Measures of Resting-State Brain Network Segregation and Integration Vary in Relation to Data Quantity: Implications for Within and Between Subject Comparisons of Functional Brain Network Organization

Authors

Liang Han
Micaela Y Chan
Phillip F Agres
Ezra Winter-Nelson
Ziwei Zhang
Gagan S Wig

Language

English

Publication Date

1-31-2024

Journal

Cereb Cortex

DOI

10.1093/cercor/bhad506

PMID

38385891

PMCID

PMC10883417

PubMedCentral® Posted Date

2-22-2024

PubMedCentral® Full Text Version

Post-print

Abstract

Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.

Keywords

Young Adult, Humans, Cross-Sectional Studies, Reproducibility of Results, Brain, Cognition, Aging, resting-state functional correlations, brain networks, reliability, system segregation, modularity, integration

Published Open-Access

yes

Recommended Citation

Han, Liang; Chan, Micaela Y; Agres, Phillip F; et al., "Measures of Resting-State Brain Network Segregation and Integration Vary in Relation to Data Quantity: Implications for Within and Between Subject Comparisons of Functional Brain Network Organization" (2024). Faculty, Staff and Student Publications. 6142.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6142