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Graphical Structural Learning of rs-fMRI data in Heavy Smokers

arXiv 2409.08395 ↗First posted 12 Sep 2024q-bio.QM cs.LG stat.APcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 12 Sep 2024.

Builds on

8 of 22 references.

References · 8

  1. 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inferencecited by 26
  2. 2024Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) datacited by 26
  3. 2024CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 datasetcited by 24
  4. 2024Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learningcited by 17
  5. 2024Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learningcited by 17
  6. 2023Twitter Sentiment Analysis of Covid Vaccinesscited by 13
  7. 2024Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Modelscited by 9
  8. 2024Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Seriescited by 6

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Citing papers per year: 2024: 10; 2025: 1.

11 papers.