Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

Eye-Tracking Biomarkers and Autism Diagnosis in Primary Care.

Importance: Finding effective and scalable solutions to address diagnostic delays and disparities in autism is a public health imperative. Approaches that integrate eye-tracking biomarkers into tiered community-based models of autism evaluation hold promise for addressing this problem. Objective: To determine whether a battery of eye-tracking biomarkers can reliably differentiate young children with and without autism in a community-referred sample collected during clinical evaluation in the primary care setting and to evaluate whether combining eye-tracking biomarkers with primary care practitioner (PCP) diagnosis and diagnostic certainty is associated with diagnostic outcome. Design, Setting, and Participants: Early Autism Evaluation (EAE) Hub system PCPs referred a consecutive sample of children to this prospective diagnostic study for blinded eye-tracking index test and follow-up expert evaluation from June 7, 2019, to September 23, 2022. Participants included 146 children (aged 14-48 months) consecutively referred by 7 EAE Hubs. Of 154 children enrolled, 146 provided usable data for at least 1 eye-tracking measure. Main Outcomes and Measures: The primary outcomes were sensitivity and specificity of a composite eye-tracking (ie, index) test, which was a consolidated measure based on significant eye-tracking indices, compared with reference standard expert clinical autism diagnosis. Secondary outcome measures were sensitivity and specificity of an integrated approach using an index test and PCP diagnosis and certainty. Results: Among 146 children (mean [SD] age, 2.6 [0.6] years; 104 [71%] male; 21 [14%] Hispanic or Latine and 96 [66%] non-Latine White; 102 [70%] with a reference standard autism diagnosis), 113 (77%) had concordant autism outcomes between the index (composite biomarker) and reference outcomes, with 77.5% sensitivity (95% CI, 68.4%-84.5%) and 77.3% specificity (95% CI, 63.0%-87.2%). When index diagnosis was based on the combination of a composite biomarker, PCP diagnosis, and diagnostic certainty, outcomes were concordant with reference standard for 114 of 127 cases (90%) with a sensitivity of 90.7% (95% CI, 83.3%-95.0%) and a specificity of 86.7% (95% CI, 70.3%-94.7%). Conclusions and Relevance: In this prospective diagnostic study, a composite eye-tracking biomarker was associated with a best-estimate clinical diagnosis of autism, and an integrated diagnostic model including PCP diagnosis and diagnostic certainty demonstrated improved sensitivity and specificity. These findings suggest that equipping PCPs with a multimethod diagnostic approach has the potential to substantially improve access to timely, accurate diagnosis in local communities.

A hybrid CNN-SVM model for enhanced autism diagnosis.

Autism is a representative disorder of pervasive developmental disorder. It exerts influence upon an individual's behavior and performance, potentially co-occurring with other mental illnesses. Consequently, an effective diagnostic approach proves to be invaluable in both therapeutic interventions and the timely provision of medical support. Currently, most scholars' research primarily relies on neuroimaging techniques for auxiliary diagnosis and does not take into account the distinctive features of autism's social impediments. In order to address this deficiency, this paper introduces a novel convolutional neural network-support vector machine model that integrates resting state functional magnetic resonance imaging data with the social responsiveness scale metrics for the diagnostic assessment of autism. We selected 821 subjects containing the social responsiveness scale measure from the publicly available Autism Brain Imaging Data Exchange dataset, including 379 subjects with autism spectrum disorder and 442 typical controls. After preprocessing of fMRI data, we compute the static and dynamic functional connectivity for each subject. Subsequently, convolutional neural networks and attention mechanisms are utilized to extracts their respective features. The extracted features, combined with the social responsiveness scale features, are then employed as novel inputs for the support vector machine to categorize autistic patients and typical controls. The proposed model identifies salient features within the static and dynamic functional connectivity, offering a possible biological foundation for clinical diagnosis. By incorporating the behavioral assessments, the model achieves a remarkable classification accuracy of 94.30%, providing a more reliable support for auxiliary diagnosis.

Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification.

