  Motivation: In many fMRI studies, respiratory signals are often missing or of
poor quality. Therefore, it could be highly beneficial to have a tool to
extract respiratory variation (RV) waveforms directly from fMRI data without
the need for peripheral recording devices.
  Goal(s): Investigate the hypothesis that head motion parameters contain
valuable information regarding respiratory patter, which can help machine
learning algorithms estimate the RV waveform.
  Approach: This study proposes a CNN model for reconstruction of RV waveforms
using head motion parameters and BOLD signals.
  Results: This study showed that combining head motion parameters with BOLD
signals enhances RV waveform estimation.
  Impact: It is expected that application of the proposed method will lower the
cost of fMRI studies, reduce complexity, and decrease the burden on
participants as they will not be required to wear a respiratory bellows.
