Premium
Analyzing effective connectivity with functional magnetic resonance imaging
Author(s) -
Stephan Klaas Enno,
Friston Karl J.
Publication year - 2010
Publication title -
wiley interdisciplinary reviews: cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.526
H-Index - 49
eISSN - 1939-5086
pISSN - 1939-5078
DOI - 10.1002/wcs.58
Subject(s) - functional magnetic resonance imaging , neuroimaging , functional neuroimaging , cognitive science , functional connectivity , context (archaeology) , computer science , neuroscience , cognitive neuroscience , functional integration , cognition , artificial intelligence , psychology , cognitive psychology , data science , biology , paleontology , mathematical analysis , mathematics , integral equation
Abstract 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. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Neuroscience > Cognition