In recent years, research has been demonstrating that movement analysis, utilizing machine learning methods, can be a promising aid for clinicians in supporting autism diagnostic process. Within this field of research, we aim to explore new models and delve into the detailed observation of certain features that previous literature has identified as prominent in the classification process. Our study employs a game-based tablet application to collect motor data. We use artificial neural networks to analyze raw trajectories in a "drag and drop" task. We compare a two-features model (utilizing only raw coordinates) with a four-features model (including velocities and accelerations). The aim is to assess the effectiveness of raw data analysis and determine the impact of acceleration on autism classification. Our results revealed that both models demonstrate promising accuracy in classifying motor trajectories. The four-features model consistently outperforms the two-features model, as evidenced by accuracy values (0.90 vs. 0.76). However, our findings support the potential of raw data analysis in objectively assessing motor behaviors related to autism. While the four-features model excels, the two-features model still achieves reasonable accuracy. Addressing limitations related to sample size and noise is essential for future research. Our study emphasizes the importance of integrating intelligent solutions to enhance and assist autism traditional diagnostic process and intervention, paving the way for more effective tools in assessing motor skills.

Transtorno do Espectro Autista e Transtorno Desafiante de Oposição: Dificuldades no diagnostico / Transtorno del Espectro Autista y Trastorno Negativista Desafiante: Dificultades en el diagnóstico / Autism Spectrum Disorder and Oppositional Defiant Disorder: Difficulties in diagnosis

Introdução: O Transtorno do Espectro Autista e Transtorno Desafiante de Oposição, são desordens comumente diagnosticadas em indivíduos ainda na infância. Objetivo: Identificar possíveis fatores dificultadores no diagnóstico diferencial dos referidos transtornos. Metodologia: Foi realizada uma revisão integrativa da literatura, a qual selecionou artigos nas bases de dados Biblioteca Virtual de Saúde, periódico Coordenação de Aperfeiçoamento de Pessoal de Nível Superior e Periódicos Eletrônicos de Psicologia entre os meses de setembro e outubro de 2021. Para tanto, foram utilizadas as palavras chaves Transtorno do Espectro Autista, autismo, Transtorno Desafiante de Oposição, Transtorno Opositor Desafiador, diagnóstico, comorbidades, comportamentos disruptivos e dificuldades diagnósticas. Resultados: Oito artigos foram selecionados para extração de dados. O diagnóstico correto desses transtornos pode ser desafiador devido à sobreposição de sinais com outros transtornos e comorbidades, bem como à diversidade presente no espectro autista e à variedade de manifestações dos transtornos disruptivos. Além disso, a maioria dos estudos destacam os prejuízos na área da comunicação, o comprometimento na área social e os graus de severidade, como sendo características semelhantes entre os dois transtornos, podendo serem possíveis fatores que podem dificultar no diagnóstico do Transtorno do Espectro Autista e Transtorno Desafiante de Oposição, de maneira diferencial ou concomitante. Conclusões: O número de pesquisas relacionadas aos transtornos citados acima é inferior ao que se faz necessário para melhor conhecimento sobre o tema. No que diz respeito as pesquisas de materiais científicos, foram encontradas dificuldades para obtenção de estudos que estivessem de acordo com a nossa pesquisa. Com isso, faz-se necessário mais pesquisas que tentem investigar e compreender o porquê da escassez de material que estudem tais diagnósticos de maneira concomitante (AU).

