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Front Hum Neurosci. 2019 Dec 10;13:430. doi: 10.3389/fnhum.2019.00430. eCollection 2019.

Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks.

Author information

1
Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States.
2
Department of Biomedical Engineering, Virginia Tech-Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, NC, United States.
3
Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
4
Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, United States.

Abstract

As the field of dynamic brain networks continues to expand, new methods are needed to allow for optimal handling and understanding of this explosion in data. We propose here a novel approach that embeds dynamic brain networks onto a two-dimensional (2D) manifold based on similarities and differences in network organization. Each brain network is represented as a single point on the low dimensional manifold with networks of similar topology being located in close proximity. The rich spatio-temporal information has great potential for visualization, analysis, and interpretation of dynamic brain networks. The fact that each network is represented by a single point makes it possible to switch between the low-dimensional space and the full connectivity of any given brain network. Thus, networks in a specific region of the low-dimensional space can be examined to identify network features, such as the location of brain network hubs or the interconnectivity between brain circuits. In this proof-of-concept manuscript, we show that these low dimensional manifolds contain meaningful information, as they were able to successfully discriminate between cognitive tasks and study populations. This work provides evidence that embedding dynamic brain networks onto low dimensional manifolds has the potential to help us better visualize and understand dynamic brain networks with the hope of gaining a deeper understanding of normal and abnormal brain dynamics.

KEYWORDS:

PCA; connectivity pattern; dynamic brain networks; embedding; fMRI; t-SNE

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