Abstract
There is an increasing consensus in the scientific and medical communtities that functional brain analysis should be conducted from a connectionist standpoint. Most connectivity studies to date rely on derived measures of graph properties. In this paper, we show that brain networks can be analyzed effectively by considering them as elements of the Riemannian manifold of symmetric positive definite matrices \(\text {Sym}^+\). Using recently proposed methods for manifold multivariate linear modelling, we analyze the developing functional connectivity of a small cohort of children aged 6 to 13 of both genders with strongly varying handedness indices at both rest and task simultaneously. We show that even with small sample sizes we can obtain results that reflect findings on large cohorts, and that \(\text {Sym}^+\) is a better framework for analyzing functional brain connectivity compared to Euclidean space.
G. Langs—This project was supported by FWF (KLI 544-B27, I 2714-B31) and OeNB (15356, 15929).
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Schwartz, E., Nenning, KH., Kasprian, G., Schuller, AL., Bartha-Doering, L., Langs, G. (2017). Multivariate Manifold Modelling of Functional Connectivity in Developing Language Networks. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_25
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