Introduction: Autism Spectrum Disorder and Oppositional Defiant Disorderare disorders commonly diagnosed in individuals in childhood. Objective:Identify possible factors that hinder the differential diagnosis of these disorders. Methodology:An integrative review of the literature was carried out, which selected articles from the Virtual Health Library databases, Coordination for the Improvement of Higher Education Personnel journal and Electronic Psychology Journalsdatabases between the months of September and October 2021. To this end, the keywords Autistic Spectrum Disorder, autism, Disorder Defiant Disorder, Opposition, Oppositional Defiant Disorder, diagnosis, comorbidities, disruptive behaviors and diagnostic difficulties.Results:Eight articles were selected for data extraction. Correctly diagnosing these disorders can be challenging due to overlapping signs with other disorders and comorbidities, as well as the diversity present in the autism spectrum and the variety of manifestations of disruptive disorders. Furthermore, most studies highlight losses in the area of communication, impairment in the social area and degrees of severity, as being similar characteristics between the two disorders, and may be possible factors that can make it difficult to diagnose Autism Spectrum Disorder and Oppositional Defiant Disorder, differentially or concomitantly. Conclusions:The number of studies related to the disorders mentioned above is lower than what is needed for a better understanding of the subject. With regard to research on scientific materials, difficulties were encountered in obtaining studies that were in accordance with our research. With this, more research is needed to try to investigate and understand the reason for the scarcity of material that studies such diagnoses concomitantly (AU).

Introducción: El Trastorno del Espectro Autista y el Trastorno Negativista Desafiante son trastornos comúnmente diagnosticados en individuos en la infancia. Objetivo: Identificar posibles factores que puedan dificultar el diagnóstico diferencial de los trastornos antes mencionados.Metodología:Se realizó una revisión integrativa de la literatura, que seleccionó artículos en las bases de datos Biblioteca Virtual en Salud, revista Coordinación para el Perfeccionamiento del Personal de Educación Superior y Revistas Electrónicas de Psicología entre septiembre y octubre de 2021. Para ello, se utilizaron las palabras clave Trastorno del espectro autista, autismo, Trastorno negativista desafiante, Trastorno negativista desafiante, diagnóstico, comorbilidades, conductas disruptivas y dificultades diagnósticas. Resultados: Se seleccionaron ocho artículos para la extracción de datos. El diagnóstico correcto de estos trastornos puede ser un desafío debido a la superposición de síntomas con otros trastornos y comorbilidades, así como a la diversidad presente en el espectro del autismo y la variedad de manifestaciones de los trastornos disruptivos. Además, la mayoría de los estudios destacan las deficiencias en el área de la comunicación, la deficiencia en el área social y los grados de gravedad, como características similares entre ambos trastornos, que pueden ser posibles factores que dificulten el diagnóstico del Trastorno del Espectro Autista y Trastorno de Oposición Desafiante, ya sea de forma diferencial o concomitante. Conclusiones: El número de estudios relacionados con los trastornos antes mencionados es inferior al necesario para una mejor comprensión del tema. En cuanto a la investigación sobre materiales científicos, se encontraron dificultades para obtener estudios que estuvieran de acuerdo con nuestra investigación. Con esto, se necesita más investigación para tratar de investigar y comprender la razón de la escasez de material que estudie dichos diagnósticos de forma concomitante (AU).

Clinical correlates of diagnostic certainty in children and youths with Autistic Disorder.

BACKGROUND: Clinicians diagnosing autism rely on diagnostic criteria and instruments in combination with an implicit knowledge based on clinical expertise of the specific signs and presentations associated with the condition. This implicit knowledge influences how diagnostic criteria are interpreted, but it cannot be directly observed. Instead, insight into clinicians' understanding of autism can be gained by investigating their diagnostic certainty. Modest correlations between the certainty of an autism diagnosis and symptom load have been previously reported. Here, we investigated the associations of diagnostic certainty with specific items of the ADOS as well as other clinical features including head circumference. METHODS: Phenotypic data from the Simons Simplex Collection was used to investigate clinical correlates of diagnostic certainty in individuals diagnosed with Autistic Disorder (n = 1511, age 4 to 18 years). Participants were stratified by the ADOS module used to evaluate them. We investigated how diagnostic certainty was associated with total ADOS scores, age, and ADOS module. We calculated the odds-ratios of being diagnosed with the highest possible certainty given the presence or absence of different signs during the ADOS evaluation. Associations between diagnostic certainty and other cognitive and clinical variables were also assessed. RESULTS: In each ADOS module, some items showed a larger association with diagnostic certainty than others. Head circumference was significantly higher for individuals with the highest certainty rating across all three ADOS modules. In turn, head circumference was positively correlated with some of the ADOS items that were associated with diagnostic certainty, and was negatively correlated with verbal/nonverbal IQ ratio among those assessed with ADOS module 2. LIMITATIONS: The investigated cohort was heterogeneous, e.g. in terms of age, IQ, language level, and total ADOS score, which could impede the identification of associations that only exist in a subgroup of the population. The variability of the certainty ratings in the sample was low, limiting the power to identify potential associations with other variables. Additionally, the scoring of diagnostic certainty may vary between clinicians. CONCLUSION: Some ADOS items may better capture the signs that are most associated with clinicians' implicit knowledge of Autistic Disorder. If replicated in future studies, new diagnostic instruments with differentiated weighting of signs may be needed to better reflect this, possibly resulting in better specificity in standardized assessments.

