Brief report: A confirmatory factor analysis of the Child Behavior Checklist in a large sample of autistic youth / Laura DE LA ROCHE in Research in Autism Spectrum Disorders, 118 (October 2024)  

Public ISBD [article]  inResearch in Autism Spectrum Disorders > 118 (October 2024) . - 102487 Titre : Brief report: A confirmatory factor analysis of the Child Behavior Checklist in a large sample of autistic youth Type de document : texte imprimé Auteurs : Laura DE LA ROCHE, Auteur ; Brianne DERBY, Auteur ; Molly PASCOE, Auteur ; Melissa SUSKO, Auteur ; Sabrina LUTCHMEAH, Auteur ; Jessica JONES, Auteur ; Stelios GEORGIADES, Auteur ; Rob NICOLSON, Auteur ; Evdokia ANAGNOSTOU, Auteur ; Elizabeth KELLEY, Auteur Article en page(s) : 102487 Langues : Anglais (eng) Mots-clés : Autism  Child Behavior Checklist  Factor structure  Co-occurring conditions Index. décimale : PER Périodiques Résumé : Background Autistic youth often experience co-occurring psychiatric conditions. Checklist measures such as the Child Behavior Checklist (CBCL) can assist clinicians and researchers in assessing the symptom profiles of such conditions. Symptom profiles often overlap between autism and cooccurring psychiatric conditions (e.g., depression) in which the same symptoms occur in both. Previous research investigating the validity of the CBCL in autistic populations using factor structure has been mixed. Method Seven-hundred-and-fourteen autistic youth (293 females) aged 6-18 years (M = 11.25, SD = 3.29) participated. A confirmatory factor analysis of the 8-factor CBCL-6-18 was completed. Results Results suggest a poor model fit in autistic samples of the widely used eight-scale factor structure. Conclusions This model may not fit this sample due to the overlap of symptomatology autism has with other psychiatric condition profiles (e.g., communication and behaviors). Future research and implications, including an exploratory factor analysis on the CBCL/6-18 for autistic populations, are discussed. En ligne : https://doi.org/10.1016/j.rasd.2024.102487 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=540 [article] Brief report: A confirmatory factor analysis of the Child Behavior Checklist in a large sample of autistic youth [texte imprimé] / Laura DE LA ROCHE, Auteur ; Brianne DERBY, Auteur ; Molly PASCOE, Auteur ; Melissa SUSKO, Auteur ; Sabrina LUTCHMEAH, Auteur ; Jessica JONES, Auteur ; Stelios GEORGIADES, Auteur ; Rob NICOLSON, Auteur ; Evdokia ANAGNOSTOU, Auteur ; Elizabeth KELLEY, Auteur . - 102487. Langues : Anglais (eng) in Research in Autism Spectrum Disorders > 118 (October 2024) . - 102487 Mots-clés : Autism  Child Behavior Checklist  Factor structure  Co-occurring conditions Index. décimale : PER Périodiques Résumé : Background Autistic youth often experience co-occurring psychiatric conditions. Checklist measures such as the Child Behavior Checklist (CBCL) can assist clinicians and researchers in assessing the symptom profiles of such conditions. Symptom profiles often overlap between autism and cooccurring psychiatric conditions (e.g., depression) in which the same symptoms occur in both. Previous research investigating the validity of the CBCL in autistic populations using factor structure has been mixed. Method Seven-hundred-and-fourteen autistic youth (293 females) aged 6-18 years (M = 11.25, SD = 3.29) participated. A confirmatory factor analysis of the 8-factor CBCL-6-18 was completed. Results Results suggest a poor model fit in autistic samples of the widely used eight-scale factor structure. Conclusions This model may not fit this sample due to the overlap of symptomatology autism has with other psychiatric condition profiles (e.g., communication and behaviors). Future research and implications, including an exploratory factor analysis on the CBCL/6-18 for autistic populations, are discussed. En ligne : https://doi.org/10.1016/j.rasd.2024.102487 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=540

