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Convolution models for fMRI.
Authors:
HENSON, R.N.A. & Friston, K.J.
Reference:
In Statistical Parametric Mapping: The analysis of functional brain images, K. Friston, J. Ashburner, S. Kiebel, T. Nichols, and W. Penny (Eds). Elsevier, London, 2006.
Year of publication:
2006
CBU number:
6363
Abstract:
This chapter reviews issues specific to the analysis of fMRI data. It extends the General Linear Model (GLM) introduced in Chapter 8 to convolution models, in which the Blood Oxygenation Level Dependent (BOLD) signal is modelled by neuronal causes that are expressed via a hemodynamic response function (HRF). We begin by considering linear convolution models, and introduce the concept of temporal basis functions. We then consider the related issues of temporal filtering and temporal autocorrelation. Finally, we extend the convolution model to include nonlinear terms, and conclude with some example analyses of fMRI data.
