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

Nature Neuroscience volume 21, pages 765–772 (2018)Cite this article

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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.

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Fig. 1: Methods overview for combining resting-state functional connectivity MRI, random resampling and PLSR.
Fig. 2: Within-network and between-networks associations with mean maternal IL-6.
Fig. 3: Predictive features (ROIs) within and between networks associated with mean maternal IL-6.
Fig. 4: Relationship between maternal IL-6, neonatal functional connectivity & working memory.

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Acknowledgements

We greatly acknowledge the assistance of E. R. Earl at OHSU for pipeline preparation and assistance. This work was funded by the National Institute of Mental Health (grants R01 MH091351 to C.B. and P.D.W. and R01 MH105538 to C.B., D.A.F and P.D.W.) and was supported by the National Institutes of Health (grants R01 MH096773 and K99/R00 MH091238 to D.A.F.), Oregon Clinical and Translational Research Institute (D.A.F), NIMH K99 MH111805 (A.G.), the Gates Foundation (D.A.F, R.N., A.G., C.B.), the Destafano Innovation Fund (D.A.F.), an OHSU Fellowship for Diversity and Inclusion in Research Program (O.M.-D.) and a National Library of Medicine Postdoctoral Fellowship (E.F.).

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Authors and Affiliations

  1. Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA

    Marc D. Rudolph, Alice M. Graham, Eric Feczko, Oscar Miranda-Dominguez & Damien A. Fair

  2. Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA

    Eric Feczko

  3. Development, Health and Disease Research Program, University of California, Irvine, Irvine, CA, USA

    Jerod M. Rasmussen, Sonja Entringer, Pathik D. Wadhwa & Claudia Buss

  4. Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, OR, USA

    Rahel Nardos

  5. Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Berlin, Germany

    Sonja Entringer & Claudia Buss

  6. Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA

    Damien A. Fair

  7. Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA

    Damien A. Fair

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Contributions

M.D.R., A.M.G., O.M.-D., E.F., R.N. and D.A.F. drafted the manuscript and designed analyses. M.D.R. and S.E. managed data. M.D.R. performed data analysis under supervision of D.A.F. and A.M.G. C.B., S.E., P.D.W. and D.A.F. designed the study and provided insight regarding developmental models incorporating prenatal health factors. C.B., S.E. and J.M.R. collected and disseminated all raw behavioral and imaging data. All authors provided critical revisions and have approved the final version of the manuscript.

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Correspondence to Claudia Buss or Damien A. Fair.

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Supplementary Figure 1 Relationship between maternal IL-6, neonatal functional connectivity and working memory using leave-one-out cross-validation (LOOCV).

Similar to Fig. 4 in the main text, in panel a) predictive features, here using LOOCV, are overlaid on top of voxelwise activation maps (reverse inference) related to working memory generated via Neurosynth.org. NeuroSynth activation maps for working memory are comprised of results reported from 901 fMRI studies (results are corrected for multiple comparisons). In panel b) we show that regions involved in working memory (N=54) are also more predictive of IL-6 on average (than those not overlapping with active working memory voxels; N=210) even when using LOOCV using a two-tailed independent t-test assuming unequal variances (t(64)=2.60,p=.012). In panel c) we, again show that using all three gestational time points for IL-6, we can also predict future working memory performance at two years of age (d=0.747). All analyses are performed in our sample of neonates with working memory scores (N=46). Boxplot: x shows the mean, center lines show the medians; box limits indicate the 25th and 75th percentiles, whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, outliers are represented by dots.

Supplementary Figure 2 Relationship between maternal IL-6, neonatal functional connectivity and language.

Similar to Fig. 4 in the main text, in panel a) predictive features shown to estimate maternal IL-6 in our sample of neonates (N=84) are overlaid on top of voxelwise activation maps (forward inference, less strict) related to language generated via NeuroSynth.org. In panel b), using an independent two-tailed t-test assuming unequal variances, we show a non-significant trend (t(69)=2, p=0.117) for regions predictive of maternal IL-6 overlapping (N=49) with language-related activations generated from the meta-analysis as compared to regions with no overlap (N=215). Of note this trend is only present for the forward inference map, not the reverse as in the main manuscript with working memory. Boxplot: x shows the mean, center lines show the medians; box limits indicate the 25th and 75th percentiles, whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, outliers are represented by dots

Supplementary Figure 3 Relationship between infant negative emotionality, neonatal functional connectivity and working memory.

In panel a) predictive features estimating infant negative emotionality (N=63) are overlaid on top of voxelwise activation maps (forward inference, less strict) related to working memory generated via Neurosynth.org. In panel b), using an independent two-tailed t-test assuming unequal variances, we show that unlike the strong relationship in the original report for maternal IL-6, the comparison of regions predictive of negative emotionality overlapping (N=54) working memory regions from the meta-analysis with those not overlapping (N=210) is statistically non-significant (t(96)=1.856, p=0.415). Boxplot: x shows the mean, center lines show the medians; box limits indicate the 25th and 75th percentiles, whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, outliers are represented by dots

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Rudolph, M.D., Graham, A.M., Feczko, E. et al. Maternal IL-6 during pregnancy can be estimated from newborn brain connectivity and predicts future working memory in offspring. Nat Neurosci 21, 765–772 (2018). https://doi.org/10.1038/s41593-018-0128-y

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