What makes individuals unique is a long-standing research question of science. Growing studies of functional magnetic resonance imaging point to the utility of co-activation pattern, which is called functional connectivity (FC), of the brain to elucidate neural mechanisms of a great variety of individual differences.
Previous studies in this research field can be characterized by group-level, local-circuit based, and single-state approach. However, a number of studies have revealed that such approaches have serious limitations because they ignore significant variability among individuals, contributions of the other brain circuits, or commonality across subjectively different states.
Here, we propose a novel approach based on sophisticated statistical techniques which can consider the whole-brain FC from diverse states. We first demonstrated that our approach could construct a reliable individual-level classifier for the obsessive-compulsive disorder from whole-brain FC. Second, we characterized a common brain network among state, trait, and pathological anxiety by analyzing whole-brain FC of different states simultaneously. Finally, we investigated a potential factor underlying state-unspecific inter-individual variability of the whole-brain pattern of FC.
In this thesis, we show that our approach allowed us to go beyond the previous coarse, hypothesis-driven regime and to embark on the fine-grained exploration for neural substrates of individual differences.