Modeling the Shape of the Brain Connectome via Deep Neural Networks
This addresses a long-standing issue in brain imaging for researchers and clinicians by improving the accuracy of fiber tractography, though it is incremental as it builds on existing geodesic methods.
The paper tackles the problem of modeling brain structural connectivity from diffusion-weighted MRI by representing the connectome as a Riemannian manifold and using geodesics to represent neural connections, achieving excellent performance in aligning geodesics with white matter pathways and recovering crossing fibers with high fidelity.
The goal of diffusion-weighted magnetic resonance imaging (DWI) is to infer the structural connectivity of an individual subject's brain in vivo. To statistically study the variability and differences between normal and abnormal brain connectomes, a mathematical model of the neural connections is required. In this paper, we represent the brain connectome as a Riemannian manifold, which allows us to model neural connections as geodesics. This leads to the challenging problem of estimating a Riemannian metric that is compatible with the DWI data, i.e., a metric such that the geodesic curves represent individual fiber tracts of the connectomics. We reduce this problem to that of solving a highly nonlinear set of partial differential equations (PDEs) and study the applicability of convolutional encoder-decoder neural networks (CEDNNs) for solving this geometrically motivated PDE. Our method achieves excellent performance in the alignment of geodesics with white matter pathways and tackles a long-standing issue in previous geodesic tractography methods: the inability to recover crossing fibers with high fidelity.