We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each streamline curve in a tractography as an observed event in connectome space,here a product space of cortical white matter boundaries. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden,we develop a fast parameter estimation method by pre-computing associated Legendre products of the data,leveraging properties of the spherical heat kernel. We show how our approach can be used to assess the quality of cortical parcellations with respect to connectivty. We further present empirical results that suggest the “discrete” connectomes derived from our model have substantially higher test-retest reliability compared to standard methods.
CITATION STYLE
Moyer, D., Gutman, B. A., Faskowitz, J., Jahanshad, N., & Thompson, P. M. (2016). A continuous model of cortical connectivity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9900 LNCS, pp. 157–165). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_19
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