Deviation from normative brain development is associated with symptom severity in autism spectrum disorder / B. TUNC in Molecular Autism, 10 (2019)  

Public ISBD [article]  inMolecular Autism > 10 (2019) . - 46 p. Titre : Deviation from normative brain development is associated with symptom severity in autism spectrum disorder Type de document : Texte imprimé et/ou numérique Auteurs : B. TUNC, Auteur ; L. D. YANKOWITZ, Auteur ; D. PARKER, Auteur ; J. A. ALAPPATT, Auteur ; J. PANDEY, Auteur ; Robert T. SCHULTZ, Auteur ; R. VERMA, Auteur Article en page(s) : 46 p. Langues : Anglais (eng) Mots-clés : Autism  Brain development  Heterogeneity  Machine learning  Normative modeling  Symptom severity Index. décimale : PER Périodiques Résumé : Background: Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity. Methods: The developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6-25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity. Results: Machine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = - 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity. Limitations: This work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable. Conclusions: Findings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD. En ligne : http://dx.doi.org/10.1186/s13229-019-0301-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=414 [article] Deviation from normative brain development is associated with symptom severity in autism spectrum disorder [Texte imprimé et/ou numérique] / B. TUNC, Auteur ; L. D. YANKOWITZ, Auteur ; D. PARKER, Auteur ; J. A. ALAPPATT, Auteur ; J. PANDEY, Auteur ; Robert T. SCHULTZ, Auteur ; R. VERMA, Auteur . - 46 p. Langues : Anglais (eng) in Molecular Autism > 10 (2019) . - 46 p. Mots-clés : Autism  Brain development  Heterogeneity  Machine learning  Normative modeling  Symptom severity Index. décimale : PER Périodiques Résumé : Background: Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity. Methods: The developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6-25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity. Results: Machine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = - 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity. Limitations: This work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable. Conclusions: Findings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD. En ligne : http://dx.doi.org/10.1186/s13229-019-0301-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=414

Diagnostic shifts in autism spectrum disorder can be linked to the fuzzy nature of the diagnostic boundary: a data-driven approach / B. TUNC in Journal of Child Psychology and Psychiatry, 62-10 (October 2021)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 62-10 (October 2021) . - p.1236-1245 Titre : Diagnostic shifts in autism spectrum disorder can be linked to the fuzzy nature of the diagnostic boundary: a data-driven approach Type de document : Texte imprimé et/ou numérique Auteurs : B. TUNC, Auteur ; J. PANDEY, Auteur ; T. ST JOHN, Auteur ; S. S. MEERA, Auteur ; J. E. MALDARELLI, Auteur ; Lonnie ZWAIGENBAUM, Auteur ; Heather C. HAZLETT, Auteur ; Stephen R. DAGER, Auteur ; Kelly N. BOTTERON, Auteur ; J. B. GIRAULT, Auteur ; R. C. MCKINSTRY, Auteur ; R. VERMA, Auteur ; J. T. ELISON, Auteur ; J. R. PRUETT, Auteur ; J. PIVEN, Auteur ; A. M. ESTES, Auteur ; Robert T. SCHULTZ, Auteur Article en page(s) : p.1236-1245 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnosis  Child, Preschool  Cohort Studies  Early Diagnosis  Humans  Phenotype  Siblings  Autism spectrum disorders  diagnosis  infancy  machine learning  stability  interest Index. décimale : PER Périodiques Résumé : BACKGROUND: Diagnostic shifts at early ages may provide invaluable insights into the nature of separation between autism spectrum disorder (ASD) and typical development. Recent conceptualizations of ASD suggest the condition is only fuzzily separated from non-ASD, with intermediate cases between the two. These intermediate cases may shift along a transition region over time, leading to apparent instability of diagnosis. METHODS: We used a cohort of children with high ASD risk, by virtue of having an older sibling with ASD, assessed at 24 months (N = 212) and 36 months (N = 