Association-sensory spatiotemporal hierarchy and functional gradient-regularised recurrent neural network with implications for schizophrenia

Abstract

The human neocortex is functionally organised at its highest level along a continuous sensory-to-association (AS) hierarchy. This study investigates two questions—how this hierarchy is structurally altered in schizophrenia, and what these alterations imply for neural dynamics and cognitive computation. Using a large fMRI dataset (N=355), we extracted individual AS gradients via spectral analysis of brain connectivity and quantified hierarchical organisation by the gradient range. Schizophrenia showed a compressed AS hierarchy, indicating reduced functional differentiation. Estimating neural timescale (autocorrelation decay constant) with the Ornstein-Uhlenbeck process, we observed that the most specialised, locally cohesive regions at the gradient extremes exhibit longer timescales, an empirical spatiotemporal mapping that is attenuated in schizophrenia. To probe the computational consequences of this compression, we used the gradients to regularise subject-specific recurrent neural networks (RNNs) trained on working memory tasks. Networks endowed with greater gradient range learned more efficiently, plateaued at lower task loss, and maintained stronger alignment to the prescribed AS hierarchical geometry. Fixed point linearisation showed that high-range networks settled into more stable neural states during memory delay, evidenced by lower energy and smaller maximal Jacobian eigenvalues. This gradient-regularised RNN framework thereby links large-scale cortical architecture with fixed point stability, providing a computational hypothesis that AS gradient de-differentiation can destabilise neural computations in schizophrenia, convergently supported by empirical timescale flattening along AS gradient and model-based evidence of less stable fixed points.