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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.00219 |
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| _version_ | 1866910429354131456 |
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| author | Addeh, Abdoljalil Vega, Fernando Williams, Rebecca J. Pike, G. Bruce MacDonald, M. Ethan |
| author_facet | Addeh, Abdoljalil Vega, Fernando Williams, Rebecca J. Pike, G. Bruce MacDonald, M. Ethan |
| contents | Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for peripheral recording devices.
Goal(s): Investigate the hypothesis that head motion parameters contain valuable information regarding respiratory patter, which can help machine learning algorithms estimate the RV waveform.
Approach: This study proposes a CNN model for reconstruction of RV waveforms using head motion parameters and BOLD signals.
Results: This study showed that combining head motion parameters with BOLD signals enhances RV waveform estimation.
Impact: 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00219 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters Addeh, Abdoljalil Vega, Fernando Williams, Rebecca J. Pike, G. Bruce MacDonald, M. Ethan Machine Learning Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for peripheral recording devices. Goal(s): Investigate the hypothesis that head motion parameters contain valuable information regarding respiratory patter, which can help machine learning algorithms estimate the RV waveform. Approach: This study proposes a CNN model for reconstruction of RV waveforms using head motion parameters and BOLD signals. Results: This study showed that combining head motion parameters with BOLD signals enhances RV waveform estimation. Impact: 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. |
| title | Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.00219 |