding. Here we use a cross-language design to examine the stability of language experience-dependent subcortical plasticity over multiple days. We then examine the extent to which behavioral relevance induced by sound-to-category training leads to plastic changes in subcortical pitch encoding in adulthood relative to adolescence, a period of ongoing maturation of subcortical and cortical auditory processing. Frequency-following responses (FFRs), which reflect phase-locked activity from subcortical neural ensembles, were elicited while participants passively listened to pitch patterns reflective of Mandarin tones. In experiment 1, FFRs were recorded across three consecutive days from native Chinese-speaking (n = 10) and English-speaking (n = 10) adults. In experiment 2, FFRs were recorded from native English-speaking adolescents (n = 20) and adults (n = 15) before, during, and immediately after a session of sound-to-category training, as well as a day after training ceased. Experiment 1 demonstrated the stability of language experience-dependent subcortical plasticity in pitch encoding across multiple days of passive exposure to linguistic pitch patterns. In contrast, experiment 2 revealed an enhancement in subcortical pitch encoding that emerged a day after the sound-to-category training, with some developmental differences observed. Taken together, these findings suggest that behavioral relevance is a critical component for the observation of plasticity in the subcortical encoding of pitch.NEW & NOTEWORTHY We examine the timescale of experience-dependent auditory plasticity to linguistically relevant pitch patterns. We find extreme stability in lifelong experience-dependent plasticity. We further demonstrate that subcortical function in adolescents and adults is modulated by a single session of sound-to-category training. Our results suggest that behavioral relevance is a necessary ingredient for neural changes in pitch encoding to be observed throughout human development. These findings contribute to the neurophysiological understanding of long- and short-term experience-dependent modulation of pitch.

The role of age and executive function in auditory category learning.

Auditory categorization is a natural and adaptive process that allows for the organization of high-dimensional, continuous acoustic information into discrete representations. Studies in the visual domain have identified a rule-based learning system that learns and reasons via a hypothesis-testing process that requires working memory and executive attention. The rule-based learning system in vision shows a protracted development, reflecting the influence of maturing prefrontal function on visual categorization. The aim of the current study was twofold: (a) to examine the developmental trajectory of rule-based auditory category learning from childhood through adolescence and into early adulthood and (b) to examine the extent to which individual differences in rule-based category learning relate to individual differences in executive function. A sample of 60 participants with normal hearing-20 children (age range=7-12years), 21 adolescents (age range=13-19years), and 19 young adults (age range=20-23years)-learned to categorize novel dynamic "ripple" sounds using trial-by-trial feedback. The spectrotemporally modulated ripple sounds are considered the auditory equivalent of the well-studied "Gabor" patches in the visual domain. Results reveal that auditory categorization accuracy improved with age, with young adults outperforming children and adolescents. Computational modeling analyses indicated that the use of the task-optimal strategy (i.e., a conjunctive rule-based learning strategy) improved with age. Notably, individual differences in executive flexibility significantly predicted auditory category learning success. The current findings demonstrate a protracted development of rule-based auditory categorization. The results further suggest that executive flexibility coupled with perceptual processes play important roles in successful rule-based auditory category learning.

Objective measurement of movement variability using wearable sensors predicts ASD outcomes in infants at high likelihood for ASD and ADHD.

Early motor delays and differences are common among children with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Yet, little work has shown whether there are early atypical motor signs that differentiate these groups. Quantitative measures of movement variability hold promise for improving the identification of subtle and specific differences in motor function among infants and toddlers at high likelihood for ASD and ADHD. To this end, we created a novel quantitative measure of movement variability (movement curvature) and conducted a preliminary investigation as to whether this measure improves outcome predictions. We used a wearable triaxial accelerometer to evaluate continuous motion-based activity in infants at high and low likelihood for ASD and ADHD at 12, 18, 24, and 36 months of age. At 36 months, participants were categorized into three outcome groups: ASD (n = 19), ADHD concerns (n = 17), and a comparison group (n = 82). We examined group differences in movement curvature and whether movement curvature is predictive of a later ASD or ADHD concerns classification. We found that movement curvature was significantly lower in infants with later ASD diagnosis at 18, 24, and 36 months of age compared to infants with either ADHD concerns or those in the comparison group. Movement curvature was also a significant predictor of ASD at 18, 24, and 36 months (AUC 0.66-0.71; p = 0.005-0.039) and when adjusting for high ASD likelihood at 18 and 24 months (AUC 0.90, p = 0.05-0.019). These results indicate that lower movement curvature may be a feature of early motor differences in infants with later ASD diagnosis as early as 18 months of age.

