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International Journal of Biomedical Imaging
Volume 2006, Article ID 27483, 7 pages

Intervention Models in Functional Connectivity Identification Applied to fMRI

1Departamento de Estatística, Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Sp 05508-090, Brazil
2Laboratório de Neuroimagem Funcional (NIF), Lim 44, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Sp 05403-001, Brazil
3Departamento de Radiología, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Sp 05403-001, Brazil

Received 31 January 2006; Revised 26 June 2006; Accepted 26 June 2006

Copyright © 2006 João Ricardo Sato et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recent advances in neuroimaging techniques have provided precise spatial localization of brain activation applied in several neuroscience subareas. The development of functional magnetic resonance imaging (fMRI), based on the BOLD signal, is one of the most popular techniques related to the detection of neuronal activation. However, understanding the interactions between several neuronal modules is also an important task, providing a better comprehension about brain dynamics. Nevertheless, most connectivity studies in fMRI are based on a simple correlation analysis, which is only an association measure and does not provide the direction of information flow between brain areas. Other proposed methods like structural equation modeling (SEM) seem to be attractive alternatives. However, this approach assumes prior information about the causality direction and stationarity conditions, which may not be satisfied in fMRI experiments. Generally, the fMRI experiments are related to an activation task; hence, the stimulus conditions should also be included in the model. In this paper, we suggest an intervention analysis, which includes stimulus condition, allowing a nonstationary modeling. Furthermore, an illustrative application to real fMRI dataset from a simple motor task is presented.