A large-scale high-density weighted structural connectome of the macaque brain acquired by predicting missing links

Yuhan Chen*, Zi Ke Zhang, Yong He, Changsong ZHOU

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

As a substrate for function, large-scale brain structural networks are crucial for fundamental and systems-level understanding of primate brains. However, it is challenging to acquire a complete primate whole-brain structural connectome using track tracing techniques. Here, we acquired a weighted brain structural network across 91 cortical regions of a whole macaque brain hemisphere with a connectivity density of 59% by predicting missing links from the CoCoMac-based binary network with a low density of 26.3%. The prediction model combines three factors, including spatial proximity, topological similarity, and cytoarchitectural similarity-to predict missing links and assign connection weights. The model was tested on a recently obtained high connectivity density yet partial-coverage experimental weighted network connecting 91 sources to 29 target regions; the model showed a prediction sensitivity of 74.1% in the predicted network. This predicted macaque hemisphere-wide weighted network has module segregation closely matching functional domains. Interestingly, the areas that act as integrators linking the segregated modules are mainly distributed in the frontoparietal network and correspond to the regions with large wiring costs in the predicted weighted network. This predicted weighted network provides a high-density structural dataset for further exploration of relationships between structure, function, and metabolism in the primate brain.

Original languageEnglish
Pages (from-to)4771-4789
Number of pages19
JournalCerebral Cortex
Volume30
Issue number9
DOIs
Publication statusPublished - 1 Sep 2020

Scopus Subject Areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

User-Defined Keywords

  • Hemisphere-wide weighted network
  • Link prediction
  • Macaque brain connectome
  • Segregation and integration
  • Structural network

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