Fast Connectivity Gradient Approximation: Maintaining spatially fine-grained connectivity gradients while reducing computational costs

Abstract Brain connectome analysis suffers from the high dimensionality of connectivity data, often forcing a reduced representation of the brain at a lower spatial resolution or parcellation. However, maintaining high spatial resolution can both allow fine-grained topographical analysis and preserve subtle individual differences otherwise lost. This work presents a computationally efficient approach to estimate spatially fine-grained connectivity gradients and demonstrates its application in improving brain-behavior predictions.