Graphical Structural Learning of rs-fMRI data in Heavy Smokers
This provides insights for clinical research on smoking-related brain changes, but it is incremental as it applies an existing method to new data.
The study tackled the problem of identifying specific changes in topological brain connections in heavy smokers using Gaussian Undirected Graphs with graphical lasso on rs-fMRI data, finding high stability in estimated graphs and several significantly affected brain regions.
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.