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Poster
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
Cesar F Caiafa · Olaf Sporns · Andrew Saykin · Franco Pestilli

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #151
in Posters Wed »

Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models. We provide a theoretical analysis of the accuracy of the sparse decomposed model, LiFESD, and demonstrate that it can reduce the size of the model significantly. Also, we develop algorithms to implement the optimisation solver using the tensor representation in an efficient way.

Keywords: Matrix and Tensor Factorization · Connectomics
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Author Information

Cesar F Caiafa (CONICET)
Olaf Sporns (Department of Psychological and Brain Sciences - Indiana University)
Andrew Saykin (IUPUI)
Franco Pestilli (Indiana University)

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