Detection of spatial activation patterns as unsupervised segmentation of fMRI data

Abstract

In functional connectivity analysis, networks of interest are defined based on correlation with the mean time course of a user-selected 'seed' region. In this work we propose to simultaneously estimate the optimal representative time courses that summarize the fMRI data well and the partition of the volume into a set of disjoint regions that are best explained by these representative time courses. Our approach offers two advantages. First, is removes the sensitivity of the analysis to the details of the seed selection. Second, it substantially simplifies group analysis by eliminating the need for a subject-specific threshold at which correlation values are deemed significant. This unsupervised technique generalizes connectivity analysis to situations where candidate seeds are difficult to identify reliably or are unknown. Our experimental results indicate that the functional segmentation provides a robust, anatomically meaningful and consistent model for functional connectivity in fMRI.

Fig. 1

Functional segmentation examples. (a,b) Subject-specific segmentation results for two and three systems respectively (flattened view). Green: intrinsic system, blue: stimulus-driven cortex, red: visual cortex. Solid lines show the boundaries of the intrinsic system determined through seed selection. (c) Group average of the subject-specific 2-system maps. Color shading shows the proportion of subjects whose clustering agreed with the majority label. (d) Group average of the subject-specific segmentation of the intrinsic system into two sub-systems. Only voxels consistently labeled across subjects are shown. (e) Subject-specific segmentation into a large number of systems. Browsing of all preceding levels (not shown here) revealed the hierarchy displayed on the right. Colors show matching systems in the image (left) and labels in the hierarchy (right).

Fig. 2

Comparison with the regression-based detection. (a) Color shows the statistical parametric map; solid lines indicate the boundaries of the visual system obtained through clustering. (b) 3-system segmentation of the visual cortex for subjects 1,2,5,7. Only the posterior half of the flattened view is shown for each subject. The black lines indicate the boundaries of V1-V4 regions.

Fig. 3

Performance statistics. (a) Proportion of runs that resulted in the segmentation that was close to the best (max likelihood) segmentation. (b) Null hypothesis distribution for the number of voxels that showed perfect agreement across all subjects.