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  Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

Rieck, B., Yates, T., Bock, C., Borgwardt, K., Wolf, G., Turk-Browne, N., et al. (2020). Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. Advances in Neural Information Processing Systems (NeurIPS 2020), 33, 6900-6912. doi:10.48550/arXiv.2006.07882.

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https://arxiv.org/pdf/2006.07882.pdf (Any fulltext)
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 Creators:
Rieck, Bastian, Author
Yates, Tristan, Author
Bock, Christian, Author
Borgwardt, Karsten1, Author                 
Wolf, Guy, Author
Turk-Browne, Nicholas, Author
Krishnaswamy, Smita, Author
Affiliations:
1ETH Zürich, ou_persistent22              

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 Abstract: Functional magnetic resonance imaging (fMRI) is a crucial technology for gaining insights into cognitive processes in humans. Data amassed from fMRI measurements result in volumetric data sets that vary over time. However, analysing such data presents a challenge due to the large degree of noise and person-to-person variation in how information is represented in the brain. To address this challenge, we present a novel topological approach that encodes each time point in an fMRI data set as a persistence diagram of topological features, i.e. high-dimensional voids present in the data. This representation naturally does not rely on voxel-by-voxel correspondence and is robust towards noise. We show that these time-varying persistence diagrams can be clustered to find meaningful groupings between participants, and that they are also useful in studying within-subject brain state trajectories of subjects performing a particular task. Here, we apply both clustering and trajectory analysis techniques to a group of participants watching the movie 'Partly Cloudy'. We observe significant differences in both brain state trajectories and overall topological activity between adults and children watching the same movie.

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 Dates: 20202020
 Publication Status: Issued
 Pages: 6900-6912
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.48550/arXiv.2006.07882
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Title: Advances in Neural Information Processing Systems (NeurIPS 2020)
Source Genre: Journal
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Pages: - Volume / Issue: 33 Sequence Number: - Start / End Page: 6900 - 6912 Identifier: -