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 Auteurs : B. TUNC, Auteur ; Lisa D. YANKOWITZ, Auteur ; Drew PARKER, Auteur ; Jacob A. ALAPPATT, Auteur ; Juhi PANDEY, Auteur ; Robert T. SCHULTZ, Auteur ; Ragini 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] / B. TUNC, Auteur ; Lisa D. YANKOWITZ, Auteur ; Drew PARKER, Auteur ; Jacob A. ALAPPATT, Auteur ; Juhi PANDEY, Auteur ; Robert T. SCHULTZ, Auteur ; Ragini 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 Auteurs : B. TUNC, Auteur ; Juhi PANDEY, Auteur ; Tanya ST JOHN, Auteur ; Shoba S. MEERA, Auteur ; Jennifer E. MALDARELLI, Auteur ; Lonnie ZWAIGENBAUM, Auteur ; Heather C. HAZLETT, Auteur ; Stephen R. DAGER, Auteur ; Kelly N. BOTTERON, Auteur ; Jessica B. GIRAULT, Auteur ; Robert C. MCKINSTRY, Auteur ; Ragini VERMA, Auteur ; Jed T. ELISON, Auteur ; John R. PRUETT, Auteur ; Joseph PIVEN, Auteur ; Annette 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] / B. TUNC, Auteur ; Juhi PANDEY, Auteur ; Tanya ST JOHN, Auteur ; Shoba S. MEERA, Auteur ; Jennifer E. MALDARELLI, Auteur ; Lonnie ZWAIGENBAUM, Auteur ; Heather C. HAZLETT, Auteur ; Stephen R. DAGER, Auteur ; Kelly N. BOTTERON, Auteur ; Jessica B. GIRAULT, Auteur ; Robert C. MCKINSTRY, Auteur ; Ragini VERMA, Auteur ; Jed T. ELISON, Auteur ; John R. PRUETT, Auteur ; Joseph PIVEN, Auteur ; Annette 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

DTI and Tractography in the Autistic Brain / Timothy P.L. ROBERTS

Public ISBD in The Neuroscience of Autism Spectrum Disorders / Joseph D. BUXBAUM Titre : DTI and Tractography in the Autistic Brain Type de document : texte imprimé Auteurs : Timothy P.L. ROBERTS, Auteur ; Jeffrey I. BERMAN, Auteur ; Ragini VERMA, Auteur Année de publication : 2013 Importance : p.267-279 Langues : Anglais (eng) Index. décimale : SCI-D SCI-D - Neurosciences Résumé : Diffusion tensor imaging (DTI) and associated fiber tractography are an emerging MRI technique for studying white matter of the brain. This chapter presents an introduction to the physical and biological bases of diffusion in white matter and the development and analysis of diffusion tensor imaging. It also includes visualization of white matter fiber tracts and quantification of physical diffusion parameters, such as the mean diffusivity (MD) and fractional anisotropy (FA) that might be used to index white matter maturation. A review of the recent findings made using DTI in ASD (autism spectrum disorder) is presented, focusing on studies with large (gt;20) sample sizes. Common themes of elevated mean diffusivity and diminished fractional anisotropy emerge, especially in structures of the frontal and temporal lobes, but also in the corpus callosum. Voxel-based as well as regional connectivity approaches to extracting quantitative information from DTI are discussed along with approaches involving machine learning of pattern classifiers to distinguish ASD from TD and also identify key features (structures, regions or connections) that contribute most to that discrimination ability. Limitations of tractography based on DTI are discussed along with the emerging advance of high angular resolution diffusion imaging (HARDI) as a means to overcome DTI limitations in regions of complex white matter organization. Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=189 in The Neuroscience of Autism Spectrum Disorders / Joseph D. BUXBAUM DTI and Tractography in the Autistic Brain [texte imprimé] / Timothy P.L. ROBERTS, Auteur ; Jeffrey I. BERMAN, Auteur ; Ragini VERMA, Auteur . - 2013 . - p.267-279. Langues : Anglais (eng) Index. décimale : SCI-D SCI-D - Neurosciences Résumé : Diffusion tensor imaging (DTI) and associated fiber tractography are an emerging MRI technique for studying white matter of the brain. This chapter presents an introduction to the physical and biological bases of diffusion in white matter and the development and analysis of diffusion tensor imaging. It also includes visualization of white matter fiber tracts and quantification of physical diffusion parameters, such as the mean diffusivity (MD) and fractional anisotropy (FA) that might be used to index white matter maturation. A review of the recent findings made using DTI in ASD (autism spectrum disorder) is presented, focusing on studies with large (gt;20) sample sizes. Common themes of elevated mean diffusivity and diminished fractional anisotropy emerge, especially in structures of the frontal and temporal lobes, but also in the corpus callosum. Voxel-based as well as regional connectivity approaches to extracting quantitative information from DTI are discussed along with approaches involving machine learning of pattern classifiers to distinguish ASD from TD and also identify key features (structures, regions or connections) that contribute most to that discrimination ability. Limitations of tractography based on DTI are discussed along with the emerging advance of high angular resolution diffusion imaging (HARDI) as a means to overcome DTI limitations in regions of complex white matter organization. Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=189

