Model-Free Analysis of Single fMRI Time Series Based on Spatial Synchronization.

We propose a new model-free approach to analyze fMRI time series data. Instead of testing a parametric model for statistical significance and thereby obtaining an activation map of the brain, we use synchronization of fMRI voxels as the basis of our analysis. Synchronization between large voxel cross sections are expected when the brain responds to a stimulus. Therefore, the dependency of analysis to the baseline assumptions can be avoided by using a definition of synchronization in which the phenomenon is insensitive to the choice of thresholds over a wide range. We also provide an example on how a functional picture of the brain’s response to a visual-motor task can be obtained by examining differential fractional synchronizations in the primary motor cortex, the supplementary motor cortex, the primary visual cortex, and the occipital cortex.