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PreprintCC BYbioRxiv

Identifying Network Factors Driving Changes in Brain Connectivity During Aging via Stochastic Actor-Oriented Models

  • E. K. Garrison1
  • R. C. Sotero
  • J. G. Orlandi
  • for the Alzheimer's Disease Neuroimaging Initiative
  1. 1University of Calgary
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Abstract

Brain network organization emerges from ongoing network dynamics that continuously create, maintain, and reorganize connectivity patterns. At both the structural and functional levels, these dynamics shape network evolution throughout the lifespan. Here, we introduce a data-driven framework based on Stochastic Actor-Oriented Models (SAOMs), which model longitudinal network changes as a process of local edge formation and dissolution driven by local-connectivity factors. Applied to functional and structural brain networks from an aging cohort, the resulting models recovered a set of interacting mechanisms that collectively promote hub formation, short-range connectivity, and modular organization. These factors were derived through a novel data-driven factor-selection procedure rather than specified a priori, allowing network mechanisms underlying longitudinal change to be identified directly from observations. Functional networks exhibited substantially higher rates of change than structural networks, suggesting more frequent opportunities for network maintenance and reorganization. Structural networks additionally showed despecification in the hub-forming mechanisms with cognitive impairment. These results support a view of brain network organization as an actively maintained process emerging from ongoing local connectivity dynamics. More broadly, this framework provides a general approach for studying longitudinal network dynamics through the estimation of network-generating mechanisms over time.

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Cite

BibTeXRIS
BibTeX
@misc{garrison2026identifying,
  title = {{Identifying Network Factors Driving Changes in Brain Connectivity During Aging via Stochastic Actor-Oriented Models}},
  author = {E. K. Garrison and R. C. Sotero and J. G. Orlandi and for the Alzheimer's Disease Neuroimaging Initiative},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.02.756273},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.02.756273v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Identifying Network Factors Driving Changes in Brain Connectivity During Aging via Stochastic Actor-Oriented Models
AU  - E. K. Garrison
AU  - R. C. Sotero
AU  - J. G. Orlandi
AU  - for the Alzheimer's Disease Neuroimaging Initiative
PY  - 2026
DA  - 2026/10/02
PB  - bioRxiv
DO  - 10.64898/2026.10.02.756273
UR  - https://www.biorxiv.org/content/10.64898/2026.10.02.756273v1
AB  - Brain network organization emerges from ongoing network dynamics that continuously create, maintain, and reorganize connectivity patterns. At both the structural and functional levels, these dynamics shape network evolution throughout the lifespan. Here, we introduce a data-driven framework based on Stochastic Actor-Oriented Models (SAOMs), which model longitudinal network changes as a process of local edge formation and dissolution driven by local-connectivity factors. Applied to functional and structural brain networks from an aging cohort, the resulting models recovered a set of interacting mechanisms that collectively promote hub formation, short-range connectivity, and modular organization. These factors were derived through a novel data-driven factor-selection procedure rather than specified a priori, allowing network mechanisms underlying longitudinal change to be identified directly from observations. Functional networks exhibited substantially higher rates of change than structural networks, suggesting more frequent opportunities for network maintenance and reorganization. Structural networks additionally showed despecification in the hub-forming mechanisms with cognitive impairment. These results support a view of brain network organization as an actively maintained process emerging from ongoing local connectivity dynamics. More broadly, this framework provides a general approach for studying longitudinal network dynamics through the estimation of network-generating mechanisms over time.
ER  -