Analyzing for pattern-information,
not regional-average activation

Pattern-information analysis can reveal effects lost in regional-average activation analysis. Toy simulation illustrating the potential benefits of analyzing for information in fine-grained activity patterns. (a) A region of interest (ROI) consisting of 113 voxels. (b) The fMRI data matrix (time by voxel) for the ROI. The voxels are lined up in arbitrary order along the space axis. (c) In a standard activation-based analysis of the ROI, all 113 time courses are first averaged across voxels, yielding an average time course (black line). This shows that there is little difference in the overall activation of the region during the two experimental conditions indicated by the rectangular reference functions (red for condition 1, blue for condition 2). (d) Modeling each voxel time course with a separate hemodynamic predictor for each trial suggests that each condition (red, blue) is associated with a replicable and distinct spatial activity pattern. (e) The average spatial patterns associated with the two conditions (red, blue) are plotted with standard-error bars. (Only 30 of the 113 voxels are shown.) For all voxels, the error bars overlap—indicating that the univariate effects are not significant in any voxel, even without correction for multiple comparisons. A mapping analysis would not mark any voxel in this toy simulation. However, a multivariate test performed on all voxels (multivariate analysis of variance here) demonstrates that the two activity patterns (red, blue) are in fact significantly different (p<0.05).

References

Functional magnetic resonance imaging of the human object-vision system
Kriegeskorte, N. (2004) PhD Thesis. Universiteit Maastricht.

Analyzing for information, not activation, to exploit high-resolution fMRI
Kriegeskorte N, Bandettini P. (2007) Neuroimage 38(4):649-62.

Revealing representational content with pattern-information fMRI – an introductory guide
Mur M, Bandettini P, Kriegeskorte N (2009) Social Cognitive and Affective Neuroscience 4(1): 101-9.