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| 그래프 뇌 네트워크 분석× | 구조방정식 모형× | |
|---|---|---|
| 분야≠ | 신경영상 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2009 | 1921 |
| 창시자≠ | Ed Bullmore | Sewall Wright |
| 유형≠ | Brain network graph analysis pipeline | Method |
| 원전≠ | Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. DOI ↗ | Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗ |
| 별칭≠ | graph theory, brain network analysis, network neuroscience | SEM, path analysis, latent variable modeling, causal modeling |
| 관련 | 3 | 3 |
| 요약≠ | Graph Theoretical Brain Network Analysis applies network science to understand brain organization, treating the brain as a complex network of interconnected nodes (regions) and edges (connections). Formalized by Bullmore and Sporns in 2009, graph analysis reveals fundamental organizational principles—modularity, efficiency, resilience—that characterize healthy and diseased brains. | Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis. |
| ScholarGate데이터셋 ↗ |
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