Associations between parental psychiatric disorders and autism spectrum disorder in the offspring / Yi-Ling CHIEN in Autism Research, 15-12 (December 2022)  

Public ISBD [article]  inAutism Research > 15-12 (December 2022) . - p.2409-2419 Titre : Associations between parental psychiatric disorders and autism spectrum disorder in the offspring Type de document : texte imprimé Auteurs : Yi-Ling CHIEN, Auteur ; Chi-Shin WU, Auteur ; Yen-Chen CHANG, Auteur ; Mei-Leng CHEONG, Auteur ; Tsung-Chieh YAO, Auteur ; Hui-Ju TSAI, Auteur Article en page(s) : p.2409-2419 Langues : Anglais (eng) Mots-clés : Child  Female  Humans  Autism Spectrum Disorder/epidemiology/genetics/complications  Case-Control Studies  Parents/psychology  Attention Deficit Disorder with Hyperactivity/epidemiology/genetics  Mothers/psychology  autism spectrum disorder  offspring  parental  psychiatric disorders Index. décimale : PER Périodiques Résumé : Whether parental psychiatric disorders are associated with autism spectrum disorder (ASD) in offspring has remained inconclusive. We examined the associations of parental psychiatric disorders with ASD in offspring. This population-based case-control study used Taiwan's National Health Insurance Research Database to identify a cohort of children born from 2004 to 2017 and their parents. A total of 24,279 children with ASD (diagnostic ICD-9-CM code: 299.x or ICD-10 code F84.x) and 97,715 matched controls were included. Parental psychiatric disorders, including depressive disorders, bipolar spectrum disorders, anxiety disorders, obsessive-compulsive disorder, schizophrenia, substance use disorders, autism spectrum disorder, attention-deficit hyperactivity disorder (ADHD), and adjustment disorders were identified. Conditional logistic regressions with covariate adjustment were performed. The results suggest that parental diagnosis with any of the psychiatric disorders is associated with ASD in offspring (adjusted odds ratio [AOR] = 1.45, 95%CI: 1.40-1.51 for mothers; and AOR = 1.12, 95%CI: 1.08-1.17 for fathers). ASD in offspring was associated with schizophrenia, depressive disorders, obsessive-compulsive disorder, adjustment disorders, ADHD and ASD in both parents. The relationship between parental psychiatric disorders and the timing of the child's birth and ASD diagnosis varied across the different psychiatric disorders. The present study provides supportive evidence that parental psychiatric disorders are associated with autistic children. Furthermore, because the associations between parental psychiatric disorders and the timing of child's birth and ASD diagnosis varied across psychiatric disorders, the observed relationships may be affected by both genetic and environmental factors. Future studies are needed to disentangle the potential influence of genetic and environmental factors on the observed associations. En ligne : http://dx.doi.org/10.1002/aur.2835 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=488 [article] Associations between parental psychiatric disorders and autism spectrum disorder in the offspring [texte imprimé] / Yi-Ling CHIEN, Auteur ; Chi-Shin WU, Auteur ; Yen-Chen CHANG, Auteur ; Mei-Leng CHEONG, Auteur ; Tsung-Chieh YAO, Auteur ; Hui-Ju TSAI, Auteur . - p.2409-2419. Langues : Anglais (eng) in Autism Research > 15-12 (December 2022) . - p.2409-2419 Mots-clés : Child  Female  Humans  Autism Spectrum Disorder/epidemiology/genetics/complications  Case-Control Studies  Parents/psychology  Attention Deficit Disorder with Hyperactivity/epidemiology/genetics  Mothers/psychology  autism spectrum disorder  offspring  parental  psychiatric disorders Index. décimale : PER Périodiques Résumé : Whether parental psychiatric disorders are associated with autism spectrum disorder (ASD) in offspring has remained inconclusive. We examined the associations of parental psychiatric disorders with ASD in offspring. This population-based case-control study used Taiwan's National Health Insurance Research Database to identify a cohort of children born from 2004 to 2017 and their parents. A total of 24,279 children with ASD (diagnostic ICD-9-CM code: 299.x or ICD-10 code F84.x) and 97,715 matched controls were included. Parental psychiatric disorders, including depressive disorders, bipolar spectrum disorders, anxiety disorders, obsessive-compulsive disorder, schizophrenia, substance use disorders, autism spectrum disorder, attention-deficit hyperactivity disorder (ADHD), and adjustment disorders were identified. Conditional logistic regressions with covariate adjustment were performed. The results suggest that parental diagnosis with any of the psychiatric disorders is associated with ASD in offspring (adjusted odds ratio [AOR] = 1.45, 95%CI: 1.40-1.51 for mothers; and AOR = 1.12, 95%CI: 1.08-1.17 for fathers). ASD in offspring was associated with schizophrenia, depressive disorders, obsessive-compulsive disorder, adjustment disorders, ADHD and ASD in both parents. The relationship between parental psychiatric disorders and the timing of the child's birth and ASD diagnosis varied across the different psychiatric disorders. The present study provides supportive evidence that parental psychiatric disorders are associated with autistic children. Furthermore, because the associations between parental psychiatric disorders and the timing of child's birth and ASD diagnosis varied across psychiatric disorders, the observed relationships may be affected by both genetic and environmental factors. Future studies are needed to disentangle the potential influence of genetic and environmental factors on the observed associations. En ligne : http://dx.doi.org/10.1002/aur.2835 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=488

