Functional properties of focused vs. distributed edges in dynamic topographic informational networks: Implications for the autism spectrum / Peter S. PRESSMAN in Research in Autism, 133 (May 2026)  

Public ISBD [article]  inResearch in Autism > 133 (May 2026) . - p.202901 Titre : Functional properties of focused vs. distributed edges in dynamic topographic informational networks: Implications for the autism spectrum Type de document : texte imprimé Auteurs : Peter S. PRESSMAN, Auteur ; Wan-Tai M. AU-YEUNG, Auteur ; Joel STEELE, Auteur ; Miranda M. LIM, Auteur ; Jeremy SLATER, Auteur ; David B. ARCINIEGAS, Auteur Article en page(s) : p.202901 Langues : Anglais (eng) Mots-clés : Connectivity Architecture  Information Theory  Autism Spectrum  Signal Processing  Neural Networks  Long-Range Connectivity Index. décimale : PER Périodiques Résumé : Background Long-distance connections comprise ∼30% of neural connections but 50–60% of brain volume, rendering them disproportionately vulnerable to diffuse injury. Additional vulnerabilities include synchronization challenges, excitation/inhibition balance requirements, and dependence on coordination pathways. We developed a mathematical framework characterizing how reduced long-distance connectivity produces predictable functional consequences based on network architecture. Method We integrated principles from computational neuroscience, graph theory, and signal processing to relate connectivity architecture to information processing capacity. Core relationships were derived from first principles and tested through computational simulation. Explanatory scope was assessed against observed symptom patterns and seemingly contradictory neuroimaging findings. Results Parallel connectivity enhances temporal precision through signal averaging and multiplexing, with precision scaling as nα (1 ≤ α ≤ 2). Distributed connectivity facilitates information integration, with independent variance scaling as sin²(θ) where θ is angular diversity between inputs. Simulations confirmed these relationships: reduced parallel fibers degrade temporal precision; reduced distributed fibers impair integration of disparate information. Functional connectivity modeling demonstrates increased inter-region correlation with connectivity loss, reflecting reduced signal complexity. Conclusions Relative paucity of long-range connectivity predicts different symptoms depending on connection architecture. Diminished parallel connectivity affects temporal precision, such as speech articulation, fine motor control, auditory processing. Diminished distributed connectivity impairs integration, limiting coherent representation and abstract understanding. This framework provides mechanistic explanations for diverse autism spectrum symptoms, unifies thirty existing theoretical frameworks, and holds therapeutic implications. En ligne : https://doi.org/10.1016/j.reia.2026.202901 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=585 [article] Functional properties of focused vs. distributed edges in dynamic topographic informational networks: Implications for the autism spectrum [texte imprimé] / Peter S. PRESSMAN, Auteur ; Wan-Tai M. AU-YEUNG, Auteur ; Joel STEELE, Auteur ; Miranda M. LIM, Auteur ; Jeremy SLATER, Auteur ; David B. ARCINIEGAS, Auteur . - p.202901. Langues : Anglais (eng) in Research in Autism > 133 (May 2026) . - p.202901 Mots-clés : Connectivity Architecture  Information Theory  Autism Spectrum  Signal Processing  Neural Networks  Long-Range Connectivity Index. décimale : PER Périodiques Résumé : Background Long-distance connections comprise ∼30% of neural connections but 50–60% of brain volume, rendering them disproportionately vulnerable to diffuse injury. Additional vulnerabilities include synchronization challenges, excitation/inhibition balance requirements, and dependence on coordination pathways. We developed a mathematical framework characterizing how reduced long-distance connectivity produces predictable functional consequences based on network architecture. Method We integrated principles from computational neuroscience, graph theory, and signal processing to relate connectivity architecture to information processing capacity. Core relationships were derived from first principles and tested through computational simulation. Explanatory scope was assessed against observed symptom patterns and seemingly contradictory neuroimaging findings. Results Parallel connectivity enhances temporal precision through signal averaging and multiplexing, with precision scaling as nα (1 ≤ α ≤ 2). Distributed connectivity facilitates information integration, with independent variance scaling as sin²(θ) where θ is angular diversity between inputs. Simulations confirmed these relationships: reduced parallel fibers degrade temporal precision; reduced distributed fibers impair integration of disparate information. Functional connectivity modeling demonstrates increased inter-region correlation with connectivity loss, reflecting reduced signal complexity. Conclusions Relative paucity of long-range connectivity predicts different symptoms depending on connection architecture. Diminished parallel connectivity affects temporal precision, such as speech articulation, fine motor control, auditory processing. Diminished distributed connectivity impairs integration, limiting coherent representation and abstract understanding. This framework provides mechanistic explanations for diverse autism spectrum symptoms, unifies thirty existing theoretical frameworks, and holds therapeutic implications. En ligne : https://doi.org/10.1016/j.reia.2026.202901 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=585

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