Independent Component Analysis (ICA) is a data-driven method to decompose functional neuroimaging data into independent signals that represent a mixture of real functional brain networks and noise. This work highlights the beauty of those components, demonstrating thresholded spatial maps of the decompositions from an individual with schizophrenia (magenta gradient), and a healthy control (aqua gradient). The color gradients coincide with the percentage of variance in the data that each component accounts for, with purples and blues accounting for more variance, and pinks and greens less. The viewer might try to find matching networks between groups, or distinguish between functional networks and noise.