Associations between parental psychiatric disorders and autism spectrum disorder in the offspring-A response / Yi-Ling CHIEN in Autism Research, 16-5 (May 2023)  

Public ISBD [article]  inAutism Research > 16-5 (May 2023) . - p.877-878 Titre : Associations between parental psychiatric disorders and autism spectrum disorder in the offspring-A response Type de document : texte imprimé Auteurs : Yi-Ling CHIEN, Auteur ; Chi-Shin WU, Auteur ; Yen-Chen CHANG, Auteur ; Mei-Leng CHEONG, Auteur ; Tsung-Chieh YAO, Auteur ; Hui-Ju TSAI, Auteur Article en page(s) : p.877-878 Langues : Anglais (eng) Index. décimale : PER Périodiques En ligne : http://dx.doi.org/https://doi.org/10.1002/aur.2908 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=503 [article] Associations between parental psychiatric disorders and autism spectrum disorder in the offspring-A response [texte imprimé] / Yi-Ling CHIEN, Auteur ; Chi-Shin WU, Auteur ; Yen-Chen CHANG, Auteur ; Mei-Leng CHEONG, Auteur ; Tsung-Chieh YAO, Auteur ; Hui-Ju TSAI, Auteur . - p.877-878. Langues : Anglais (eng) in Autism Research > 16-5 (May 2023) . - p.877-878 Index. décimale : PER Périodiques En ligne : http://dx.doi.org/https://doi.org/10.1002/aur.2908 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=503

Clinical correlates of errors in machine-learning diagnostic model of autism spectrum disorder: Impact of sample cohorts / Yen-Chin WANG in Autism, 29-12 (December 2025)  

