Abstract
Though fairly well-studied in adults, less is known about the manifestation of resting state networks (RSN) in children. We examined the validity of RSN derived in an ethnically diverse group of typically developing 6- to 7-year-old children. We hypothesized that the RSNs in young children would be robust and would reliably show significant concordance with previously published RSN in adults. Additionally, we hypothesized that a smaller sample size using this robust technique would be comparable in quality to pediatric RSNs found in a larger cohort study. Furthermore, we posited that compared to the adult RSNs, the primary sensorimotor and the default mode networks (DMNs) in this pediatric group would demonstrate the greatest correspondence, while the executive function networks would exhibit a lesser degree of spatial overlap. Resting state functional magnetic resonance images (rs-fMRI) were acquired in 18 children between 6 and 7 years recruited from an ethnically diverse population in the Mid-South region of the United States. Twenty RSNs were derived using group independent component analysis and their spatial correspondence with previously published adult RSNs was examined. We demonstrate that the rs-fMRI in this group can be deconstructed into the fundamental RSN as all the major RSNs previously described in adults and in a large sample that included older children can be observed in our sample of young children. Further, the primary visual, auditory, and somatosensory networks, as well as the default mode, and frontoparietal networks derived in this group exhibited a greater spatial concordance with those seen in adults. The motor, temporoparietal, executive control, dorsal attention, and cerebellar networks in children had less spatial overlap with the corresponding RSNs in adults. Our findings suggest that several salient RSNs can be mapped reliably in small and diverse pediatric cohort within a narrow age range and the evolution of these RSNs can be studied reliably in such groups during early childhood and adolescence.
Highlights
Over the course of the past decade, there has been an increased effort toward understanding the functional architecture of brain, in relation to resting brain networks (Gusnard et al, 2001)
We have shown that in a small sample of racially diverse, healthy, typically developing, right handed, 6- to 7-year-old children, resting-state fMRI data can be deconstructed into the fundamental resting state networks (RSN) shown in normal adult populations through an independent component analysis (ICA)
All the major RSNs previously described in adults (Smith et al, 2009) and in a large sample that included older children (Muetzel et al, 2016) were observed in our smaller sample of young children
Summary
Over the course of the past decade, there has been an increased effort toward understanding the functional architecture of brain, in relation to resting brain networks (Gusnard et al, 2001). The most common approach for characterizing the resting brain networks is to quantify the temporal correlation of neuronal activity between anatomically distant brain regions, termed functional connectivity. The distributed functional connectivity patterns between brain areas that share similar variation in their activity over time are identified using a data driven technique, the independent component analysis (ICA) (Jafri et al, 2008; Sohn et al, 2012). Each independent component represents a functional network that consists of constituent brain regions having a closely correlated time course. Several such components or networks, each with its own temporal characteristic can be simultaneously derived without specifying brain regions. The functional relevance of these resting state networks (RSN) are interpreted, primarily, on the basis of their spatial profile and include: default mode (DMN), medial, lateral, and parietal visual, auditory, somatosensory, motor, attention, executive control, cerebellar, and frontoparietal networks
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