Neural correlates of face processing associated with development of social communication in 12-month infants with familial risk of autism spectrum disorder / Joshua GLAUSER in Journal of Neurodevelopmental Disorders, 14 (2022)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 14 (2022) Titre : Neural correlates of face processing associated with development of social communication in 12-month infants with familial risk of autism spectrum disorder Type de document : texte imprimé Auteurs : Joshua GLAUSER, Auteur ; Carol L. WILKINSON, Auteur ; Laurel J. GABARD-DURNAM, Auteur ; Boin CHOI, Auteur ; Helen TAGER-FLUSBERG, Auteur ; Charles A. NELSON, Auteur Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnosis  Communication  Evoked Potentials/physiology  Facial Recognition  Female  Genetic Predisposition to Disease  Humans  Infant  Autism  Eeg  Event-related potential  Face processing  Language development Index. décimale : PER Périodiques Résumé : BACKGROUND: Differences in face processing in individuals with ASD is hypothesized to impact the development of social communication skills. This study aimed to characterize the neural correlates of face processing in 12-month-old infants at familial risk of developing ASD by (1) comparing face-sensitive event-related potentials (ERP) (Nc, N290, P400) between high-familial-risk infants who develop ASD (HR-ASD), high-familial-risk infants without ASD (HR-NoASD), and low-familial-risk infants (LR), and (2) evaluating how face-sensitive ERP components are associated with development of social communication skills. METHODS: 12-month-old infants participated in a study in which they were presented with alternating images of their mother's face and the face of a stranger (LR = 45, HR-NoASD = 41, HR-ASD = 24) as EEG data were collected. Parent-reported and laboratory-observed social communication measures were obtained at 12 and 18 months. Group differences in ERP responses were evaluated using ANOVA, and multiple linear regressions were conducted with maternal education and outcome groups as covariates to assess relationships between ERP and behavioral measures. RESULTS: For each of the ERP components (Nc [negative-central], N290, and P400), the amplitude difference between mother and stranger (Mother-Stranger) trials was not statistically different between the three outcome groups (Nc p = 0.72, N290 p = 0.88, P400 p = 0.91). Marginal effects analyses found that within the LR group, a greater Nc Mother-Stranger response was associated with better expressive language skills on the Mullen Scales of Early Learning, controlling for maternal education and outcome group effects (marginal effects dy/dx = 1.15; p < 0.01). No significant associations were observed between the Nc and language or social measures in HR-NoASD or HR-ASD groups. In contrast, specific to the HR-ASD group, amplitude difference between the Mother versus Stranger P400 response was positively associated with expressive (dy/dx = 2.1, p < 0.001) and receptive language skills at 12 months (dy/dx = 1.68, p < 0.005), and negatively associated with social affect scores on the Autism Diagnostic Observation Schedule (dy/dx = - 1.22, p < 0.001) at 18 months. CONCLUSIONS: In 12-month-old infant siblings with subsequent ASD, increased P400 response to Mother over Stranger faces is positively associated with concurrent language and future social skills. En ligne : https://dx.doi.org/10.1186/s11689-021-09413-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574 [article] Neural correlates of face processing associated with development of social communication in 12-month infants with familial risk of autism spectrum disorder [texte imprimé] / Joshua GLAUSER, Auteur ; Carol L. WILKINSON, Auteur ; Laurel J. GABARD-DURNAM, Auteur ; Boin CHOI, Auteur ; Helen TAGER-FLUSBERG, Auteur ; Charles A. NELSON, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 14 (2022) Mots-clés : Autism Spectrum Disorder/diagnosis  Communication  Evoked Potentials/physiology  Facial Recognition  Female  Genetic Predisposition to Disease  Humans  Infant  Autism  Eeg  Event-related potential  Face processing  Language development Index. décimale : PER Périodiques Résumé : BACKGROUND: Differences in face processing in individuals with ASD is hypothesized to impact the development of social communication skills. This study aimed to characterize the neural correlates of face processing in 12-month-old infants at familial risk of developing ASD by (1) comparing face-sensitive event-related potentials (ERP) (Nc, N290, P400) between high-familial-risk infants who develop ASD (HR-ASD), high-familial-risk infants without ASD (HR-NoASD), and low-familial-risk infants (LR), and (2) evaluating how face-sensitive ERP components are associated with development of social communication skills. METHODS: 12-month-old infants participated in a study in which they were presented with alternating images of their mother's face and the face of a stranger (LR = 45, HR-NoASD = 41, HR-ASD = 24) as EEG data were collected. Parent-reported and laboratory-observed social communication measures were obtained at 12 and 18 months. Group differences in ERP responses were evaluated using ANOVA, and multiple linear regressions were conducted with maternal education and outcome groups as covariates to assess relationships between ERP and behavioral measures. RESULTS: For each of the ERP components (Nc [negative-central], N290, and P400), the amplitude difference between mother and stranger (Mother-Stranger) trials was not statistically different between the three outcome groups (Nc p = 0.72, N290 p = 0.88, P400 p = 0.91). Marginal effects analyses found that within the LR group, a greater Nc Mother-Stranger response was associated with better expressive language skills on the Mullen Scales of Early Learning, controlling for maternal education and outcome group effects (marginal effects dy/dx = 1.15; p < 0.01). No significant associations were observed between the Nc and language or social measures in HR-NoASD or HR-ASD groups. In contrast, specific to the HR-ASD group, amplitude difference between the Mother versus Stranger P400 response was positively associated with expressive (dy/dx = 2.1, p < 0.001) and receptive language skills at 12 months (dy/dx = 1.68, p < 0.005), and negatively associated with social affect scores on the Autism Diagnostic Observation Schedule (dy/dx = - 1.22, p < 0.001) at 18 months. CONCLUSIONS: In 12-month-old infant siblings with subsequent ASD, increased P400 response to Mother over Stranger faces is positively associated with concurrent language and future social skills. En ligne : https://dx.doi.org/10.1186/s11689-021-09413-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574

Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months / Fleming C. PECK in Journal of Neurodevelopmental Disorders, 13 (2021)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 13 (2021) Titre : Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months Type de document : texte imprimé Auteurs : Fleming C. PECK, Auteur ; Laurel J. GABARD-DURNAM, Auteur ; Carol L. WILKINSON, Auteur ; William BOSL, Auteur ; Helen TAGER-FLUSBERG, Auteur ; Charles A. NELSON, Auteur Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnosis  Autistic Disorder  Electroencephalography/methods  Humans  Infant  Language  Machine Learning  Autism  Eeg  Language development  Sensitive period Index. décimale : PER Périodiques Résumé : BACKGROUND: Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. METHODS: Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). RESULTS: Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. CONCLUSIONS: These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms. En ligne : https://dx.doi.org/10.1186/s11689-021-09405-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574 [article] Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months [texte imprimé] / Fleming C. PECK, Auteur ; Laurel J. GABARD-DURNAM, Auteur ; Carol L. WILKINSON, Auteur ; William BOSL, Auteur ; Helen TAGER-FLUSBERG, Auteur ; Charles A. NELSON, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 13 (2021) Mots-clés : Autism Spectrum Disorder/diagnosis  Autistic Disorder  Electroencephalography/methods  Humans  Infant  Language  Machine Learning  Autism  Eeg  Language development  Sensitive period Index. décimale : PER Périodiques Résumé : BACKGROUND: Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. METHODS: Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). RESULTS: Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. CONCLUSIONS: These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms. En ligne : https://dx.doi.org/10.1186/s11689-021-09405-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574

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