Beyond cortical geometry: brain dynamics shaped by long-range connections

Abstract

A fundamental topological principle is that the container always shapes the content. In neuroscience, this translates into how the brain anatomy shapes brain dynamics. From neuroanatomy, the topology of the mammalian brain can be approximated by local connectivity, accurately described by an exponential distance rule (EDR). The compact, folded geometry of the cortex is shaped by this local connectivity and the geometric harmonic modes can reconstruct much of the functional dynamics. However, this ignores the fundamental role of the rare long-range cortical connections, crucial for improving information processing in the mammalian brain, but not captured by local cortical folding and geometry. Here we show the superiority of harmonic modes combining rare long-range connectivity with EDR (EDR+LR) in capturing functional dynamics (specifically long-range functional connectivity and task-evoked brain activity) compared to geometry and EDR representations. Importantly, the orchestration of dynamics is carried out by a more efficient manifold made up of a low number of fundamental EDR+LR modes. Our results show the importance of rare long-range connectivity for capturing the complexity of functional brain activity through a low-dimensional manifold shaped by fundamental EDR+LR modes. Significance StatementExplaining how structure of the brain gives rise to its emerging dynamics is a primary pursuit in neuroscience. We describe a fundamental anatomical constraint that emphasises the key role of rare long-range connections in explaining functional organisation of the brain in terms of spontaneous and task-evoked activity. Specifically, this constraint unifies brain geometry and local connectivity through the Exponential Distance Rule while considering the long-range exceptions to this local connectivity as derived from the structural connectome. In addition, when using this structural information, we show that the task-evoked brain activity is described by a low-dimensional manifold of several modes suggesting that less is more for the efficient information processing in the brain.