Fig 3. Schematic showing the perfusion-weighted denoising procedure.

Echoes were first despiked, volume registered, and coregistered to MNI space. Each echo was then individually low-pass filtered at f ≤ 0.09 Hz. Echoes were then combined using a T2*-weighted approach. This low-pass filtered, multi-echo combined dataset was fed into the MEICA algorithm, which extracted independent components and classified them as artifact, BOLD, or indeterminate. The BOLD and artifact components were regressed from the unfiltered first-echo data resulting in a denoised first-echo dataset. Surround subtraction and high-pass filtering followed by demodulation were performed on this data leading to denoised PW datasets. A GLM was employed on this data to determine activation.