Identification and support of autistic individuals within the UK Criminal Justice System: a practical approach based upon professional consensus with input from lived experience.

BACKGROUND: Autism spectrum disorder (hereafter referred to as autism) is characterised by difficulties with (i) social communication, social interaction, and (ii) restricted and repetitive interests and behaviours. Estimates of autism prevalence within the criminal justice system (CJS) vary considerably, but there is evidence to suggest that the condition can be missed or misidentified within this population. Autism has implications for an individual's journey through the CJS, from police questioning and engagement in court proceedings through to risk assessment, formulation, therapeutic approaches, engagement with support services, and long-term social and legal outcomes. METHODS: This consensus based on professional opinion with input from lived experience aims to provide general principles for consideration by United Kingdom (UK) CJS personnel when working with autistic individuals, focusing on autistic offenders and those suspected of offences. Principles may be transferable to countries beyond the UK. Multidisciplinary professionals and two service users were approached for their input to address the effective identification and support strategies for autistic individuals within the CJS. RESULTS: The authors provide a consensus statement including recommendations on the general principles of effective identification, and support strategies for autistic individuals across different levels of the CJS. CONCLUSION: Greater attention needs to be given to this population as they navigate the CJS.

ESDM intervention in severe preschool autism: an Italian Case report, psychological and social medicine reflections.

BACKGROUND: The goal of our contribution is to discuss a preschool intervention based on the Early Start Denver Model and the use of the main tools for the detection of adaptive behaviour in cases of autism: Vineland, ABAS. CASE PRESENTATION: the work is the presentation of a clinical case that has benefited from an intervention with the Early Start Denver Model methodology for the benefit of a child with socio-cultural and economic disadvantages. This early intervention, in a child of 36 months, which followed the diagnosis, was possible thanks to the intervention of many third-sector organizations which allowed this child, with a serious autism profile, to receive an evidence-based intervention for free. At the beginning of the intervention, the child presented a diagnosis of severe autism with absence of gaze, vocalizations and other communicative impairments. The level of motor clumsiness was also quite high, as were stereotypies. CONCLUSIONS: Research has shown the usefulness of intervening in this area with an early assessment and/or diagnosis and immediate intervention; however, public health services are not always able to maintain this pace. Our contribution therefore shows on the one hand the evidence of the improvements achieved by the child despite the low intensity of the treatment, and on the other hand, demonstrates the total versatility and adaptability of the Denver Model to the Italian context. In our conclusions, there are also some reflections on the tools used to measure adaptive behavior which seem to have a number of limitations and criticalities.

Conducting head-mounted eye-tracking research with young children with autism and children with increased likelihood of later autism diagnosis.

