 In our paper, we're dealing with functional magnetic resonance imaging, or fMRI, for short. This method results in a 3D view of a person's brain, but the data are actually 4D because the state of the brain varies over time. So we get a volume of those voxels, and we can take a look inside the brain by clipping away some parts. Of course, this remains a 3D volume that we can rotate. We model each time step of such a volume as a cubicle complex. The voxels are becoming the vertices of the cubes, and edges connect adjacent voxels. We now start tracking topological features such as connected components, cycles and voids as we filtrate this cubicle complex according to the fMRI activation function values. From this, we obtain a persistence diagram that is a descriptor containing information about the creation and destruction of each topological feature in the data. By calculating a summary statistic over the time axis, we can summarize the topological activity of each input sample. We use this to predict the age of participants in our dataset, for instance. Next to such summaries, we can also calculate brain state trajectories for cohorts in our dataset, such as adults or children. We observe that their topological activity is markedly different while watching the same movie, indicating that adults and children process this same stimuli quite differently. To learn more about how topological features can uncover characteristics of fMRI datasets, please visit our poster or read our paper. Thank you very much for your attention.