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Related Experiment Video

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fMRI Validation of fNIRS Measurements During a Naturalistic Task
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DeepPhysioRecon: Tracing peripheral physiology in low frequency fMRI dynamics.

Roza G Bayrak1,2, Colin B Hansen2,3, Jorge A Salas1,4

  • 1Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States.

Imaging Neuroscience (Cambridge, Mass.)
|September 29, 2025
PubMed
Summary

This study introduces DeepPhysioRecon, a novel AI tool that extracts physiological data like breathing and heart rate from brain imaging (fMRI). This enhances fMRI analysis by revealing crucial brain-body interactions.

Keywords:
brain-bodyfMRIheart raterespiration

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Area of Science:

  • Neuroimaging and Computational Neuroscience
  • Physiological Monitoring and Signal Processing

Background:

  • Functional magnetic resonance imaging (fMRI) studies often omit vital physiological measurements.
  • Autonomic physiological fluctuations (respiration, heart rate) significantly influence fMRI signals and brain function.
  • Lack of physiological data limits the interpretation and depth of fMRI research.

Purpose of the Study:

  • To develop a method for decoding physiological variations from fMRI data.
  • To assess the generalizability and utility of the developed approach across different datasets and conditions.
  • To underscore the importance of integrating physiological measures into fMRI analyses.

Main Methods:

  • Developed DeepPhysioRecon, a Long-Short-Term-Memory (LSTM)-based neural network.
  • The network decodes continuous respiration amplitude and heart rate from whole-brain fMRI dynamics.
  • Conducted systematic evaluations to test generalizability across datasets and experimental settings.

Main Results:

  • Successfully decoded continuous physiological variations (respiration, heart rate) from fMRI data.
  • Demonstrated the generalizability of the DeepPhysioRecon approach.
  • Quantified the impact of including physiological measures in fMRI analyses.

Conclusions:

  • DeepPhysioRecon provides a powerful tool for studying brain-body interactions.
  • Integrating physiological decoding can enhance fMRI's efficacy as a biomarker.
  • The open-source software promotes wider application in neuroscience research.