Deep Learning the Shape of the Brain Connectome

• URL: http://arxiv.org/abs/2203.06122v1
• Date: Sun, 6 Mar 2022 17:51:31 GMT
• Title: Deep Learning the Shape of the Brain Connectome
• Authors: Haocheng Dai, Martin Bauer, P. Thomas Fletcher, Sarang C. Joshi
• Abstract summary: We show for the first time how one can leverage deep neural networks to estimate a geodesic metric of the brain. Our method achieves excellent performance in geodesic-white-matter-pathway alignment.
• Score: 6.165163123577484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. We show for the first time how one can leverage deep neural networks to estimate a Riemannian metric of the brain that can accommodate fiber crossings and is a natural modeling tool to infer the shape of the brain from DWMRI. Our method achieves excellent performance in geodesic-white-matter-pathway alignment and tackles the long-standing issue in previous methods: the inability to recover the crossing fibers with high fidelity.

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