Comparative Analysis of Phenotypic and Genotypic Differences Between Individuals Affected by Regressive and Non-Regressive Autism: A Cross-Sectional Study / Alana IABONI ; Brett TROST ; Miriam REUTER ; Zsuzsa LINDENMAIER ; Azadeh KUSHKI ; Elizabeth KELLEY ; Jessica JONES ; Muhammad AYUB ; Stelios GEORGIADES ; Robert NICOLSON ; Elim CHAN ; Andrada CRETU ; Jessica BRIAN ; Evdokia ANAGNOSTOU in Autism Research, 18-6 (June 2025)  

Public ISBD [article]  inAutism Research > 18-6 (June 2025) . - p.1290-1300 Titre : Comparative Analysis of Phenotypic and Genotypic Differences Between Individuals Affected by Regressive and Non-Regressive Autism: A Cross-Sectional Study Type de document : texte imprimé Auteurs : Alana IABONI, Auteur ; Brett TROST, Auteur ; Miriam REUTER, Auteur ; Zsuzsa LINDENMAIER, Auteur ; Azadeh KUSHKI, Auteur ; Elizabeth KELLEY, Auteur ; Jessica JONES, Auteur ; Muhammad AYUB, Auteur ; Stelios GEORGIADES, Auteur ; Robert NICOLSON, Auteur ; Elim CHAN, Auteur ; Andrada CRETU, Auteur ; Jessica BRIAN, Auteur ; Evdokia ANAGNOSTOU, Auteur Article en page(s) : p.1290-1300 Langues : Anglais (eng) Mots-clés : autism Spectrum disorder  child  genotype  neurodevelopmental disorder  phenotype  regressive autism  retrospective studies Index. décimale : PER Périodiques Résumé : ABSTRACT Development among autistic youth varies widely. A subgroup of children experiences regression, defined as the loss of previously acquired developmental skills. Various genetic and environmental factors have been suggested as potential contributors. This study aimed to compare the developmental profiles of children and youth with regression to those without and identify factors associated with regression. Data from the Province of Ontario Neurodevelopmental Disorders (POND) Network was analyzed, including 930 eligible participants. Regression classification was based on the Autism Diagnostic Interview-Revised (ADI-R). Differences in demographic information, medical history, mental health, cognitive and adaptive functioning, and molecular genetic findings were examined between individuals with regressive and non-regressive autism. Among participants, 211 (22.7%) had regressive autism. Lower Full-Scale IQ (p corrected 0.015) and adaptive function (ABAS-2) scores (p corrected 0.015) were identified in the regressive group. No statistically significant differences in mental health outcomes (measured by the Child Behavior Checklist, CBCL) or socialization and core symptom severity (measured by the Social Communication Questionnaire, SCQ) were found. There were no notable differences in other factors hypothesized to contribute to regression, such as pregnancy duration, family history of autism, caregivers' education levels, or sleep disorders, except for a higher prevalence of epilepsy in the regressive group (p 0.001). Rare and common genetic features of both groups are described. In conclusion, autistic youth with regression tend to have lower cognitive and adaptive scores and may experience higher epilepsy rates. Further powered studies are needed to explore the genomic architecture of autistic regression. En ligne : https://doi.org/10.1002/aur.70029 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=558 [article] Comparative Analysis of Phenotypic and Genotypic Differences Between Individuals Affected by Regressive and Non-Regressive Autism: A Cross-Sectional Study [texte imprimé] / Alana IABONI, Auteur ; Brett TROST, Auteur ; Miriam REUTER, Auteur ; Zsuzsa LINDENMAIER, Auteur ; Azadeh KUSHKI, Auteur ; Elizabeth KELLEY, Auteur ; Jessica JONES, Auteur ; Muhammad AYUB, Auteur ; Stelios GEORGIADES, Auteur ; Robert NICOLSON, Auteur ; Elim CHAN, Auteur ; Andrada CRETU, Auteur ; Jessica BRIAN, Auteur ; Evdokia ANAGNOSTOU, Auteur . - p.1290-1300. Langues : Anglais (eng) in Autism Research > 18-6 (June 2025) . - p.1290-1300 Mots-clés : autism Spectrum disorder  child  genotype  neurodevelopmental disorder  phenotype  regressive autism  retrospective studies Index. décimale : PER Périodiques Résumé : ABSTRACT Development among autistic youth varies widely. A subgroup of children experiences regression, defined as the loss of previously acquired developmental