Modelling low-dimensional interacting brain networks reveals organising principle in human cognition

The discovery of resting-state networks shifted the focus from the role of local regions in cognitive tasks to the ongoing spontaneous dynamics in global networks. Recently, efforts have been invested to reduce the complexity of brain activity recordings through the application of nonlinear dimensionality reduction algorithms. Here, we investigate how the interaction between these networks emerges as an organising principle in human cognition. We combine deep variational autoencoders with computational modelling to construct a dynamical model of brain networks fitted to the whole-brain dynamics measured with functional magnetic resonance imaging (fMRI). Crucially, this allows us to infer the interaction between these networks in resting state and seven different cognitive tasks by determining the effective functional connectivity between networks. We found a high flexible reconfiguration of task-driven network interaction patterns and we demonstrate that it can be used to classify different cognitive tasks. Importantly, compared with using all the nodes in a parcellation, we obtain better results by modelling the dynamics of interacting networks in both model and classification performance. These findings show the key causal role of manifolds as a fundamental organising principle of brain function, providing evidence that interacting networks are the computational engines’ brain during cognitive tasks.

The discovery of resting-state networks has greatly influenced the investigation of brain functioning, shifting the focus from local regions involved in cognitive tasks to the ongoing spontaneous dynamics in global networks. This research goes beyond that shift and proposes investigating how human cognition is shaped by the interactions between whole-brain networks embedded in a low-dimensional manifold space. To achieve this, a combination of deep variational autoencoders with computational modelling is used to construct a dynamic model of brain networks, fitted to whole-brain dynamics measured with functional magnetic resonance imaging (fMRI). The results show that during cognitive tasks, highly flexible reconfigurations of task-driven network interaction patterns occur, and these patterns, in turn, can be used to accurately classify different cognitive tasks. Importantly, using this low-dimensional whole-brain network model provides significantly better results than working in the conventional brain space.