Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder / Sanna STROTH in Molecular Autism, 13 (2022)  

Public ISBD [article]  inMolecular Autism > 13 (2022) . - 11 p. Titre : Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder Type de document : Texte imprimé et/ou numérique Auteurs : Sanna STROTH, Auteur ; Johannes TAUSCHER, Auteur ; Nicole WOLFF, Auteur ; Charlotte KÜPPER, Auteur ; Luise POUSTKA, Auteur ; Stefan ROEPKE, Auteur ; Veit ROESSNER, Auteur ; Dominik HEIDER, Auteur ; Inge KAMP-BECKER, Auteur Article en page(s) : 11 p. Langues : Anglais (eng) Mots-clés : Affect  Autism Spectrum Disorder/diagnosis  Autistic Disorder  Female  Humans  Intellectual Disability/diagnosis  Male  Adi-r  Ados  Asd  Diagnostics  Female autism  Phenotype  Sex Index. décimale : PER Périodiques Résumé : BACKGROUND: Although autism spectrum disorder (ASD) is a common developmental disorder, our knowledge about a behavioral and neurobiological female phenotype is still scarce. As the conceptualization and understanding of ASD are mainly based on the investigation of male individuals, females with ASD may not be adequately identified by routine clinical diagnostics. The present machine learning approach aimed to identify diagnostic information from the Autism Diagnostic Observation Schedule (ADOS) that discriminates best between ASD and non-ASD in females and males. METHODS: Random forests (RF) were used to discover patterns of symptoms in diagnostic data from the ADOS (modules 3 and 4) in 1057 participants with ASD (18.1% female) and 1230 participants with non-ASD (17.9% % female). Predictive performances of reduced feature models were explored and compared between females and males without intellectual disabilities. RESULTS: Reduced feature models relied on considerably fewer features from the ADOS in females compared to males, while still yielding similar classification performance (e.g., sensitivity, specificity). LIMITATIONS: As in previous studies, the current sample of females with ASD is smaller than the male sample and thus, females may still be underrepresented, limiting the statistical power to detect small to moderate effects. CONCLUSION: Our results do not suggest the need for new or altered diagnostic algorithms for females with ASD. Although we identified some phenotypic differences between females and males, the existing diagnostic tools seem to sufficiently capture the core autistic features in both groups. En ligne : http://dx.doi.org/10.1186/s13229-022-00491-9 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477 [article] Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder [Texte imprimé et/ou numérique] / Sanna STROTH, Auteur ; Johannes TAUSCHER, Auteur ; Nicole WOLFF, Auteur ; Charlotte KÜPPER, Auteur ; Luise POUSTKA, Auteur ; Stefan ROEPKE, Auteur ; Veit ROESSNER, Auteur ; Dominik HEIDER, Auteur ; Inge KAMP-BECKER, Auteur . - 11 p. Langues : Anglais (eng) in Molecular Autism > 13 (2022) . - 11 p. Mots-clés : Affect  Autism Spectrum Disorder/diagnosis  Autistic Disorder  Female  Humans  Intellectual Disability/diagnosis  Male  Adi-r  Ados  Asd  Diagnostics  Female autism  Phenotype  Sex Index. décimale : PER Périodiques Résumé : BACKGROUND: Although autism spectrum disorder (ASD) is a common developmental disorder, our knowledge about a behavioral and neurobiological female phenotype is still scarce. As the conceptualization and understanding of ASD are mainly based on the investigation of male individuals, females with ASD may not be adequately identified by routine clinical diagnostics. The present machine learning approach aimed to identify diagnostic information from the Autism Diagnostic Observation Schedule (ADOS) that discriminates best between ASD and non-ASD in females and males. METHODS: Random forests (RF) were used to discover patterns of symptoms in diagnostic data from the ADOS (modules 3 and 4) in 1057 participants with ASD (18.1% female) and 1230 participants with non-ASD (17.9% % female). Predictive performances of reduced feature models were explored and compared between females and males without intellectual disabilities. RESULTS: Reduced feature models relied on considerably fewer features from the ADOS in females compared to males, while still yielding similar classification performance (e.g., sensitivity, specificity). LIMITATIONS: As in previous studies, the current sample of females with ASD is smaller than the male sample and thus, females may still be underrepresented, limiting the statistical power to detect small to moderate effects. CONCLUSION: Our results do not suggest the need for new or altered diagnostic algorithms for females with ASD. Although we identified some phenotypic differences between females and males, the existing diagnostic tools seem to sufficiently capture the core autistic features in both groups. En ligne : http://dx.doi.org/10.1186/s13229-022-00491-9 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477

