用图模型分析吸烟者脑连接变化，发现多个关键区域受损。
Graphical Structural Learning of rs-fMRI data in Heavy Smokers
- 基于rs-fMRI数据构建高斯无向图，用图lasso算法挖掘脑连接模式。
- 识别出多个受吸烟显著影响的脑区，结果具有高稳定性。
- 为神经精神疾病临床研究提供潜在生物标志物参考。
近期研究揭示了重度吸烟者存在结构性和功能性脑改变，但其拓扑脑连接的具体变化尚不明确。本研究利用高斯无向图与图lasso算法，对吸烟者与非吸烟者的rs-fMRI数据进行建模，以识别显著的脑连接差异。结果表明所估计的脑网络图具有高度稳定性，并识别出若干受吸烟显著影响的脑区，为未来临床研究提供了重要洞见。
原文摘要 · Abstract (English)
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.
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