Optimizing network neuroscience computation of individual differences
Published in NETWORK NEUROSCI, 2023
It is an essential mission for neuroscience to understand the individual differences in brain function. Graph or network theory offer novel methods of network neuroscience to address such a challenge. This article documents optimal strategies on the test-retest reliability of measuring individual differences in intrinsic brain networks of spontaneous activity. The analytical pipelines are identified to optimize for highly reliable, individualized network measurements. These pipelines optimize network metrics for high interindividual variances and low inner-individual variances by defining network nodes with whole-brain parcellations, deriving the connectivity with spontaneous high-frequency slow-band oscillations, constructing brain graphs with topology-based methods for edge filtering, and favoring multilevel or multimodal metrics. These psychometric findings are critical for translating the functional network neuroscience into clinical or other personalized practices requiring neuroimaging markers.
Recommended citation: C. Jiang, Y. He, R. F. Betzel, Y. S. Wang, X. X. Xing & X. N. Zuo. (2023). "Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability." Network Neuroscience, 7:1080–1108. https://doi.org/10.1162/netn_a_00315