Hostname: page-component-f554764f5-44mx8 Total loading time: 0 Render date: 2025-04-18T23:13:09.043Z Has data issue: false hasContentIssue false
  • English
  • Français

Prenatal stress and the developing brain: Risks for neurodevelopmental disorders

Published online by Cambridge University Press:  02 August 2018

Bea R. H. Van den Bergh ,
Robert Dahnke  and
Maarten Mennes
Bea R. H. Van den Bergh*
Affiliation:
University of Leuven Belgian Department for Welfare, Public Health and Family
Robert Dahnke
Affiliation:
University Hospital Jena
Maarten Mennes
Affiliation:
Radboud University
*
Address correspondence and reprint requests to: Bea R. H. Van den Bergh, Health Psychology, KU Leuven, University of Leuven, Tiensestraat 102, B-3000 Leuven, Belgium; E-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

The prenatal period is increasingly considered as a crucial target for the primary prevention of neurodevelopmental and psychiatric disorders. Understanding their pathophysiological mechanisms remains a great challenge. Our review reveals new insights from prenatal brain development research, involving (epi)genetic research, neuroscience, recent imaging techniques, physical modeling, and computational simulation studies. Studies examining the effect of prenatal exposure to maternal distress on offspring brain development, using brain imaging techniques, reveal effects at birth and up into adulthood. Structural and functional changes are observed in several brain regions including the prefrontal, parietal, and temporal lobes, as well as the cerebellum, hippocampus, and amygdala. Furthermore, alterations are seen in functional connectivity of amygdalar–thalamus networks and in intrinsic brain networks, including default mode and attentional networks. The observed changes underlie offspring behavioral, cognitive, emotional development, and susceptibility to neurodevelopmental and psychiatric disorders. It is concluded that used brain measures have not yet been validated with regard to sensitivity, specificity, accuracy, or robustness in predicting neurodevelopmental and psychiatric disorders. Therefore, more prospective long-term longitudinal follow-up studies starting early in pregnancy should be carried out, in order to examine brain developmental measures as mediators in mediating the link between prenatal stress and offspring behavioral, cognitive, and emotional problems and susceptibility for disorders.

Type
Special Issue Articles
Information
Development and Psychopathology , Volume 30 , Special Issue 3: Developmental Origins of Psychopathology: Mechanisms, Processes, and Pathways Linking the Prenatal Environment to Postnatal Outcomes , August 2018 , pp. 743 - 762
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

This research was supported by funding from EU FP7/Health.2011.2.22-2 and GA2798219 (to B.v.d.B.).

References

Alansary, A., Rajchl, M., McDonagh, S. G., Murgasova, M., Damodaram, M., Lloyd, D. F., … Kainz, B. (2017). PVR: Patch-to-volume reconstruction for large area motion correction of fetal MRI. IEEE Transactions on Medical Imaging, 36. doi:10.1109/TMI.2017.2737081Google Scholar
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.Google Scholar
Arcos-Burgos, M., Vélez, J. I., Solomon, B. D., & Muenke, M. (2012). A common genetic network underlies substance use disorders and disruptive or externalizing disorders. Human Genetics, 131, 917–929. doi:10.1007/s00439-012-1164-4Google Scholar
Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—The methods. NeuroImage, 11(6, Pt. 1), 805–821. doi:10.1006/nimg.2000.0582Google Scholar
Ashburner, J., & Friston, K. J. (2011). Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. NeuroImage, 55, 954–967. doi:10.1016/j.neuroimage.2010.12.049Google Scholar
