  Task-based functional magnetic resonance imaging (task fMRI) is a
non-invasive technique that allows identifying brain regions whose activity
changes when individuals are asked to perform a given task. This contributes to
the understanding of how the human brain is organized in functionally distinct
subdivisions. Task fMRI experiments from high-resolution scans provide hundred
of thousands of longitudinal signals for each individual, corresponding to
measurements of brain activity over each voxel of the brain along the duration
of the experiment. In this context, we propose some visualization techniques
for high dimensional functional data relying on depth-based notions that allow
for computationally efficient 2-dim representations of tfMRI data and that shed
light on sample composition, outlier presence and individual variability. We
believe that this step is crucial previously to any inferential approach
willing to identify neuroscientific patterns across individuals, tasks and
brain regions. We illustrate the proposed technique through a simulation study
and demonstrate its application on a motor and language task fMRI experiment.