191). We applied machine learning to empirically characterize the classification boundary between ASD and non-ASD, using variables quantifying developmental and adaptive skills. We computed the distance of children to the classification boundary. RESULTS: Children who switched diagnostic labels from 24 to 36 months, in both directions, (dynamic group) had intermediate phenotypic profiles. They were closer to the classification boundary compared to children who had stable diagnoses, both at 24 months (Cohen's d = .52) and at 36 months (d = .75). The magnitude of change in distance between the two time points was similar for the dynamic and stable groups (Cohen's d = .06), and diagnostic shifts were not associated with a large change. At the individual level, a few children in the dynamic group showed substantial change. CONCLUSIONS: Our results suggested that a diagnostic shift was largely due to a slight movement within a transition region between ASD and non-ASD. This fact highlights the need for more vigilant surveillance and intervention strategies. Young children with intermediate phenotypes may have an increased susceptibility to gain or lose their diagnosis at later ages, calling attention to the inherently dynamic nature of early ASD diagnoses. En ligne : http://dx.doi.org/10.1111/jcpp.13406 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=456 [article] Diagnostic shifts in autism spectrum disorder can be linked to the fuzzy nature of the diagnostic boundary: a data-driven approach [Texte imprimé et/ou numérique] / B. TUNC, Auteur ; J. PANDEY, Auteur ; T. ST JOHN, Auteur ; S. S. MEERA, Auteur ; J. E. MALDARELLI, Auteur ; Lonnie ZWAIGENBAUM, Auteur ; Heather C. HAZLETT, Auteur ; Stephen R. DAGER, Auteur ; Kelly N. BOTTERON, Auteur ; J. B. GIRAULT, Auteur ; R. C. MCKINSTRY, Auteur ; R. VERMA, Auteur ; J. T. ELISON, Auteur ; J. R. PRUETT, Auteur ; J. PIVEN, Auteur ; A. M. ESTES, Auteur ; Robert T. SCHULTZ, Auteur . - p.1236-1245. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 62-10 (October 2021) . - p.1236-1245 Mots-clés : Autism Spectrum Disorder/diagnosis  Child, Preschool  Cohort Studies  Early Diagnosis  Humans  Phenotype  Siblings  Autism spectrum disorders  diagnosis  infancy  machine learning  stability  interest Index. décimale : PER Périodiques Résumé : BACKGROUND: Diagnostic shifts at early ages may provide invaluable insights into the nature of separation between autism spectrum disorder (ASD) and typical development. Recent conceptualizations of ASD suggest the condition is only fuzzily separated from non-ASD, with intermediate cases between the two. These intermediate cases may shift along a transition region over time, leading to apparent instability of diagnosis. METHODS: We used a cohort of children with high ASD risk, by virtue of having an older sibling with ASD, assessed at 24 months (N = 212) and 36 months (N = 191). We applied machine learning to empirically characterize the classification boundary between ASD and non-ASD, using variables quantifying developmental and adaptive skills. We computed the distance of children to the classification boundary. RESULTS: Children who switched diagnostic labels from 24 to 36 months, in both directions, (dynamic group) had intermediate phenotypic profiles. They were closer to the classification boundary compared to children who had stable diagnoses, both at 24 months (Cohen's d = .52) and at 36 months (d = .75). The magnitude of change in distance between the two time points was similar for the dynamic and stable groups (Cohen's d = .06), and diagnostic shifts were not associated with a large change. At the individual level, a few children in the dynamic group showed substantial change. CONCLUSIONS: Our results suggested that a diagnostic shift was largely due to a slight movement within a transition region between ASD and non-ASD. This fact highlights the need for more vigilant surveillance and intervention strategies. Young children with intermediate phenotypes may have an increased susceptibility to gain or lose their diagnosis at later ages, calling attention to the inherently dynamic nature of early ASD diagnoses. En ligne : http://dx.doi.org/10.1111/jcpp.13406 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=456

