Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the Human Connectome Development Cohort

TL;DR

This paper introduces a CNN-based method to estimate respiratory signals from resting-state BOLD-fMRI data, enabling respiratory correction without additional equipment, thus simplifying and reducing costs of fMRI studies.

Contribution

A novel 1D CNN model for reconstructing respiratory measures from BOLD signals, eliminating the need for respiratory bellows in fMRI research.

Findings

CNN accurately reconstructs RV and RVT from BOLD signals

Method reduces equipment and participant burden

Potential to lower fMRI study costs

Abstract

In many fMRI studies, respiratory signals are unavailable or do not have acceptable quality. Consequently, the direct removal of low-frequency respiratory variations from BOLD signals is not possible. This study proposes a one-dimensional CNN model for reconstruction of two respiratory measures, RV and RVT. Results show that a CNN can capture informative features from resting BOLD signals and reconstruct realistic RV and RVT timeseries. 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.