Public ISBD [article]  inAutism > 29-12 (December 2025) . - p.3083-3099 Titre : Clinical correlates of errors in machine-learning diagnostic model of autism spectrum disorder: Impact of sample cohorts Type de document : texte imprimé Auteurs : Yen-Chin WANG, Auteur ; Chung-Yuan CHENG, Auteur ; Chi-Shin WU, Auteur ; Chi-Chun LEE, Auteur ; Susan Shur-Fen GAU, Auteur Article en page(s) : p.3083-3099 Langues : Anglais (eng) Mots-clés : autism spectrum disorder  diagnostic models  error analysis  machine-learning Index. décimale : PER Périodiques Résumé : Machine-learning models can assist in diagnosing autism but have biases. We examines the correlates of misclassifications and how training data affect model generalizability. The Social Responsive Scale data were collected from two cohorts in Taiwan: the clinical cohort comprised 1203 autistic participants and 1182 non-autistic comparisons, and the community cohort consisted of 35 autistic participants and 3297 non-autistic comparisons. Classification models were trained, and the misclassification cases were investigated regarding their associations with sex, age, intelligence quotient (IQ), symptoms from the child behavioral checklist (CBCL), and co-occurring psychiatric diagnosis. Models showed high within-cohort accuracy (clinical: sensitivity 0.91-0.95, specificity 0.93-0.94; community: sensitivity 0.91-1.00, specificity 0.89-0.96), but generalizability across cohorts was limited. When the community-trained model was applied to the clinical cohort, performance declined (sensitivity 0.65, specificity 0.95). In both models, non-autistic individuals misclassified as autistic showed elevated behavioral symptoms and attention-deficit hyperactivity disorder (ADHD) prevalence. Conversely, autistic individuals who were misclassified tended to show fewer behavioral symptoms and, in the community model, higher IQ and aggressive behavior but less social and attention problems. Error patterns of machine-learning model and the impact of training data warrant careful consideration in future research.Lay Abstract Machine-learning is a type of computer model that can help identify patterns in data and make predictions. In autism research, these models may support earlier or more accurate identification of autistic individuals. But to be useful, they need to make reliable predictions across different groups of people. In this study, we explored when and why these models might make mistakes-and how the kind of data used to train them affects their accuracy. Training models means using information to teach the computer model how to tell the difference between autistic and non-autistic individuals. We used the information from the Social Responsiveness Scale (SRS), which is a questionnaire that measures autistic features. We tested these models on two different groups: one from clinical settings and one from the general community. The models worked well when tested within the same type of group they were trained. However, a model trained on the community group did not perform as accurately when tested on the clinical group. Sometimes, the model got it wrong. For example, in the clinical group, some autistic individuals were mistakenly identified as non-autistic. These individuals tended to have fewer emotional or behavioral difficulties. In the community group, autistic individuals who were mistakenly identified as non-autistic had higher IQs and showed more aggressive behaviors but fewer attention or social problems. On the contrary, some non-autistic people were incorrectly identified as autistic. These people had more emotional or behavioral challenges and were more likely to have attention-deficit hyperactivity disorder (ADHD). These findings highlight that machine-learning models are sensitive to the type of data they are trained on. To build fair and accurate models for predicting autism, it is essential to consider where the training data come from and whether it represents the full diversity of individuals. Understanding these patterns of error can help improve future tools used in both research and clinical care. En ligne : https://dx.doi.org/10.1177/13623613251360271 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=572 [article] Clinical correlates of errors in machine-learning diagnostic model of autism spectrum disorder: Impact of sample cohorts [texte imprimé] / Yen-Chin WANG, Auteur ; Chung-Yuan CHENG, Auteur ; Chi-Shin WU, Auteur ; Chi-Chun LEE, Auteur ; Susan Shur-Fen GAU, Auteur . - p.3083-3099. Langues : Anglais (eng) in Autism > 29-12 (December 2025) . - p.3083-3099 Mots-clés : autism spectrum disorder  diagnostic models  error analysis  machine-learning Index. décimale : PER Périodiques Résumé : Machine-learning models can assist in diagnosing autism but have biases. We examines the correlates of misclassifications and how training data affect model generalizability. The Social Responsive Scale data were collected from two cohorts in Taiwan: the clinical cohort comprised 1203 autistic participants and 1182 non-autistic comparisons, and the community cohort consisted of 35 autistic participants and 3297 non-autistic comparisons. Classification models were trained, and the misclassification cases were investigated regarding their associations with sex, age, intelligence quotient (IQ), symptoms from the child behavioral checklist (CBCL), and co-occurring psychiatric diagnosis. Models showed high within-cohort accuracy (clinical: sensitivity 0.91-0.95, specificity 0.93-0.94; community: sensitivity 0.91-1.00, specificity 0.89-0.96), but generalizability across cohorts was limited. When the community-trained model was applied to the clinical cohort, performance declined (sensitivity 0.65, specificity 0.95). In both models, non-autistic individuals misclassified as autistic showed elevated behavioral symptoms and attention-deficit hyperactivity disorder (ADHD) prevalence. Conversely, autistic individuals who were misclassified tended to show fewer behavioral symptoms and, in the community model, higher IQ and aggressive behavior but less social and attention problems. Error patterns of machine-learning model and the impact of training data warrant careful consideration in future research.Lay Abstract Machine-learning is a type of computer model that can help identify patterns in data and make predictions. In autism research, these models may support earlier or more accurate identification of autistic individuals. But to be useful, they need to make reliable predictions across different groups of people. In this study, we explored when and why these models might make mistakes-and how the kind of data used to train them affects their accuracy. Training models means using information to teach the computer model how to tell the difference between autistic and non-autistic individuals. We used the information from the Social Responsiveness Scale (SRS), which is a questionnaire that measures autistic features. We tested these models on two different groups: one from clinical settings and one from the general community. The models worked well when tested within the same type of group they were trained. However, a model trained on the community group did not perform as accurately when tested on the clinical group. Sometimes, the model got it wrong. For example, in the clinical group, some autistic individuals were mistakenly identified as non-autistic. These individuals tended to have fewer emotional or behavioral difficulties. In the community group, autistic individuals who were mistakenly identified as non-autistic had higher IQs and showed more aggressive behaviors but fewer attention or social problems. On the contrary, some non-autistic people were incorrectly identified as autistic. These people had more emotional or behavioral challenges and were more likely to have attention-deficit hyperactivity disorder (ADHD). These findings highlight that machine-learning models are sensitive to the type of data they are trained on. To build fair and accurate models for predicting autism, it is essential to consider where the training data come from and whether it represents the full diversity of individuals. Understanding these patterns of error can help improve future tools used in both research and clinical care. En ligne : https://dx.doi.org/10.1177/13623613251360271 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=572

