CYFIP1 overexpression increases fear response in mice but does not affect social or repetitive behavioral phenotypes / C. FRICANO-KUGLER in Molecular Autism, 10 (2019)  

Public ISBD [article]  inMolecular Autism > 10 (2019) . - 25p. Titre : CYFIP1 overexpression increases fear response in mice but does not affect social or repetitive behavioral phenotypes Type de document : Texte imprimé et/ou numérique Auteurs : C. FRICANO-KUGLER, Auteur ; A. GORDON, Auteur ; G. SHIN, Auteur ; K. GAO, Auteur ; J. NGUYEN, Auteur ; J. BERG, Auteur ; M. STARKS, Auteur ; Daniel H. GESCHWIND, Auteur Article en page(s) : 25p. Langues : Anglais (eng) Mots-clés : Autism spectrum disorder (ASD)  Cyfip1  Dup15q  Fear conditioning  Mouse behavior  Neurodevelopmental disorders  RNA sequencing Index. décimale : PER Périodiques Résumé : Background: CYFIP1, a protein that interacts with FMRP and regulates protein synthesis and actin dynamics, is overexpressed in Dup15q syndrome as well as autism spectrum disorder (ASD). While CYFIP1 heterozygosity has been rigorously studied due to its loss in 15q11.2 deletion, Prader-Willi and Angelman syndrome, the effects of CYFIP1 overexpression, as is observed in patients with CYFIP1 duplication, are less well understood. Methods: We developed and validated a mouse model of human CYFIP1 overexpression (CYFIP1 OE) using qPCR and western blot analysis. We performed a large battery of behavior testing on these mice, including ultrasonic vocalizations, three-chamber social assay, home-cage behavior, Y-maze, elevated plus maze, open field test, Morris water maze, fear conditioning, prepulse inhibition, and the hot plate assay. We also performed RNA sequencing and analysis on the basolateral amygdala. Results: Extensive behavioral testing in CYFIP1 OE mice reveals no changes in the core behaviors related to ASD: social interactions and repetitive behaviors. However, we did observe mild learning deficits and an exaggerated fear response. Using RNA sequencing of the basolateral amygdala, a region associated with fear response, we observed changes in pathways related to cytoskeletal regulation, oligodendrocytes, and myelination. We also identified GABA-A subunit composition changes in basolateral amygdala neurons, which are essential components of the neural fear conditioning circuit. Conclusion: Overall, this research identifies the behavioral and molecular consequences of CYFIP1 overexpression and how they contribute to the variable phenotype seen in Dup15q syndrome and in ASD patients with excess CYFIP1. En ligne : http://dx.doi.org/10.1186/s13229-019-0278-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=402 [article] CYFIP1 overexpression increases fear response in mice but does not affect social or repetitive behavioral phenotypes [Texte imprimé et/ou numérique] / C. FRICANO-KUGLER, Auteur ; A. GORDON, Auteur ; G. SHIN, Auteur ; K. GAO, Auteur ; J. NGUYEN, Auteur ; J. BERG, Auteur ; M. STARKS, Auteur ; Daniel H. GESCHWIND, Auteur . - 25p. Langues : Anglais (eng) in Molecular Autism > 10 (2019) . - 25p. Mots-clés : Autism spectrum disorder (ASD)  Cyfip1  Dup15q  Fear conditioning  Mouse behavior  Neurodevelopmental disorders  RNA sequencing Index. décimale : PER Périodiques Résumé : Background: CYFIP1, a protein that interacts with FMRP and regulates protein synthesis and actin dynamics, is overexpressed in Dup15q syndrome as well as autism spectrum disorder (ASD). While CYFIP1 heterozygosity has been rigorously studied due to its loss in 15q11.2 deletion, Prader-Willi and Angelman syndrome, the effects of CYFIP1 overexpression, as is observed in patients with CYFIP1 duplication, are less well understood. Methods: We developed and validated a mouse model of human CYFIP1 overexpression (CYFIP1 OE) using qPCR and western blot analysis. We performed a large battery of behavior testing on these mice, including ultrasonic vocalizations, three-chamber social assay, home-cage behavior, Y-maze, elevated plus maze, open field test, Morris water maze, fear conditioning, prepulse inhibition, and the hot plate assay. We also performed RNA sequencing and analysis on the basolateral amygdala. Results: Extensive behavioral testing in CYFIP1 OE mice reveals no changes in the core behaviors related to ASD: social interactions and repetitive behaviors. However, we did observe mild learning deficits and an exaggerated fear response. Using RNA sequencing of the basolateral amygdala, a region associated with fear response, we observed changes in pathways related to cytoskeletal regulation, oligodendrocytes, and myelination. We also identified GABA-A subunit composition changes in basolateral amygdala neurons, which are essential components of the neural fear conditioning circuit. Conclusion: Overall, this research identifies the behavioral and molecular consequences of CYFIP1 overexpression and how they contribute to the variable phenotype seen in Dup15q syndrome and in ASD patients with excess CYFIP1. En ligne : http://dx.doi.org/10.1186/s13229-019-0278-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=402