skills. Various genetic and environmental factors have been suggested as potential contributors. This study aimed to compare the developmental profiles of children and youth with regression to those without and identify factors associated with regression. Data from the Province of Ontario Neurodevelopmental Disorders (POND) Network was analyzed, including 930 eligible participants. Regression classification was based on the Autism Diagnostic Interview-Revised (ADI-R). Differences in demographic information, medical history, mental health, cognitive and adaptive functioning, and molecular genetic findings were examined between individuals with regressive and non-regressive autism. Among participants, 211 (22.7%) had regressive autism. Lower Full-Scale IQ (p corrected 0.015) and adaptive function (ABAS-2) scores (p corrected 0.015) were identified in the regressive group. No statistically significant differences in mental health outcomes (measured by the Child Behavior Checklist, CBCL) or socialization and core symptom severity (measured by the Social Communication Questionnaire, SCQ) were found. There were no notable differences in other factors hypothesized to contribute to regression, such as pregnancy duration, family history of autism, caregivers' education levels, or sleep disorders, except for a higher prevalence of epilepsy in the regressive group (p 0.001). Rare and common genetic features of both groups are described. In conclusion, autistic youth with regression tend to have lower cognitive and adaptive scores and may experience higher epilepsy rates. Further powered studies are needed to explore the genomic architecture of autistic regression. En ligne : https://doi.org/10.1002/aur.70029 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=558

Harmonizing two measures of adaptive functioning using computational approaches: prediction of vineland adaptive behavior scales II (VABS-II) from the adaptive behavior assessment system II (ABAS-II) scores / Alexandra LAUTARESCU ; Tony CHARMAN ; Jennifer CROSBIE ; Russell J SCHACHAR ; Alana IABONI ; Stelios GEORGIADES ; Robert NICOLSON ; Elizabeth KELLEY ; Muhammad AYUB ; Jessica JONES ; Paul D. ARNOLD ; Jason P LERCH ; Evdokia ANAGNOSTOU ; Azadeh KUSHKI in Molecular Autism, 15 (2024)  

Public ISBD [article]  inMolecular Autism > 15 (2024) . - 51 Titre : Harmonizing two measures of adaptive functioning using computational approaches: prediction of vineland adaptive behavior scales II (VABS-II) from the adaptive behavior assessment system II (ABAS-II) scores Type de document : texte imprimé Auteurs : Alexandra LAUTARESCU, Auteur ; Tony CHARMAN, Auteur ; Jennifer CROSBIE, Auteur ; Russell J SCHACHAR, Auteur ; Alana IABONI, Auteur ; Stelios GEORGIADES, Auteur ; Robert NICOLSON, Auteur ; Elizabeth KELLEY, Auteur ; Muhammad AYUB, Auteur ; Jessica JONES, Auteur ; Paul D. ARNOLD, Auteur ; Jason P LERCH, Auteur ; Evdokia ANAGNOSTOU, Auteur ; Azadeh KUSHKI, Auteur Article en page(s) : 51 Langues : Anglais (eng) Mots-clés : Humans  Male  Female  Child  Adolescent  *Adaptation, Psychological  Autistic Disorder/diagnosis/psychology Index. décimale : PER Périodiques Résumé : BACKGROUND: Very large sample sizes are often needed to capture heterogeneity in autism, necessitating data sharing across multiple studies with diverse assessment instruments. In these cases, data harmonization can be a critical tool for deriving a single dataset for analysis. This can be done through computational approaches that enable the conversion of scores across various instruments. To this end, our study examined the use of analytical approaches for mapping scores on two measures of adaptive functioning, namely predicting the scores on the vineland adaptive behavior scales II (VABS) from the scores on the adaptive behavior assessment system II (ABAS). METHODS: Data from the province of Ontario neurodevelopmental disorders network were used. The dataset included scores VABS and the ABAS for 720 participants (autism n = 547, 433 male, age: 11.31+3.63 years; neurotypical n = 173, 95 male, age: 