Individual uniqueness of connectivity gradients is driven by the complexity of the embedded networks and their dispersion.

Abstract

Connectivity gradients are widely used to characterize meaningful principles of functional brain organization in health and disease. However, the degree of individual uniqueness and shared common principles is not yet fully understood. Here, we leveraged the Hangzhou test-retest dataset, comprising repeated resting-state fMRI scans over the span of 1 month, to investigate the balance between individual variation and shared patterns of brain organization. We quantified the short- and long-term stability for the first three connectivity gradients and used connectome fingerprinting to establish the associated individual identification rate. We found that all three connectivity gradients are highly correlated over both short and long time intervals, demonstrating connectome fingerprinting utility. Individual uniqueness was dictated by the complexity of the networks such that heteromodal networks had higher connectome fingerprinting rates than unimodal networks. Importantly, the dispersion of the gradient coefficients associated with canonical functional networks was correlated with identification rates, irrespective of the position along the gradients. Beyond individual uniqueness, between subject similarity was high along the first connectivity gradient, which captures the dissociation between unimodal and heteromodal cortices, and the second connectivity gradient, which differentiates sensory cortices. Our results support the usage of connectivity gradients for the purposes of both group comparisons and prediction of individual behaviours. Our work adds to existing knowledge on the shared versus unique organizational principles and offers insights into the importance of network dispersion to the individual uniqueness it carries.