Abstract #3064

Examining Global White Matter Development via the Sparse Coding Properties of Diffusion-Weighted MRI

Vishal Patel¹, Mariko Fitzgibbons¹, Paul M Thompson², Arthur W Toga², and Noriko Salamon¹

¹University of California, Los Angeles, Los Angeles, CA, United States, ²University of Southern California, Los Angeles, CA, United States

To avoid the assumptions inherent in diffusion modeling and tractography, we develop a new approach for studying global white matter development that operates on diffusion-weighted MR images directly. We apply a sparse coding method, K-SVD, to decompose a diffusion-weighted series. We quantify the efficiency of the resulting encoding by computing the Gini coefficient. We then show that this measure increases in a predictable manner throughout normal pediatric development. Our results support the hypotheses that more organized white matter can be more sparsely encoded and that the sparsity of the encoding may thus be used to infer the state of development.

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