BACKGROUND: Over the past years, researchers have been using head-mounted eye-tracking systems to study young children's gaze behaviors in everyday activities through which children learn about the world. This method has great potential to further our understanding of how millisecond-level gaze behaviors create multisensory experiences and fluctuate around social environments. While this line of work can yield insight into early perceptual experiences and potential learning mechanisms, the majority of the work is exclusively conducted with typically-developing children. Sensory sensitivities, social-communication difficulties, and challenging behaviors (e.g., disruption, elopement) are common among children with developmental disorders, and they may represent potential methodological challenges for collecting high-quality data. RESULTS: In this paper, we describe our research practices of using head-mounted eye trackers with 41 autistic children and 17 children with increased likelihood of later autism diagnosis without auditory or visual impairments, including those who are minimally or nonspeaking and/or have intellectual disabilities. The success rate in gathering data among children with autism was 92.68%. 3 of 41 children failed to complete the play-session, resulting in an 86.36% success rate among 1-4-year-olds and a 100.00% success rate among 5-8-year-olds. 1 of 17 children with increased likelihood of later autism diagnosis failed to complete the play-session, resulting in a success rate of 94.11%. There were numerous "challenging" behaviors relevant to the method. The most common challenging behaviors included taking the eye-tracking device off, elopement, and becoming distressed. Overall, among children with autism, 88.8% of 1-4-year-olds and 29.4% of 5-8-year-olds exhibited at least one challenging behavior. CONCLUSIONS: Research capitalizing on this methodology has the potential to reveal early, socially-mediated gaze behaviors that are relevant for autism screening, diagnosis, and intervention purposes. We hope that our efforts in documenting our study methodology will help researchers and clinicians effectively study early naturally-occuring gaze behaviors of children during non-experimental contexts across the spectrum and other developmental disabilities using head-mounted eye-tracking. Ultimately, such applications may increase the generalizability of results, better reflect the diversity of individual characteristics, and offer new ways in which this method can contribute to the field.

Biosignal comparison for autism assessment using machine learning models and virtual reality.

Clinical assessment procedures encounter challenges in terms of objectivity because they rely on subjective data. Computational psychiatry proposes overcoming this limitation by introducing biosignal-based assessments able to detect clinical biomarkers, while virtual reality (VR) can offer ecological settings for measurement. Autism spectrum disorder (ASD) is a neurodevelopmental disorder where many biosignals have been tested to improve assessment procedures. However, in ASD research there is a lack of studies systematically comparing biosignals for the automatic classification of ASD when recorded simultaneously in ecological settings, and comparisons among previous studies are challenging due to methodological inconsistencies. In this study, we examined a VR screening tool consisting of four virtual scenes, and we compared machine learning models based on implicit (motor skills and eye movements) and explicit (behavioral responses) biosignals. Machine learning models were developed for each biosignal within the virtual scenes and then combined into a final model per biosignal. A linear support vector classifier with recursive feature elimination was used and tested using nested cross-validation. The final model based on motor skills exhibited the highest robustness in identifying ASD, achieving an AUC of 0.89 (SD = 0.08). The best behavioral model showed an AUC of 0.80, while further research is needed for the eye-movement models due to limitations with the eye-tracking glasses. These findings highlight the potential of motor skills in enhancing objectivity and reliability in the early assessment of ASD compared to other biosignals.

Improved emotion differentiation under reduced acoustic variability of speech in autism.

