6 recherche sur le mot-clé 'artificial intelligence'

Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes / Joseph C.Y. LAU in Autism, 29-5 (May 2025)  

Public ISBD [article]  inAutism > 29-5 (May 2025) . - p.1346-1358 Titre : Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes Type de document : texte imprimé Auteurs : Joseph C.Y. LAU, Auteur ; Emily LANDAU, Auteur ; Qingcheng ZENG, Auteur ; Ruichun ZHANG, Auteur ; Stephanie CRAWFORD, Auteur ; Rob VOIGT, Auteur ; Molly LOSH, Auteur Article en page(s) : p.1346-1358 Langues : Anglais (eng) Mots-clés : artificial intelligence  autism  broad autism phenotype  FMR1 premutation  fragile X  pragmatic language  pre-trained language model Index. décimale : PER Périodiques Résumé : Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor intensive, and not readily applied in ample sample sizes. This proof-of-concept methodological study employed an artificial intelligence pre-trained language model, Bidirectional Encoder Representations from Transformers, as a tool to address such challenges. We applied Bidirectional Encoder Representations from Transformers to computationally index pragmatic-related variability in autism and in genetically related phenotypes displaying pragmatic differences, namely, in parents of autistic individuals, fragile X syndrome, and FMR1 premutation. Findings suggest that without model fine-tuning, Bidirectional Encoder Representations from Transformers?s Next Sentence Prediction module was able to derive estimates that differentiate autistic from non-autistic groups. Moreover, such computational estimates correlated with manually coded characterization of pragmatic abilities that contribute to conversational coherence, not only in autism but also in the other genetically related phenotypes. This study represents a step forward in evaluating the efficacy of artificial intelligence language models for capturing clinically important pragmatic differences and variability related to autism, showcasing the potential of artificial intelligence to provide automatized, efficient, and objective tools for pragmatic characterization to help advance the field.Lay abstract Autism is clinically defined by challenges with social language, including difficulties offering on-topic language in a conversation. Similar differences are also seen in genetically related conditions such as fragile X syndrome (FXS), and even among those carrying autism-related genes who do not have clinical diagnoses (e.g., the first-degree relatives of autistic individuals and carriers of the FMR1 premutation), which suggests there are genetic influences on social language related to the genes involved in autism. Characterization of social language is therefore important for informing potential intervention strategies and understanding the causes of communication challenges in autism. However, current tools for characterizing social language in both clinical and research settings are very time and labor intensive. In this study, we test an automized computational method that may address this problem. We used a type of artificial intelligence known as pre-trained language model to measure aspects of social language in autistic individuals and their parents, non-autistic comparison groups, and individuals with FXS and the FMR1 premutation. Findings suggest that these artificial intelligence approaches were able to identify differences in social language in autism, and to provide insight into the individuals' ability to keep a conversation on-topic. These findings also were associated with broader measures of participants' social communication ability. This study is one of the first to use artificial intelligence models to capture important differences in social language in autism and genetically related groups, demonstrating how artificial intelligence might be used to provide automatized, efficient, and objective tools for language characterization. En ligne : https://dx.doi.org/10.1177/13623613241304488 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555 [article] Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes [texte imprimé] / Joseph C.Y. LAU, Auteur ; Emily LANDAU, Auteur ; Qingcheng ZENG, Auteur ; Ruichun ZHANG, Auteur ; Stephanie CRAWFORD, Auteur ; Rob VOIGT, Auteur ; Molly LOSH, Auteur . - p.1346-1358. Langues : Anglais (eng) in Autism > 29-5 (May 2025) . - p.1346-1358 Mots-clés : artificial intelligence  autism  broad autism phenotype  FMR1 premutation  fragile X  pragmatic language  pre-trained language model Index. décimale : PER Périodiques Résumé : Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor intensive, and not readily applied in ample sample sizes. This proof-of-concept methodological study employed an artificial intelligence pre-trained language model, Bidirectional Encoder