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging / K. GAO in Autism Research, 14-12 (December 2021)  

Public ISBD [article]  inAutism Research > 14-12 (December 2021) . - p.2512-2523 Titre : Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging Type de document : Texte imprimé et/ou numérique Auteurs : K. GAO, Auteur ; Y. SUN, Auteur ; S. NIU, Auteur ; L. WANG, Auteur Article en page(s) : p.2512-2523 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnostic imaging  Autistic Disorder  Brain/diagnostic imaging  Child, Preschool  Humans  Infant  Magnetic Resonance Imaging  Neuroimaging  autism Spectrum disorder (ASD)  deep learning algorithm  early-stage status prediction  infant structural MRI  subject-specific autism attention  interest. Index. décimale : PER Périodiques Résumé : Autism, or autism spectrum disorder (ASD), is a developmental disability that is diagnosed at about 2?years of age based on abnormal behaviors. Existing neuroimaging-based methods for the prediction of ASD typically focus on functional magnetic resonance imaging (fMRI); however, most of these fMRI-based studies include subjects older than 5?years of age. Due to challenges in the application of fMRI for infants, structural magnetic resonance imaging (sMRI) has increasingly received attention in the field for early status prediction of ASD. In this study, we propose an automated prediction framework based on infant sMRI at about 24?months of age. Specifically, by leveraging an infant-dedicated pipeline, iBEAT V2.0 Cloud, we derived segmentation and parcellation maps from infant sMRI. We employed a convolutional neural network to extract features from pairwise maps and a Siamese network to distinguish whether paired subjects were from the same or different classes. As compared to T1w imaging without segmentation and parcellation maps, our proposed approach with segmentation and parcellation maps yielded greater sensitivity, specificity, and accuracy of ASD prediction, which was validated using two datasets with different imaging protocols/scanners and was confirmed by receiver operating characteristic analysis. Furthermore, comparison with state-of-the-art methods demonstrated the superior effectiveness and robustness of the proposed method. Finally, attention maps were generated to identify subject-specific autism effects, supporting the reasonability of the predictive results. Collectively, these findings demonstrate the utility of our unified framework for the early-stage status prediction of ASD by sMRI. LAY SUMMARY: The status prediction of autism spectrum disorder (ASD) at an early age is highly desirable, as early intervention may significantly reduce autism symptoms. However, current methods for diagnosing young children are limited to behavioral assays. In this study, we propose an automated method for ASD status prediction at the age of 24?months that uses infant structural magnetic resonance imaging to identify neural features. En ligne : http://dx.doi.org/10.1002/aur.2626 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=450 [article] Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging [Texte imprimé et/ou numérique] / K. GAO, Auteur ; Y. SUN, Auteur ; S. NIU, Auteur ; L. WANG, Auteur . - p.2512-2523. Langues : Anglais (eng) in Autism Research > 14-12 (December 2021) . - p.2512-2523 Mots-clés : Autism Spectrum Disorder/diagnostic imaging  Autistic Disorder  Brain/diagnostic imaging  Child, Preschool  Humans  Infant  Magnetic Resonance Imaging  Neuroimaging  autism Spectrum disorder (ASD)  deep learning algorithm  early-stage status prediction  infant structural MRI  subject-specific autism attention  interest. Index. décimale : PER Périodiques Résumé : Autism, or autism spectrum disorder (ASD), is a developmental disability that is diagnosed at about 2?years of age based on abnormal behaviors. Existing neuroimaging-based methods for the prediction of ASD typically focus on functional magnetic resonance imaging (fMRI); however, most of these fMRI-based studies include subjects older than 5?years of age. Due to challenges in the application of fMRI for infants, structural magnetic resonance imaging (sMRI) has increasingly received attention in the field for early status prediction of ASD. In this study, we propose an automated prediction framework based on infant sMRI at about 24?months of age. Specifically, by leveraging an infant-dedicated pipeline, iBEAT V2.0 Cloud, we derived segmentation and parcellation maps from infant sMRI. We employed a convolutional neural network to extract features from pairwise maps and a Siamese network to distinguish whether paired subjects were from the same or different classes. As compared to T1w imaging without segmentation and parcellation maps, our proposed approach with segmentation and parcellation maps yielded greater sensitivity, specificity, and accuracy of ASD prediction, which was validated using two datasets with different imaging protocols/scanners and was confirmed by receiver operating characteristic analysis. Furthermore, comparison with state-of-the-art methods demonstrated the superior effectiveness and robustness of the proposed method. Finally, attention maps were generated to identify subject-specific autism effects, supporting the reasonability of the predictive results. Collectively, these findings demonstrate the utility of our unified framework for the early-stage status prediction of ASD by sMRI. LAY SUMMARY: The status prediction of autism spectrum disorder (ASD) at an early age is highly desirable, as early intervention may significantly reduce autism symptoms. However, current methods for diagnosing young children are limited to behavioral assays. In this study, we propose an automated method for ASD status prediction at the age of 24?months that uses infant structural magnetic resonance imaging to identify neural features. En ligne : http://dx.doi.org/10.1002/aur.2626 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=450

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