12.53+4.05 years). Six regression approaches (ordinary least squares (OLS) linear regression, ridge regression, ElasticNet, LASSO, AdaBoost, random forest) were used to predict VABS total scores from the ABAS scores, demographic variables (age, sex), and phenotypic measures (diagnosis; core and co-occurring features; IQ; internalizing and externalizing symptoms). RESULTS: The VABS scores were significantly higher than the ABAS scores in the autism group, but not the neurotypical group (median difference: 8, 95% CI = (7,9)). The difference was negatively associated with age (beta = -1.2+0.12, t = -10.6, p < 0.0001). All estimators demonstrated similar performance, with no statistically significant differences in mean absolute error (MAE) values across estimators (MAE range: 4.96-6.91). The highest contributing features to the prediction model were ABAS composite score, diagnosis, and age. LIMITATIONS: This study has several strengths, including the large sample. We did not examine the conversion of domain scores across the two measures of adaptive functioning and suggest this as a future area of investigation. CONCLUSION: Overall, our results supported the feasibility of harmonization. Our results suggest that a linear regression model trained on the ABAS composite score, the ABAS raw domain scores, and age, sex, and diagnosis would provide an acceptable trade-off between accuracy, parsimony, and data collection and processing complexity. En ligne : https://dx.doi.org/10.1186/s13229-024-00630-4 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555 [article] Harmonizing two measures of adaptive functioning using computational approaches: prediction of vineland adaptive behavior scales II (VABS-II) from the adaptive behavior assessment system II (ABAS-II) scores [texte imprimé] / Alexandra LAUTARESCU, Auteur ; Tony CHARMAN, Auteur ; Jennifer CROSBIE, Auteur ; Russell J SCHACHAR, Auteur ; Alana IABONI, Auteur ; Stelios GEORGIADES, Auteur ; Robert NICOLSON, Auteur ; Elizabeth KELLEY, Auteur ; Muhammad AYUB, Auteur ; Jessica JONES, Auteur ; Paul D. ARNOLD, Auteur ; Jason P LERCH, Auteur ; Evdokia ANAGNOSTOU, Auteur ; Azadeh KUSHKI, Auteur . - 51. Langues : Anglais (eng) in Molecular Autism > 15 (2024) . - 51 Mots-clés : Humans  Male  Female  Child  Adolescent  *Adaptation, Psychological  Autistic Disorder/diagnosis/psychology Index. décimale : PER Périodiques Résumé : BACKGROUND: Very large sample sizes are often needed to capture heterogeneity in autism, necessitating data sharing across multiple studies with diverse assessment instruments. In these cases, data harmonization can be a critical tool for deriving a single dataset for analysis. This can be done through computational approaches that enable the conversion of scores across various instruments. To this end, our study examined the use of analytical approaches for mapping scores on two measures of adaptive functioning, namely predicting the scores on the vineland adaptive behavior scales II (VABS) from the scores on the adaptive behavior assessment system II (ABAS). METHODS: Data from the province of Ontario neurodevelopmental disorders network were used. The dataset included scores VABS and the ABAS for 720 participants (autism n = 547, 433 male, age: 11.31+3.63 years; neurotypical n = 173, 95 male, age: 12.53+4.05 years). Six regression approaches (ordinary least squares (OLS) linear regression, ridge regression, ElasticNet, LASSO, AdaBoost, random forest) were used to predict VABS total scores from the ABAS scores, demographic variables (age, sex), and phenotypic measures (diagnosis; core and co-occurring features; IQ; internalizing and externalizing symptoms). RESULTS: The VABS scores were significantly higher than the ABAS scores in the autism group, but not the neurotypical group (median difference: 8, 95% CI = (7,9)). The difference was negatively associated with age (beta = -1.2+0.12, t = -10.6, p < 0.0001). All estimators demonstrated similar performance, with no statistically significant differences in mean absolute error (MAE) values across