Infants later diagnosed with autism have lower canonical babbling ratios in the first year of life / L. D. YANKOWITZ in Molecular Autism, 13 (2022)  

Public ISBD [article]  inMolecular Autism > 13 (2022) . - 28 p. Titre : Infants later diagnosed with autism have lower canonical babbling ratios in the first year of life Type de document : Texte imprimé et/ou numérique Auteurs : L. D. YANKOWITZ, Auteur ; V. PETRULLA, Auteur ; S. PLATE, Auteur ; B. TUNC, Auteur ; W. GUTHRIE, Auteur ; S. S. MEERA, Auteur ; K. TENA, Auteur ; J. PANDEY, Auteur ; M. R. SWANSON, Auteur ; J. R. Jr PRUETT, Auteur ; M. COLA, Auteur ; A. RUSSELL, Auteur ; N. MARRUS, Auteur ; Heather C. HAZLETT, Auteur ; K. BOTTERON, Auteur ; J. N. CONSTANTINO, Auteur ; Stephen R. DAGER, Auteur ; A. ESTES, Auteur ; Lonnie ZWAIGENBAUM, Auteur ; J. PIVEN, Auteur ; Robert T. SCHULTZ, Auteur ; Julia PARISH-MORRIS, Auteur Article en page(s) : 28 p. Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnosis  Autistic Disorder  Humans  Infant  Language Development Disorders/diagnosis  Longitudinal Studies  Reproducibility of Results Index. décimale : PER Périodiques Résumé : BACKGROUND: Canonical babbling-producing syllables with a mature consonant, full vowel, and smooth transition-is an important developmental milestone that typically occurs in the first year of life. Some studies indicate delayed or reduced canonical babbling in infants at high familial likelihood for autism spectrum disorder (ASD) or who later receive an ASD diagnosis, but evidence is mixed. More refined characterization of babbling in the first year of life in infants with high likelihood for ASD is needed. METHODS: Vocalizations produced at 6 and 12 months by infants (n=267) taking part in a longitudinal study were coded for canonical and non-canonical syllables. Infants were categorized as low familial likelihood (LL), high familial likelihood diagnosed with ASD at 24 months (HL-ASD) or not diagnosed (HL-Neg). Language delay was assessed based on 24-month expressive and receptive language scores. Canonical babble ratio (CBR) was calculated by dividing the number of canonical syllables by the number of total syllables. Generalized linear (mixed) models were used to assess the relationship between group membership and CBR, controlling for site, sex, and maternal education. Logistic regression was used to assess whether canonical babbling ratios at 6 and 12 months predict 24-month diagnostic outcome. RESULTS: No diagnostic group differences in CBR were detected at 6 months, but HL-ASD infants produced significantly lower CBR than both the HL-Neg and LL groups at 12 months. HL-Neg infants with language delay also showed reduced CBR at 12 months. Neither 6- nor 12-month CBR was significant predictors of 24-month diagnostic outcome (ASD versus no ASD) in logistic regression. LIMITATIONS: Small numbers of vocalizations produced by infants at 6 months may limit the reliability of CBR estimates. It is not known if results generalize to infants who are not at high familial likelihood, or infants from more diverse racial and socioeconomic backgrounds. CONCLUSIONS: Lower canonical babbling ratios are apparent by the end of the first year of life in ASD regardless of later language delay, but are also observed for infants with later language delay without ASD. Canonical babbling may lack specificity as an early marker when used on its own. En ligne : http://dx.doi.org/10.1186/s13229-022-00503-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=491 [article] Infants later diagnosed with autism have lower canonical babbling ratios in the first year of life [Texte imprimé et/ou numérique] / L. D. YANKOWITZ, Auteur ; V. PETRULLA, Auteur ; S. PLATE, Auteur ; B. TUNC, Auteur ; W. GUTHRIE, Auteur ; S. S. MEERA, Auteur ; K. TENA, Auteur ; J. PANDEY, Auteur ; M. R. SWANSON, Auteur ; J. R. Jr PRUETT, Auteur ; M. COLA, Auteur ; A. RUSSELL, Auteur ; N. MARRUS, Auteur ; Heather C. HAZLETT, Auteur ; K. BOTTERON, Auteur ; J. N. CONSTANTINO, Auteur ; Stephen R. DAGER, Auteur ; A. ESTES, Auteur ; Lonnie ZWAIGENBAUM, Auteur ; J. PIVEN, Auteur ; Robert T. SCHULTZ, Auteur ; Julia PARISH-MORRIS, Auteur . - 28 p. Langues : Anglais (eng) in Molecular Autism > 13 (2022) . - 28 p. Mots-clés : Autism Spectrum Disorder/diagnosis  Autistic Disorder  Humans  Infant  Language Development Disorders/diagnosis  Longitudinal Studies  Reproducibility of Results Index. décimale : PER Périodiques Résumé : BACKGROUND: Canonical babbling-producing syllables with a mature consonant, full vowel, and smooth transition-is an important developmental milestone that typically occurs in the first year of life. Some studies indicate delayed or reduced canonical babbling in infants at high familial likelihood for autism spectrum disorder (ASD) or who later receive an ASD diagnosis, but evidence is mixed. More refined characterization of babbling in the first year of life in infants with high likelihood for ASD is needed. METHODS: Vocalizations produced at 6 and 12 months by infants (n=267) taking part in a longitudinal study were coded for canonical and non-canonical syllables. Infants were categorized as low familial likelihood (LL), high familial likelihood diagnosed with ASD at 24 months (HL-ASD) or not diagnosed (HL-Neg). Language delay was assessed based on 24-month expressive and receptive language scores. Canonical babble ratio (CBR) was calculated by dividing the number of canonical syllables by the number of total syllables. Generalized linear (mixed) models were used to assess the relationship between group membership and CBR, controlling for site, sex, and maternal education. Logistic regression was used to assess whether canonical babbling ratios at 6 and 12 months predict 24-month diagnostic outcome. RESULTS: No diagnostic group differences in CBR were detected at 6 months, but HL-ASD infants produced significantly lower CBR than both the HL-Neg and LL groups at 12 months. HL-Neg infants with language delay also showed reduced CBR at 12 months. Neither 6- nor 12-month CBR was significant predictors of 24-month diagnostic outcome (ASD versus no ASD) in logistic regression. LIMITATIONS: Small numbers of vocalizations produced by infants at 6 months may limit the reliability of CBR estimates. It is not known if results generalize to infants who are not at high familial likelihood, or infants from more diverse racial and socioeconomic backgrounds. CONCLUSIONS: Lower canonical babbling ratios are apparent by the end of the first year of life in ASD regardless of later language delay, but are also observed for infants with later language delay without ASD. Canonical babbling may lack specificity as an early marker when used on its own. En ligne : http://dx.doi.org/10.1186/s13229-022-00503-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=491

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