Behavioural relevance of functional connectivity-based individual-specific brain representations

COEX 

Resting-state functional connectivity (RSFC) has shown great promise as a tool for characterizing the human brain. Until recently, most functional connectivity-based brain representations have relied on data averaged across many individuals. However, such population-level brain representation might obscure biologically meaningful individual-specific features. Here, we present a multi-session hierarchical Bayesian model to generate high-quality individual-specific parcellations. We further explored the behavioural relevance of our individual-specific parcellations and other individual-specific representations, including principal RSFC gradients, local gradients and independent components. We found that the principal gradient approach required at least 40 to 60 gradients to perform as well as parcellations and independent components. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients could provide significant behaviourally relevant information. If time permits, we will also discuss potential clinical applications of individual-specific parcellations.