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February 9, 20260 citationsOpen Access

Hidden Pseudoreplication in Disorder-Averaged Transport Simulations on Parcellated Connectomes

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ODOleg Dolgikh

Key Points

  • The aim is to identify and resolve hidden statistical dependencies in transport simulations using brain parcellation.
  • Conducted simulations on Human Connectome Project data using GKSL/Lindblad transport models.
  • Analyzed effects of shared-disorder seeding on intra-class correlation (ICC).
  • Implemented a method to hash base seeds with subject identity to eliminate correlation.
  • Observed intra-class correlation up to ICC = 0.74, indicating strong hidden dependencies.
  • Effective sample size reduced from 60 to approximately 15 due to shared disorder influences.
  • Proposed recommendations for hybrid experimental designs in future studies.

Abstract

Standard brain parcellation atlases assign identical region labels to all subjects, creating a hidden statistical dependency when diagonal site disorder is generated from node identity. We demonstrate that, in GKSL/Lindblad transport simulations on Human Connectome Project connectomes (5 subjects, Desikan-Killiany atlas), shared-disorder seeding produces intra-class correlations up to ICC = 0.74 on a 2-target basal ganglia pathway, reducing the effective sample size from 60 to approximately 15. A one-line fix—hashing the base seed with subject identity—eliminates the correlation. We propose a hybrid experimental design and recommend reporting ICC for all disorder-averaged connectome studies. Companion paper: "Noise-Assisted Transport Windows in Human Connectome Subgraphs" (doi:10.5281/zenodo.18519173).

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Cite This Study

Oleg Dolgikh (2026) studied this question.

synapsesocial.com/papers/69897a14f0ec2af6756e85f3https://doi.org/10.5281/zenodo.18520884
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