Analysis of fMRI Time-Series Revisited

• DOI: 10.1006/nimg.1995.1007
• Publication Date: 2002-10-07T10:29:07Z
• Authors: K.J. Friston; A.P. Holmes; J-B. Poline; P.J. Grasby; S.C.R. Williams; R.S.J. Frackowiak; R. Turner
• Keywords: Brain Mapping; Hemodynamics; Brain; Magnetic Resonance Imaging; Sensitivity and Specificity; 510; 03 medical and health sciences; 0302 clinical medicine; Reference Values; Regional Blood Flow; Image Processing, Computer-Assisted; Linear Models; Humans; Nerve Net; Arousal; Artifacts

This paper presents a general approach to the analysis of functional MRI time-series from one or more subjects. The approach is predicated on an extension of the general linear model that allows for correlations between error terms due to physiological noise or correlations that ensue after temporal smoothing. This extension uses the effective degrees of freedom associated with the error term. The effective degrees of freedom are a simple function of the number of scans and the temporal auto correlation function. A specific form for the latter can be assumed if the data are smoothed, in time, to accentuate hemodynamic responses with a neural basis. This assumption leads to an expedient implementation of a flexible statistical framework. The importance of this small extension is that, in contradistinction to our previous approach, any parametric statistical analysis can be implemented. We demonstrate this point using a multiple regression analysis that tests for effects of interest (activations due to word generation), while taking explicit account of some obvious confounds.