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

Understand

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.