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Record W3010608265 · doi:10.1186/s13229-020-0317-x

Social brain activation during mentalizing in a large autism cohort: the Longitudinal European Autism Project

2020· article· en· W3010608265 on OpenAlexaff
Carolin Moessnang, Sarah Baumeister, Julian Tillmann, David Goyard, Tony Charman, Sara Ambrosino, Simon Baron‐Cohen, Christian F. Beckmann, Sven Bölte, Carsten Bours, Daisy Crawley, Flavio Dell’Acqua, Sarah Durston, Christine Ecker, Vincent Frouin, Hannah Hayward, Rosemary Holt, Mark H. Johnson, Emily J. H. Jones, Meng‐Chuan Lai, Michael Lombardo, Luke Mason, Marianne Oldenhinkel, Antonio M. Persico, Antonia San José Cáceres, Will Spooren, Eva Loth, Declan Murphy, Jan K. Buitelaar, Tobias Banaschewski, Daniel Brandeis, Heike Tost, Andreas Meyer‐Lindenberg

Bibliographic record

VenueMolecular Autism · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids FoundationCentre for Addiction and Mental Health
FundersSeventh Framework ProgrammeInnovative Medicines InitiativeBundesministerium für Bildung und ForschungAutism SpeaksSimons Foundation Autism Research InitiativeMedical Research CouncilEuropean Federation of Pharmaceutical Industries and AssociationsBrandeis UniversityEuropean Commission
KeywordsAutismPsychologyMentalizationFunctional magnetic resonance imagingNeuropsychologyAutism spectrum disorderDevelopmental psychologyNeurodevelopmental disorderSocial cognitionTheory of mindNeuroscienceCognitive psychologyCognition

Abstract

fetched live from OpenAlex

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition with key deficits in social functioning. It is widely assumed that the biological underpinnings of social impairment are neurofunctional alterations in the "social brain," a neural circuitry involved in inferring the mental state of a social partner. However, previous evidence comes from small-scale studies and findings have been mixed. We therefore carried out the to-date largest study on neural correlates of mentalizing in ASD. METHODS: As part of the Longitudinal European Autism Project, we performed functional magnetic resonance imaging at six European sites in a large, well-powered, and deeply phenotyped sample of individuals with ASD (N = 205) and typically developing (TD) individuals (N = 189) aged 6 to 30 years. We presented an animated shapes task to assess and comprehensively characterize social brain activation during mentalizing. We tested for effects of age, diagnosis, and their association with symptom measures, including a continuous measure of autistic traits. RESULTS: We observed robust effects of task. Within the ASD sample, autistic traits were moderately associated with functional activation in one of the key regions of the social brain, the dorsomedial prefrontal cortex. However, there were no significant effects of diagnosis on task performance and no effects of age and diagnosis on social brain responses. Besides a lack of mean group differences, our data provide no evidence for meaningful differences in the distribution of brain response measures. Extensive control analyses suggest that the lack of case-control differences was not due to a variety of potential confounders. CONCLUSIONS: Contrary to prior reports, this large-scale study does not support the assumption that altered social brain activation during mentalizing forms a common neural marker of ASD, at least with the paradigm we employed. Yet, autistic individuals show socio-behavioral deficits. Our work therefore highlights the need to interrogate social brain function with other brain measures, such as connectivity and network-based approaches, using other paradigms, or applying complementary analysis approaches to assess individual differences in this heterogeneous condition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.316
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations67
Published2020
Admission routes1
Has abstractyes

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