Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain

Miao Cao, Yong He, Zhengjia Dai, Xuhong Liao, Tina Jeon, Minhui Ouyang, Lina Chalak, Yanchao Bi, Nancy Rollins, Qi Dong, Hao Huang, Miao Cao, Yong He, Zhengjia Dai, Xuhong Liao, Tina Jeon, Minhui Ouyang, Lina Chalak, Yanchao Bi, Nancy Rollins, Qi Dong, Hao Huang

Abstract

Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small-worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting-state functional MRI and voxel-based graph theory analysis to systematically investigate the topological organization of whole-brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high-order default-mode and executive-control regions. The hub and rich-club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth.

Keywords: connectome; functional connectivity; hub; preterm; rich club.

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Figures

Figure 1.
Figure 1.
Age-dependent changes in FCS from 31.3 to 41.7 weeks. (A) The mean FCS and the heterogeneity of FCS increased with age. The line plots show the regression line with 95% prediction error bounds. (B) Age effects on nodal FCS. (C) Developing nodal FCS from 31 to 41 weeks demonstrating age-dependent gradual increase of nodal FCS. A map of FCS averaged from 10 term infants (>38.7 weeks at birth) was also presented as a reference. Nodal FCS was calculated as the fitted values of the general linear model with gender and head motion parameters mFD as covariates. (D) Age effects on nodal FCS differentiated within different distance bins. The values were mapped onto the cortical surface using BrainNet Viewer (Xia et al. 2013). The R2 values were adjusted using gender and mFD as covariates. PMA (weeks), postmenstrual age in weeks; FCS, functional connectivity strength; Het, heterogeneity.
Figure 2.
Figure 2.
Developmental changes of seed-based functional connectivity. (A) Right thalamus; (B) left lingual gyrus; (C) left precentral gyrus; (D) PCC/PCu; (E) Right rolandic operculum; (F) left rolandic operculum. For each seed ROI, the first row showed the regions with significant age effects on seed-based functional connectivity in axial slices. The 4 rows below showed the regions with significant functional connectivity with the seed regions within 3 different age groups of infants and within a group of term controls (>38.7 weeks at birth, n = 10) at a relatively strict threshold of a corrected P< 0.01 (which corresponded to an uncorrected single voxel significance level of P< 0.01 and a minimum cluster size of 351 mm3). Representative axial slices for spatial patterns of connectivity are overlaid onto the customized template. PCC/PCu, posterior cingulate/precuneus cortex; MPFC, medial prefrontal cortex; ROI, region of interest.
Figure 3.
Figure 3.
Age-dependent changes of small-world and modular properties from 31.3 to 41.7 weeks. The (A) Cp, (B) Lp, (C) Lamda increased with age. The (D) mean PC and (E) number of connectors (nodes with PC > 0.45) decreased with age. The scatter plots show the regression line with 95% prediction error bounds. The R2 values were adjusted using gender and mFD as covariates. PMA (weeks), postmenstrual age in weeks; Num, number.
Figure 4.
Figure 4.
Developmental changes in nodal degree and degree distribution from 31.3 to 41.7 weeks. (A) Regions showing significant age-related changes in nodal degree. (B) The fitted degree map of each week of postmenstrual age. The hubs are delineated with blue lines. The map of nodal degree values averaged from 10 term infants (>38.7 weeks at birth) is also presented as a reference. (C) The heterogeneity of nodal degree increased with age. (D) The number of hubs increased with age. The hubs included regions with nodal degrees > 1.5 SD beyond the mean. (E) The cumulative distributions of degree for each subject. The blue circles indicate the original data. The solid lines indicate the best fitted distributions. The yellow to red colors correspond to ages from younger to older. (F) The distribution parameters (kc) significantly increased with age. For (C), (D), and (F), the R2 values were adjusted using gender and mFD as covariates; the dashed lines are the regression lines with 95% prediction error bounds. PMA (weeks), postmenstrual age in weeks; Het, heterogeneity; Cul, cumulative. The values were mapped onto the cortical surface using BrainNet Viewer (Xia et al. 2013).
Figure 5.
Figure 5.
Statistical maps showing regions with significant developmental changes in the (A) nodal clustering coefficient, (B) participation coefficient, and (C) nodal efficiency. The values were mapped onto the cortical surface using BrainNet Viewer (Xia et al. 2013).
Figure 6.
Figure 6.
Development of rich-club organization from 31.3 to 41.7 weeks. (A) Individual normalized rich-club curves (Φnorm) for each baby. (B) The size of rich club and (C) the normalized rich-club coefficients significantly changed with age. (D) Maps showing the membership probabilities of regions belonging to the rich-club organization at each week of postmenstrual age. Rich-club probability map of 10 term infants (>38.7 weeks at birth) is also presented. (E) An illustration figure of the rich-club organization. The age-related changes in (F) edge number and (G) edge strength of different types of connections (rich club, feeder, and local connections) are shown. The R2 values were adjusted using gender and mFD as covariates; the dashed lines are the regression lines with 95% prediction error bounds. PMA (weeks), postmenstrual age in weeks. The values were mapped onto the cortical surface using BrainNet Viewer (Xia et al. 2013).
Figure 7.
Figure 7.
The prediction results of individual brain age based on nodal FCS. (A) The prediction results of nodal FCS. The scatter plots show the correlation line of actual verses predicted age with 95% confidential interval. Pearson correlation coefficient between the actual and predicted ages was calculated to assess the prediction accuracy. Ten thousand permutation tests were performed to determine the statistical significance. (B) Absolute predicting map of support vector regression analysis to predict brain age using FCS. The values were mapped onto the cortical surface using BrainNet Viewer (Xia et al. 2013).
Figure 8.
Figure 8.
Developmental changes of nodal functional connectivity strength (FCS) with data after global signal removal and with data after head motion scrubbing. The FCS values were mapped onto the cortical surface using BrainNet Viewer (Xia et al. 2013).
Figure 9.
Figure 9.
Age-dependent changes of Cp, Lp, and Lamda from 31.3 to 41.3 weeks with head motion scrubbing and different processing strategies. (A) Data after head motion scrubbing. (B) Different densities (3 and 7%). (C) Weighted brain networks. The scatter plots show the regression line with 95% prediction error bounds. The R2 values were adjusted with gender and mFD as covariates. PMA (weeks), postmenstrual age in weeks.

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