Groupwise whole-brain parcellation from resting-state fMRI data for network node identification - PubMed (original) (raw)

Groupwise whole-brain parcellation from resting-state fMRI data for network node identification

X Shen et al. Neuroimage. 2013.

Abstract

In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages.

Keywords: Functional MRI; Graph-theory-based parcellation; Network analysis; Resting-state connectivity; Whole-brain atlas.

Copyright © 2013 Elsevier Inc. All rights reserved.

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Figures

Figure 1

Figure 1

Multigraph (groupwise) _K_-way Clustering Algorithm

Figure 2

Figure 2

Simulated data includes six regions in a single slice. The boundaries between the six regions are set to change randomly for each virtual subject. The image on the right shows the maximal probability that a voxel belongs to one of the six regions.

Figure 3

Figure 3

Mean timecourses from 10 subjects for the above six regions were used to generate the synthetic data. These six regions were obtained from a group parcellation using the groupwise multigraph clustering approach.

Figure 4

Figure 4

Examples of parcellation results at different signal to noise level with increasing noise added as α = 0.2 (top row),α =0.3(middle row) and_α_ =0.4 (bottom row). Our proposed multigraph clustering algorithm and two other parcellation approaches were applied to obtain the results. Left: the proposed multigraph clustering parcellation; Middle: GP1; Right: GP2. By visual inspection, the multigraph clustering parcellation approach shows the best performance at all three noise levels in term of the classification accuracy and regional homogeneity.

Figure 5

Figure 5

Quantitative evaluation using the Dice’s coefficient, the Hausdorff distance and the median minimal distance. The colors indicate different approaches: our multigraph clustering parcellation (red), GP1 (blue), GP2 (green). α = 0.2, 0.3 and 0.4 from top row to bottom row respectively. The quantitative analysis confirms the visual inspection that the proposed multigraph clustering approach outperformed both the GP1 and GP2 approaches. At the lowest noise level α_=0.2, all three approaches were successful at identifying the six regions. At an increased noise level α =0.3, the multigraph approach was still good at identifying regions 1, 3 and 5 with high accuracy (mean Dice’s coefficients being above 0.9). At this noise level, GP1 was able to identify regions 1, 3 and 5 with lower accuracy and GP2 basically failed the classification task. At the highest noise level_α =0.4, only the proposed multigraph approach was able to identify region 5 with good accuracy. The other two approaches both failed.

Figure 6

Figure 6

Whole-brain parcellation using the multigraph _K_-way clustering method. The number of regions K are 50, 100, 150 for each hemisphere yielding total nodes of 94, 189 and 281 (for the three parcellations shown in this figure). The cross-subject reproducibility is indicated by the shading of each subunit in the parcellation. The same heat colormap and reproducibility scale was used for all levels of parcellation.

Figure 7

Figure 7

Whole-brain reproducibility distribution at different number of_K_’s. Each boxplot was generated based on parcellations from 20 pairs of mutually exclusive groups of subjects.

Figure 8

Figure 8

Sizes of subunits of the groupwise parcellation at different number of_K_’s. Each boxplot was generated based on parcellations from 40 groups of randomly selected subjects. The sizes were measured by the number of voxels of dimension 3 × 3 × 3_mm_3. The boxplot shows that the variation of the sizes of the subunits is relative small across the whole brain.

Figure 9

Figure 9

Curves of whole-brain inhomogeneity indices for 79 subjects from four parcellations, namely the AAL atlas (red), groupwise parcellation using the multigraph clustering algorithm with 50 regions for each hemisphere (blue_K_ = 50), groupwise parcellation using the multigraph clustering algorithm with 100 regions for each hemisphere (green_K_ = 100), groupwise parcellation using the multigraph clustering algorithm with 150 regions for each hemisphere (black_K_ = 150). The x-axis is the subject index. The subjects were ordered according to their whole-brain inhomogeneity indices based on the AAL atlas. The calculation of the inhomogeneity indices used data without the Gaussian spatial smoothing.

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