Emergence of resting state networks in the preterm human brain

Abstract

The functions of the resting state networks (RSNs) revealed by functional MRI remain unclear, but it has seemed possible that networks emerge in parallel with the development of related cognitive functions. We tested the alternative hypothesis: that the full repertoire of resting state dynamics emerges during the period of rapid neural growth before the normal time of birth at term (around 40 wk of gestation). We used a series of independent analytical techniques to map in detail the development of different networks in 70 infants born between 29 and 43 wk of postmenstrual age (PMA). We characterized and charted the development of RSNs from recognizable but often fragmentary elements at 30 wk of PMA to full facsimiles of adult patterns at term. Visual, auditory, somatosensory, motor, default mode, frontoparietal, and executive control networks developed at different rates; however, by term, complete networks were present, several of which were integrated with thalamic activity. These results place the emergence of RSNs largely during the period of rapid neural growth in the third trimester of gestation, suggesting that they are formed before the acquisition of cognitive competencies in later childhood.

Fig. 1.

Probabilistic ICA, group analysis. Spontaneous activity patterns in the three age groups of infants born prematurely and in the group of control babies born at term. Example sagittal, coronal, and axial slices for meaningful spatial patterns (corresponding to independent components from probabilistic ICA) are overlaid onto the age-specific templates. Images were thresholded controlling the false discovery rate at P < 0.05 and are shown as z-statistics in radiological convention. The following networks have been detected: medial visual (A), lateral visual (B), auditory (C), somatosensory (D), motor (E), cerebellum (F), brainstem and thalami (G), default mode (H), dorsal visual stream (left and right components) (I and J), and executive control (K).

Fig. 2.

Whole-brain regression analyses. Seed regions are shown in the first column on the left. Example sagittal, coronal, and axial slices for meaningful spatial patterns are overlaid onto the age-specific templates. Images were thresholded using a false discovery rate correction at P < 0.05 and are shown as z-statistics in radiological convention. The following networks have been detected: visual (A), auditory (B), somatosensory (C), motor (D), default mode (E), dorsal visual stream (right component) (F), dorsal visual stream (left component) (G), and executive control (H).

Fig. 3.

Quantification of developmental changes of connectivity in the different networks. For the auditory (A), motor (B), somatosensory (C), and visual (D) networks, the connectivity is expressed as the z-transformed partial correlation coefficient between the right and left cortices. For the complex networks [DMN (E), executive control (F), and left and right components of the dorsal visual stream (G and H)], the connectivity is expressed as the average of the partial correlation coefficients computed between the different pair of nodes within each network and z-transformed. The line plots show the robust regression line with prediction error bounds.

Fig. 4.

Frames at given weeks illustrate the motor network through development using the regression of connectivity strength projected onto the 4D spatiotemporal template for visualization. Images were thresholded using a consistent thresholding across different ages, controlling the false discovery rate, and using an empirical null distribution at each time point at P < 0.01. Images are shown as z-statistics in radiological convention.