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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Buka, S. L. et al. Maternal infections and subsequent psychosis among offspring. Arch. Gen. Psychiatry 58, 1032–1037 (2001).
Patterson, P. H. Immune involvement in schizophrenia and autism: etiology, pathology and animal models. Behav. Brain Res. 204, 313–321 (2009).
Hava, G., Vered, L., Yael, M., Mordechai, H. & Mahoud, H. Alterations in behavior in adult offspring mice following maternal inflammation during pregnancy. Dev. Psychobiol. 48, 162–168 (2006).
Estes, M. L. & McAllister, A. K. Maternal immune activation: implications for neuropsychiatric disorders. Science 353, 772–777 (2016).
Knuesel, I. et al. Maternal immune activation and abnormal brain development across CNS disorders. Nat. Rev. Neurol. 10, 643–660 (2014).
Buss, C. et al. Intergenerational transmission of maternal childhood maltreatment exposure: implications for fetal brain development. J. Am. Acad. Child Adolesc. Psychiatry 56, 373–382 (2017).
Monk, C., Spicer, J. & Champagne, F. A. Linking prenatal maternal adversity to developmental outcomes in infants: the role of epigenetic pathways. Dev. Psychopathol. 24, 1361–1376 (2012).
Deverman, B. E. & Patterson, P. H. Cytokines and CNS development. Neuron 64, 61–78 (2009).
Boulanger, L. M. Immune proteins in brain development and synaptic plasticity. Neuron 64, 93–109 (2009).
Kohli, S. et al. Self-reported cognitive impairment in patients with cancer. J. Oncol. Pract. 3, 54–59 (2007).
Buss, C., Entringer, S. & Wadhwa, P. D. Fetal programming of brain development: intrauterine stress and susceptibility to psychopathology. Sci. Signal. 5, pt7 (2012).
Smith, S. E. P., Li, J., Garbett, K., Mirnics, K. & Patterson, P. H. Maternal immune activation alters fetal brain development through interleukin-6. J. Neurosci. 27, 10695–10702 (2007).
Wu, W.-L., Hsiao, E. Y., Yan, Z., Mazmanian, S. K. & Patterson, P. H. The placental interleukin-6 signaling controls fetal brain development and behavior. Brain Behav. Immun. 62, 11–23 (2017).
Graham, A. M. et al. Maternal systemic interleukin-6 during pregnancy is associated with newborn amygdala phenotypes and subsequent behavior at 2 years of age. Biol. Psychiatry 83, 109–119 (2018).
Buss, C., Entringer, S., Swanson, J. M. & Wadhwa, P. D. The role of stress in brain development: the gestational environment’s long-term effects on the brain. Cerebrum 2012, 4 (2012).
Wei, H. et al. Brain IL-6 elevation causes neuronal circuitry imbalances and mediates autism-like behaviors. Biochim. Biophys. Acta 1822, 831–842 (2012).
Smyser, C. D., Snyder, A. Z. & Neil, J. J. Functional connectivity MRI in infants: exploration of the functional organization of the developing brain. Neuroimage 56, 1437–1452 (2011).
Gao, W. et al. Temporal and spatial evolution of brain network topology during the first two years of life. PLoS One 6, e25278 (2011).
Graham, A. M. & Fair, D. A. Commentary: developmental connectomics to advance our understanding of typical and atypical brain development – a commentary on Vértes and Bullmore (2015). J. Child Psychol. Psychiatry 56, 321–323 (2015).
Grayson, D. S. & Fair, D. A. Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature. Neuroimage 160, 15–31 (2017).
Thomason, M. E. et al. Cross-hemispheric functional connectivity in the human fetal brain. Sci. Transl. Med. 5, 173ra24 (2013).
Fair, D. A. et al. Development of distinct control networks through segregation and integration. Proc. Natl Acad. Sci. USA 104, 13507–13512 (2007).
Khambhati, A. N., Sizemore, A. E., Betzel, R. F. & Bassett, D. S. Modelling and interpreting network dynamics. Preprint at bioRxiv https://doi.org/10.1101/124016 (2017).
Sporns, O. Structure and function of complex brain networks. Dialogues Clin. Neurosci. 15, 247–262 (2013).
Sporns, O. Towards network substrates of brain disorders. Brain 137, 2117–2118 (2014).
Beck, D. M., Schaefer, C., Pang, K. & Carlson, S. M. Executive function in preschool children: test-retest reliability. J. Cogn. Dev. 12, 169–193 (2011).
Hughes, C. & Ensor, R. Executive function and theory of mind: predictive relations from ages 2 to 4. Dev. Psychol. 43, 1447–1459 (2007).
Schwarz, E., Tost, H. & Meyer-Lindenberg, A. Working memory genetics in schizophrenia and related disorders: an RDoC perspective. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 171B, 121–131 (2016).
Yarkoni, T., Poldrack, R., Nichols, T., Van Essen, D. & Wager, T. NeuroSynth: a new platform for large scale automated synthesis of human functional neuroimaging data. in Frontiers in Neuroinformatics Conf. Abstr.:4th INCF Congress of Neuroinformatics https://doi.org/10.3389/conf.fninf.2011.08.00058 (2011).