estimators (MAE range: 4.96-6.91). The highest contributing features to the prediction model were ABAS composite score, diagnosis, and age. LIMITATIONS: This study has several strengths, including the large sample. We did not examine the conversion of domain scores across the two measures of adaptive functioning and suggest this as a future area of investigation. CONCLUSION: Overall, our results supported the feasibility of harmonization. Our results suggest that a linear regression model trained on the ABAS composite score, the ABAS raw domain scores, and age, sex, and diagnosis would provide an acceptable trade-off between accuracy, parsimony, and data collection and processing complexity. En ligne : https://dx.doi.org/10.1186/s13229-024-00630-4 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555

Investigating the general psychopathology factor in autistic youth / Hannah Muriel Robb BURROWS in Research in Autism Spectrum Disorders, 119 (January 2025)  

Public ISBD [article]  inResearch in Autism Spectrum Disorders > 119 (January 2025) . - p.102519 Titre : Investigating the general psychopathology factor in autistic youth Type de document : texte imprimé Auteurs : Hannah Muriel Robb BURROWS, Auteur ; Brianne DERBY, Auteur ; Laura DE LA ROCHE, Auteur ; Melissa SUSKO, Auteur ; Rob NICOLSON, Auteur ; Stelios GEORGIADES, Auteur ; Jessica JONES, Auteur ; Evdokia ANAGNOSTOU, Auteur ; Elizabeth KELLEY, Auteur Article en page(s) : p.102519 Langues : Anglais (eng) Mots-clés : General psychopathology  factor  Autism  Internalizing  Externalizing Index. décimale : PER Périodiques Résumé : Background Autistic youth are at higher risk of presenting with co-occurring internalizing (I) (i.e., anxiety and depression) and externalizing (E) (i.e., aggression and impulsivity) disorders (Bauminger et al., 2010). The Child Behavior Checklist for ages 6-18 (CBCL/6-18; Achenbach & Rescorla, 2001) is a measure of I-E disorders and symptoms in autistic and neurotypical youth, providing norm-referenced subscales as factors for each form of psychopathology. The general psychopathology or "p" factor may provide a better measure of co-occurring disorders in autism as it has not been evaluated in this population contextually to date. The p factor proposes that psychopathological disorders come from the same etiological factor, implying that we can measure all I-E disorders as indicators of p. Method Using archival data from the Province of Ontario Neurodevelopmental Disorders (POND) Network, (N = 782) autistic youths' raw scores from the CBCL/6-18 were analyzed using two confirmatory factor analyses (CFAs): an I-E CFA and a p factor CFA. An exploratory factor analysis (EFA) was also conducted to determine the best-fitting factor structure. Results A chi-square difference test compared each CFA to find the best model fit. Results reported each model as individually significant, however, based on recommendations from Hoyle and Panter (1995), neither model had an acceptable fit. Conclusions Given that neither the p factor nor the internalizing/externalizing factor models had appropriate fit, it is recommended that future research investigate whether the CBCL/6-18 is the most appropriate measure for assessing co-occurring symptoms in autistic youth. The results of the EFA also suggest that the CBCL may not be the most appropriate measure for autistic youth. En ligne : https://dx.doi.org/10.1016/j.rasd.2024.102519 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=545 [article] Investigating the general psychopathology factor in autistic youth [texte imprimé] / Hannah Muriel Robb BURROWS, Auteur ; Brianne DERBY, Auteur ; Laura DE LA ROCHE, Auteur ; Melissa SUSKO, Auteur ; Rob NICOLSON, Auteur ; Stelios GEORGIADES, Auteur ; Jessica JONES, Auteur ; Evdokia ANAGNOSTOU, Auteur ; Elizabeth KELLEY, Auteur . - p.102519. Langues : Anglais (eng) in Research in Autism Spectrum Disorders > 119 (January 2025) . - p.102519 Mots-clés : General psychopathology  factor  Autism  Internalizing  Externalizing Index. décimale : PER Périodiques Résumé : Background