Representations from Transformers, as a tool to address such challenges. We applied Bidirectional Encoder Representations from Transformers to computationally index pragmatic-related variability in autism and in genetically related phenotypes displaying pragmatic differences, namely, in parents of autistic individuals, fragile X syndrome, and FMR1 premutation. Findings suggest that without model fine-tuning, Bidirectional Encoder Representations from Transformers?s Next Sentence Prediction module was able to derive estimates that differentiate autistic from non-autistic groups. Moreover, such computational estimates correlated with manually coded characterization of pragmatic abilities that contribute to conversational coherence, not only in autism but also in the other genetically related phenotypes. This study represents a step forward in evaluating the efficacy of artificial intelligence language models for capturing clinically important pragmatic differences and variability related to autism, showcasing the potential of artificial intelligence to provide automatized, efficient, and objective tools for pragmatic characterization to help advance the field.Lay abstract Autism is clinically defined by challenges with social language, including difficulties offering on-topic language in a conversation. Similar differences are also seen in genetically related conditions such as fragile X syndrome (FXS), and even among those carrying autism-related genes who do not have clinical diagnoses (e.g., the first-degree relatives of autistic individuals and carriers of the FMR1 premutation), which suggests there are genetic influences on social language related to the genes involved in autism. Characterization of social language is therefore important for informing potential intervention strategies and understanding the causes of communication challenges in autism. However, current tools for characterizing social language in both clinical and research settings are very time and labor intensive. In this study, we test an automized computational method that may address this problem. We used a type of artificial intelligence known as pre-trained language model to measure aspects of social language in autistic individuals and their parents, non-autistic comparison groups, and individuals with FXS and the FMR1 premutation. Findings suggest that these artificial intelligence approaches were able to identify differences in social language in autism, and to provide insight into the individuals' ability to keep a conversation on-topic. These findings also were associated with broader measures of participants' social communication ability. This study is one of the first to use artificial intelligence models to capture important differences in social language in autism and genetically related groups, demonstrating how artificial intelligence might be used to provide automatized, efficient, and objective tools for language characterization. En ligne : https://dx.doi.org/10.1177/13623613241304488 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants / Arjun P. ATHREYA in Journal of Child Psychology and Psychiatry, 63-11 (November 2022)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 63-11 (November 2022) . - p.1347-1358 Titre : Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants Type de document : texte imprimé Auteurs : Arjun P. ATHREYA, Auteur ; Jennifer L. VANDE VOORT, Auteur ; Julia SHEKUNOV, Auteur ; Sandra J. RACKLEY, Auteur ; Jarrod M. LEFFLER, Auteur ; Alastair J. MCKEAN, Auteur ; Magdalena ROMANOWICZ, Auteur ; Betsy KENNARD, Auteur ; Graham J. EMSLIE, Auteur ; Taryn MAYES, Auteur ; Madhukar TRIVEDI, Auteur ; Liewei WANG, Auteur ; Richard M. WEINSHILBOUM, Auteur ; William V. BOBO, Auteur ; Paul E. CROARKIN, Auteur Article en page(s) : p.1347-1358 Langues : Anglais (eng) Mots-clés : Child  Humans  Adolescent  Fluoxetine/therapeutic use  Depressive Disorder, Major/therapy  Duloxetine Hydrochloride/therapeutic use  Artificial Intelligence  Double-Blind Method  Antidepressive Agents  Treatment Outcome  Machine Learning  Depression  adolescents  decision support tools Index. décimale : PER Périodiques Résumé : BACKGROUND: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS: The study samples included training datasets (N=271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N=255) or placebo (N=265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression. En ligne : http://dx.doi.org/10.1111/jcpp.13580 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490 [article] Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants [texte imprimé] / Arjun P. ATHREYA, Auteur ; Jennifer L. VANDE VOORT, Auteur ; Julia SHEKUNOV, Auteur ; Sandra J. RACKLEY, Auteur ; Jarrod M. LEFFLER, Auteur ; Alastair J. MCKEAN, Auteur ; Magdalena ROMANOWICZ, Auteur ; Betsy KENNARD, Auteur ; Graham J. EMSLIE, Auteur ; Taryn MAYES, Auteur ; Madhukar TRIVEDI, Auteur ; Liewei WANG, Auteur ; Richard M. WEINSHILBOUM, Auteur ; William V. BOBO, Auteur ; Paul E. CROARKIN, Auteur . - p.1347-1358. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 63-11 (November 2022) . - p.1347-1358 Mots-clés : Child  Humans  Adolescent  Fluoxetine/therapeutic use  Depressive Disorder, Major/therapy  Duloxetine Hydrochloride/therapeutic use  Artificial Intelligence  Double-Blind Method  Antidepressive Agents  Treatment Outcome  Machine Learning  Depression  adolescents  decision support tools Index. décimale : PER Périodiques Résumé : BACKGROUND: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS: The study samples included training datasets (N=271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N=255) or placebo (N=265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression. En ligne : http://dx.doi.org/10.1111/jcpp.13580 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490

Annual Research Review: Translational machine learning for child and adolescent psychiatry / Dominic DWYER in Journal of Child Psychology and Psychiatry, 63-4 (April 2022)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 63-4 (April 2022) . - p.421-443 Titre : Annual Research Review: Translational machine learning for child and adolescent psychiatry Type de document : texte imprimé Auteurs : Dominic DWYER, Auteur ; Nikolaos KOUTSOULERIS, Auteur Article en page(s) : p.421-443 Langues : Anglais (eng) Mots-clés : Adhd  Machine learning  artificial intelligence  autism spectrum disorders  depression  psychosis Index. décimale : PER Périodiques Résumé : Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents. En ligne : http://dx.doi.org/10.1111/jcpp.13545 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=475 [article] Annual Research Review: Translational machine learning for child and adolescent psychiatry [texte imprimé] / Dominic DWYER, Auteur ; Nikolaos KOUTSOULERIS, Auteur . - p.421-443. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 63-4 (April 2022) . - p.421-443 Mots-clés : Adhd  Machine learning  artificial intelligence  autism spectrum disorders  depression  psychosis Index. décimale : PER Périodiques Résumé : Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents. En ligne : http://dx.doi.org/10.1111/jcpp.13545 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=475

Deep Neural Network Reveals the World of Autism From a First-Person Perspective / Mindi RUAN in Autism Research, 14-2 (February 2021)  

Public ISBD [article]  inAutism Research > 14-2 (February 2021) . - p.333-342 Titre : Deep Neural Network Reveals the World of Autism From a First-Person Perspective Type de document : texte imprimé Auteurs : Mindi RUAN, Auteur ; Paula J. WEBSTER, Auteur ; Xin LI, Auteur ; Shuo WANG, Auteur Article en page(s) : p.333-342 Langues : Anglais (eng) Mots-clés : artificial intelligence  attention  autism spectrum disorder  deep neural network  faces  photos  saliency Index. décimale : PER Périodiques Résumé : People with autism spectrum disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories, but was particularly successful at classifying photos of people with >80% accuracy. Importantly, visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time we showed that photos taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first-person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD. LAY SUMMARY: People with autism spectrum disorder (ASD) demonstrate atypical visual attention to social stimuli. However, it remains largely unclear how they perceive the world from a first-person perspective. In this study, we employed a deep learning approach to analyze a unique dataset of photos taken by people with and without ASD. Our computer modeling was not only able to discern which photos were taken by individuals with ASD, outperforming ASD experts, but importantly, it revealed critical features that led to successful discrimination, revealing aspects of atypical visual attention in ASD from their first-person perspective. En ligne : http://dx.doi.org/10.1002/aur.2376 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=441 [article] Deep Neural Network Reveals the World of Autism From a First-Person Perspective [texte imprimé] / Mindi RUAN, Auteur ; Paula J. WEBSTER, Auteur ; Xin LI, Auteur ; Shuo WANG, Auteur . - p.333-342. Langues : Anglais (eng) in Autism Research > 14-2 (February 2021) . - p.333-342 Mots-clés : artificial intelligence  attention  autism spectrum disorder  deep neural network  faces  photos  saliency Index. décimale : PER Périodiques Résumé : People with autism spectrum disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories, but was particularly successful at classifying photos of people with >80% accuracy. Importantly, visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time we showed that photos taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first-person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD. LAY SUMMARY: People with autism spectrum disorder (ASD) demonstrate atypical visual attention to social stimuli. However, it remains largely unclear how they perceive the world from a first-person perspective. In this study, we employed a deep learning approach to analyze a unique dataset of photos taken by people with and without ASD. Our computer modeling was not only able to discern which photos were taken by individuals with ASD, outperforming ASD experts, but importantly, it revealed critical features that led to successful discrimination, revealing aspects of atypical visual attention in ASD from their first-person perspective. En ligne : http://dx.doi.org/10.1002/aur.2376 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=441

Sentiment Analysis in Children with Neurodevelopmental Disorders in an Ingroup/Outgroup Setting / E. VAUCHERET PAZ in Journal of Autism and Developmental Disorders, 50-1 (January 2020)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 50-1 (January 2020) . - p.162-170 Titre : Sentiment Analysis in Children with Neurodevelopmental Disorders in an Ingroup/Outgroup Setting Type de document : texte imprimé Auteurs : E. VAUCHERET PAZ, Auteur ; M. MARTINO, Auteur ; M. HYLAND, Auteur ; M. CORLETTO, Auteur ; C. PUGA, Auteur ; M. PERALTA, Auteur ; N. DELTETTO, Auteur ; T. KUHLMANN, Auteur ; D. CAVALIE, Auteur ; M. LEIST, Auteur ; B. DUARTE, Auteur ; I. LASCOMBES, Auteur Article en page(s) : p.162-170 Langues : Anglais (eng) Mots-clés : Artificial intelligence  Empathy  Morality  Neurodevelopmental disorders  Social norms  Third-party punishment Index. décimale : PER Périodiques Résumé : People punish transgressors with different intensity depending if they are members of their group or not. We explore this in a cross-sectional analytical study with paired samples in children with developmental disorders who watched two videos and expressed their opinion. In Video-1, a football-player from the participant's country scores a goal with his hand. In Video-2, a player from another country does the same against the country of the participant. Each subject watched the two videos and their answers were compared. The autism spectrum disorder (ASD) group showed negative feelings in Video 1 (M = - .1; CI 95% - .51 to .31); and in Video 2 (M = - .43; CI 95% .77 to - .09; t(8) = 1.64, p = .13), but the attention deficit hyperactivity disorder, learning disabilities, intellectual disability groups showed positive opinion in Video-1 and negative in Video-2. This suggests that children with ASD respect rules regardless of whether those who break them belong or not to their own group, possibly due to lower degrees of empathy. En ligne : http://dx.doi.org/10.1007/s10803-019-04242-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=414 [article] Sentiment Analysis in Children with Neurodevelopmental Disorders in an Ingroup/Outgroup Setting [texte imprimé] / E. VAUCHERET PAZ, Auteur ; M. MARTINO, Auteur ; M. HYLAND, Auteur ; M. CORLETTO, Auteur ; C. PUGA, Auteur ; M. PERALTA, Auteur ; N. DELTETTO, Auteur ; T. KUHLMANN, Auteur ; D. CAVALIE, Auteur ; M. LEIST, Auteur ; B. DUARTE, Auteur ; I. LASCOMBES, Auteur . - p.162-170. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 50-1 (January 2020) . - p.162-170 Mots-clés : Artificial intelligence  Empathy  Morality  Neurodevelopmental disorders  Social norms  Third-party punishment Index. décimale : PER Périodiques Résumé : People punish transgressors with different intensity depending if they are members of their group or not. We explore this in a cross-sectional analytical study with paired samples in children with developmental disorders who watched two videos and expressed their opinion. In Video-1, a football-player from the participant's country scores a goal with his hand. In Video-2, a player from another country does the same against the country of the participant. Each subject watched the two videos and their answers were compared. The autism spectrum disorder (ASD) group showed negative feelings in Video 1 (M = - .1; CI 95% - .51 to .31); and in Video 2 (M = - .43; CI 95% .77 to - .09; t(8) = 1.64, p = .13), but the attention deficit hyperactivity disorder, learning disabilities, intellectual disability groups showed positive opinion in Video-1 and negative in Video-2. This suggests that children with ASD respect rules regardless of whether those who break them belong or not to their own group, possibly due to lower degrees of empathy. En ligne : http://dx.doi.org/10.1007/s10803-019-04242-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=414