Superior temporal sulcus folding, functional network connectivity, and autistic-like traits in a non-clinical population / Igor NENADI? in Molecular Autism, 15 (2024)  

Public ISBD [article]  inMolecular Autism > 15 (2024) . - 44p. Titre : Superior temporal sulcus folding, functional network connectivity, and autistic-like traits in a non-clinical population Type de document : Texte imprimé et/ou numérique Auteurs : Igor NENADI?, Auteur ; Yvonne SCHRÖDER, Auteur ; Jonas HOFFMANN, Auteur ; Ulrika EVERMANN, Auteur ; Julia-Katharina PFARR, Auteur ; Aliénor BERGMANN, Auteur ; Daniela Michelle HOHMANN, Auteur ; Boris KEIL, Auteur ; Ahmad ABU-AKEL, Auteur ; Sanna STROTH, Auteur ; Inge KAMP-BECKER, Auteur ; Andreas JANSEN, Auteur ; Sarah GREZELLSCHAK, Auteur ; Tina MELLER, Auteur Article en page(s) : 44p. Langues : Anglais (eng) Mots-clés : Humans  Male  Female  Adult  Temporal Lobe/diagnostic imaging  Magnetic Resonance Imaging  Young Adult  Autistic Disorder/diagnostic imaging/physiopathology  Adolescent  Middle Aged  Nerve Net/diagnostic imaging  Autism Spectrum Disorder/diagnostic imaging/physiopathology  Brain Mapping/methods  Phenotype  Autism quotient (AQ)  Autism spectrum disorder (ASD)  Cortical surface complexity  Interpersonal  Subclinical Index. décimale : PER Périodiques Résumé : BACKGROUND: Autistic-like traits (ALT) are prevalent across the general population and might be linked to some facets of a broader autism spectrum disorder (ASD) phenotype. Recent studies suggest an association of these traits with both genetic and brain structural markers in non-autistic individuals, showing similar spatial location of findings observed in ASD and thus suggesting a potential neurobiological continuum. METHODS: In this study, we first tested an association of ALTs (assessed with the AQ questionnaire) with cortical complexity, a cortical surface marker of early neurodevelopment, and then the association with disrupted functional connectivity. We analysed structural T1-weighted and resting-state functional MRI scans in 250 psychiatrically healthy individuals without a history of early developmental disorders, in a first step using the CAT12 toolbox for cortical complexity analysis and in a second step we used regional cortical complexity findings to apply the CONN toolbox for seed-based functional connectivity analysis. RESULTS: Our findings show a significant negative correlation of both AQ total and AQ attention switching subscores with left superior temporal sulcus (STS) cortical folding complexity, with the former being significantly correlated with STS to left lateral occipital cortex connectivity, while the latter showed significant positive correlation of STS to left inferior/middle frontal gyrus connectivity (n = 233; all p < 0.05, FWE cluster-level corrected). Additional analyses also revealed a significant correlation of AQ attention to detail subscores with STS to left lateral occipital cortex connectivity. LIMITATIONS: Phenotyping might affect association results (e.g. choice of inventories); in addition, our study was limited to subclinical expressions of autistic-like traits. CONCLUSIONS: Our findings provide further evidence for biological correlates of ALT even in the absence of clinical ASD, while establishing a link between structural variation of early developmental origin and functional connectivity. En ligne : https://dx.doi.org/10.1186/s13229-024-00623-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=538 [article] Superior temporal sulcus folding, functional network connectivity, and autistic-like traits in a non-clinical population [Texte imprimé et/ou numérique] / Igor NENADI?, Auteur ; Yvonne