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Joint Analysis of Band-Specific Functional Connectivity and Signal Complexity in Autism / Yasser GHANBARI in Journal of Autism and Developmental Disorders, 45-2 (February 2015)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 45-2 (February 2015) . - p.444-460 Titre : Joint Analysis of Band-Specific Functional Connectivity and Signal Complexity in Autism Type de document : texte imprimé Auteurs : Yasser GHANBARI, Auteur ; Luke BLOY, Auteur ; J. CHRISTOPHER EDGAR, Auteur ; Lisa BLASKEY, Auteur ; Ragini VERMA, Auteur ; Timothy P.L. ROBERTS, Auteur Article en page(s) : p.444-460 Langues : Anglais (eng) Mots-clés : Autism  Magnetoencephalography (MEG)  Resting-state  Connectivity  Complexity  Synchronization likelihood (SL)  Multi-scale entropy (MSE) Index. décimale : PER Périodiques Résumé : Examination of resting state brain activity using electrophysiological measures like complexity as well as functional connectivity is of growing interest in the study of autism spectrum disorders (ASD). The present paper jointly examined complexity and connectivity to obtain a more detailed characterization of resting state brain activity in ASD. Multi-scale entropy was computed to quantify the signal complexity, and synchronization likelihood was used to evaluate functional connectivity (FC), with node strength values providing a sensor-level measure of connectivity to facilitate comparisons with complexity. Sensor level analysis of complexity and connectivity was performed at different frequency bands computed from resting state MEG from 26 children with ASD and 22 typically developing controls (TD). Analyses revealed band-specific group differences in each measure that agreed with other functional studies in fMRI and EEG: higher complexity in TD than ASD, in frontal regions in the delta band and occipital-parietal regions in the alpha band, and lower complexity in TD than in ASD in delta (parietal regions), theta (central and temporal regions) and gamma (frontal-central boundary regions); increased short-range connectivity in ASD in the frontal lobe in the delta band and long-range connectivity in the temporal, parietal and occipital lobes in the alpha band. Finally, and perhaps most strikingly, group differences between ASD and TD in complexity and FC appear spatially complementary, such that where FC was elevated in ASD, complexity was reduced (and vice versa). The correlation of regional average complexity and connectivity node strength with symptom severity scores of ASD subjects supported the overall complementarity (with opposing sign) of connectivity and complexity measures, pointing to either diminished connectivity leading to elevated entropy due to poor inhibitory regulation or chaotic signals prohibiting effective measure of connectivity. En ligne : http://dx.doi.org/10.1007/s10803-013-1915-7 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=258 [article] Joint Analysis of Band-Specific Functional Connectivity and Signal Complexity in Autism [texte imprimé] / Yasser GHANBARI, Auteur ; Luke BLOY, Auteur ; J. CHRISTOPHER EDGAR, Auteur ; Lisa BLASKEY, Auteur ; Ragini VERMA, Auteur ; Timothy P.L. ROBERTS, Auteur . - p.444-460. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 45-2 (February 2015) . - p.444-460 Mots-clés : Autism  Magnetoencephalography (MEG)  Resting-state  Connectivity  Complexity  Synchronization likelihood (SL)  Multi-scale entropy (MSE) Index. décimale : PER Périodiques Résumé : Examination of resting state brain activity using electrophysiological measures like complexity as well as functional connectivity is of growing interest in the study of autism spectrum disorders (ASD). The present paper jointly examined complexity and connectivity to obtain a more detailed characterization of resting state brain activity in ASD. Multi-scale entropy was computed to quantify the signal complexity, and synchronization likelihood was used to evaluate functional connectivity (FC), with node strength values providing a sensor-level measure of connectivity to facilitate comparisons with complexity. Sensor level analysis of complexity and connectivity was performed at different frequency bands computed from resting state MEG from 26 children with ASD and 22 typically developing controls (TD). Analyses revealed band-specific group differences in each measure that agreed with other functional studies in fMRI and EEG: higher complexity in TD than ASD, in frontal regions in the delta band and occipital-parietal regions in the alpha band, and lower complexity in TD than in ASD in delta (parietal regions), theta (central and temporal regions) and gamma (frontal-central boundary regions); increased short-range connectivity in ASD in the frontal lobe in the delta band and long-range connectivity in the temporal, parietal and occipital lobes in the alpha band. Finally, and perhaps most strikingly, group differences between ASD and TD in complexity and FC appear spatially complementary, such that where FC was elevated in ASD, complexity was reduced (and vice versa). The correlation of regional average complexity and connectivity node strength with symptom severity scores of ASD subjects supported the overall complementarity (with opposing sign) of connectivity and complexity measures, pointing to either diminished connectivity leading to elevated entropy due to poor inhibitory regulation or chaotic signals prohibiting effective measure of connectivity. En ligne : http://dx.doi.org/10.1007/s10803-013-1915-7 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=258

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