Maternal IL-6 during pregnancy can be estimated from newborn brain connectivity and predicts future working memory in offspring

Abstract

Several lines of evidence support the link between maternal inflammation during pregnancy and increased likelihood of neurodevelopmental and psychiatric disorders in offspring. This longitudinal study seeks to advance understanding regarding implications of systemic maternal inflammation during pregnancy, indexed by plasma interleukin-6 (IL-6) concentrations, for large-scale brain system development and emerging executive function skills in offspring. We assessed maternal IL-6 during pregnancy, functional magnetic resonance imaging acquired in neonates, and working memory (an important component of executive function) at 2 years of age. Functional connectivity within and between multiple neonatal brain networks can be modeled to estimate maternal IL-6 concentrations during pregnancy. Brain regions heavily weighted in these models overlap substantially with those supporting working memory in a large meta-analysis. Maternal IL-6 also directly accounts for a portion of the variance of working memory at 2 years of age. Findings highlight the association of maternal inflammation during pregnancy with the developing functional architecture of the brain and emerging executive function.

Figure 1. Methods overview for combining rs-fcMRI, random resampling and PLSR

The above diagram provides a step-by-step overview visually depicting the process of associating neonatal functional connectivity data with mean maternal IL-6. After standard preprocessing steps, for each individual neonate, functional timecourses representing regional activation for a given ROI are extracted and pairwise cross-correlation matrices are constructed for 264 regions as described in Power et al. 2011. From here, individual subnetworks are extracted; specifically, matrices are extracted for each of the 10 networks assessed within (a) and between (b) previously identified large-scale systems (i.e. DFM, VIS, etc.). Connections between ROIs for a given within or between network functional connectivity matrix are used as features to estimate mean maternal IL-6 using partial-least squares regression (PLSR). Using a repeated (k=4000) hold-out random resampling procedure, the data is randomly partitioned into training (80%) and test (20%) sets, and the resulting distribution of actual versus predicted IL-6 values is tested for significance against a null distribution (i.e. random chance).

Figure 2. Within and between functional network associations with mean maternal IL-6

In panel a) the distribution of correlations between actual and estimated mean maternal IL-6 values in our sample of neonates (N=84) obtained via PLSR with randomized holdouts (4000 iterations; blue) is shown for each within (diagonal) and between (off diagonal) network model that passed statistical threshold (see Methods). The corresponding null distribution for each model is shown in peach. Brighter highlighted cells denote stronger results according to effect size, the primary outcome of interest, indicative of the strength of the model in accurately estimating IL-6 (see Table 1 for actual statistics; Figure 4). In panel b) a network schematic depicting significant associations within and between large-scale functional networks and mean maternal IL-6 is visualized using the Gephi network visualization software. Circles (or nodes) represent individual networks and are scaled according their overall degree of association with mean maternal IL-6 (number of associations passing criteria for statistical significance and effect size). Nodes with thick borders represent significant within network associations with mean maternal IL-6. Line width between nodes represents the relative effect size of between network models. Note: The graph is undirected and used for illustrative purposes and does not represent graph theoretical relationships between communities.

Figure 3. Predictive features (ROIs) within and between networks associated with mean maternal IL-6

Predictive features representing individual brain regions for a given network associated with mean maternal IL-6 are visualized on a standardized brain surface using Caret 5 software. ROIs are scaled proportionally; node (circle) sizes are determined by the overall degree of importance of a region in estimating IL-6 (beta-weights). ROIs for networks significantly associated with maternal IL-6 (see Table 1 for statistics) include the SAL (black), DAN (green), SUB (orange), VAN (turquoise), CER (pink), CON (purple), FP (yellow), and VIS (blue) networks.

Figure 4. Relationship between maternal IL-6, neonatal functional connectivity & working memory

In panel a) predictive features are overlaid on top of voxelwise, meta-analysis maps related to working memory in our sample of neonates with assessment data (N=46) generated via Neurosynth.org. Neurosynth meta-analysis maps for working memory are comprised of results reported from 901 fMRI studies (reverse-inference; corrected for multiple comparisons using a false discovery rate (FDR) criterion of .01 as previously described). In panel b) we show the combined sum of the beta weights of a given region (i.e. node strength) for those regions (N=54) within (overlapping) the meta-analysis working memory mask, and those regions (N=210) outside (non-overlapping) the mask. On average, regions within the working memory mask are more predictive of IL-6 as indicated by an independent two-tailed t-test assuming unequal variances (t(70)=2.90, p=.005). In the boxplot, the x indicates the mean value, horizontal lines within the box represent the medians; box limits indicate the 25th and 75^th percentiles, whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, outliers are represented by dots. In panel c) we show directly that using all three gestational time points for IL-6, we can also predict future working memory performance (d=0.747) at two years of age in these same infants (N=46) using PLSR.