Academic Journal
Local Interaction Rules Drive Global Organization of the Human Connectome.
| Τίτλος: | Local Interaction Rules Drive Global Organization of the Human Connectome. |
|---|---|
| Συγγραφείς: | Tozzi A; ASL Napoli 1 Centro, Distretto 27, Naples, Italy. |
| Πηγή: | Brain connectivity [Brain Connect] 2026 Oct-Nov; Vol. 16 (8-9), pp. 274-283. Date of Electronic Publication: 2026 Aug 23. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Mary Ann Liebert, Inc Country of Publication: United States NLM ID: 101550313 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2158-0022 (Electronic) Linking ISSN: 21580014 NLM ISO Abbreviation: Brain Connect Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New Rochelle, NY : Mary Ann Liebert, Inc. |
| Ιατρικοί όροι (MeSH): | Connectome*/methods , Brain*/physiology , Brain*/diagnostic imaging , Nerve Net*/physiology , Models, Neurological*, Neural Pathways/physiology ; Humans ; Computer Simulation |
| Περίληψη: | Introduction: The human connectome exhibits nontrivial large-scale organization despite emerging from decentralized local biological interactions. Most existing generative models reproduce connectomic features through global optimization principles, predefined wiring targets, or developmental templates, leaving unresolved which properties arise from locality alone and which require additional nonlocal mechanisms. Methods: We implemented a simulation framework showing that global coherence can emerge from local compatibility constraints. Networks were generated exclusively through bounded spatial interactions, probabilistic local edge formation, and suppression of incompatible configurations, without global objectives, target topologies, or long-range coordination. Simulated ensembles were analyzed using graph-theoretical metrics, scaling relationships, and rule-based structural classification relative to published reference values of human connectome descriptors. Results: Simulations consistently generated mesoscopic organization characterized by high clustering, modular structure, motif enrichment, and strong short-range connectivity bias. Degree distributions were broad and right-skewed, while edge-length distributions showed pronounced spatial localization. In contrast, several higher-order integrative properties were not reproduced, including empirical connectivity scale, rich-club organization, and long-range hub-to-hub connectivity. Although global metrics displayed substantial quantitative divergence from reported empirical values, several structural regimes and scaling relationships were preserved across parameter ranges. Discussion: Our results distinguish connectome properties structurally compatible with local compatibility constraints from those underdetermined under locality alone. We provide a diagnostic framework designed to isolate the explanatory contribution of local interaction rules to connectome organization through simulations that identify which structural properties emerge directly from locality and which require additional mechanisms beyond local constraints.Impact StatementWe introduce a constraint-based framework for determining which features of human connectome organization can emerge from local interaction rules alone and which require additional mechanisms. By treating divergence from empirical connectomes as evidence of underdetermination rather than simply model error, we shift emphasis from reproducing network statistics to identifying their mechanistic requirements. Our results distinguish locally generated properties, including clustering, modular organization, motif enrichment and short-range wiring bias, from features associated with large-scale integration and hub organization. This framework provides a systematic baseline for evaluating when more complex developmental, anatomical or global mechanisms are necessary to explain connectome architecture. |
| Competing Interests: | Author Disclosure StatementThe author does not have any known or potential conflict of interest including any financial, personal, or other relationships with other people or organizations within 3 years of beginning the submitted work that could inappropriately influence or be perceived to influence their work. |
| Contributed Indexing: | Keywords: mesoscopic organization; modularity; motif statistics; scale dependence; wiring cost |
| Entry Date(s): | Date Created: 20260824 Date Completed: 20260916 Latest Revision: 20260916 |
| Update Code: | 20260916 |
| DOI: | 10.1177/21580014261479958 |
| PMID: | 42634234 |
| Βάση Δεδομένων: | MEDLINE |
| FullText | Text: Availability: 0 |
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| Header | DbId: cmedm DbLabel: MEDLINE An: 42634234 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Local Interaction Rules Drive Global Organization of the Human Connectome. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Tozzi+A%22">Tozzi A</searchLink>; ASL Napoli 1 Centro, Distretto 27, Naples, Italy. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101550313%22">Brain connectivity</searchLink> [Brain Connect] 2026 Oct-Nov; Vol. 16 (8-9), pp. 274-283. <i>Date of Electronic Publication: </i>2026 Aug 23. