Feature similarity: a sensitive method to capture the functional interaction of brain regions and networks to support flexible behavior
The brain is a dynamic system where complex behaviours emerge from interactions across distributed regions. Accurately linking brain function to cognition requires tools that are sensitive to these dynamics. We introduce a novel technique - Feature Similarity (FS) - to capture intricate interaction patterns between brain systems. Our results show that FS can capture functional brain organisation: regions within the same functional network have greater FS compared to those in different networks, and FS also identifies the principal gradient that spans from unimodal to transmodal cortices. FS was found to be more sensitive to task modulation than traditional functional connectivity (FC). Specifically, FS reveals interaction patterns missed by FC, such as a double dissociation in the Dorsal Attention Network (DAN): greater interaction with the Visual network during working memory tasks and greater interaction with the default mode network (DMN) during long-term memory tasks. This study highlights FS as a promising tool for understanding flexibility in brain network dynamics.