Connectomics modeling of regional networks of white-matter fractional anisotropy to predict the severity of young adult drinking
The connectomics of white matter FA can predict the severity of alcohol consumption, and by incorporating brain network pathways, identify sex differences. This approach provides new clues to the biological basis of alcohol abuse and evaluates how these regions interact in broader brain networks for understanding alcohol misuse and its comorbidities.
Authors
- Guangfei Li (ORCID: https://orcid.org/0000-0002-5527-8996)
- Dongmei Hao (ORCID: https://orcid.org/0000-0002-5715-5930)
- Chiang-Shan Ray Li (ORCID: https://orcid.org/0000-0002-9393-1212)
- Yan Yan (ORCID: https://orcid.org/0000-0003-2247-8024)
- Yashuang Li
- Mingdong Gao
- Lin Yang (ORCID: https://orcid.org/0000-0001-7369-796X)
- Ning Zhang (ORCID: https://orcid.org/0000-0002-1559-111X)
- Yiyao Ye-Lin
Institutions
- Hebei Medical University (CN)
- Capital Medical University (CN)
- Yale University (US)
- Beijing University of Technology (CN)
- China International Science and Technology Cooperation (CN)
- First Affiliated Hospital of Hebei Medical University (CN)
- Universitat Politècnica de València (ES)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-03-01
- DOI
- https://doi.org/10.21037/qims-24-2131
- Citations
- 1
- Primary Topic
- Advanced Neuroimaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36