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Conference Paper

Analyzing Spatial Distributions of FMRI "Bold" Signals by RQA Variables

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Bianciardi, M., Sirabella, P., Hagberg, G., Giuliani, A., Zbilut, J., & Colosimo, A. (2004). Analyzing Spatial Distributions of FMRI "Bold" Signals by RQA Variables. In R. Benigni, A. Colosimo, A. Giuliani, P. Sirabella, & J. Zbilut (Eds.), International Meeting Complexity in the Living: A Problem-Oriented Approach (pp. 238-243). Roma, Italy: Istituto Superiore di Sanità.

Cite as: http://hdl.handle.net/21.11116/0000-0005-5126-5
Recurrence Quantification Analysis (RQA) is a model-free method sensitive to both linear and non-linear time-dependent processes. The assumption-free RQ estimation of brain activation patterns thus offers an extension and improvement of conventional General Linear Modeling (GLM) approaches. In the present work we check the conjecture that parameters obtained by RQA can be used as indicators of significant MR signal changes during activation in the human brain. We produced recurrence plots of experimental fMRI data acquired on a subject performing a motor task and, by means of RQA variables, we analyzed signals generated from different areas or volume elements of the brain. If brain activity can be reliably identified and imaged by such an approach, a spatial picture of the time dependent changes in the system may be developed and active/non active areas discriminated without too strict a priori assumptions.