Breakdown of Whole-brain Dynamics in Preterm-born Children

Abstract

The brain operates at a critical point that is balanced between order and disorder. Even during rest, unstable periods of random behavior are interspersed with stable periods of balanced activity patterns that support optimal information processing. Being born preterm may cause deviations from this normal pattern of development. We compared 33 extremely preterm (EPT) children born at < 27 weeks of gestation and 28 full-term controls. Two approaches were adopted in both groups, when they were 10 years of age, using structural and functional brain magnetic resonance imaging data. The first was using a novel intrinsic ignition analysis to study the ability of the areas of the brain to propagate neural activity. The second was a whole-brain Hopf model, to define the level of stability, desynchronization, or criticality of the brain. EPT-born children exhibited fewer intrinsic ignition events than controls; nodes were related to less sophisticated aspects of cognitive control, and there was a different hierarchy pattern in the propagation of information and suboptimal synchronicity and criticality. The largest differences were found in brain nodes belonging to the rich-club architecture. These results provide important insights into the neural substrates underlying brain reorganization and neurodevelopmental impairments related to prematurity.

Keywords: brain development; brain dynamics; functional connectivity; neurodevelopment; prematurity.

Figure 1

Overview of the Methodology. (a) Intrinsic ignition. The blood oxygen level–dependent (BOLD) neuroimaging signal of a previously defined brain region (A, green signal) can be treated as spiking data by applying a threshold method (see Materials and Methods) to define an ignition event. For each driving event the activity of the rest of the network is measured (B, read area) in the time window (gray rectangle). For each ignition event this gives rise to a connectivity matrix where integration can be measured. (b) Whole-brain computational model. SC, structural connectivity; FC, functional connectivity. SC data were computed from dMRI-based tractography. The anatomical labeling atlas was used to parcellate the dMRI data in 90 anatomical regions. Then, a structural connectivity matrix (90 × 90) for each participant was created. The functional data from fMRI-BOLD activity was also used to create an FC matrix (empirical) following similar aforementioned steps (see Materials and Methods). Based on the SC matrix, a computational model is built to construct a simulated FC matrix (FC model) that is fitted to the empirical FC matrix, using different model parameter combinations. The optimal fit corresponds to a minimal Euclidian distance. (c) Hopf bifurcation model. The local dynamics of each node is modeled by using a normal form of a Hopf bifurcation. The nodes can behave synchronously (oscillations), asynchronously (noise), or critically (noise + oscillations), and these characteristics are represented by a bifurcation alpha value (bifurcation parameter, BP) with positive, negative, and values around 0, respectively. The simulated signal looks like the empirical data when the nodes are at the border between noisy and oscillatory behavior (bifurcation point).

Figure 2

Ignition. (a) Ignition values across the brain and variability across events for the preterm (orange) and the term (green) groups. Standard error is represented at a shaded area. (b) Ranked (descending) ignition and variability values where top 10 regions (highlighted in gray within the plot) for both groups are described at the top right insert. Rendered brains show the top 10 regions with the highest values in ignition and variability across events.

Figure 3

Fitting between empirical and simulated brain activity. Static (left) stands for the correlation between empirical and simulated functional connectivity by means of a Pearson correlation coefficient. Dynamic (right) represents the Kolmogorov–Smirnov distance between the distributions of ongoing dynamics. The closer simulated and empirical dynamics are the lower the distance. The optimal coupling values are described in the top right corner of each plot. Preterm group (orange) and term group (green). Solid lines represent mean fitting values, while shadowed areas represent the standard deviation over simulations (as many as subjects per group).

Figure 4

Whole-brain Hopf bifurcation model. (a) Bar plot of alpha bifurcation values across the brain for preterm (orange) and term (green) groups. The third blue plot shows the absolute difference (term and preterm) between alpha bifurcation values on each region. Asterisks show the top 10 regions with highest difference. (b) Ranked absolute differences between the term and preterm alpha vectors. The top 10 regions presenting the highest difference are marked with red and described in the top right insert and (c) displayed in the rendered brains

Figure 5

Differences in bifurcation (alpha) and ignition values. (a) and (c) represent rendered brains showing the top 10 regions with the highest differences (delta) in alpha bifurcation and ignition values. (b) Plot representing the ranked delta in alpha (red) and ignition values (green), with the top 10 regions representing the highest differences described in the insert. (d) Scatter plot between delta alpha and delta ignition with a fitted linear regression (straight gray line) and 95% confidence bounds (dotted lines). Spearman’s rank correlation coefficient between these two variables is indicated by r.