Diagnostic certainty during in-person and telehealth autism evaluations.

Background: Many diagnostic evaluations abruptly shifted to telehealth during the COVID-19 pandemic; however, little is known about the impact on diagnosis patterns for children evaluated for autism spectrum disorder (ASD). The purpose of this clinical research study was to examine (1) the frequency of diagnoses evaluated beyond ASD; (2) the frequency of diagnoses made, including ASD; and (3) clinician diagnostic certainty for all diagnoses evaluated for children who received an evaluation due to primary concerns about ASD via telehealth during the pandemic compared to those evaluated in person before the pandemic at an ASD specialty clinic. Methods: The sample included 2192 children, 1-17 years (M = 6.5 years; SD = 3.9), evaluated by a physician/psychologist at an ASD specialty center. A total of 649 children were evaluated in-person September 1, 2019-March 13, 2020 (pre-pandemic) and 1543 were evaluated via telehealth March 14, 2020-July 26, 2021 (during pandemic). Upon completion of each evaluation, clinicians provided a final diagnostic determination (i.e., "Yes," "No," "Possible," or "Not Assessed") for the following DSM-5 conditions: ASD, attention-deficit/hyperactivity disorder (ADHD), intellectual developmental disorder (IDD), anxiety (ANX), depression (DEP), and behavioral disorder (BD). "Possible" indicated lower certainty and the diagnosis was not provided. "Not Assessed" indicated the disorder was not evaluated. Results: Diagnostic certainty for ASD and ADHD was lower and clinicians evaluated for and made diagnoses of IDD less often during evaluations that occurred via telehealth during the pandemic versus in person before the pandemic. DEP and BD were diagnosed more frequently, diagnostic certainty of DEP was lower, and no differences in the frequency of ANX diagnoses emerged during evaluations conducted via telehealth during the pandemic compared to those conducted in person before the pandemic. Conclusions: Differences emerged in the frequency of diagnoses evaluated and made and diagnostic certainty for evaluations conducted via telehealth during the pandemic compared to in person before the pandemic, which likely impacted patients and reflect real-word challenges. Future work should examine whether these patterns are generalizable and the mechanisms that contribute to these differences.

Neural correlates and predictors of speech and language development in infants at elevated likelihood for autism: a systematic review.

Autism spectrum disorder (ASD) is an increasingly prevalent and heterogeneous neurodevelopmental condition, characterized by social communicative differences, and a combination of repetitive behaviors, focused interests, and sensory sensitivities. Early speech and language delays are characteristic of young autistic children and are one of the first concerns reported by parents; often before their child's second birthday. Elucidating the neural mechanisms underlying these delays has the potential to improve early detection and intervention efforts. To fill this gap, this systematic review aimed to synthesize evidence on early neurobiological correlates and predictors of speech and language development across different neuroimaging modalities in infants with and without a family history of autism [at an elevated (EL infants) and low likelihood (LL infants) for developing autism, respectively]. A comprehensive, systematic review identified 24 peer-reviewed articles published between 2012 and 2023, utilizing structural magnetic resonance imaging (MRI; n = 2), functional MRI (fMRI; n = 4), functional near-infrared spectroscopy (fNIRS; n = 4), and electroencephalography (EEG; n = 14). Three main themes in results emerged: compared to LL infants, EL infants exhibited (1) atypical language-related neural lateralization; (2) alterations in structural and functional connectivity; and (3) mixed profiles of neural sensitivity to speech and non-speech stimuli, with some differences detected as early as 6 weeks of age. These findings suggest that neuroimaging techniques may be sensitive to early indicators of speech and language delays well before overt behavioral delays emerge. Future research should aim to harmonize experimental paradigms both within and across neuroimaging modalities and additionally address the feasibility, acceptability, and scalability of implementing such methodologies in non-academic, community-based settings.

