A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in complex brain function. However, the variability of methodologies applied across 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 practices by systematically comparing the reliability of human brain network measurements of individual differences under different analytical strategies using the test-retest design of the resting-state functional magnetic resonance imaging from the Human Connectome Project. The results uncovered four essential principles to guide reliable network neuroscience of individual differences: 1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions, 2) construct functional connectome using spontaneous brain activity in multiple slow bands, 3) optimize topological economy of networks at individual level, 4) characterise information flow with metrics of integration and segregation.
Jiang, C., Betzel, R., He, Y., & Zuo, X.-N. (2021). Toward Reliable Network Neuroscience for Mapping Individual Differences. bioRxiv, 2021.05.06.442886. https://doi.org/10.1101/2021.05.06.442886