A whole brain fMRI atlas generated via spatially constrained spectral clustering

Abstract

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/.

Figure 1

Comparison of r _t and r _s similarity metrics calculated from the resting state data of a single subject. Each point corresponds to a pair of voxels. Voxel pairs for which one metric is positive and the other is negative are shown in red. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Figure 2

Illustration of the method for creating connectivity matrices from a clustering solution. On the left is a two‐dimensional patch of 9 voxels that are clustered into two regions: white and gray. This is translated into a 9 × 9 connectivity matrix where a voxel from a cluster is connected to every other voxel from the same cluster with connection strength of 1, and unconnected to voxels that are not in the same cluster. The connectivity matrix entry for voxel 1 is highlighted on the right. It is connected to voxels 2, 3, 4, and 7, which are also in the white cluster.

Figure 3

Illustration of clustering results for the four combinations of functional parcellation methods and random parcellation containing 50, 200, or 1000 ROIs. Each parcellation is depicted as three orthogonal views. Color‐coding is arbitrarily used to best emphasize ROI boundaries. ROI coloring was matched across parcellation results to emphasize similarities in ROI locations between methods.

Figure 4

Comparison of parameter selection performance for each combination of parcellation strategy (r _t group mean, r _t two‐level, r _s group mean, and r _s two‐level) for K between 50 and 1,000, in multiples of 50. Panel (a) compares the LOOCV accuracy of r _t group mean to r _s group mean, (b) compares r _t two‐level to r _s two‐level, (c) compares r _s group mean to r _s two‐level, and (d) compares r _t group mean to r _t two‐level. The similarity between random parcellation and individual level functional parcellation results using r _t and r _s are included in panels (c) and (d) for comparison. Panel (e) compares the methods in terms of silhouette width calculated with the r _t metric and (f) in terms of silhouette width calculated with the r _s metric. Symbols are located at the median and error bars indicate 0.25 and 0.75 quartiles. Results from different parcellation strategies are overlapping and obscure one another. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Figure 5

Comparison of ROI homogeneity for r _t 2‐level functional parcellation, anatomical atlas (A: AAL, E: Eickhoff‐Zilles, H: Harvard‐Oxford, T: Talairach and Tournoux), and random parcellation. The vertical axis corresponds to the average correlation between every pair of voxels (temporal in panel a, spatial in panel b) within an ROI, averaged across ROIs. Symbol is located at the subject median and error bars indicate inter‐quartile range. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Figure 6

Comparison of accuracy of representation for r _t 2‐level functional parcellation, anatomical atlas (A: AAL, E: Eickhoff‐Zilles, H: Harvard‐Oxford, T: Talairach and Tournoux), and random parcellation. Symbol is located at the mean and error bars indicate standard deviation. Voxel‐wise FC maps for vPCC (ventral posterior cingulate cortex), M1 (left primary motor cortex), and V1 (primary visual cortex) were compared to ROI‐wise FC maps calculated from the same seeds using Pearson's correlation. ROI summary time courses were derived either by averaging (a–c) or using the first eigenvariate of a principle components analysis (PCA) (d–f).

Figure 7

Comparison of voxel‐wise FC map of the default mode network to maps generated from a 200 ROI parcellation using r _t 2‐level, random parcellation, the Harvard‐Oxford atlas and the Talairach and Tournoux atlas. FC maps were Fischer transformed, combined across subjects using a one‐sample t‐test and converted to z‐scores. No threshold was applied to the images.