Differences in white matter segments in autistic males, non-autistic siblings, and non-autistic participants: An intermediate phenotype approach / Yi-Ling CHIEN in Autism, 27-4 (May 2023)  

Public ISBD [article]  inAutism > 27-4 (May 2023) . - p.1036-1052 Titre : Differences in white matter segments in autistic males, non-autistic siblings, and non-autistic participants: An intermediate phenotype approach Type de document : texte imprimé Auteurs : Yi-Ling CHIEN, Auteur ; Yu-Jen CHEN, Auteur ; Wan-Ling TSENG, Auteur ; Yung-Chin HSU, Auteur ; Chi-Shin WU, Auteur ; Wen-Yih Isaac TSENG, Auteur ; Susan Shur-Fen GAU, Auteur Article en page(s) : p.1036-1052 Langues : Anglais (eng) Mots-clés : autism spectrum disorder,diffusion spectrum imaging,intermediate phenotype,unaffected siblings,white matter properties Index. décimale : PER Périodiques Résumé : Whether altered white matter microstructural property of autistic people also exists in non-autistic siblings is uncertain. The microstructures of a neural tract may not be consistent throughout the whole track. We assessed 38 cognitive-able autistic males (aged 15.8+4.4 years), 39 non-autistic siblings (16.5+5.7 years), and 78 age- and sex-matched non-autistic comparison people (14.4+5.3 years) using tract-based automatic analysis of diffusion spectrum imaging and threshold-free cluster-weighted method. First, we identified segments within the right frontal aslant tract, frontostriatal tract, and thalamic radiation to precentral areas in both autistic people and non-autistic siblings that differed from those in non-autistic comparison people. Second, segments within bilateral cingulate gyri and callosal fibers connecting superior temporal lobes differed between autistic people and non-autistic comparison people but not between siblings and non-autistic comparison people. Third, segments within the left inferior longitudinal fasciculus and callosal fibers connecting precuneus showed increased generalized fractional anisotropy in non-autistic siblings. Our findings suggest microstructural properties of some potential neural segments that were similar between autistic people and their non-autistic siblings may serve as intermediate phenotypes of autism, facilitating further etiological searching for autism. Meanwhile, increased microstructural properties in unaffected siblings alone might indicate compensatory processes in the light of genetic predisposition for autism.Lay abstractWhite matter is the neural pathway that connects neurons in different brain regions. Although research has shown white matter differences between autistic and non-autistic people, little is known about the properties of white matter in non-autistic siblings. In addition, past studies often focused on the whole neural tracts; it is unclear where differences exist in specific segments of the tracts. This study identified neural segments that differed between autistic people, their non-autistic siblings, and the age- and non-autistic people. We found altered segments within the tracts connected to anterior brain regions corresponding to several higher cognitive functions (e.g. executive functions) in autistic people and non-autistic siblings. Segments connecting to regions for social cognition and Theory of Mind were altered only in autistic people, explaining a large portion of autistic traits and may serve as neuroimaging markers. Segments within the tracts associated with fewer autistic traits or connecting brain regions for diverse highly integrated functions showed compensatory increases in the microstructural properties in non-autistic siblings. Our findings suggest that differential white matter segments that are shared between autistic people and non-autistic siblings may serve as potential ''intermediate phenotypes''-biological or neuropsychological characteristics in the causal link between genetics and symptoms-of autism. These findings shed light on a promising neuroimaging model to refine the intermediate phenotype of autism which may facilitate further identification of the genetic and biological bases of autism. Future research exploring links between compensatory segments and neurocognitive strengths in non-autistic siblings may help understand brain adaptation to autism. En ligne : https://doi.org/10.1177/13623613221125620 