BACKGROUND: Socio-emotional impairments are among the diagnostic criteria for autism spectrum disorder (ASD), but the actual knowledge has substantiated both altered and intact emotional prosodies recognition. Here, a Bayesian framework of perception is considered suggesting that the oversampling of sensory evidence would impair perception within highly variable environments. However, reliable hierarchical structures for spectral and temporal cues would foster emotion discrimination by autistics. METHODS: Event-related spectral perturbations (ERSP) extracted from electroencephalographic (EEG) data indexed the perception of anger, disgust, fear, happiness, neutral, and sadness prosodies while listening to speech uttered by (a) human or (b) synthesized voices characterized by reduced volatility and variability of acoustic environments. The assessment of mechanisms for perception was extended to the visual domain by analyzing the behavioral accuracy within a non-social task in which dynamics of precision weighting between bottom-up evidence and top-down inferences were emphasized. Eighty children (mean 9.7 years old; standard deviation 1.8) volunteered including 40 autistics. The symptomatology was assessed at the time of the study via the Autism Diagnostic Observation Schedule, Second Edition, and parents' responses on the Autism Spectrum Rating Scales. A mixed within-between analysis of variance was conducted to assess the effects of group (autism versus typical development), voice, emotions, and interaction between factors. A Bayesian analysis was implemented to quantify the evidence in favor of the null hypothesis in case of non-significance. Post hoc comparisons were corrected for multiple testing. RESULTS: Autistic children presented impaired emotion differentiation while listening to speech uttered by human voices, which was improved when the acoustic volatility and variability of voices were reduced. Divergent neural patterns were observed from neurotypicals to autistics, emphasizing different mechanisms for perception. Accordingly, behavioral measurements on the visual task were consistent with the over-precision ascribed to the environmental variability (sensory processing) that weakened performance. Unlike autistic children, neurotypicals could differentiate emotions induced by all voices. CONCLUSIONS: This study outlines behavioral and neurophysiological mechanisms that underpin responses to sensory variability. Neurobiological insights into the processing of emotional prosodies emphasized the potential of acoustically modified emotional prosodies to improve emotion differentiation by autistics. TRIAL REGISTRATION: BioMed Central ISRCTN Registry, ISRCTN18117434. Registered on September 20, 2020.

Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews.

Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.

Feasibility of a 2-minute eye-tracking protocol to support the early identification of autism.

We tested the potential for Gazefinder eye-tracking to support early autism identification, including feasible use with infants, and preliminary concurrent validity of trial-level gaze data against clinical assessment scores. We embedded the ~ 2-min 'Scene 1S4' protocol within a comprehensive clinical assessment for 54 consecutively-referred, clinically-indicated infants (prematurity-corrected age 9-14 months). Alongside % tracking rate as a broad indicator of feasible assessment/data capture, we report infant gaze data to pre-specified regions of interest (ROI) across four trial types and associations with scores on established clinical/behavioural tools. Most infants tolerated Gazefinder eye-tracking well, returning high overall % tracking rate. As a group, infants directed more gaze towards social vs. non-social (or more vs. less socially-salient) ROIs within trials. Behavioural autism features were correlated with increased gaze towards non-social/geometry (vs. social/people) scenes. No associations were found for gaze directed to ROIs within other stimulus types. Notably, there were no associations between developmental/cognitive ability or adaptive behaviour with gaze towards any ROI. Gazefinder assessment seems highly feasible with clinically-indicated infants, and the people vs. geometry stimuli show concurrent predictive validity for behavioural autism features. Aggregating data across the ~ 2-min autism identification protocol might plausibly offer greater utility than stimulus-level analysis alone.

Exploring Motor Speech Disorders in Low and Minimally Verbal Autistic Individuals: An Auditory-Perceptual Analysis.

PURPOSE: Motor deficits are widely documented among autistic individuals, and speech characteristics consistent with a motor speech disorder have been reported in prior literature. We conducted an auditory-perceptual analysis of speech production skills in low and minimally verbal autistic individuals as a step toward clarifying the nature of speech production impairments in this population and the potential link between oromotor functioning and language development. METHOD: Fifty-four low or minimally verbal autistic individuals aged 4-18 years were video-recorded performing nonspeech oromotor tasks and producing phonemes, syllables, and words in imitation. Three trained speech-language pathologists provided auditory perceptual ratings of 11 speech features reflecting speech subsystem performance and overall speech production ability. The presence, attributes, and severity of signs of oromotor dysfunction were analyzed, as were relative performance on nonspeech and speech tasks and correlations between perceptual speech features and language skills. RESULTS AND CONCLUSIONS: Our findings provide evidence of a motor speech disorder in this population, characterized by perceptual speech features including reduced intelligibility, decreased consonant and vowel precision, and impairments of speech coordination and consistency. Speech deficits were more associated with articulation than with other speech subsystems. Speech production was more impaired than nonspeech oromotor abilities in a subgroup of the sample. Oromotor deficits were significantly associated with expressive and receptive language skills. Findings are interpreted in the context of known characteristics of the pediatric motor speech disorders childhood apraxia of speech and childhood dysarthria. These results, if replicated in future studies, have significant potential to improve the early detection of language impairments, inform the development of speech and language interventions, and aid in the identification of neurobiological mechanisms influencing communication development.

