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Structural Balance and Hub Heterogeneity Alterations in Functional Brain Networks of ADHD: A Signed-Graph Topological Study
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1
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| Subject:
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Artificial Intelligence and Brain Mapping
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Presenter Author:
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Rouzbh Fakhari
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Author:
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| Name 1:
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Rouzbh Fakhari - Corresponding - Presenter
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| Affiliation 1:
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Ph.D. in Cognitive Science — Computational Neuroscience & AI, Institute for Cognitive Science & AI, Tehran, Iran
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Abstract
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Original Research
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introduction :
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This work characterizes signed topology of resting-state functional networks in Attention-Deficit/Hyperactivity Disorder (ADHD). While conventional unsigned analyses emphasize synchrony, signed-graph theory explicitly models cooperative and antagonistic interactions and can reveal triadic stability and hub organization that underlie network adaptability. We examine whether structural balance energy and negative-subnetwork hub heterogeneity (TMH) relate to diagnosis, clinical subtype, and symptom severity.
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Methods:
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Data: ADHD-200 multi-site sample (N=947; ages ≈7–21; controls + inattentive, hyperactive-impulsive, combined subtypes). Shared preprocessing (Athena pipeline: slice timing, motion correction, normalization, nuisance regression, band-pass filtering) and parcellation strategies (AAL, CC200, CC400) are described in Supplement. For each subject we computed signed correlation matrices (positive → +, negative → −). Primary metrics: global structural balance energy (mean triadic energy), TMH computed on the negative subnetwork, average path length, modularity, and node centralities. Group comparisons used permutation testing with FDR correction; correlations with clinical symptom scales used robust statistics; localized inference employed Network-Based Statistics (NBS). (See Supplement for full parameter settings, motion thresholds, and statistical pipelines.)
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Results:
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No significant group mean difference in global balance energy was found between combined ADHD (or individual subtypes) and controls. However, within ADHD participants, higher clinical symptom severity (ADHD index; hyperactivity/inattention subscales) correlated with lower balance energy (FDR-corrected), indicating a shift toward more structurally balanced — and thus less flexible — network configurations. The hyperactive-impulsive subtype showed significantly reduced TMH in the negative subnetwork compared to controls and other subtypes (permutation tested, FDR-corrected), suggesting diminished centralization of anticorrelated hubs. NBS identified spatially specific edge differences concentrated in default-mode and prefrontal circuits; average path length changes indicated subgroup-dependent modulation of integration.
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Conclusion:
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This work shows that signed-graph measures—particularly structural balance and negative-subnetwork TMH—reveal clinically relevant topological reorganizations in ADHD that unsigned metrics miss. Paper 1 focuses on descriptive topology and clinical correlations and recommends external replication and longitudinal validation to assess developmental trajectories and treatment sensitivity.
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| Attachment:
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| Keywords:
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Attention Deficit Disorder with Hyperactivity; Magnetic Resonance Imaging; Brain Mapping; Graph Theory; Biomarkers
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| Key message:
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