Atasoy, S., Donnelly, I., & Pearson, J. (2016). Human brain networks function in connectome-specific harmonic waves. Nature Communications, 7, 10340. doi:10.1038/ncomms10340Google Scholar
Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54, 2033–2044. doi:10.1016/j.neuroimage.2010.09.025Google Scholar
Babb, J. A., Deligiannidis, K. M., Murgatroyd, C. A., & Nephew, B. C. (2015). Peripartum depression and anxiety as an integrative cross domain target for psychiatric preventative measures. Behavioural Brain Research, 276, 32–44. doi:10.1016/j.bbr.2014.03.039Google Scholar
Ball, G., Aljabar, P., Zebari, S., Tusor, N., Arichi, T., Merchant, N., … Counsell, S. J. (2014). Rich-club organization of the newborn human brain. Proceedings of the National Academy of Sciences, 111, 7456–7461. doi:10.1073/pnas.1324118111Google Scholar
Barch, D. M., & Carter, C. S. (2016). Functional and structural brain connectivity in psychopathology. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1, 196–198. doi:10.1016/j.bpsc.2016.03.006Google Scholar
Barker, D. J. (1990). The fetal and infant origins of adult disease. British Medical Journal, 301, 1111.Google Scholar
Batalle, D., Hughes, E. J., Zhang, H., Tournier, J. D., Tusor, N., Aljabar, P., … Counsell, S. J. (2017). Early development of structural networks and the impact of prematurity on brain connectivity. NeuroImage, 149, 379–392. doi:10.1016/j.neuroimage.2017.01.065Google Scholar
Bauer, A., Parsonage, M., Knapp, M., Iemmi, V., & Adelaja, B. (2014). Costs of perinatal mental health problems London: London School of Economics.Google Scholar
Bayly, P. V., Taber, L. A., & Kroenke, C. D. (2014). Mechanical forces in cerebral cortical folding: A review of measurements and models. Journal of the Mechanical Behavior of Biomedical Materials, 29, 568–581. doi:10.1016/j.jmbbm.2013.02.018Google Scholar
Behan, P., & Geschwind, N. (1985). Dyslexia, congenital anomalies, and immune disorders: The role of the fetal environmenta. Annals of the New York Academy of Sciences, 457, 13–18. doi:10.1111/j.1749-6632.1985.tb20796.xGoogle Scholar
Behrens, T. E. J., & Sporns, O. (2012). Human connectomics. Current Opinion in Neurobiology, 22, 144–153. doi:10.1016/j.conb.2011.08.005Google Scholar
Ben-Ari, Y. (2008). Neuro-archaeology: Pre-symptomatic architecture and signature of neurological disorders. Trends in Neurosciences, 31, 626–636. doi:10.1016/j.tins.2008.09.002Google Scholar
Ben-Ari, Y., & Spitzer, N. C. (2010). Phenotypic checkpoints regulate neuronal development. Trends in Neurosciences, 33, 485–492. doi:10.1016/j.tins.2010.08.005Google Scholar
Benasich, A. A., Choudhury, N., Friedman, J. T., Realpe-Bonilla, T., Chojnowska, C., & Gou, Z. (2006). The infant as a prelinguistic model for language learning impairments: Predicting from event-related potentials to behavior. Neuropsychologia, 44, 396–411. doi:10.1016/j.neuropsychologia.2005.06.004Google Scholar
Benes, F. M., Vincent, S. L., & Todtenkopf, M. (2001). The density of pyramidal and nonpyramidal neurons in anterior cingulate cortex of schizophrenic and bipolar subjects. Biological Psychiatry, 50, 395–406. doi:10.1016/S0006-3223(01)01084-8Google Scholar
Bernadskaya, Y., & Christiaen, L. (2016). Transcriptional control of developmental cell behaviors. Annual Review of Cell and Developmental Biology, 32, 77–101. doi:10.1146/annurev-cellbio-111315-125218Google Scholar
Billiet, T., Vandenbulcke, M., Mädler, B., Peeters, R., Dhollander, T., Zhang, H., … Emsell, L. (2015). Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiology of Aging, 36, 2107–2121. doi:10.1016/j.neurobiolaging.2015.02.029Google Scholar
Bock, J., Rether, K., Gröger, N., Xie, L., & Braun, K. (2014). Perinatal programming of emotional brain circuits: An integrative view from systems to molecules. Frontiers in Neuroscience, 8, 11. doi:10.3389/fnins.2014.00011Google Scholar
