Building functional network neuroscience for reliable individual differences

Abstract A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in complex brain function, i.e., the functional network neuroscience (FNN). However, the variability of methodologies applied across FNN 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 FNN practices by systematically comparing the measurement reliability of individual differences in FNN under different analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide reliable FNN: 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 specific metrics of integration and segregation. We built an interactive online resource for reliable FNN (http://ibraindata.com/research/reliableFNN).