Prevalence of gastrointestinal symptoms among autistic individuals, with and without co-occurring intellectual disability.

Gastrointestinal symptoms (GI) are very common among individuals on the autism spectrum. Prior research reports mixed findings regarding whether individuals with autism and co-occurring intellectual disability (ID) have elevated risk of gastrointestinal symptoms relative to individuals with autism alone. GI symptoms can be challenging to assess in individuals with autism spectrum disorder (ASD) and/or ID given challenges with language, communication, and interoception. Prior research has tended to only include individuals with documented presence or absence of GI symptoms or conditions, that is, to exclude observations in which there is uncertainty regarding presence of GI symptoms. Therefore, none of the prior autism studies reported the association between ID and the certainty regarding presence or absence of GI symptoms. The objective of this study was to examine differences in parental certainty and odds of reporting gastrointestinal signs and symptoms among children on the autism spectrum, with and without intellectual disability. Participants were 308 children (36% ID) with a clinical diagnosis of autism spectrum disorder (6-17 years). Parents endorsed whether their child had experienced or displayed a range of signs or symptoms related to GI problems in the past 3 months. Parents of autistic children with ID were less certain about the presence of more subjective symptoms, including abdominal pain, nausea, and bloating. Conversely, certainty regarding more objective signs (e.g., constipation, diarrhea, spitting up, etc.) was not significantly different. More accurate measures for GI signs/symptoms are needed for this population.

Diagnostic Stability and Phenotypic Differences Among School-Age Children Diagnosed With ASD Before Age 2.

Given the importance of early detection, it is critical to understand the non-linearity in manifestation of ASD before age 24 months, when ASD symptoms are beginning to consolidate, through the age of 36 months when stability of ASD diagnosis is reportedly high into school-age when increased demands may challenge previously successful compensatory processes and permit first ASD detection. We employed a prospective, longitudinal design focused on children with an older sibling with ASD (n = 210) who received diagnostic evaluations at mean ages of 15.4 months (Time 1), 36.6 months (Time 2), and 5.7 years (Time 3) to examine: (1) diagnostic stability, (2) developmental trajectories associated with different patterns of ASD vs. non-ASD classifications, and (3) predictors of classification group over time. Clinical best estimate (CBE) diagnosis of ASD or non-ASD was made at each time point. Linear mixed-effects models were implemented to examine differences in developmental trajectories of stable and dynamic diagnostic groups. Multinomial logistic regression analyses were used to examine predictors of the likelihood of belonging to each CBE diagnostic classification group. Results revealed that sensitivity and stability of an ASD diagnosis significantly increased from Time 1 (sensitivity: 52%; stability: 63%) to Time 2 (sensitivity: 86%; stability: 68%). Different developmental trajectories of autism symptom severity and non-verbal and verbal IQ were observed across groups, with differences first observed at Time 1 and becoming more pronounced through Time 3. Presence of restricted and repetitive behaviors as well as limitations in initiation of joint attention and expressive language skills differentially predicted the likelihood of belonging to the different CBE diagnostic classification groups. Results suggest that ASD symptoms may emerge or attenuate over time, with some children meeting diagnosis at follow-up, and other children no longer meeting diagnostic criteria. From a systems perspective, diagnostic non-linearity may be viewed as a dynamic developmental process, where emergent properties arising from various biological, genetic, and experiential factors interact, culminating in phenotypic phenomena that change over time. Clinical implications include extending universal ASD and social communication screening into school-age, supporting families' understanding of diagnostic shifts, and ensuring unbiased diagnostic decision-making when following children with ASD.

