The Useful Side of Motion: Using Head Motion Parameters to Correct for Respiratory Confounds in BOLD fMRI

TL;DR

This paper explores using head motion parameters derived from BOLD fMRI data, processed with bandpass filtering, to improve the estimation of respiratory variation through neural networks, addressing challenges in respiratory data acquisition.

Contribution

It introduces a novel method combining raw and filtered head motion parameters with 1D-CNNs to enhance respiratory variation estimation from resting-state fMRI data.

Findings

Bandpass-filtered head motion parameters improve RV estimation accuracy.

Integrating head motion data with neural networks outperforms traditional filtering methods.

The approach offers a robust alternative for respiratory monitoring in fMRI studies.

Abstract

Acquiring accurate external respiratory data during functional Magnetic Resonance Imaging (fMRI) is challenging, prompting the exploration of machine learning methods to estimate respiratory variation (RV) from fMRI data. Respiration induces head motion, including real and pseudo motion, which likely provides useful information about respiratory events. Recommended notch filters mitigate respiratory-induced motion artifacts, suggesting that a bandpass filter at the respiratory frequency band isolates respiratory-induced head motion. This study seeks to enhance the accuracy of RV estimation from resting-state BOLD-fMRI data by integrating estimated head motion parameters. Specifically, we aim to determine the impact of incorporating raw versus bandpass-filtered head motion parameters on RV reconstruction accuracy using one-dimensional convolutional neural networks (1D-CNNs). This approach addresses the limitations of traditional filtering techniques and leverages the potential of head motion data to provide a more robust estimation of respiratory-induced variations.