Bock, J., Wainstock, T., Braun, K., & Segal, M. (2015). Stress in utero: Prenatal programming of brain plasticity and cognition. Biological Psychiatry, 78, 315–326. doi:10.1016/j.biopsych.2015.02.036Google Scholar
Bowers, M. E., & Yehuda, R. (2016). Intergenerational transmission of stress in humans. Neuropsychopharmacology, 41, 232–244. doi:10.1038/npp.2015.247Google Scholar
Braun, K., Bock, J., Wainstock, T., Matas, E., Gaisler-Salomon, I., Fegert, J., … Segal, M. (2017). Experience-induced transgenerational (re-)programming of neuronal structure and functions: Impact of stress prior and during pregnancy. Neuroscience & Biobehavioral Reviews. Advance online publication. doi:10.1016/j.neubiorev.2017.05.021Google Scholar
Brummelte, S. (2017). Introduction: Early adversity and brain development. Neuroscience, 342, 1–3. doi:10.1016/j.neuroscience.2016.09.041Google Scholar
Budday, S., Steinmann, P., & Kuhl, E. (2015). Physical biology of human brain development. Frontiers in Cellular Neuroscience, 9. doi:10.3389/fncel.2015.00257Google Scholar
Buss, C., Davis, E. P., Muftuler, L. T., Head, K., & Sandman, C. A. (2010). High pregnancy anxiety during mid-gestation is associated with decreased gray matter density in 6–9-year-old children. Psychoneuroendocrinology, 35, 141–153. doi:10.1016/j.psyneuen.2009.07.010Google Scholar
Buss, C., Davis, E. P., Shahbaba, B., Pruessner, J. C., Head, K., & Sandman, C. A. (2012). Maternal cortisol over the course of pregnancy and subsequent child amygdala and hippocampus volumes and affective problems. Proceedings of the National Academy of Sciences, 109, E1312–E1319. doi:10.1073/pnas.1201295109Google Scholar
Cao, M., Wang, Z., & He, Y. (2015). Connectomics in psychiatric research: Advances and applications. Neuropsychiatric Disease Treatment, 11, 2801–2810.Google Scholar
Castellanos, F. X., & Aoki, Y. (2016). Intrinsic functional connectivity in attention-deficit/hyperactivity disorder: A science in development. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1, 253–261. doi:10.1016/j.bpsc.2016.03.004Google Scholar
Castellanos, F. X., & Proal, E. (2012). Large-scale brain systems in ADHD: Beyond the prefrontal–striatal model. Trends in Cognitive Sciences, 16, 17–26. doi:10.1016/j.tics.2011.11.007Google Scholar
Chang, Y. S., Owen, J. P., Pojman, N. J., Thieu, T. U., Bukshpun, P., Wakahiro, M. L. J., … Mukherjee, P. (2015). White matter changes of neurite density and fiber orientation dispersion during human brain maturation. PLOS ONE, 10, 1–23. doi:10.1371/journal.pone.0123656Google Scholar
Charil, A., Laplante, D. P., Vaillancourt, C., & King, S. (2010). Prenatal stress and brain development. Brain Research Reviews, 65, 56–79. doi:10.1016/j.brainresrev.2010.06.002Google Scholar
Chen, L., Pan, H., Tuan, T. A., Teh, A. L., MacIsaac, J. L., Mah, S. M., … Holbrook, J. D. (2015). Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism influences the association of the methylome with maternal anxiety and neonatal brain volumes. Development and Psychopathology, 27, 137–150. doi:10.1017/S0954579414001357Google Scholar
Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 587–600.Google Scholar
Choudhry, Z., Sengupta, S. M., Grizenko, N., Fortier, M.-E., Thakur, G. A., Bellingham, J., & Joober, R. (2012). LPHN3 and attention-deficit/hyperactivity disorder: Interaction with maternal stress during pregnancy. Journal of Child Psychology and Psychiatry, 53, 892–902. doi:10.1111/j.1469-7610.2012.02551.xGoogle Scholar
Cicchetti, D., Handley, E. D., & Rogosch, F. A. (2015). Child maltreatment, inflammation, and internalizing symptoms: Investigating the roles of C-reactive protein, gene variation, and neuroendocrine regulation. Development and Psychopathology, 27, 553–566. doi:10.1017/S0954579415000152Google Scholar
