A 4D approach to the analysis of functional brain images: Application to FMRI data

This paper presents a new approach to functional magnetic resonance imaging (FMRI) data analysis. The main difference lies in the view of what compris es an observation. Here we treat the data from one scanning session (compri sing t volumes, say) as one observation. This is contrary to the convention al way of looking at the data where each session is treated as t different observations. Thus instead of viewing the v voxels comprising the 3D volume of the brain as the variables, we suggest the usage of the vt hypervoxels comprising the 4D volume of the brain-over-session as the variables. A line ar model is fitted to the 4D volumes originating from different sessions. P arameter estimation and hypothesis testing in this model can be performed w ith standard techniques. The hypothesis testing generates 4D statistical im ages (SIs) to which any relevant test statistic can be applied. In this pap er we describe two test statistics, one voxel based and one cluster based, that can be used to test a range of hypotheses. There are several benefits in treating the data from each session as one observation, two of which are : (i) the temporal characteristics of the signal can be investigated withou t an explicit model for the blood oxygenation level dependent (BOLD) contra st response function, and (ii) the observations (sessions) can be assumed t o be independent and hence inference on the 4D SI can be made by nonparamet ric or Monte Carlo methods. The suggested 4D approach is applied to FMRI da ta and is shown to accurately detect the expected signal. (C) 2001 Wiley-Li ss, Inc.