October 31, 2020

Intrinsic brain activity gradients dynamically coordinate functional connectivity states

Brain areas are organized into functionally connected networks though the mechanism underlying this widespread coordination remains unclear. Here we apply deep learning-based dimensionality reduction to task-free functional magnetic resonance images to discover the principal latent dimensions of human brain functional activity. We find that each dimension corresponds to a distinct and stable spatial activity gradient. Brain areas are distributed non-uniformly along each gradient, reflecting modular boundaries and hub properties. Gradients appear to dynamically steepen or flatten to produce task-specific activation patterns. Dynamical systems modelling reveals that gradients can interact via state-specific coupling parameters, allowing accurate forecasts and simulations of brain activity during different tasks. Together, these findings indicate that a small set of overlapping global activity gradients determine the repertoire of possible functional connectivity states.

 bioRxiv Subject Collection: Neuroscience

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