SCHRÖDER, Auteur ; Jonas HOFFMANN, Auteur ; Ulrika EVERMANN, Auteur ; Julia-Katharina PFARR, Auteur ; Aliénor BERGMANN, Auteur ; Daniela Michelle HOHMANN, Auteur ; Boris KEIL, Auteur ; Ahmad ABU-AKEL, Auteur ; Sanna STROTH, Auteur ; Inge KAMP-BECKER, Auteur ; Andreas JANSEN, Auteur ; Sarah GREZELLSCHAK, Auteur ; Tina MELLER, Auteur . - 44p. Langues : Anglais (eng) in Molecular Autism > 15 (2024) . - 44p. Mots-clés : Humans  Male  Female  Adult  Temporal Lobe/diagnostic imaging  Magnetic Resonance Imaging  Young Adult  Autistic Disorder/diagnostic imaging/physiopathology  Adolescent  Middle Aged  Nerve Net/diagnostic imaging  Autism Spectrum Disorder/diagnostic imaging/physiopathology  Brain Mapping/methods  Phenotype  Autism quotient (AQ)  Autism spectrum disorder (ASD)  Cortical surface complexity  Interpersonal  Subclinical Index. décimale : PER Périodiques Résumé : BACKGROUND: Autistic-like traits (ALT) are prevalent across the general population and might be linked to some facets of a broader autism spectrum disorder (ASD) phenotype. Recent studies suggest an association of these traits with both genetic and brain structural markers in non-autistic individuals, showing similar spatial location of findings observed in ASD and thus suggesting a potential neurobiological continuum. METHODS: In this study, we first tested an association of ALTs (assessed with the AQ questionnaire) with cortical complexity, a cortical surface marker of early neurodevelopment, and then the association with disrupted functional connectivity. We analysed structural T1-weighted and resting-state functional MRI scans in 250 psychiatrically healthy individuals without a history of early developmental disorders, in a first step using the CAT12 toolbox for cortical complexity analysis and in a second step we used regional cortical complexity findings to apply the CONN toolbox for seed-based functional connectivity analysis. RESULTS: Our findings show a significant negative correlation of both AQ total and AQ attention switching subscores with left superior temporal sulcus (STS) cortical folding complexity, with the former being significantly correlated with STS to left lateral occipital cortex connectivity, while the latter showed significant positive correlation of STS to left inferior/middle frontal gyrus connectivity (n = 233; all p < 0.05, FWE cluster-level corrected). Additional analyses also revealed a significant correlation of AQ attention to detail subscores with STS to left lateral occipital cortex connectivity. LIMITATIONS: Phenotyping might affect association results (e.g. choice of inventories); in addition, our study was limited to subclinical expressions of autistic-like traits. CONCLUSIONS: Our findings provide further evidence for biological correlates of ALT even in the absence of clinical ASD, while establishing a link between structural variation of early developmental origin and functional connectivity. En ligne : https://dx.doi.org/10.1186/s13229-024-00623-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=538

Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses / Martin SCHULTE-RUTHER in Journal of Child Psychology and Psychiatry, 64-1 (January 2023)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 64-1 (January 2023) . - p.16-26 Titre : Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses Type de document : Texte imprimé et/ou numérique Auteurs : Martin SCHULTE-RUTHER, Auteur ; Tomas KULVICIUS, Auteur ; Sanna STROTH, Auteur ; Nicole WOLFF, Auteur ; Veit ROESSNER, Auteur ; Peter B. MARSCHIK, Auteur ; Inge KAMP-BECKER, Auteur ; Luise POUSTKA, Auteur Article en page(s) : p.16-26 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Background Diagnostic assessment of ASD requires substantial clinical experience and is particularly difficult in the context of other disorders with behavioral symptoms in the domain of social interaction and communication. Observation measures such as the Autism Diagnostic Observation Schedule (ADOS) do not take into account such co-occurring disorders. Method We used a well-characterized clinical sample of individuals (n=1,251) that had received detailed outpatient evaluation for the presence of an ASD diagnosis (n=481) and covered a range of additional overlapping diagnoses, including anxiety-related disorders (ANX, n=122), ADHD (n=439), and conduct disorder (CD, n=194). We focused on ADOS module 3, covering the age range with particular high prevalence of such differential diagnoses. We used machine learning (ML) and trained random forest models on ADOS single item scores to predict a clinical best-estimate diagnosis of ASD in the context of these differential diagnoses (ASD vs. ANX, ASD vs. ADHD, ASD vs. CD), in the context of co-occurring ADHD, and an unspecific model using all available data. We employed nested cross-validation for an unbiased estimate of classification performance and made available a Webapp to showcase the results and feasibility for translation into clinical practice. Results We obtained very good overall sensitivity (0.89-0.94) and specificity (0.87-0.89). In particular for individuals with less severe symptoms, our models showed increases of up to 35% in sensitivity or specificity. Furthermore, we analyzed item importance profiles of the ANX, ADHD, and CD models in comparison with the unspecific model revealing distinct patterns of importance for specific ADOS items with respect to differential diagnoses. Conclusions ML-based diagnostic classification may improve clinical decisions by utilizing the full range of information from detailed diagnostic observation instruments such as the ADOS. Importantly, this strategy might be of particular relevance for older children with less severe symptoms for whom the diagnostic decision is often particularly difficult. En ligne : https://doi.org/10.1111/jcpp.13650 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490 [article] Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses [Texte imprimé et/ou numérique] / Martin SCHULTE-RUTHER, Auteur ; Tomas KULVICIUS, Auteur ; Sanna STROTH, Auteur ; Nicole WOLFF, Auteur ; Veit ROESSNER, Auteur ; Peter B. MARSCHIK, Auteur ; Inge KAMP-BECKER, Auteur ; Luise POUSTKA, Auteur . - p.16-26. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 64-1 (January 2023) . - p.16-26 Index. décimale : PER Périodiques Résumé : Background Diagnostic assessment of ASD requires substantial clinical experience and is particularly difficult in the context of other disorders with behavioral symptoms in the domain of social interaction and communication. Observation measures such as the Autism Diagnostic Observation Schedule (ADOS) do not take into account such co-occurring disorders. Method We used a well-characterized clinical sample of individuals (n=1,251) that had received detailed outpatient evaluation for the presence of an ASD diagnosis (n=481) and covered a range of additional overlapping diagnoses, including anxiety-related disorders (ANX, n=122), ADHD (n=439), and conduct disorder (CD, n=194). We focused on ADOS module 3, covering the age range with particular high prevalence of such differential diagnoses. We used machine learning (ML) and trained random forest models on ADOS single item scores to predict a clinical best-estimate diagnosis of ASD in the context of these differential diagnoses (ASD vs. ANX, ASD vs. ADHD, ASD vs. CD), in the context of co-occurring ADHD, and an unspecific model using all available data. We employed nested cross-validation for an unbiased estimate of classification performance and made available a Webapp to showcase the results and feasibility for translation into clinical practice. Results We obtained very good overall sensitivity (0.89-0.94) and specificity (0.87-0.89). In particular for individuals with less severe symptoms, our models showed increases of up to 35% in sensitivity or specificity. Furthermore, we analyzed item importance profiles of the ANX, ADHD, and CD models in comparison with the unspecific model revealing distinct patterns of importance for specific ADOS items with respect to differential diagnoses. Conclusions ML-based diagnostic classification may improve clinical decisions by utilizing the full range of information from detailed diagnostic observation instruments such as the ADOS. Importantly, this strategy might be of particular relevance for older children with less severe symptoms for whom the diagnostic decision is often particularly difficult. En ligne : https://doi.org/10.1111/jcpp.13650 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490

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