Uncovering the Topology of Time-Varying fMRI Data using Cubical  Persistence (spooky version)
Channel: Bastian Grossenbacher-Rieck
Date: 06/11/2020

This is the spooky variant of our spotlight presentation video for our NeurIPS 2020 paper 'Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence'.

Paper: https://arxiv.org/abs/2006.07882
Code: https://github.com/BorgwardtLab/fMRI_Cubical_Persistence

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 to 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.

The background music is "Instant Crush" by Corbyn Kites, used from the YouTube Audio Library.