Correlation analysis between depression and gambling behavior using graph theory(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2024年第11期
- Page:
- 1374-1382
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Correlation analysis between depression and gambling behavior using graph theory
- Author(s):
- MA Ningxin; WANG Yu; XIAO Hongbing; XING Suxia; XU Ran
- School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
- Keywords:
- Keywords: depression functional brain network functional magnetic resonance imaging graph theory analysis
- PACS:
- R318；TP181
- DOI:
- DOI:10.3969/j.issn.1005-202X.2024.11.009
- Abstract:
- Abstract: The correlations of global/local properties of brain networks with gambling behavioral scales in depression are explored. The task-state brain functional magnetic resonance imaging data of 24 patients with gambling behavior and depression and 24 healthy controls are analyzed, and preprocessed by SPM software. Graph theory analysis method is used to establish the functional brain networks in which local and global properties are calculated. Two sets of local attribute index including node degree and node efficiency are used to make edge analysis in different depression groups (major, moderate and mild depression groups, with 8 patients in each group) and healthy control group, and the changes in global properties are also discussed. Additionally, the correlations of scoring scale related to gambling behavior with 3 criteria on the global properties (small world attribute, global efficiency and local efficiency) are analyzed. The two-sample t-tests on depression groups and healthy control group confirm the significant connections among brain regions (P<0.05), and reveal the significant negative correlations between the global brain network attribute indexes and different behavioral scales, which fully verifies the correlation between gambling behavior and depression, and provides the basis for further exploring correlation between the individual behavior attribute and depression, thereby assisting clinical diagnosis and treatment of depression patients.
Last Update: 2024-11-26