Cole, D. M., Smith, S. M., & Beckmann, C. F. (2010). Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers in Systems Neuroscience, 4, 8. doi:10.3389/fnsys.2010.00008Google Scholar
Collin, G., & van den Heuvel, M. P. (2013). The ontogeny of the human connectome: Development and dynamic changes of brain connectivity across the life span. Neuroscientist, 19, 616–628. doi:10.1177/1073858413503712Google Scholar
Conti, E., Mitra, J., Calderoni, S., Pannek, K., Shen, K. K., Pagnozzi, A., … Guzzetta, A. (2017). Network over-connectivity differentiates autism spectrum disorder from other developmental disorders in toddlers: A diffusion MRI study. Human Brain Mapping, 38, 2333–2344. doi:10.1002/hbm.23520Google Scholar
Czepielewski, L. S., Wang, L., Gama, C. S., & Barch, D. M. (2017). The relationship of intellectual functioning and cognitive performance to brain structure in schizophrenia. Schizophrenia Bulletin, 43, 355–364. doi:10.1093/schbul/sbw090Google Scholar
Dahnke, R., Yotter, R. A., & Gaser, C. (2013). Cortical thickness and central surface estimation. NeuroImage, 65, 336–348. doi:10.1016/j.neuroimage.2012.09.050Google Scholar
Davis, E. P., Sandman, C. A., Buss, C., Wing, D. A., & Head, K. (2013). Fetal glucocorticoid exposure is associated with preadolescent brain development. Biological Psychiatry, 74, 647–655. doi:10.1016/j.biopsych.2013.03.009Google Scholar
Davis, J., Eyre, H., Jacka, F. N., Dodd, S., Dean, O., McEwen, S., … Berk, M. (2016). A review of vulnerability and risks for schizophrenia: Beyond the two hit hypothesis. Neuroscience and Biobehavioral Reviews, 65, 185–194. doi:10.1016/j.neubiorev.2016.03.017Google Scholar
Dawson, G., Frey, K., Panagiotides, H., Osterling, J., & Hessl, D. (1997). Infants of depressed mothers exhibit atypical frontal brain activity: A replication and extension of previous findings. Journal of Child Psychology and Psychiatry, 38, 179–186. doi:10.1111/j.1469-7610.1997.tb01852.xGoogle Scholar
Dennis, E. L., & Thompson, P. M. (2013). Typical and atypical brain development: A review of neuroimaging studies. Dialogues in Clinical Neuroscience, 15, 359–382.Google Scholar
Deoni, S. C. L., Dean, D. C. III, Piryatinsky, I., O'Muircheartaigh, J., Waskiewicz, N., Lehman, K., … Dirks, H. (2013). Breastfeeding and early white matter development: A cross-sectional study. NeuroImage, 82, 77–86. doi:10.1016/j.neuroimage.2013.05.090Google Scholar
Deoni, S. C. L., Zinkstok, J. R., Daly, E., Ecker, C., Williams, S. C. R., & Murphy, D. G. M. (2014). White-matter relaxation time and myelin water fraction differences in young adults with autism. Psychological Medicine, 45, 795–805. doi:10.1017/S0033291714001858Google Scholar
Di Martino, A., Fair, D. A., Kelly, C., Satterthwaite, T. D., Castellanos, F. X., Thomason, M. E., … Milham, M. P. (2014). Unraveling the miswired connectome: A developmental perspective. Neuron, 83, 1335–1353. doi:10.1016/j.neuron.2014.08.050Google Scholar
Donovan, A. P. A., & Basson, M. A. (2017). The neuroanatomy of autism—A developmental perspective. Journal of Anatomy, 230, 4–15. doi:10.1111/joa.12542Google Scholar
Doria, V., Beckmann, C. F., Arichi, T., Merchant, N., Groppo, M., Turkheimer, F. E., … Edwards, A. D. (2010). Emergence of resting state networks in the preterm human brain. Proceedings of the National Academy of Sciences, 107, 20015–20020. doi:10.1073/pnas.1007921107Google Scholar
Dubois, J., Dehaene-Lambertz, G., Kulikova, S., Poupon, C., Hüppi, P. S., & Hertz-Pannier, L. (2014). The early development of brain white matter: A review of imaging studies in fetuses, newborns and infants. Neuroscience, 276, 48–71. doi:10.1016/j.neuroscience.2013.12.044Google Scholar
