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README.md
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Physiological Signal Reconstruction directly from Functional Magnetic Resonance Imaging Data
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Description
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Functional magnetic resonance imaging (fMRI) is a powerful technique for studying human brain activity and large-scale neural circuits. However, fMRI signals can be strongly modulated by slow changes in respiration volume (RV) and heart rate (HR). Monitoring cardiac and respiratory signals during fMRI enables modeling and/or reducing such effects; yet, physiological measurements are often unavailable in practice, and are missing from a large number of fMRI datasets. Here, we propose learning approaches for inferring RV and HR signals directly from fMRI time-series dynamics.
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If you use these models, please cite:
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```
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- fmri
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**Physiological Signal Reconstruction directly from Functional Magnetic Resonance Imaging Data**
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Functional magnetic resonance imaging (fMRI) is a powerful technique for studying human brain activity and large-scale neural circuits. However, fMRI signals can be strongly modulated by slow changes in respiration volume (RV) and heart rate (HR). Monitoring cardiac and respiratory signals during fMRI enables modeling and/or reducing such effects; yet, physiological measurements are often unavailable in practice, and are missing from a large number of fMRI datasets. Here, we propose learning approaches for inferring RV and HR signals directly from fMRI time-series dynamics.
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---
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If you use these models, please cite:
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```
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