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Record W4415474264 · doi:10.1038/s41380-025-03205-8

Connectome-based symptom mapping and in silico related gene expression in children with autism and/or attention-deficit/hyperactivity disorder

2025· article· en· W4415474264 on OpenAlexfundno aff
Patricia Segura, Marco Pagani, Somer Bishop, Phoebe Thomson, Stanley J. Colcombe, Ting Xu, Zekiel Z. Factor, Emily C. Hector, So Hyun Kim, Michael Lombardo, Alessandro Gozzi, F. Xavier Castellanos, Catherine Lord, Michael P. Milham, Adriana Di Martino

Bibliographic record

VenueMolecular Psychiatry · 2025
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsnot available
FundersNational Institute of Mental HealthNational Institutes of HealthPartenariat Canadien Contre Le CancerMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaNational Research FoundationChild Mind Institute
KeywordsAutismNeuroimagingNeurodevelopmental disorderNeuroanatomyAttention deficit hyperactivity disorderAutism spectrum disorderAnterior cingulate cortexNeuroliginDefault mode network

Abstract

fetched live from OpenAlex

Clinical, neuroimaging and genomics evidence have increasingly underscored a degree of overlap between autism and attention-deficit/hyperactivity disorder (ADHD). This study explores the specific contribution of their core symptoms to shared biology in N = 166 verbal children (6-12 years) with rigorously-established primary diagnoses of either autism or ADHD (without autism). We investigated the associations between inter-individual differences in low motion whole-brain intrinsic functional connectivity (iFC) and dimensional measures of autism and ADHD symptoms indexed by clinician-based observation and parent interview, respectively. Additionally, we explored their linked gene expression patterns in silico. Whole-brain multivariate distance matrix regression revealed a transdiagnostic association between autism severity and iFC of two nodes primarily on the left hemisphere: the middle frontal gyrus of the frontoparietal network and the posterior cingulate cortex of the default mode network. Across children, the greater the iFC between these nodes, the more severe the autism symptoms, even after controlling for ADHD ratings. Results from secondary segregation analyses were consistent with primary findings, underscoring the significance of internetwork iFC for autism symptom severity across diagnoses. No statistically significant brain-behavior relationships were observed for ADHD symptoms. Genetic enrichment analyses of the iFC maps associated with autism symptoms implicated genes known to: (i) have greater rate of variance in autism and ADHD, and (ii) be involved in neuron projections, suggesting shared genetic mechanisms for this specific brain-clinical phenotype. These findings underscore the relevance of transdiagnostic dimensional approaches in linking clinically-defined and observation-based phenomena to shared presentations at the macroscale circuit- and genomic-levels across diagnoses.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.233
Teacher spread0.225 · 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 designSimulation or modeling
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

Citations4
Published2025
Admission routes1
Has abstractyes

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