Identifying autism using EEG: unleashing the power of feature selection and machine learning.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by communication barriers, societal disengagement, and monotonous actions. Currently, the diagnosis of ASD is made by experts through a subjective and time-consuming qualitative behavioural examination using internationally recognized descriptive standards. In this paper, we present an EEG-based three-phase novel approach comprising 29 autistic subjects and 30 neurotypical people. In the first phase, preprocessing of data is performed from which we derived one continuous dataset and four condition-based datasets to determine the role of each dataset in the identification of autism from neurotypical people. In the second phase, time-domain and morphological features were extracted and four different feature selection techniques were applied. In the last phase, five-fold cross-validation is used to evaluate six different machine learning models based on the performance metrics and computational efficiency. The neural network outperformed when trained with maximum relevance and minimum redundancy (MRMR) algorithm on the continuous dataset with 98.10% validation accuracy and 0.9994 area under the curve (AUC) value for model validation, and 98.43% testing accuracy and AUC test value of 0.9998. The decision tree overall performed the second best in terms of computational efficiency and performance accuracy. The results indicate that EEG-based machine learning models have the potential for ASD identification from neurotypical people with a more objective and reliable method.

Validation of the Hindi Versions of Three Autism Specific Screening Tools (M-CHAT-R/F, RBSK-ASQ and TABC) Widely Used in India in 16-30-Month-Old Children.

OBJECTIVE: To determine the diagnostic accuracy of MCHAT-R/F, RBSK-ASQ and TABC for screening children aged 16 to 30 months for autism spectrum disorder (ASD). METHOD: Children aged 16 to 30 months were recruited from the pediatrics department. Those with known neurodevelopmental disorders, disabilities, severe medical illnesses, unavailable mothers, or lack of maternal understanding of Hindi, were excluded. The three index tools were translated into Hindi; each tool was piloted on 25 mothers and modified accordingly. The researcher was trained in administration, scoring and interpretation of the three tools. After enrollment the index tools and Developmental Profile (DP-3) were administered to each participant. The reference tool was a comprehensive assessment by experts that included clinical evaluation, computation of DP-3 scores, and application of diagnostic criteria of ASD; the final diagnosis being ASD or Non-ASD. RESULTS: Sensitivity and specificity of M-CHAT-R/F were 95.2% and 94.4%, of RBSK-ASQ were 100% and 93.9%, and of TABC were 100% and 94.4%, respectively. Convergent validity was high (Spearman's correlation coefficient 0.98). Test-retest and inter-rater reliability of each tool was excellent (Intra-class correlation coefficient 1.00). CONCLUSION: All three tools had acceptable psychometric properties, high convergent validity and excellent test-retest and inter-rater reliability.

Measurement properties of tools used to assess self-harm in autistic and general population adults.

Autistic people are at increased risk of experiencing self-harm compared to the general population. However, it is unclear which tools are being used to assess self-harm in autistic people, or whether existing tools need to be adapted for this group. This two-stage systematic review aimed to identify tools used to assess self-harm in autistic and general population adults, evaluate these tools on their measurement properties, and make recommendations for their appropriate use in research and clinical practice. Four databases were systematically searched (PsycINFO, Embase, MEDLINE and Web of Science). Eight frequently used self-harm assessment tools were identified and assessed for risk of bias, criteria for good measurement properties, and quality of evidence using the COSMIN checklist. Of these, two tools had sufficient evidence of internal consistency (ISAS, QNSSI), and one had been frequently used with autistic adults (NSSI-AT). These three tools may have potential for use with autistic adults but require further investigation for content validity and measurement properties in the autistic population. More research and potential adaptations to current self-harm assessment tools are recommended in order to better conceptualise and understand self-harm and its measurement in autism.