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=499 [article] Differences in white matter segments in autistic males, non-autistic siblings, and non-autistic participants: An intermediate phenotype approach [texte imprimé] / Yi-Ling CHIEN, Auteur ; Yu-Jen CHEN, Auteur ; Wan-Ling TSENG, Auteur ; Yung-Chin HSU, Auteur ; Chi-Shin WU, Auteur ; Wen-Yih Isaac TSENG, Auteur ; Susan Shur-Fen GAU, Auteur . - p.1036-1052. Langues : Anglais (eng) in Autism > 27-4 (May 2023) . - p.1036-1052 Mots-clés : autism spectrum disorder,diffusion spectrum imaging,intermediate phenotype,unaffected siblings,white matter properties Index. décimale : PER Périodiques Résumé : Whether altered white matter microstructural property of autistic people also exists in non-autistic siblings is uncertain. The microstructures of a neural tract may not be consistent throughout the whole track. We assessed 38 cognitive-able autistic males (aged 15.8+4.4 years), 39 non-autistic siblings (16.5+5.7 years), and 78 age- and sex-matched non-autistic comparison people (14.4+5.3 years) using tract-based automatic analysis of diffusion spectrum imaging and threshold-free cluster-weighted method. First, we identified segments within the right frontal aslant tract, frontostriatal tract, and thalamic radiation to precentral areas in both autistic people and non-autistic siblings that differed from those in non-autistic comparison people. Second, segments within bilateral cingulate gyri and callosal fibers connecting superior temporal lobes differed between autistic people and non-autistic comparison people but not between siblings and non-autistic comparison people. Third, segments within the left inferior longitudinal fasciculus and callosal fibers connecting precuneus showed increased generalized fractional anisotropy in non-autistic siblings. Our findings suggest microstructural properties of some potential neural segments that were similar between autistic people and their non-autistic siblings may serve as intermediate phenotypes of autism, facilitating further etiological searching for autism. Meanwhile, increased microstructural properties in unaffected siblings alone might indicate compensatory processes in the light of genetic predisposition for autism.Lay abstractWhite matter is the neural pathway that connects neurons in different brain regions. Although research has shown white matter differences between autistic and non-autistic people, little is known about the properties of white matter in non-autistic siblings. In addition, past studies often focused on the whole neural tracts; it is unclear where differences exist in specific segments of the tracts. This study identified neural segments that differed between autistic people, their non-autistic siblings, and the age- and non-autistic people. We found altered segments within the tracts connected to anterior brain regions corresponding to several higher cognitive functions (e.g. executive functions) in autistic people and non-autistic siblings. Segments connecting to regions for social cognition and Theory of Mind were altered only in autistic people, explaining a large portion of autistic traits and may serve as neuroimaging markers. Segments within the tracts associated with fewer autistic traits or connecting brain regions for diverse highly integrated functions showed compensatory increases in the microstructural properties in non-autistic siblings. Our findings suggest that differential white matter segments that are shared between autistic people and non-autistic siblings may serve as potential ''intermediate phenotypes''-biological or neuropsychological characteristics in the causal link between genetics and symptoms-of autism. These findings shed light on a promising neuroimaging model to refine the intermediate phenotype of autism which may facilitate further identification of the genetic and biological bases of autism. Future research exploring links between compensatory segments and neurocognitive strengths in non-autistic siblings may help understand brain adaptation to autism. En ligne : https://doi.org/10.1177/13623613221125620 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=499

Ophthalmologic diagnoses in youths with autism spectrum disorder: Prevalence and clinical correlates / Chi-Shin WU in Autism Research, 16-10 (October 2023)  

