Resting-state networks in 6-to-10 year old children

Abstract

Resting-state functional magnetic resonance imaging provides a non-invasive approach to the study of intrinsic functional brain networks. When applied to the study of brain development, most studies consist of relatively small samples that are not always representative of the general population. Descriptions of these networks in the general population offer important insight for clinical studies examining, for instance, psychopathology or neurological conditions. Thus our goal was to characterize resting-state networks in a large sample of children using independent component analysis (ICA). The study further aimed to describe the robustness of these networks by examining which networks occur frequently after repeated ICA. Resting-state networks were obtained from a sample of 536 6-to-10 year old children. Distributions of networks were built from repeated subsampling and group ICA analyses, and meta-ICA was used to construct a representative set of components. Within- and between-network properties were tested for age-related developmental associations using spatio-temporal regression. After repeated ICA, many networks were present over 95% of the time suggesting the components are highly reproducible. Some networks were less robust, and were observed less than 70% of the time. Age-related associations were also observed in a selection of networks, including the default-mode network, offering further evidence of development in these networks at an early age. ICA-derived resting-state networks appear to be robust, although some networks should further scrutinized if subjected to group-level statistical analyses, such as spatiotemporal regression. The final set of ICA-derived networks and an age-appropriate T₁ -weighted template are made available to the neuroimaging community, https://www.nitrc.org/projects/genr. Hum Brain Mapp 37:4286-4300, 2016. © 2016 Wiley Periodicals, Inc.

Keywords: age-related; brain development; dual regression; functional MRI; independent component analysis.

Figure 1

Axial slices of components 1‐to‐10 resulting from meta ICA of 500 repeated ICA samples. Components are thresholded at z = 3.09 (P < 0.001). Component Labels: 1 = Default Mode Network I, 2 = Sensory, 3 = Right Frontoparietal, 4 = Sensorimotor, 5 = Inferior Frontal, 6 = Lower Brainstem, 7 = Brainstem, 8 = Middle Frontal, 9 = Cerebellar, 10 = Anterior Visual. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 2

Axial slices of components 11‐to‐20 resulting from meta ICA of 500 repeated ICA samples. Components are thresholded at z = 3.09 (P < 0.001). Component Labels: 11 = Precuneus, 12 = Lateral Frontal, 13 = Parietal, 14 = Visual, 15 = Ventricular, 16 = Executive Control, 17 = Left Frontoparietal, 18 = Default Mode Network II, 19 = Cerebellar‐Occipital, 20 = Insular. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 3

Axial slices of components 21–25 resulting from meta ICA of 500 repeated ICA samples. Components are thresholded at z = 3.09 (P < 0.001). Component Labels: 21 = Motor, 22 = Upper Brainstem, 23 = Frontal‐temporal‐Parietal, 24 = Lateral Visual, 25 = Lateral Middle Frontal. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 4

Frequencies (presence) of components observed after repeated ICA subsampling. Components observed in only the meta‐ICA (black), and those found in repeated subsamples but not represented in the meta‐ICA (gray) are shown.

Figure 5

Within‐network associations with age using spatiotemporal regression. Clusters are t‐values indicating significant association at P _corrected < 0.05, with red indicating a positive association with age and blue indicating a negative association with age. (a) meta‐ICA component 3/right frontoparietal, (b) meta‐ICA component 1/default mode network, and (c) meta‐ICA component 16/executive control network. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 6

Between‐network associations with age. The x‐axis reflects age in years and the y‐axis is the pairwise correlations between time courses of two components, transformed to the Z distribution. A = Components 11 and 12, B = Components 3 and 13, and C = Components 2 and 3.