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Related Experiment Video

Updated: Feb 28, 2026

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos
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Structural Covariance Networks in Children with Autism or ADHD.

R A I Bethlehem1,2, R Romero-Garcia2, E Mak2

  • 1Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK.

Cerebral Cortex (New York, N.Y. : 1991)
|June 22, 2017
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Autism and ADHD show overlapping and distinct brain network differences. Both conditions exhibit altered connectivity patterns, but diverge in modular organization and hub region characteristics, suggesting an integrated approach is needed.

Keywords:
ADHDautismcortical thicknessgraph theorystructural covariance

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Area of Science:

  • Neuroscience
  • Developmental Neuroscience
  • Computational Neuroscience

Background:

  • Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are distinct diagnoses but share clinical features and high comorbidity.
  • Most research focuses on each condition separately, leading to heterogeneous findings.
  • A dual-condition approach can reveal shared and distinct neural characteristics.

Purpose of the Study:

  • To investigate shared and distinct topological properties of structural covariance networks in autism and ADHD.
  • To compare network characteristics between individuals with autism, individuals with ADHD, and neurotypical controls.
  • To examine the relationship between graph theory measures and cortical thickness in these groups.

Main Methods:

  • Utilized graph theory to analyze structural covariance networks derived from regional cortical thickness data.
  • Included data from the ABIDE (autism) and ADHD-200 (ADHD) datasets, alongside a neurotypical (NT) control group.
  • Focused on differences in long and short-range connectivity, and the relation between graph measures and cortical thickness.

Main Results:

  • Both autism and ADHD groups showed decreased cortical thickness (CT) covariance with increased Euclidean distance compared to NTs, indicating convergence.
  • The conditions diverged in modular overlap, with less overlap between autism and ADHD than between each condition and NTs.
  • The ADHD group exhibited reduced CT and lower hub region degree, along with reduced wiring costs, compared to the autism group.

Conclusions:

  • Findings support an integrated approach for highly comorbid conditions like autism and ADHD.
  • Both autism and ADHD display altered inter-regional covariance patterns related to distance.
  • Divergent network features, including modular organization and hub characteristics, highlight distinctions between autism and ADHD.