Analysis of Race and Sex Bias in the Autism Diagnostic Observation Schedule (ADOS-2).

Importance: There are long-standing disparities in the prevalence of autism spectrum disorder (ASD) across race and sex. Surprisingly, few studies have examined whether these disparities arise partially out of systematic biases in the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), the reference standard measure of ASD. Objective: To examine differential item functioning (DIF) of ADOS-2 items across sex and race. Design, Setting, and Participants: This is a cross-sectional study of children who were evaluated for ASD between 2014 and 2020 at a specialty outpatient clinic located in the Mid-Atlantic region of the US. Data were analyzed from July 2021 to February 2022. Exposures: Child race (Black/African American vs White) and sex (female vs male). Main Outcomes and Measures: Item-level biases across ADOS-2 harmonized algorithm items, including social affect (SA; 10 items) and repetitive/restricted behaviors (RRBs; 4 items), were evaluated across 3 modules. Measurement bias was identified by examining DIF and differential test functioning (DTF), within a graded response, item response theory framework. Statistical significance was determined by a likelihood ratio χ2 test, and a series of metrics was used to examine the magnitude of DIF and DTF. Results: A total of 6269 children (mean [SD] age, 6.77 [3.27] years; 1619 Black/African American [25.9%], 3151 White [50.3%], and 4970 male [79.4%]), were included in this study. Overall, 16 of 140 ADOS-2 diagnostic items (11%) had a significant DIF. For race, 8 items had a significant DIF, 6 of which involved SA. No single item showed DIF consistently across all modules. Most items with DIF had greater difficulty and poorer discrimination in Black/African American children compared with White children. For sex, 5 items showed significant DIF. DIF was split across SA and RRB. However, hand mannerisms evidenced DIF across all 5 algorithms, with generally greater difficulty. The magnitude of DIF was only moderate to large for 2 items: hand mannerisms (among female children) and repetitive interests (among Black/African American children). The overall estimated effect of DIF on total DTF was not large. Conclusions and Relevance: These findings suggest that the ADOS-2 does not have widespread systematic measurement bias across race or sex. However, the findings raise some concerns around underdetection that warrant further research.

The influence of race on parental beliefs and concerns during an autism diagnosis: A mixed-method analysis.

LAY ABSTRACT: The goal of this study was to examine if there were differences between races in parental concern and belief about autism spectrum disorder (ASD) and the perspectives of clinicians. We studied 489 children with ASD who were having their first evaluation at an ASD clinic. Parents of White children most often believed that their child had ASD. However, White children whose parents believed the child had ASD were less severe in their symptoms. Parents of Black/African American or Hispanic children were more likely to have concerns about communication than parents of White children. In Hispanic families, parental concern about social communication was related to more severe symptoms in children. We discuss the implications of our findings for diagnosis.

Replication study for ADOS-2 cut-offs to assist evaluation of autism spectrum disorder.

The Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) has been widely used for ASD assessment. While prior studies investigated sensitivity and specificity of ADOS-2 Modules 1-3, there has been limited research addressing algorithm cut-off scores to optimize ADOS-2 classification. The goal of this study was to assess algorithm cut-off scores for diagnosing ASD with Modules 1-3, and to evaluate alignment of the ADOS-2 classification with the best estimate clinical diagnosis. Participants included 3144 children aged 31 months or older who received ADOS-2 Modules 1-3, as well as the best estimate clinical diagnosis. Five classification statistics were reported for each module: sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (i.e., Receiver Operator Classification Statistic), and these statistics were calculated for the optimal cut-off score. Frequency tables were used to compare ADOS-2 classification and the best estimate clinical diagnosis. Half of the sample received Module 3, 21% received Module 2, and 29% received Module 1. The overall prevalence of ASD was 60%; the male-to-female ratio was 4:1, and half of the sample was non-White. Across all modules, the autism spectrum cut-off score from the ADOS-2 manual resulted in high sensitivity (95%+) and low specificity (63%-73%). The autism cut-off score resulted in better specificity (76%-86%) with favorable sensitivity (81%-94%). The optimal cut-off scores for all modules based on the current sample were within the autism spectrum classification range except Module 2 Algorithm 2. In the No ASD group, 29% had false positives (ADOS-2 autism spectrum classification or autism classification). The ADOS-2 autism spectrum classification did not indicate directionality for diagnostic outcome (ASD 56% vs. No ASD 44%). While cut-off scores of ADOS-2 Modules 1-3 in the manual yielded good clinical utility in ASD assessment, false positives and low predictability of the autism spectrum classification remain challenging for clinicians.

