Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters

TL;DR

This paper presents a machine learning approach using CNNs to estimate respiratory waveforms from fMRI data and head motion parameters, eliminating the need for peripheral respiratory recording devices.

Contribution

It introduces a novel CNN-based method that combines head motion and BOLD signals to accurately estimate respiratory fluctuations from fMRI data.

Findings

Combining head motion with BOLD signals improves respiratory waveform estimation.

The method reduces the need for external respiratory measurement devices.

Potential to lower costs and participant burden in fMRI studies.

Abstract

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.