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Deviation from normative brain development is associated with symptom severity in autism spectrum disorder / B. TUNC in Molecular Autism, 10 (2019)
[article]
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
in Molecular Autism > 10 (2019) . - 46 p.[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 Patterns of connectome variability in autism across five functional activation tasks: findings from the LEAP project / Tristan LOODEN in Molecular Autism, 13 (2022)
[article]
Titre : Patterns of connectome variability in autism across five functional activation tasks: findings from the LEAP project Type de document : Texte imprimé et/ou numérique Auteurs : Tristan LOODEN, Auteur ; Dorothea L. FLORIS, Auteur ; Alberto LLERA, Auteur ; Roselyne J. CHAUVIN, Auteur ; Tony CHARMAN, Auteur ; Tobias BANASCHEWSKI, Auteur ; Declan MURPHY, Auteur ; Andre F. MARQUAND, Auteur ; Jan K. BUITELAAR, Auteur ; Christian F. BECKMANN, Auteur ; AIMS-2-TRIALS GROUP, Auteur Article en page(s) : 53 p. Langues : Anglais (eng) Mots-clés : Autism Canonical correlation analysis Functional connectivity Heterogeneity Normative modeling fMRI Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism spectrum disorder (autism) is a complex neurodevelopmental condition with pronounced behavioral, cognitive, and neural heterogeneities across individuals. Here, our goal was to characterize heterogeneity in autism by identifying patterns of neural diversity as reflected in BOLD fMRI in the way individuals with autism engage with a varied array of cognitive tasks. METHODS: All analyses were based on the EU-AIMS/AIMS-2-TRIALS multisite Longitudinal European Autism Project (LEAP) with participants with autism (n=282) and typically developing (TD) controls (n=221) between 6 and 30Â years of age. We employed a novel task potency approach which combines the unique aspects of both resting state fMRI and task-fMRI to quantify task-induced variations in the functional connectome. Normative modelling was used to map atypicality of features on an individual basis with respect to their distribution in neurotypical control participants. We applied robust out-of-sample canonical correlation analysis (CCA) to relate connectome data to behavioral data. RESULTS: Deviation from the normative ranges of global functional connectivity was greater for individuals with autism compared to TD in each fMRI task paradigm (all tasks p< 0.001). The similarity across individuals of the deviation pattern was significantly increased in autistic relative to TD individuals (p< 0.002). The CCA identified significant and robust brain-behavior covariation between functional connectivity atypicality and autism-related behavioral features. CONCLUSIONS: Individuals with autism engage with tasks in a globally atypical way, but the particular spatial pattern of this atypicality is nevertheless similar across tasks. Atypicalities in the tasks originate mostly from prefrontal cortex and default mode network regions, but also speech and auditory networks. We show how sophisticated modeling methods such as task potency and normative modeling can be used toward unravelling complex heterogeneous conditions like autism. En ligne : http://dx.doi.org/10.1186/s13229-022-00529-y Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=491
in Molecular Autism > 13 (2022) . - 53 p.[article] Patterns of connectome variability in autism across five functional activation tasks: findings from the LEAP project [Texte imprimé et/ou numérique] / Tristan LOODEN, Auteur ; Dorothea L. FLORIS, Auteur ; Alberto LLERA, Auteur ; Roselyne J. CHAUVIN, Auteur ; Tony CHARMAN, Auteur ; Tobias BANASCHEWSKI, Auteur ; Declan MURPHY, Auteur ; Andre F. MARQUAND, Auteur ; Jan K. BUITELAAR, Auteur ; Christian F. BECKMANN, Auteur ; AIMS-2-TRIALS GROUP, Auteur . - 53 p.
Langues : Anglais (eng)
in Molecular Autism > 13 (2022) . - 53 p.
Mots-clés : Autism Canonical correlation analysis Functional connectivity Heterogeneity Normative modeling fMRI Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism spectrum disorder (autism) is a complex neurodevelopmental condition with pronounced behavioral, cognitive, and neural heterogeneities across individuals. Here, our goal was to characterize heterogeneity in autism by identifying patterns of neural diversity as reflected in BOLD fMRI in the way individuals with autism engage with a varied array of cognitive tasks. METHODS: All analyses were based on the EU-AIMS/AIMS-2-TRIALS multisite Longitudinal European Autism Project (LEAP) with participants with autism (n=282) and typically developing (TD) controls (n=221) between 6 and 30Â years of age. We employed a novel task potency approach which combines the unique aspects of both resting state fMRI and task-fMRI to quantify task-induced variations in the functional connectome. Normative modelling was used to map atypicality of features on an individual basis with respect to their distribution in neurotypical control participants. We applied robust out-of-sample canonical correlation analysis (CCA) to relate connectome data to behavioral data. RESULTS: Deviation from the normative ranges of global functional connectivity was greater for individuals with autism compared to TD in each fMRI task paradigm (all tasks p< 0.001). The similarity across individuals of the deviation pattern was significantly increased in autistic relative to TD individuals (p< 0.002). The CCA identified significant and robust brain-behavior covariation between functional connectivity atypicality and autism-related behavioral features. CONCLUSIONS: Individuals with autism engage with tasks in a globally atypical way, but the particular spatial pattern of this atypicality is nevertheless similar across tasks. Atypicalities in the tasks originate mostly from prefrontal cortex and default mode network regions, but also speech and auditory networks. We show how sophisticated modeling methods such as task potency and normative modeling can be used toward unravelling complex heterogeneous conditions like autism. En ligne : http://dx.doi.org/10.1186/s13229-022-00529-y Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=491