Profiles and correlates of language and social communication differences among young autistic children.

Delays in early language development are characteristic of young autistic children, and one of the most recognizable first concerns that motivate parents to seek a diagnostic evaluation for their child. Although early language abilities are one of the strongest predictors of long-term outcomes, there is still much to be understood about the role of language impairment in the heterogeneous phenotypic presentation of autism. Using a person-centered, Latent Profile Analysis, we first aimed to identify distinct patterns of language and social communication ability in a clinic-based sample of 498 autistic children, ranging in age from 18 to 60 months (M = 33 mo, SD = 12 mo). Next, a multinomial logistic regression analysis was implemented to examine sociodemographic and child-based developmental differences among the identified language and social communication profiles. Three clinically meaningful profiles were identified from parent-rated and clinician-administered measures: Profile 1 (48% of the sample) "Relatively Low Language and Social Communication Abilities," Profile 2 (34% of the sample) "Relatively Elevated Language and Social Communication Abilities," and Profile 3 (18% of the sample) "Informant Discrepant Language and Relatively Elevated Social Communication Abilities." Overall, young autistic children from the lowest-resource households exhibited the lowest language and social communication abilities, and the lowest non-verbal problem-solving and fine-motor skills, along with more features of attention-deficit/hyperactivity disorder and atypical auditory processing. These findings highlight the need for effective community-based implementation strategies for young autistic children from low-resource households and underrepresented communities to improve access to individualized quality care.

Neural tracking of the speech envelope is differentially modulated by attention and language experience.

The ability to selectively attend to a speech signal amid competing sounds is a significant challenge, especially for listeners trying to comprehend non-native speech. Attention is critical to direct neural processing resources to the most essential information. Here, neural tracking of the speech envelope of an English story narrative and cortical auditory evoked potentials (CAEPs) to non-speech stimuli were simultaneously assayed in native and non-native listeners of English. Although native listeners exhibited higher narrative comprehension accuracy, non-native listeners exhibited enhanced neural tracking of the speech envelope and heightened CAEP magnitudes. These results support an emerging view that although attention to a target speech signal enhances neural tracking of the speech envelope, this mechanism itself may not confer speech comprehension advantages. Our findings suggest that non-native listeners may engage neural attentional processes that enhance low-level acoustic features, regardless if the target signal contains speech or non-speech information.

Parental relationship status and age at autism spectrum disorder diagnosis of their child.

LAY ABSTRACT: Autism spectrum disorder (ASD) can be diagnosed as early as 18 months of age. However, the average age at diagnosis in the United States is over 2 years later. A lot has been written about the many barriers families face when seeking a diagnosis for their child. One area of research that has received no attention is whether separation between a child's biological parents affects the age at which a child is diagnosed with ASD. This study was conducted among 561 children who were receiving an ASD diagnosis for the first time. On average, these children were 5 years of age. The study took place in an urban, outpatient specialty autism clinic in the United States. Biological parents self-reported their relationship status during the evaluation. This was categorized as either "together" (married or living together but not married) or "not together" (separated, divorced, or never married). At the time of diagnosis, most children's biological parents were together (69%). We found children of parents who were together were diagnosed 1.4 years earlier than those who were not together. These findings have important implications for providing support to families that separate early in a child's life, with the goal of reducing the age at ASD evaluation among single parents and those who have been separated from their child's other biological parent. Providing support to these families is important since earlier age at diagnosis leads to earlier intervention, which can improve long-term outcomes for the child, family, and community as a whole.