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Mary+Ann+Liebert%2C+Inc%22">Mary Ann Liebert, Inc </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101550313 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2158-0022 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2221580014%22">21580014 </searchLink><i>NLM ISO Abbreviation: </i>Brain Connect <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Original Publication</i>: New Rochelle, NY : Mary Ann Liebert, Inc. – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Connectome%22">Connectome*</searchLink>/<searchLink fieldCode="MM" term="%22Connectome+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Brain%22">Brain*</searchLink>/<searchLink fieldCode="MM" term="%22Brain+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Brain%22">Brain*</searchLink>/<searchLink fieldCode="MM" term="%22Brain+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Nerve+Net%22">Nerve Net*</searchLink>/<searchLink fieldCode="MM" term="%22Nerve+Net+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Models%2C+Neurological%22">Models, Neurological*</searchLink><br /><searchLink fieldCode="MH" term="%22Neural+Pathways%22">Neural Pathways</searchLink>/<searchLink fieldCode="MH" term="%22Neural+Pathways+physiology%22">physiology</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Computer+Simulation%22">Computer Simulation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Introduction: The human connectome exhibits nontrivial large-scale organization despite emerging from decentralized local biological interactions. Most existing generative models reproduce connectomic features through global optimization principles, predefined wiring targets, or developmental templates, leaving unresolved which properties arise from locality alone and which require additional nonlocal mechanisms.<br />Methods: We implemented a simulation framework showing that global coherence can emerge from local compatibility constraints. Networks were generated exclusively through bounded spatial interactions, probabilistic local edge formation, and suppression of incompatible configurations, without global objectives, target topologies, or long-range coordination. Simulated ensembles were analyzed using graph-theoretical metrics, scaling relationships, and rule-based structural classification relative to published reference values of human connectome descriptors.<br />Results: Simulations consistently generated mesoscopic organization characterized by high clustering, modular structure, motif enrichment, and strong short-range connectivity bias. Degree distributions were broad and right-skewed, while edge-length distributions showed pronounced spatial localization. In contrast, several higher-order integrative properties were not reproduced, including empirical connectivity scale, rich-club organization, and long-range hub-to-hub connectivity. Although global metrics displayed substantial quantitative divergence from reported empirical values, several structural regimes and scaling relationships were preserved across parameter ranges.<br />Discussion: Our results distinguish connectome properties structurally compatible with local compatibility constraints from those underdetermined under locality alone. We provide a diagnostic framework designed to isolate the explanatory contribution of local interaction rules to connectome organization through simulations that identify which structural properties emerge directly from locality and which require additional mechanisms beyond local constraints.Impact StatementWe introduce a constraint-based framework for determining which features of human connectome organization can emerge from local interaction rules alone and which require additional mechanisms. By treating divergence from empirical connectomes as evidence of underdetermination rather than simply model error, we shift emphasis from reproducing network statistics to identifying their mechanistic requirements. Our results distinguish locally generated properties, including clustering, modular organization, motif enrichment and short-range wiring bias, from features associated with large-scale integration and hub organization. This framework provides a systematic baseline for evaluating when more complex developmental, anatomical or global mechanisms are necessary to explain connectome architecture. – Name: Abstract Label: Competing Interests Group: Ab Data: Author Disclosure StatementThe author does not have any known or potential conflict of interest including any financial, personal, or other relationships with other people or organizations within 3 years of beginning the submitted work that could inappropriately influence or be perceived to influence their work. – Name: SubjectMinor Label: Contributed Indexing Group: Data: <i>Keywords: </i>mesoscopic organization; modularity; motif statistics; scale dependence; wiring cost – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20260824 <i>Date Completed: </i>20260916 <i>Latest Revision: </i>20260916 – Name: DateUpdate Label: Update Code Group: Date Data: 20260916 – Name: DOI Label: DOI Group: ID Data: 10.1177/21580014261479958 – Name: AN Label: PMID Group: ID Data: 42634234 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=42634234 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/21580014261479958 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 274 Subjects: – SubjectFull: Neural Pathways physiology Type: general – SubjectFull: Humans Type: general – SubjectFull: Computer Simulation Type: general – SubjectFull: Connectome methods Type: general – SubjectFull: Brain physiology Type: general – SubjectFull: Brain diagnostic imaging Type: general – SubjectFull: Nerve Net physiology Type: general – SubjectFull: Models, Neurological Type: general Titles: – TitleFull: Local Interaction Rules Drive Global Organization of the Human Connectome. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tozzi A IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: 2026 Oct-Nov Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 2158-0022 Numbering: – Type: volume Value: 16 – Type: issue Value: 8-9 Titles: – TitleFull: Brain connectivity Type: main |
| ResultId | 1 |