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

Abdoljalil Addeh, Fernando Vega, Rebecca J Williams, Ali Golestani, G. Bruce Pike +1 more
7/3/2023
eess.SPcs.AIcs.LG

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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.

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Cite this paper

@article{addeh2023using,
  title  = {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},
  author = {Abdoljalil Addeh and Fernando Vega and Rebecca J Williams and Ali Golestani and G. Bruce Pike and M. Ethan MacDonald},
  year   = {2023},
  eprint = {2307.05426},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.05426v1}
}

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