Public ISBD [article]  inAutism Research > 16-10 (October 2023) . - p.2008-2020 Titre : Ophthalmologic diagnoses in youths with autism spectrum disorder: Prevalence and clinical correlates Type de document : texte imprimé Auteurs : Chi-Shin WU, Auteur ; Tzu-Hsun TSAI, Auteur ; Wei-Li CHEN, Auteur ; Hui-Ju TSAI, Auteur ; Yi-Ling CHIEN, Auteur Article en page(s) : p.2008-2020 Index. décimale : PER Périodiques Résumé : Abstract Autism spectrum disorder (ASD) is associated with a high prevalence of visual dysfunction. This study aimed to investigate the rates of amblyopia, refractive errors, and strabismus, as well as their clinical correlates in ASD. This population-based matched-cohort study used data from the Taiwan National Health Insurance Research Database. A total of 3,551 youths with ASD and 35,510 non-autistic control participants matched by age and sex were included. All the participants were followed-up until they were 18 years old. The prevalence of amblyopia, refractive errors, and strabismus was compared between the ASD and control groups. Effect modifiers, including sex, ASD subgroup, and co-diagnosis of intelligence disability, were examined. Compared to the control group, youths with ASD had a significantly increased risk of amblyopia (adjusted odds ratio [aOR] 1.75), anisometropia (aOR 1.66), astigmatism (aOR 1.51), hypermetropia (aOR 2.08), exotropia (aOR 2.86), and esotropia (aOR 2.63), but a comparable likelihood of myopia according to age. Males with ASD had a significantly lower likelihood of exotropia, but a higher likelihood of myopia than females with ASD. The autism subgroup had a higher OR for hypermetropia, but a lower OR for myopia than the other ASD subgroups. ASD youths with intelligence disabilities demonstrated significantly higher ORs for amblyopia, hypermetropia, and all types of strabismus and lower OR for myopia than those without intelligence disabilities. In conclusion, the rates of amblyopia, refractive errors, and strabismus were higher in youths with ASD. Ocular abnormalities in youths with ASD require a comprehensive assessment and management. En ligne : https://doi.org/10.1002/aur.3019 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513 [article] Ophthalmologic diagnoses in youths with autism spectrum disorder: Prevalence and clinical correlates [texte imprimé] / Chi-Shin WU, Auteur ; Tzu-Hsun TSAI, Auteur ; Wei-Li CHEN, Auteur ; Hui-Ju TSAI, Auteur ; Yi-Ling CHIEN, Auteur . - p.2008-2020. in Autism Research > 16-10 (October 2023) . - p.2008-2020 Index. décimale : PER Périodiques Résumé : Abstract Autism spectrum disorder (ASD) is associated with a high prevalence of visual dysfunction. This study aimed to investigate the rates of amblyopia, refractive errors, and strabismus, as well as their clinical correlates in ASD. This population-based matched-cohort study used data from the Taiwan National Health Insurance Research Database. A total of 3,551 youths with ASD and 35,510 non-autistic control participants matched by age and sex were included. All the participants were followed-up until they were 18 years old. The prevalence of amblyopia, refractive errors, and strabismus was compared between the ASD and control groups. Effect modifiers, including sex, ASD subgroup, and co-diagnosis of intelligence disability, were examined. Compared to the control group, youths with ASD had a significantly increased risk of amblyopia (adjusted odds ratio [aOR] 1.75), anisometropia (aOR 1.66), astigmatism (aOR 1.51), hypermetropia (aOR 2.08), exotropia (aOR 2.86), and esotropia (aOR 2.63), but a comparable likelihood of myopia according to age. Males with ASD had a significantly lower likelihood of exotropia, but a higher likelihood of myopia than females with ASD. The autism subgroup had a higher OR for hypermetropia, but a lower OR for myopia than the other ASD subgroups. ASD youths with intelligence disabilities demonstrated significantly higher ORs for amblyopia, hypermetropia, and all types of strabismus and lower OR for myopia than those without intelligence disabilities. In conclusion, the rates of amblyopia, refractive errors, and strabismus were higher in youths with ASD. Ocular abnormalities in youths with ASD require a comprehensive assessment and management. En ligne : https://doi.org/10.1002/aur.3019 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513

The Comorbidity of Schizophrenia Spectrum and Mood Disorders in Autism Spectrum Disorder / Yi-Ling CHIEN in Autism Research, 14-3 (March 2021)  

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