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Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
Bastian Rieck · Tristan Yates · Christian Bock · Karsten Borgwardt · Guy Wolf · Nicholas Turk-Browne · Smita Krishnaswamy

Thu Dec 10 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Neuroscience
in Orals & Spotlights Track 29: Neuroscience »

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.

Author Information

Bastian Rieck (ETH Zurich)
Tristan Yates (Yale University)
Christian Bock (ETH Zurich)

I am a PhD Student at ETH Zurich, Switzerland, currently interning at Amazon. My thesis defence is planned for August 2021. I am interested in opportunities for my time after the PhD. I develop and apply methods from **topological data analysis** for machine learning tasks such as graph classification or to improve our understanding of deep learning and neuroscience. Furthermore, I utilize and develop methods for the classification of real-world **biomedical time-series**. These methods extend to statistical work on pioneering significant pattern mining for time-series data. More recently, I got interested in Gaussian Processes.

Karsten Borgwardt (ETH Zurich)

Karsten Borgwardt is Professor of Data Mining at ETH Zürich, at the Department of Biosystems located in Basel. His work has won several awards, including the NIPS 2009 Outstanding Paper Award, the Krupp Award for Young Professors 2013 and a Starting Grant 2014 from the ERC-backup scheme of the Swiss National Science Foundation. Since 2013, he is heading the Marie Curie Initial Training Network for "Machine Learning for Personalized Medicine" with 12 partner labs in 8 countries (http://www.mlpm.eu). The business magazine "Capital" listed him as one of the "Top 40 under 40" in Science in/from Germany in 2014, 2015 and 2016. For more information, visit: https://www.bsse.ethz.ch/mlcb

Guy Wolf (Université de Motréal; Mila)
Nicholas Turk-Browne (Yale University)
Smita Krishnaswamy (Yale University)

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