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Updated: Oct 2, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
Published on: September 12, 2011
Brain network localization of gray matter alterations and executive dysfunction in pediatric ADHD
Yexian Zeng1,2,3,4,5, Jia Cheng1,2,3,4,5, Mingrui Xia6,7,8
1Peking University Sixth Hospital, Beijing, China.
Background:
Attention-deficit/hyperactivity disorder (ADHD) is characterized by gray matter alterations and executive dysfunction. Whether the spatially distributed regional abnormalities reported in previous meta-analyses converge within large-scale networks of the developing brain remains unclear. Using coordinate-informed functional connectivity network mapping (FCNM), we mapped the network architecture of meta-analytic gray matter volume (GMV) and executive dysfunction coordinates in pediatric ADHD and evaluated its reproducibility and cross-disorder specificity.
Methods:
We extracted neuroimaging coordinates from published meta-analyses reporting GMV alterations and task-based executive dysfunction in pediatric ADHD (ncases = 5,015, ncontrols = 5,915). To map these disparate regional coordinates onto common functional circuits, we applied FCNM utilizing a large-scale, high-quality pediatric normative connectome from the Chinese Child Brain Development (CCBD) project (n = 2,120). The spatial overlap between the derived abnormality networks and canonical brain networks was quantified using Dice coefficients. Furthermore, network robustness was validated using independent pediatric clinical cohorts (from the ADHD-200 dataset and CCBD), and disease specificity was assessed via cross-disorder connectomic comparison with autism spectrum disorder (ASD).
Results:
Despite the spatial dispersion of the input coordinates, the FCNM-derived connectivity maps showed differential overlap with large-scale brain networks. Specifically, the network associated with gray matter alterations localized predominantly to the default mode network (DMN) (Dice = 0.457; PNCT = 0.001) and the ventral attention network (VAN) (Dice = 0.237; PNCT = 0.03), while the network underlying executive dysfunction localized primarily to the VAN (Dice = 0.319; PNCT = 0.001). Importantly, this dual-network architecture demonstrated robust reproducibility across independent clinical validation cohorts and exhibited clear topological specificity when compared to ASD-related networks.
Conclusions:
Using a large pediatric normative connectome, coordinate-informed FCNM mapped ADHD-related abnormalities onto partially overlapping DMN and VAN architectures, providing a reproducible framework for understanding distributed abnormalities in ADHD and identifying biologically defined targets for neuromodulation and early intervention.
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