Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability

Chao Jiang, Ye He, Richard F. Betzel, Yin Shan Wang, Xiu Xia Xing, Xi Nian Zuo

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies—with respect to node definition, edge construction, and graph measurements—makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).

Original languageEnglish (US)
Pages (from-to)1080-1108
Number of pages29
JournalNetwork Neuroscience
Volume7
Issue number3
DOIs
StatePublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Massachusetts Institute of Technology.

Keywords

  • Connectome
  • Individual difference
  • Open science
  • Reliability
  • Spontaneous brain activity

PubMed: MeSH publication types

  • Journal Article

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