Bayrak, Roza G.; Hansen, Colin B.; Salas, Jorge Alberto; Ahmed, Nafis; Lyu, Ilwoo; Mather, Mara M.; Huo, Yuankai; Chang, Catie E. (2025). DeepPhysioRecon: Tracing peripheral physiology in low frequency fMRI dynamics. Imaging Neuroscience, 3, IMAG.a.163. https://doi.org/10.1162/IMAG.a.163
Many brain studies that use functional magnetic resonance imaging (fMRI) do not include measurements of basic body functions like breathing or heart rate, even though these physiological signals can strongly affect brain activity patterns. Natural changes in breathing and heart rate reflect important processes related to thinking, emotion, and overall health, and they can influence how fMRI signals are interpreted.
To address this gap, researchers developed DeepPhysioRecon, a deep learning model based on a Long Short-Term Memory (LSTM) network. This model can estimate continuous changes in breathing amplitude and heart rate directly from fMRI scans of the whole brain—without the need for separate sensors. The team tested how well the model works across different datasets and experimental conditions and showed that including these reconstructed physiological signals improves how fMRI data are analyzed and interpreted.
This work emphasizes the importance of understanding the connections between the brain and the body. It also introduces a practical, open-source tool that can make fMRI a more effective biomarker for studying human health, cognition, and emotion.
Fig. 1.
DeepPhysioRecon Pipeline. The pipeline for estimating respiration volume (RV) and heart rate (HR) signals from fMRI time-series dynamics is shown. Regions of interest are defined using four published atlases that had been constructed from different imaging modalities, comprising areas in cerebral cortex, white matter, subcortex, and the ascending arousal network. ROI time-series signals are extracted from the fMRI volumes, detrended, bandpass filtered and downsampled. The preprocessed signals are provided to a candidate network as input channels. A bidirectional LSTM network architecture is adapted for joint estimation. The output of linear layers are RV and HR signals.