Parameter estimation for connectome generative models: Accuracy, reliability, and a fast parameter fitting method

Yuanzhe Liu, Caio Seguin, Sina Mansour, Stuart Oldham, Richard Betzel, Maria A. Di Biase, Andrew Zalesky

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Generative models of the human connectome enable in silico generation of brain networks based on probabilistic wiring rules. These wiring rules are governed by a small number of parameters that are typically fitted to individual connectomes and quantify the extent to which geometry and topology shape the generative process. A significant shortcoming of generative modeling in large cohort studies is that parameter estimation is computationally burdensome, and the accuracy and reliability of current estimation methods remain untested. Here, we propose a fast, reliable, and accurate parameter estimation method for connectome generative models that is scalable to large sample sizes. Our method achieves improved estimation accuracy and reliability and reduces computational cost by orders of magnitude, compared to established methods. We demonstrate an inherent tradeoff between accuracy, reliability, and computational expense in parameter estimation and provide recommendations for leveraging this tradeoff. To enable power analyses in future studies, we empirically approximate the minimum sample size required to detect between-group differences in generative model parameters. While we focus on the classic two-parameter generative model based on connection length and the topological matching index, our method can be generalized to other growth-based generative models. Our work provides a statistical and practical guide to parameter estimation for connectome generative models.

Original languageEnglish (US)
Article number119962
JournalNeuroImage
Volume270
DOIs
StatePublished - Apr 15 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

  • Accuracy
  • Connectome
  • Generative model
  • Network neuroscience
  • Reliability

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

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