Informant (Dis)Agreement on Ratings of Challenging Behaviors and Social Communication in Preschool Children With Autism Spectrum Disorder.

Purpose Cross-informant ratings are considered best practice for assessing children with autism spectrum disorder (ASD). However, informant disagreement often occurs, which can pose significant challenges to various aspects of clinical services. This study explored the degree of parent and speech-language pathologist (SLP) agreement on ratings of challenging behaviors and social communication skills in preschool children with ASD. Method Fifty-eight informant ratings of challenging behaviors and social communication skills were collected from parents and SLPs on the same 29 preschool children with ASD (M = 49.93 months, SD = 11.67 months) using the Pervasive Developmental Disorder Behavior Inventory. Parent versus SLP group rating comparisons were assessed with paired t tests and Cohen's d effect sizes. Intraclass correlation coefficients were calculated to examine interrater reliability between individual parent and SLP ratings. Bland-Altman plots were generated to evaluate informant agreement across the entire range of Pervasive Developmental Disorder Behavior Inventory composite scores. Results Group comparisons indicated that parents rated arousal regulation problems as more severe than SLPs, with no other group differences observed. Parents and SLPs exhibited poor agreement on ratings of challenging behaviors; however, moderate to good agreement was observed for social communication ratings. Conclusions These results highlight the importance of including parents in the assessment and treatment planning process for preschool children with ASD, as parents may report key behavioral concerns that clinicians may not otherwise observe. Understanding behaviors that may be more prone to informant disagreement has implications for promoting a shared understanding of behavioral concerns and treatment targets between parents and clinicians.

Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses.

Purpose Speech-evoked neurophysiological responses are often collected to answer clinically and theoretically driven questions concerning speech and language processing. Here, we highlight the practical application of machine learning (ML)-based approaches to analyzing speech-evoked neurophysiological responses. Method Two categories of ML-based approaches are introduced: decoding models, which generate a speech stimulus output using the features from the neurophysiological responses, and encoding models, which use speech stimulus features to predict neurophysiological responses. In this review, we focus on (a) a decoding model classification approach, wherein speech-evoked neurophysiological responses are classified as belonging to 1 of a finite set of possible speech events (e.g., phonological categories), and (b) an encoding model temporal response function approach, which quantifies the transformation of a speech stimulus feature to continuous neural activity. Results We illustrate the utility of the classification approach to analyze early electroencephalographic (EEG) responses to Mandarin lexical tone categories from a traditional experimental design, and to classify EEG responses to English phonemes evoked by natural continuous speech (i.e., an audiobook) into phonological categories (plosive, fricative, nasal, and vowel). We also demonstrate the utility of temporal response function to predict EEG responses to natural continuous speech from acoustic features. Neural metrics from the 3 examples all exhibit statistically significant effects at the individual level. Conclusion We propose that ML-based approaches can complement traditional analysis approaches to analyze neurophysiological responses to speech signals and provide a deeper understanding of natural speech and language processing using ecologically valid paradigms in both typical and clinical populations.

Functional Logistic Mixed-Effects Models for Learning Curves From Longitudinal Binary Data.

Purpose We present functional logistic mixed-effects models (FLMEMs) for estimating population and individual-level learning curves in longitudinal experiments. Method Using functional analysis tools in a Bayesian hierarchical framework, the FLMEM captures nonlinear, smoothly varying learning curves, appropriately accommodating uncertainty in various aspects of the analysis while also borrowing information across different model layers. An R package implementing our method is available as part of the Supplemental Materials . Results Application to speech learning data from Reetzke, Xie, Llanos, and Chandrasekaran (2018) and a simulation study demonstrate the utility of FLMEM and its many advantages over linear and logistic mixed-effects models. Conclusion The FLMEM is highly flexible and efficient in improving upon the practical limitations of linear models and logistic linear mixed-effects models. We expect the FLMEM to be a useful addition to the speech, language, and hearing scientist's toolkit. Supplemental Material https://doi.org/10.23641/asha.7822568.