Are global methods appropriate for fMRI data analysis? an in vivo fMRI study of the spatio-temporal heterogeneity of fMRI data

We propose a two-stage, data-driven exploratory approach to analyze the heterogeneity of hypothesis (i.e. no task) fMRI data. In particular, we used a screening stage followed by (local) temporal clustering. Our analysis confirms that fMRI data are extremely heterogeneous. The physiological noise varies throughout the neuroscientifically interesting regions; this poses an extreme challenge for models used for the analysis. We conclude that local, data-driven approaches such as clustering should be carried out as a mandatory first step in fMRI data analysis, since they provide important insights for the development of more flexible, locally adaptive model-based methods based on e.g,, multiscale wavelet representations or local regularization techniques. The combination of data-driven and model-driven approaches should be the strategy adopted for the analysis of fMRI data.