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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Analyzing effective connectivity with fMRI |
| Organization Unit | |
| Authors |
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| Item Subtype | Further Contribution (e.g. review article, editorial) |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Wiley Interdisciplinary Reviews: Cognitive Science |
| Publisher | Wiley-Blackwell |
| Geographical Reach | international |
| ISSN | 1939-5078 |
| Volume | 1 |
| Number | 3 |
| Page Range | 446 - 459 |
| Date | 2010 |
| Abstract Text | Functional neuroimaging techniques are used widely in cognitive neuroscience to investigate aspects of functional specialization and functional integration in the human brain. Functional integration can be characterized in two ways, functional connectivity and effective connectivity. While functional connectivity describes statistical dependencies between data, effective connectivity rests on a mechanistic model of the causal effects that generated the data. This review addresses the conceptual and methodological basis of established techniques for characterizing effective connectivity using functional magnetic resonance imaging (fMRI) data. In particular, we focus on dynamic causal modeling (DCM) of fMRI data and emphasize the importance of model selection procedures and nonlinear mechanisms for context-dependent changes in connection strengths. |
| Digital Object Identifier | 10.1002/wcs.58 |
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| Keywords | General Psychology, General Neuroscience, General Medicine |