Graphical Structural Learning of rs-fMRI data in Heavy Smokers

Yiru Gong, Qimin Zhang, Huili Zhen, Zheyan Liu, Shaohan Chen
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Abstract

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.
重度吸烟者 rs-fMRI 数据的图形结构学习
最近的研究发现,大量吸烟者的大脑结构和功能发生了变化。然而,人们对大脑拓扑连接的具体变化还不甚了解。我们在吸烟者和非吸烟者的 rs-fMRI 数据上使用高斯无向图和图形套索算法来识别大脑连接的显著变化。我们的研究结果表明,估计出的图具有很高的稳定性,并确定了几个受吸烟显著影响的脑区,为未来的临床研究提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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