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Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics

Heterogeneity in cognitive and academic abilities is a prominent feature of autism spectrum disorder (ASD), yet little is known about its underlying causes. Here we combine functional brain imaging during numerical problem-solving with hierarchical drift-diffusion models of behavior and standardized...

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Autores principales: Iuculano, T., Padmanabhan, A., Chen, L., Nicholas, J., Mitsven, S., de los Angeles, C., Menon, V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160429/
https://www.ncbi.nlm.nih.gov/pubmed/32452464
http://dx.doi.org/10.1016/j.dcn.2020.100754
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author Iuculano, T.
Padmanabhan, A.
Chen, L.
Nicholas, J.
Mitsven, S.
de los Angeles, C.
Menon, V.
author_facet Iuculano, T.
Padmanabhan, A.
Chen, L.
Nicholas, J.
Mitsven, S.
de los Angeles, C.
Menon, V.
author_sort Iuculano, T.
collection PubMed
description Heterogeneity in cognitive and academic abilities is a prominent feature of autism spectrum disorder (ASD), yet little is known about its underlying causes. Here we combine functional brain imaging during numerical problem-solving with hierarchical drift-diffusion models of behavior and standardized measures of numerical abilities to investigate neural mechanisms underlying cognitive variability in children with ASD, and their IQ-matched Typically Developing (TD) peers. Although the two groups showed similar levels of brain activation, the relation to individual abilities differed markedly in ventral temporal-occipital, parietal and prefrontal regions important for numerical cognition: children with ASD showed a positive correlation between functional brain activation and numerical abilities, whereas TD children showed the opposite pattern. Despite similar accuracy and response times, decision thresholds were significantly higher in the ASD group, suggesting greater evidence required for problem-solving. Critically, the relationship between individual abilities and engagement of prefrontal control systems anchored in the anterior insula was differentially moderated by decision threshold in subgroups of children with ASD. Our findings uncover novel cognitive and neural sources of variability in academically-relevant cognitive skills in ASD and suggest that multilevel measures and latent decision-making dynamics can aid in characterization of cognitive variability and heterogeneity in neurodevelopmental disorders.
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spelling pubmed-71604292020-04-22 Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics Iuculano, T. Padmanabhan, A. Chen, L. Nicholas, J. Mitsven, S. de los Angeles, C. Menon, V. Dev Cogn Neurosci Articles from the Special Issue on Flux 2018: Mechanisms of Learning & Plasticity; Edited by Catherine Hartley, Yana Fandakova, Silvia Bunge, Eveline Crone, Ulman Lindenberger. Heterogeneity in cognitive and academic abilities is a prominent feature of autism spectrum disorder (ASD), yet little is known about its underlying causes. Here we combine functional brain imaging during numerical problem-solving with hierarchical drift-diffusion models of behavior and standardized measures of numerical abilities to investigate neural mechanisms underlying cognitive variability in children with ASD, and their IQ-matched Typically Developing (TD) peers. Although the two groups showed similar levels of brain activation, the relation to individual abilities differed markedly in ventral temporal-occipital, parietal and prefrontal regions important for numerical cognition: children with ASD showed a positive correlation between functional brain activation and numerical abilities, whereas TD children showed the opposite pattern. Despite similar accuracy and response times, decision thresholds were significantly higher in the ASD group, suggesting greater evidence required for problem-solving. Critically, the relationship between individual abilities and engagement of prefrontal control systems anchored in the anterior insula was differentially moderated by decision threshold in subgroups of children with ASD. Our findings uncover novel cognitive and neural sources of variability in academically-relevant cognitive skills in ASD and suggest that multilevel measures and latent decision-making dynamics can aid in characterization of cognitive variability and heterogeneity in neurodevelopmental disorders. Elsevier 2020-01-09 /pmc/articles/PMC7160429/ /pubmed/32452464 http://dx.doi.org/10.1016/j.dcn.2020.100754 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Articles from the Special Issue on Flux 2018: Mechanisms of Learning & Plasticity; Edited by Catherine Hartley, Yana Fandakova, Silvia Bunge, Eveline Crone, Ulman Lindenberger.
Iuculano, T.
Padmanabhan, A.
Chen, L.
Nicholas, J.
Mitsven, S.
de los Angeles, C.
Menon, V.
Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics
title Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics
title_full Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics
title_fullStr Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics
title_full_unstemmed Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics
title_short Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics
title_sort neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics
topic Articles from the Special Issue on Flux 2018: Mechanisms of Learning & Plasticity; Edited by Catherine Hartley, Yana Fandakova, Silvia Bunge, Eveline Crone, Ulman Lindenberger.
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160429/
https://www.ncbi.nlm.nih.gov/pubmed/32452464
http://dx.doi.org/10.1016/j.dcn.2020.100754
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