Information-based functional brain mapping

Abstract

The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.

Fig. 1.

Simulated fMRI data. (A–C) The performance of activation- (black lines) and information-based mapping (colored lines) at detecting focally distributed effects (for color coding, see D). A and B each show an arrangement of four plots, where the top row displays average results obtained for large effect regions, and the bottom row displays average results obtained for small effect regions. The left and right columns display average results obtained for the two lower and the two higher functional contrast-to-noise ratios, respectively. A shows ROCs for the case of unsmoothed data. B shows the effect of spatial smoothing. The vertical axis here represents the area under the ROC. For the included case of no smoothing (i.e., full width at half maximum = 0), the areas under the ROC (marked as circles) correspond to the ROCs shown in A. Note that smoothing degrades performance for all techniques. For the crucial case of unsmoothed data, C summarizes the essential results by visually relating the detection performances afforded by the different techniques for small and large regions and low and high functional contrast-to-noise ratio. The searchlights yielding optimal performance in each case are shown in gray (4- or 5-mm radius). The circles in C replicate the circles in B reflecting the areas under the ROCs shown in A. In A and B, the line thickness measured vertically is 2 SEMs obtained by repeating the simulations and analyses 40 times with fresh noise. The shapes of the regions shown in green in A–C are exactly those used in the simulations. D illustrates the color coding in A–C and shows the searchlights used.

Fig. 2.

Real fMRI data. (A–C) Activation- and information-based mapping for a single subject. A and B show univariate t maps contrasting activity during perception of face and house images. The color scale linearly reflects the t value for the contrast between faces and houses (see color bar) for voxels above the FDR threshold. A shows the t map for unsmoothed data and B for data smoothed with a 4-mm-radius spherical kernel. Note that smoothing increases the number of voxels marked. C shows the information-based map of P values. The effect statistic is the Mahalanobis distance between face and house response patterns computed at each voxel for the contents of a 4-mm-radius spherical searchlight. The color scale linearly reflects the P value (see color bar). D shows which voxels are marked only by activation-based mapping (green), which only by information-based mapping (red), and which by both (yellow) for this subject. The sizes of these sets of voxels are related to each other in E Left. The analysis has been performed with similar results for all 11 subjects (E Center and Right). The slices shown in A–D are slices 4, 6, 8, 10, and 12 (in anatomically ascending order) of 15 axial slices acquired. The right side of each slice represents the right hemisphere.