Graphical Structural Learning of rs-fMRI data in Heavy Smokers
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917776351821824 |
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| author | Gong, Yiru Zhang, Qimin Zheng, Huili Liu, Zheyan Chen, Shaohan |
| author_facet | Gong, Yiru Zhang, Qimin Zheng, Huili Liu, Zheyan Chen, Shaohan |
| contents | Recent studies revealed structural and functional brain changes in heavy smokers. However, the specific changes in topological brain connections are not well understood. We used Gaussian Undirected Graphs with the graphical lasso algorithm on rs-fMRI data from smokers and non-smokers to identify significant changes in brain connections. Our results indicate high stability in the estimated graphs and identify several brain regions significantly affected by smoking, providing valuable insights for future clinical research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08395 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graphical Structural Learning of rs-fMRI data in Heavy Smokers Gong, Yiru Zhang, Qimin Zheng, Huili Liu, Zheyan Chen, Shaohan Quantitative Methods Machine Learning Applications Recent studies revealed structural and functional brain changes in heavy smokers. However, the specific changes in topological brain connections are not well understood. We used Gaussian Undirected Graphs with the graphical lasso algorithm on rs-fMRI data from smokers and non-smokers to identify significant changes in brain connections. Our results indicate high stability in the estimated graphs and identify several brain regions significantly affected by smoking, providing valuable insights for future clinical research. |
| title | Graphical Structural Learning of rs-fMRI data in Heavy Smokers |
| topic | Quantitative Methods Machine Learning Applications |
| url | https://arxiv.org/abs/2409.08395 |