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[Vol.34 No.3 contents]
Japanese/English
Full Text of this Articlein Japanese PDF (548K) |
| ArticleTitle | Functional integration approach in functional neuroimaging research: A brief review of Dynamic Causal Modeling |
| Language | J |
| AuthorList | Hiroki C. Tanabe |
| Affiliation | Graduate School of Informatics, Nagoya University |
| Publication | Japanese Journal of Neuropsychology: 34 (3), 192-199, 2018 |
| Received | |
| Accepted | |
| Abstract | In the study of functional neuroimaging, functional integration analysis is able to address and characterize what and how local brain areas interact with each other. There are two types of the analysis; functional connectivity and effective connectivity. Functional connectivity examines functional networks in terms of temporal correlation between spatially remote brain regions, whereas effective connectivity is defined as the causal influences that neural units exert over another. Dynamic causal modeling (DCM) is a most famous analytical scheme to examine effective connectivity. A key characteristic of DCM is that it allows for generating plausible models of neural population dynamics, and uses a biophysical forward model that describes the transformation from neural activity to hemodynamic response. A variety of Bayesian model selection and average procedure is an additional benefit of this scheme. In this article, I review the conceptual and mathematical basis of DCM, then introduce network model construction and selection process in the DCM. Finally, I touch tips and limitation in the practical use of DCM. |
| Keywords | functional magnetic resonance imaging (fMRI), dynamic causal modeling (DCM), dynamic systems, effective connectivity, functional integration analysis |
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