Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity

Abstract

Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. Altering ICA dimensionality (model order) estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD) patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference) then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.

Keywords: ICA; dual regression; fMRI; functional connectivity; model order; modularity; resting-state; seasonal affective disorder.

Figure 1

(A) RSNs total volume significantly increases as function of ICA model order reaching model order 60. Notably, higher model orders 60–150 showed relatively stable total volume. In gray, the total number of brain voxels used in the ICA analysis. (B) Total volume of between-group differences (see Figures 3, 4, green color) shows a non-linear increase in volume as a function of model order with volume maxima at model order of 70.

Figure 2

Functional segmentation of resting-state networks (RSNs) at different functional hierarchical levels superimposed on an MNI template. RSNs are thresholded at z-score > 5. Model order 20 yielded 11 large-scale networks (top). 47 and 70 fine-clustered RSNs obtained from model order 70 (middle) and 100 (bottom), respectively. The same color templates (Fslview color templates) were used to mark the fine-clustered and large-scale RSNs. Numbers at the bottom of the images refer to MNI coordinates (xyz).

Figure 3

SAD significant increased functional connectivity (green) in the motor cortex at different ICA model orders (20, 40, 60, 70, 80, 100, 120, and 150) shown superimposed on MNI template. One RSN (red–yellow) involving both the motor cortices and the auditory cortex as well at the low model order of 20. At higher model orders this large-scale RSN splits into smaller fine-grained sub-networks. Notably, some of these networks still show significant increased connectivity, while others do not. Numbers at the bottom of the images refer to MNI coordinates (xyz). The left hemisphere corresponds to the right side and t-score threshold is shown at the right.

Figure 4

SAD significant increased functional connectivity (green) in the visual cortex at different ICA model orders (20, 40, 60, 70, 80, 100, 120, and 150) shown superimposed on MNI template. While at low model order of 20 the visual cortex was segmented into one RSN (red–yellow) which also showed a significant increased connectivity, higher model orders provided a more fine-grained segmentation. A prominent increase in the number of sub-networks (red–yellow) with significant increased connectivity is evident at higher model orders. Numbers at the bottom of the images refer to MNI coordinates (xyz). The left hemisphere corresponds to the right side and t-score threshold is shown at the right.