3 recherche sur le mot-clé 'FMR1 premutation'

Associated features in females with an FMR1 premutation / Anne C. WHEELER in Journal of Neurodevelopmental Disorders, 6-1 (December 2014)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 6-1 (December 2014) . - p.30 Titre : Associated features in females with an FMR1 premutation Type de document : texte imprimé Auteurs : Anne C. WHEELER, Auteur ; Donald B. Jr BAILEY, Auteur ; Elizabeth BERRY-KRAVIS, Auteur ; Jan S. GREENBERG, Auteur ; Molly LOSH, Auteur ; Marsha R. MAILICK, Auteur ; M. MILA, Auteur ; John M. OLICHNEY, Auteur ; Laia RODRIGUEZ-REVENGA, Auteur ; Stephanie SHERMAN, Auteur ; Leann SMITH, Auteur ; Scott SUMMERS, Auteur ; Jin-Chen YANG, Auteur ; Randi J. HAGERMAN, Auteur Article en page(s) : p.30 Langues : Anglais (eng) Mots-clés : FMR1 premutation  fragile X  health risks Index. décimale : PER Périodiques Résumé : Changes in the fragile X mental retardation 1 gene (FMR1) have been associated with specific phenotypes, most specifically those of fragile X syndrome (FXS), fragile X tremor/ataxia syndrome (FXTAS), and fragile X primary ovarian insufficiency (FXPOI). Evidence of increased risk for additional medical, psychiatric, and cognitive features and conditions is now known to exist for individuals with a premutation, although some features have been more thoroughly studied than others. This review highlights the literature on medical, reproductive, cognitive, and psychiatric features, primarily in females, that have been suggested to be associated with changes in the FMR1 gene. Based on this review, each feature is evaluated with regard to the strength of evidence of association with the premutation. Areas of need for additional focused research and possible intervention strategies are suggested. En ligne : http://dx.doi.org/10.1186/1866-1955-6-30 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=346 [article] Associated features in females with an FMR1 premutation [texte imprimé] / Anne C. WHEELER, Auteur ; Donald B. Jr BAILEY, Auteur ; Elizabeth BERRY-KRAVIS, Auteur ; Jan S. GREENBERG, Auteur ; Molly LOSH, Auteur ; Marsha R. MAILICK, Auteur ; M. MILA, Auteur ; John M. OLICHNEY, Auteur ; Laia RODRIGUEZ-REVENGA, Auteur ; Stephanie SHERMAN, Auteur ; Leann SMITH, Auteur ; Scott SUMMERS, Auteur ; Jin-Chen YANG, Auteur ; Randi J. HAGERMAN, Auteur . - p.30. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 6-1 (December 2014) . - p.30 Mots-clés : FMR1 premutation  fragile X  health risks Index. décimale : PER Périodiques Résumé : Changes in the fragile X mental retardation 1 gene (FMR1) have been associated with specific phenotypes, most specifically those of fragile X syndrome (FXS), fragile X tremor/ataxia syndrome (FXTAS), and fragile X primary ovarian insufficiency (FXPOI). Evidence of increased risk for additional medical, psychiatric, and cognitive features and conditions is now known to exist for individuals with a premutation, although some features have been more thoroughly studied than others. This review highlights the literature on medical, reproductive, cognitive, and psychiatric features, primarily in females, that have been suggested to be associated with changes in the FMR1 gene. Based on this review, each feature is evaluated with regard to the strength of evidence of association with the premutation. Areas of need for additional focused research and possible intervention strategies are suggested. En ligne : http://dx.doi.org/10.1186/1866-1955-6-30 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=346

Developmental profiles of infants with an FMR1 premutation / Anne C. WHEELER in Journal of Neurodevelopmental Disorders, 8-1 (December 2016)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 8-1 (December 2016) . - p.40 Titre : Developmental profiles of infants with an FMR1 premutation Type de document : texte imprimé Auteurs : Anne C. WHEELER, Auteur ; John SIDERIS, Auteur ; Randi J. HAGERMAN, Auteur ; Elizabeth BERRY-KRAVIS, Auteur ; Flora TASSONE, Auteur ; Donald B. Jr BAILEY, Auteur Article en page(s) : p.40 Langues : Anglais (eng) Mots-clés : Early development  FMR1 premutation  Newborn screening Index. décimale : PER Périodiques Résumé : BACKGROUND: Emerging evidence suggests that a subset of FMR1 premutation carriers is at an increased risk for cognitive, emotional, and medical conditions. However, because the premutation is rarely diagnosed at birth, the early developmental trajectories of children with a premutation are not known. METHODS: This exploratory study examined the cognitive, communication, and social-behavioral profiles of 26 infants with a premutation who were identified through participation in a newborn screening for fragile X syndrome pilot study. In this study, families whose newborn screened positive for an FMR1 premutation were invited to participate in a longitudinal study of early development. Twenty-six infants with the premutation and 21 matched, screen-negative comparison babies were assessed using validated standardized measures at 6-month intervals starting as young as 3 months of age. The babies were assessed up to seven times over a 4-year period. RESULTS: The premutation group was not statistically different from the comparison group on measures of cognitive development, adaptive behavior, temperament, or overall communication. However, the babies with the premutation had a significantly different developmental trajectory on measures of nonverbal communication and hyperresponsivity to sensory experiences. They also were significantly more hyporesponsive at all ages than the comparison group. Cytosine-guanine-guanine repeat length was linearly associated with overall cognitive development. CONCLUSIONS: These results suggest that infants with a premutation may present with subtle developmental differences as young as 12 months of age that may be early markers of later anxiety, social deficits, or other challenges thought to be experienced by a subset of carriers. En ligne : http://dx.doi.org/10.1186/s11689-016-9171-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=349 [article] Developmental profiles of infants with an FMR1 premutation [texte imprimé] / Anne C. WHEELER, Auteur ; John SIDERIS, Auteur ; Randi J. HAGERMAN, Auteur ; Elizabeth BERRY-KRAVIS, Auteur ; Flora TASSONE, Auteur ; Donald B. Jr BAILEY, Auteur . - p.40. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 8-1 (December 2016) . - p.40 Mots-clés : Early development  FMR1 premutation  Newborn screening Index. décimale : PER Périodiques Résumé : BACKGROUND: Emerging evidence suggests that a subset of FMR1 premutation carriers is at an increased risk for cognitive, emotional, and medical conditions. However, because the premutation is rarely diagnosed at birth, the early developmental trajectories of children with a premutation are not known. METHODS: This exploratory study examined the cognitive, communication, and social-behavioral profiles of 26 infants with a premutation who were identified through participation in a newborn screening for fragile X syndrome pilot study. In this study, families whose newborn screened positive for an FMR1 premutation were invited to participate in a longitudinal study of early development. Twenty-six infants with the premutation and 21 matched, screen-negative comparison babies were assessed using validated standardized measures at 6-month intervals starting as young as 3 months of age. The babies were assessed up to seven times over a 4-year period. RESULTS: The premutation group was not statistically different from the comparison group on measures of cognitive development, adaptive behavior, temperament, or overall communication. However, the babies with the premutation had a significantly different developmental trajectory on measures of nonverbal communication and hyperresponsivity to sensory experiences. They also were significantly more hyporesponsive at all ages than the comparison group. Cytosine-guanine-guanine repeat length was linearly associated with overall cognitive development. CONCLUSIONS: These results suggest that infants with a premutation may present with subtle developmental differences as young as 12 months of age that may be early markers of later anxiety, social deficits, or other challenges thought to be experienced by a subset of carriers. En ligne : http://dx.doi.org/10.1186/s11689-016-9171-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=349

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