Autistic youth are at higher risk of presenting with co-occurring internalizing (I) (i.e., anxiety and depression) and externalizing (E) (i.e., aggression and impulsivity) disorders (Bauminger et al., 2010). The Child Behavior Checklist for ages 6-18 (CBCL/6-18; Achenbach & Rescorla, 2001) is a measure of I-E disorders and symptoms in autistic and neurotypical youth, providing norm-referenced subscales as factors for each form of psychopathology. The general psychopathology or "p" factor may provide a better measure of co-occurring disorders in autism as it has not been evaluated in this population contextually to date. The p factor proposes that psychopathological disorders come from the same etiological factor, implying that we can measure all I-E disorders as indicators of p. Method Using archival data from the Province of Ontario Neurodevelopmental Disorders (POND) Network, (N = 782) autistic youths' raw scores from the CBCL/6-18 were analyzed using two confirmatory factor analyses (CFAs): an I-E CFA and a p factor CFA. An exploratory factor analysis (EFA) was also conducted to determine the best-fitting factor structure. Results A chi-square difference test compared each CFA to find the best model fit. Results reported each model as individually significant, however, based on recommendations from Hoyle and Panter (1995), neither model had an acceptable fit. Conclusions Given that neither the p factor nor the internalizing/externalizing factor models had appropriate fit, it is recommended that future research investigate whether the CBCL/6-18 is the most appropriate measure for assessing co-occurring symptoms in autistic youth. The results of the EFA also suggest that the CBCL may not be the most appropriate measure for autistic youth. En ligne : https://dx.doi.org/10.1016/j.rasd.2024.102519 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=545

Subgrouping autism and ADHD based on structural MRI population modelling centiles / Clara PECCI-TERROBA in Molecular Autism, 16 (2025)  

Public ISBD [article]  inMolecular Autism > 16 (2025) . - 33 Titre : Subgrouping autism and ADHD based on structural MRI population modelling centiles Type de document : texte imprimé Auteurs : Clara PECCI-TERROBA, Auteur ; Meng-Chuan LAI, Auteur ; Michael V. LOMBARDO, Auteur ; Bhismadev CHAKRABARTI, Auteur ; Amber N. V. RUIGROK, Auteur ; John SUCKLING, Auteur ; Evdokia ANAGNOSTOU, Auteur ; Jason P. LERCH, Auteur ; Margot J. TAYLOR, Auteur ; Rob NICOLSON, Auteur ; Stelios GEORGIADES, Auteur ; Jennifer CROSBIE, Auteur ; Russell SCHACHAR, Auteur ; Elizabeth KELLEY, Auteur ; Jessica JONES, Auteur ; Paul D. ARNOLD, Auteur ; Jakob SEIDLITZ, Auteur ; Aaron F. ALEXANDER-BLOCH, Auteur ; Edward T. BULLMORE, Auteur ; Simon BARON-COHEN, Auteur ; Saashi A. BEDFORD, Auteur ; Richard A. I. BETHLEHEM, Auteur ; Clara PECCI-TERROBA, Auteur ; Meng-Chuan LAI, Auteur ; Michael V. LOMBARDO, Auteur ; Bhismadev CHAKRABARTI, Auteur ; Amber N. V. RUIGROK, Auteur ; John SUCKLING, Auteur ; Evdokia ANAGNOSTOU, Auteur ; Jason P. LERCH, Auteur ; Margot J. TAYLOR, Auteur ; Rob NICOLSON, Auteur ; Stelios GEORGIADES, Auteur ; Jennifer CROSBIE, Auteur ; Russell SCHACHAR, Auteur ; Elizabeth KELLEY, Auteur ; Jessica JONES, Auteur ; Paul D. ARNOLD, Auteur ; Jakob SEIDLITZ, Auteur ; Aaron F. ALEXANDER-BLOCH, Auteur ; Edward T. BULLMORE, Auteur ; Simon BARON-COHEN, Auteur ; Saashi A. BEDFORD, Auteur ; Richard A. I. BETHLEHEM, Auteur Article en page(s) : 33 Langues : Anglais (eng) Mots-clés : Attention Deficit Disorder with Hyperactivity/diagnostic  imaging/classification/pathology  Humans  Autistic Disorder/diagnostic imaging/classification/pathology  Magnetic Resonance Imaging/methods  Male  Female  Child  Cluster Analysis  Adolescent  Algorithms  Machine Learning  Adhd  Autism  Neuroimaging  Population modelling  Structural MRI  Subgrouping  informed consent were obtained for each primary study. The Cambridge Psychology  Research Ethics Committee (PRE.2020.104) deemed that secondary analysis of  deidentified data did not require ethical oversight. Consent for publication: Not  applicable. Competing interests: RAIB, MVL, and M-CL are Associate Editors, and  EA and BC are Editorial Board members of Molecular Autism. SBC is a former  Editor-in-Chief of the journal. ETB reports consultancy work for Boehringer  Ingelheim, Sosei Heptares, SR One, and GlaxoSmithKline. ETB, RAIB, JS, and AFA-B  are cofounders of Centile Bioscience. PDA receives research support from Biohaven  Pharmaceuticals. M-CL has received editorial honorarium from SAGE Publications.  