Summary of Reconstructing Physiological Signals From Fmri Across the Adult Lifespan, by Shiyu Wang et al.
Reconstructing physiological signals from fMRI across the adult lifespan
by Shiyu Wang, Ziyuan Xu, Laurent M. Lochard, Yamin Li, Jiawen Fan, Jingyuan E. Chen, Yuankai Huo, Mara Mather, Roza G. Bayrak, Catie Chang
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel machine learning framework is proposed to reconstruct respiratory and cardiac activity from functional magnetic resonance imaging (fMRI) signals, leveraging Transformer-based architectures. This framework outperforms previous approaches in predicting low-frequency respiratory volume (RV) and heart rate (HR) fluctuations, with median correlations of r ~ .698 for RV and r ~ .618 for HR. The study tests the model on a dataset of individuals aged 36-89 years old, demonstrating its potential to infer key physiological variables directly from fMRI data across a wide range of the adult lifespan. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand how our brains work is being developed by using special machines that take pictures of brain activity. This technology can help us understand what’s happening in people’s brains and even find signs of certain diseases. But sometimes it’s hard to get accurate readings because of extra equipment or noise in the signals. To fix this, scientists are working on computer models that can directly read brain signals and extract important information about heart rate and breathing. So far, these models have only been tested on young adults and children, but now researchers are trying them out on older adults to see if they work just as well. The results show that the models can accurately predict what’s happening with people’s heart rates and breathing patterns, even in older adults. |
Keywords
» Artificial intelligence » Machine learning » Transformer