Pruett, J. R. Jr. et al. Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data. Dev. Cogn. Neurosci. 12, 123–133 (2015).
Dosenbach, N. U. F. et al. Prediction of individual brain maturity using fMRI. Science 329, 1358–1361 (2010).
Rudolph, M. D. et al. At risk of being risky: the relationship between “brain age” under emotional states and risk preference. Dev. Cogn. Neurosci. 24, 93–106 (2017).
Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).
Gao, W., Lin, W., Grewen, K. & Gilmore, J. H. Functional connectivity of the infant human brain: plastic and modifiable. Neuroscientist 23, 169–184 (2016).
Combrisson, E. & Jerbi, K. Exceeding chance level by chance: the caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J. Neurosci. Methods 250, 126–136 (2015).
Goodman, S. A dirty dozen: twelve p-value misconceptions. Semin. Hematol. 45, 135–140 (2008).
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Intl. Joint Conf. on Articial Intelligence IJCAI https://doi.org/10.1067/mod.2000.109031 (1995).
Fair, D. A. et al. Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRIdata. Front. Syst. Neurosci. 6, 80 (2013).
Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–2356 (2007).
Menon, V. & Uddin, L. Q. Saliency, switching, attention and control: a network model of insula function. Brain Struct. Funct. 214, 655–667 (2010).
Menon, V. Salience network. in Brain Mapping: An Encyclopedic Reference Vol. 2 (ed. Toga, A. W.) 597–611 (Academic, New York, 2015).
Di Martino, A. et al. Unraveling the miswired connectome: a developmental perspective. Neuron 83, 1335–1353 (2014).
Graham, A. M. et al. Implications of newborn amygdala connectivity for fear and cognitive development at 6-months-of-age. Dev. Cogn. Neurosci. 18, 12–25 (2016).
Posner, M. I. Imaging attention networks. Neuroimage 61, 450–456 (2012).
Qin, S., Young, C. B., Supekar, K., Uddin, L. Q. & Menon, V. Immature integration and segregation of emotion-related brain circuitry in young children. Proc. Natl Acad. Sci. USA 109, 7941–7946 (2012).
Gao, W., Alcauter, S., Smith, J. K., Gilmore, J. H. & Lin, W. Development of human brain cortical network architecture during infancy. Brain Struct. Funct. 220, 1173–1186 (2015).
Stafford, J. M. et al. Large-scale topology and the default mode network in the mouse connectome. Proc. Natl Acad. Sci. USA 111, 18745–18750 (2014).
Miranda-Dominguez, O. et al. Bridging the gap between the human and macaque connectome: a quantitative comparison of global interspecies structure-function relationships and network topology. J. Neurosci. 34, 5552–5563 (2014).
Diamond, A. Executive functions. Annu. Rev. Psychol. 64, 135–168 (2013).
Li, G., Lin, W., Gilmore, J. H. & Shen, D. Spatial patterns, longitudinal development, and hemispheric asymmetries of cortical thickness in infants from birth to 2 years of age. J. Neurosci. 35, 9150–9162 (2015).
Knickmeyer, R. C. et al. A structural MRI study of human brain development from birth to 2 years. J. Neurosci. 28, 12176–12182 (2008).
Smyser, C. D. et al. Resting-state network complexity and magnitude are reduced in prematurely born infants. Cereb. Cortex 26, 322–333 (2016).
Smyser, C. D. et al. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 136, 1–9 (2016).
Short, S. J. et al. Associations between white matter microstructure and infants’ working memory. Neuroimage 64, 156–166 (2013).
Smith, S. M., Bannister, P., Beckmann, C. & Brady, M. FSL: new tools for functional and structural brain image analysis. Neuroimage https://doi.org/10.1016/S1053-8119(01)91592-7 (2001).
Fonov, V., Evans, A., McKinstry, R., Almli, C. & Collins, D. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage https://doi.org/10.1016/s1053-8119(09)70884-5 (2009).
Talairach, P. & Tournoux, J. Co-planar Stereotaxic Atlas of the Human Brain (Georg Thieme Verlag, New York, 1988).
Power, J. D. et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320–341 (2014).
Burgess, G. C. et al. Evaluation of denoising strategies to address motion-correlated artifacts in resting-state functional magnetic resonance imaging data from the human connectome project. Brain Connect. 6, 669–680 (2016).
Hallquist, M. N., Hwang, K. & Luna, B. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 82, 208–225 (2013).
Abdi, H. & Williams, L. J. Partial least squares methods: partial least squares correlation and partial least square regression. Methods Mol. Biol. 930, 549–579 (2013).
Gartstein, M. A. & Rothbart, M. K. Studying infant temperament via the Revised Infant Behavior Questionnaire. Infant Behav. Dev. 26, 64–86 (2003).
Breiman, L. & Spector, P. Submodel selection and evaluation in regression. The X-random case. Int. Stat. Rev. 60, 291–319 (1992).
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.).
The authors declare no competing interests.
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Integrated supplementary information
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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