RN reported receiving grants from Brain Canada, Hoffman La Roche, Otsuka  Pharmaceuticals, and Maplight Therapeutics outside the submitted work. EA  reported receiving grants from Roche and Anavex  receiving nonfinancial support  from AMO Pharma and CRA-Simons Foundation  and receiving personal fees from  Roche, Impel, Ono, and Quadrant outside the submitted work. Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism and attention deficit hyperactivity disorder (ADHD) are two highly heterogeneous neurodevelopmental conditions with variable underlying neurobiology. Imaging studies have yielded varied results, and it is now clear that there is unlikely to be one characteristic neuroanatomical profile of either condition. Parsing this heterogeneity could allow us to identify more homogeneous subgroups, either within or across conditions, which may be more clinically informative. This has been a pivotal goal for neurodevelopmental research using both clinical and neuroanatomical features, though results thus far have again been inconsistent with regards to the number and characteristics of subgroups. METHODS: Here, we use population modelling to cluster a multi-site dataset based on global and regional centile scores of cortical thickness, surface area and grey matter volume. We use HYDRA, a novel semi-supervised machine learning algorithm which clusters based on differences to controls and compare its performance to a traditional clustering approach. RESULTS: We identified distinct subgroups within autism and ADHD, as well as across diagnosis, often with opposite neuroanatomical alterations relatively to controls. These subgroups were characterised by different combinations of increased or decreased patterns of morphometrics. We did not find significant clinical differences across subgroups. LIMITATIONS: Crucially, however, the number of subgroups and their membership differed vastly depending on chosen features and the algorithm used, highlighting the impact and importance of careful method selection. CONCLUSIONS: We highlight the importance of examining heterogeneity in autism and ADHD and demonstrate that population modelling is a useful tool to study subgrouping in autism and ADHD. We identified subgroups with distinct patterns of alterations relative to controls but note that these results rely heavily on the algorithm used and encourage detailed reporting of methods and features used in future studies. En ligne : https://dx.doi.org/10.1186/s13229-025-00667-z Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=569 [article] Subgrouping autism and ADHD based on structural MRI population modelling centiles [texte imprimé] / Clara PECCI-TERROBA, Auteur ; Meng-Chuan LAI, Auteur ; Michael V. LOMBARDO, Auteur ; Bhismadev CHAKRABARTI, Auteur ; Amber N. V. RUIGROK, Auteur ; John SUCKLING, Auteur ; Evdokia ANAGNOSTOU, Auteur ; Jason P. LERCH, Auteur ; Margot J. TAYLOR, Auteur ; Rob NICOLSON, Auteur ; Stelios GEORGIADES, Auteur ; Jennifer CROSBIE, Auteur ; Russell SCHACHAR, Auteur ; Elizabeth KELLEY, Auteur ; Jessica JONES, Auteur ; Paul D. ARNOLD, Auteur ; Jakob SEIDLITZ, Auteur ; Aaron F. ALEXANDER-BLOCH, Auteur ; Edward T. BULLMORE, Auteur ; Simon BARON-COHEN, Auteur ; Saashi A. BEDFORD, Auteur ; Richard A. I. BETHLEHEM, Auteur ; Clara PECCI-TERROBA, Auteur ; Meng-Chuan LAI, Auteur ; Michael V. LOMBARDO, Auteur ; Bhismadev CHAKRABARTI, Auteur ; Amber N. V. RUIGROK, Auteur ; John SUCKLING, Auteur ; Evdokia ANAGNOSTOU, Auteur ; Jason P. LERCH, Auteur ; Margot J. TAYLOR, Auteur ; Rob NICOLSON, Auteur ; Stelios GEORGIADES, Auteur ; Jennifer CROSBIE, Auteur ; Russell SCHACHAR, Auteur ; Elizabeth KELLEY, Auteur ; Jessica JONES, Auteur ; Paul D. ARNOLD, Auteur ; Jakob SEIDLITZ, Auteur ; Aaron F. ALEXANDER-BLOCH, Auteur ; Edward T. BULLMORE, Auteur ; Simon BARON-COHEN, Auteur ; Saashi A. BEDFORD, Auteur ; Richard A. I. BETHLEHEM, Auteur . - 33. Langues : Anglais (eng) in Molecular Autism > 16 (2025) . - 33 Mots-clés : Attention Deficit Disorder with Hyperactivity/diagnostic  imaging/classification/pathology  Humans  Autistic Disorder/diagnostic imaging/classification/pathology  Magnetic Resonance Imaging/methods  Male  Female  Child  Cluster Analysis  Adolescent  Algorithms  Machine Learning  Adhd  Autism  Neuroimaging  Population modelling  Structural MRI  Subgrouping  informed consent were obtained for each primary study. The Cambridge Psychology  Research Ethics Committee (PRE.2020.104) deemed that secondary analysis of  deidentified data did not require ethical oversight. Consent for publication: Not  applicable. Competing interests: RAIB, MVL, and M-CL are Associate Editors, and  EA and BC are Editorial Board members of Molecular Autism. SBC is a former  Editor-in-Chief of the journal. ETB reports consultancy work for Boehringer  Ingelheim, Sosei Heptares, SR One, and GlaxoSmithKline. ETB, RAIB, JS, and AFA-B  are cofounders of Centile Bioscience. PDA receives research support from Biohaven  Pharmaceuticals. M-CL has received editorial honorarium from SAGE Publications.  RN reported receiving grants from Brain Canada, Hoffman La Roche, Otsuka  Pharmaceuticals, and Maplight Therapeutics outside the submitted work. EA  reported receiving grants from Roche and Anavex  receiving nonfinancial support  from AMO Pharma and CRA-Simons Foundation  and receiving personal fees from  Roche, Impel, Ono, and Quadrant outside the submitted work. Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism and attention deficit hyperactivity disorder (ADHD) are two highly heterogeneous neurodevelopmental conditions with variable underlying neurobiology. Imaging studies have yielded varied results, and it is now clear that there is unlikely to be one characteristic neuroanatomical profile of either condition. Parsing this heterogeneity could allow us to identify more homogeneous subgroups, either within or across conditions, which may be more clinically informative. This has been a pivotal goal for neurodevelopmental research using both clinical and neuroanatomical features, though results thus far have again been inconsistent with regards to the number and characteristics of subgroups. METHODS: Here, we use population modelling to cluster a multi-site dataset based on global and regional centile scores of cortical thickness, surface area and grey matter volume. We use HYDRA, a novel semi-supervised machine learning algorithm which clusters based on differences to controls and compare its performance to a traditional clustering approach. RESULTS: We identified distinct subgroups within autism and ADHD, as well as across diagnosis, often with opposite neuroanatomical alterations relatively to controls. These subgroups were characterised by different combinations of increased or decreased patterns of morphometrics. We did not find significant clinical differences across subgroups. LIMITATIONS: Crucially, however, the number of subgroups and their membership differed vastly depending on chosen features and the algorithm used, highlighting the impact and importance of careful method selection. CONCLUSIONS: We highlight the importance of examining heterogeneity in autism and ADHD and demonstrate that population modelling is a useful tool to study subgrouping in autism and ADHD. We identified subgroups with distinct patterns of alterations relative to controls but note that these results rely heavily on the algorithm used and encourage detailed reporting of methods and features used in future studies. En ligne : https://dx.doi.org/10.1186/s13229-025-00667-z Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=569

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