ArticleFrontiers in human neuroscience2019
Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks.
Article in Frontiers in human neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A mechanism for the emergence of low-dimensional structures in brain dynamics.NPJ systems biology and applications · 2025Article
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- Manifold Learning for fMRI time-varying FC.bioRxiv : the preprint server for biology · 2023Article
- Manifold learning for fMRI time-varying functional connectivity.Frontiers in human neuroscience · 2023Article
- Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold.Human brain mapping · 2022Article
- Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques.Journal of stroke · 2021Review
- The Laplacian eigenmaps dimensionality reduction of fMRI data for discovering stimulus-induced changes in the resting-state brain activity.Neuroimage. Reports · 2021Article
- Learning dynamic graph embeddings for accurate detection of cognitive state changes in functional brain networks.NeuroImage · 2021Article
- Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques.Frontiers in neuroinformatics · 2021Article
- A covariate-constraint method to map brain feature space into lower dimensional manifolds.Network neuroscience (Cambridge, Mass.) · 2021Article
- Dispersion of functional gradients across the adult lifespan.NeuroImage · 2020Article
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Authors and funding
6 authors.
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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.
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