Callous-unemotional traits, cognitive functioning, and externalizing problems in a propensity-matched sample from the ABCD study / Samantha PERLSTEIN ; Yael PAZ ; Jakob SEIDLITZ ; Adrian RAINE ; Samuel HAWES ; Amy L. BYRD ; Rebecca WALLER in Journal of Child Psychology and Psychiatry, 66-3 (March 2025)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 66-3 (March 2025) . - p.333-349 Titre : Callous-unemotional traits, cognitive functioning, and externalizing problems in a propensity-matched sample from the ABCD study Type de document : texte imprimé Auteurs : Samantha PERLSTEIN, Auteur ; Yael PAZ, Auteur ; Jakob SEIDLITZ, Auteur ; Adrian RAINE, Auteur ; Samuel HAWES, Auteur ; Amy L. BYRD, Auteur ; Rebecca WALLER, Auteur Article en page(s) : p.333-349 Langues : Anglais (eng) Mots-clés : callous-unemotional traits  cognitive function  aggressive behavior  externalizing disorders Index. décimale : PER Périodiques Résumé : Background Many studies show that both callous-unemotional (CU) traits (e.g., low empathy, lack of guilt) and cognitive difficulties increase risk for externalizing psychopathology across development. However, other work suggests that some aggression (e.g., relational, proactive) may rely on intact cognitive function, which could vary based on the presence of CU traits. Moreover, no prior research has adequately accounted for common risk factors shared by CU traits, cognitive difficulties, and externalizing problems, which confounds conclusions that can be drawn about their purported relationships. The current study addressed these knowledge gaps by leveraging rigorous propensity matching methods to isolate associations between CU traits and different dimensions of cognitive function and externalizing problems. Methods Associations between CU traits, cognitive functioning, and externalizing outcomes were tested within dimensional (n 11,868) and propensity-matched group-based (n 1,224) models using data from the Adolescent Brain Cognitive Development Study?, with rigorous statistical control for shared sociodemographic risk factors. Cross-sectional outcomes were parent-reported symptoms of conduct disorder (CD), oppositional defiant disorder (ODD), and attention deficit hyperactivity disorder (ADHD). Longitudinal outcomes were child-reported overt and relational aggression. Results CU traits were uniquely related to more parent-reported CD, ODD, ADHD symptoms, as well as more child-reported aggressive behaviors. Effects of cognitive difficulties were domain specific and were not consistent across dimensional and propensity matched models. There was minimal evidence for divergent associations between CU traits and externalizing outcomes as a function of cognition (i.e., no moderation). Conclusions Rigorous control for sociodemographic factors within propensity-matched models establish CU traits as a robust and unique risk factor for externalizing psychopathology, over and above difficulties with cognitive functioning. En ligne : https://doi.org/10.1111/jcpp.14062 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=548 [article] Callous-unemotional traits, cognitive functioning, and externalizing problems in a propensity-matched sample from the ABCD study [texte imprimé] / Samantha PERLSTEIN, Auteur ; Yael PAZ, Auteur ; Jakob SEIDLITZ, Auteur ; Adrian RAINE, Auteur ; Samuel HAWES, Auteur ; Amy L. BYRD, Auteur ; Rebecca WALLER, Auteur . - p.333-349. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 66-3 (March 2025) . - p.333-349 Mots-clés : callous-unemotional traits  cognitive function  aggressive behavior  externalizing disorders Index. décimale : PER Périodiques Résumé : Background Many studies show that both callous-unemotional (CU) traits (e.g., low empathy, lack of guilt) and cognitive difficulties increase risk for externalizing psychopathology across development. However, other work suggests that some aggression (e.g., relational, proactive) may rely on intact cognitive function, which could vary based on the presence of CU traits. Moreover, no prior research has adequately accounted for common risk factors shared by CU traits, cognitive difficulties, and externalizing problems, which confounds conclusions that can be drawn about their purported relationships. The current study addressed these knowledge gaps by leveraging rigorous propensity matching methods to isolate associations between CU traits and different dimensions of cognitive function and externalizing problems. Methods Associations between CU traits, cognitive functioning, and externalizing outcomes were tested within dimensional (n 11,868) and propensity-matched group-based (n 1,224) models using data from the Adolescent Brain Cognitive Development Study?, with rigorous statistical control for shared sociodemographic risk factors. Cross-sectional outcomes were parent-reported symptoms of conduct disorder (CD), oppositional defiant disorder (ODD), and attention deficit hyperactivity disorder (ADHD). Longitudinal outcomes were child-reported overt and relational aggression. Results CU traits were uniquely related to more parent-reported CD, ODD, ADHD symptoms, as well as more child-reported aggressive behaviors. Effects of cognitive difficulties were domain specific and were not consistent across dimensional and propensity matched models. There was minimal evidence for divergent associations between CU traits and externalizing outcomes as a function of cognition (i.e., no moderation). Conclusions Rigorous control for sociodemographic factors within propensity-matched models establish CU traits as a robust and unique risk factor for externalizing psychopathology, over and above difficulties with cognitive functioning. En ligne : https://doi.org/10.1111/jcpp.14062 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=548

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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