Functional connectivity gradients as a common neural architecture for predictive processing in the human brain

Abstract

Predictive processing is emerging as a common computational hypothesis to account for diverse psychological functions subserved by a brain, providing a systems-level framework for characterizing structure-function relationships of its distinct substructures. Here, we contribute to this framework by examining gradients of functional connectivity as a low dimensional spatial representation of functional variation in the brain and demonstrating their computational implications for predictive processing. Specifically, we investigated functional connectivity gradients in the cerebral cortex, the cerebellum, and the hippocampus using resting-state functional MRI data collected from large samples of healthy young adults. We then evaluated the degree to which these structures share common principles of functional organization by assessing the correspondence of their gradients. We show that the organizing principles of these structures primarily follow two functional gradients consistent with the existing hierarchical accounts of predictive processing: A model-error gradient that describes the flow of prediction and prediction error signals, and a model-precision gradient that differentiates regions involved in the representation and attentional modulation of such signals in the cerebral cortex. Using these gradients, we also demonstrated triangulation of functional connectivity involving distinct subregions of the three structures, which allows characterization of distinct ways in which these structures functionally interact with each other, possibly subserving unique and complementary aspects of predictive processing. These findings support the viability of computational hypotheses about the functional relationships between the cerebral cortex, the cerebellum, and the hippocampus that may be instrumental for understanding the brains dynamics within its large-scale predictive architecture.