El Marroun, H., Tiemeier, H., Muetzel, R. L., Thijssen, S., van der Knaap, N. J. F., Jaddoe, V. W. V., … White, T. J. H. (2016). Prenatal exposure to maternal and paternal depressive symptoms and brain morphology: A population-based prosepctive neuroimaging study in young children. Depression and Anxiety, 33, 658–666. doi:10.1002/da.22524Google Scholar
Erk, S., Mohnke, S., Ripke, S., Lett, T. A., Veer, I. M., Wackerhagen, C., … Walter, H. (2017). Functional neuroimaging effects of recently discovered genetic risk loci for schizophrenia and polygenic risk profile in five RDoC subdomains. Translational Psychiatry, 7, e997. doi:10.1038/tp.2016.272Google Scholar
Evans, A. C., & Brain Development Cooperative Group. (2006). The NIH MRI study of normal brain development. NeuroImage, 30, 184–202. doi:10.1016/j.neuroimage.2005.09.068Google Scholar
Falk, A., Heine, V. M., Harwood, A. J., Sullivan, P. F., Peitz, M., Brustle, O., … Djurovic, S. (2016). Modeling psychiatric disorders: From genomic findings to cellular phenotypes. Molecular Psychiatry, 21, 1167–1179. doi:10.1038/mp.2016.89Google Scholar
Favaro, A., Tenconi, E., Degortes, D., Manara, R., & Santonastaso, P. (2015). Neural correlates of prenatal stress in young women. Psychological Medicine, 45, 2533–2543. doi:10.1017/S003329171500046XGoogle Scholar
Field, T., & Diego, M. (2008). Maternal depression effects on infant frontal EEG asymmetry. International Journal of Neuroscience, 118, 1081–1108. doi:10.1080/00207450701769067Google Scholar
Field, T., Diego, M., Hernandez-Reif, M., Figueiredo, B., Deeds, O., Ascencio, A., … Kuhn, C. (2010). Comorbid depression and anxiety effects on pregnancy and neonatal outcome. Infant Behavior and Development, 33, 23–29. doi:10.1016/j.infbeh.2009.10.004Google Scholar
Field, T., Hernandez-Reif, M., & Diego, M. (2006). Intrusive and withdrawn depressed mothers and their infants. Developmental Review, 26, 15–30. doi:10.1016/j.dr.2005.04.001Google Scholar
Fischl, B. R. (2012). FreeSurfer. NeuroImage, 62, 774–781. doi:10.1016/j.neuroimage.2012.01.021Google Scholar
Francx, W., Llera, A., Mennes, M., Zwiers, M. P., Faraone, S. V., Oosterlaan, J., … Beckmann, C. F. (2016). Integrated analysis of gray and white matter alterations in attention-deficit/hyperactivity disorder. NeuroImage: Clinical, 11, 357–367. doi:10.1016/j.nicl.2016.03.005Google Scholar
Franke, K., Luders, E., May, A., Wilke, M., & Gaser, C. (2012). Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI. NeuroImage, 63, 1305–1312. doi:10.1016/j.neuroimage.2012.08.001Google Scholar
Franke, K., Van den Bergh, B. R. H., de Rooij, S. R., Nathanielsz, P. W., Witte, O. W., Roseboom, T. J., & Schwab, M. (2017). Effects of prenatal stress on structural brain development and aging in humans. bioRxiv. doi:10.1101/148916Google Scholar
Franke, K., Ziegler, G., Klöppel, S., & Gaser, C. (2010). Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters. NeuroImage, 50, 883–892. doi:10.1016/j.neuroimage.2010.01.005Google Scholar
Fransson, P., & Marrelec, G. (2008). The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis. NeuroImage, 42, 1178–1184. doi:10.1016/j.neuroimage.2008.05.059Google Scholar
Fransson, P., Skiold, B., Horsch, S., Nordell, A., Blennow, M., Lagercrantz, H., & Aden, U. (2007). Resting-state networks in the infant brain. Proceedings of the National Academy of Sciences, 104, 15531–15536. doi:10.1073/pnas.0704380104Google Scholar
Gaser, C., Nenadic, I., Buchsbaum, B. R., Hazlett, E. A., & Buchsbaum, M. S. (2001). Deformation-based morphometry and its relation to conventional volumetry of brain lateral ventricles in MRI. NeuroImage, 13(6, Pt. 1), 1140–1145. doi:10.1006/nimg.2001.0771Google Scholar
Gaser, C., & Schlaug, G. (2003). Brain structures differ between musicians and non-musicians. Journal of Neuroscience, 23, 9240–9245.Google Scholar
Geschwind, D. H., & Flint, J. (2015). Genetics and genomics of psychiatric disease. Science, 349, 1489–1494. doi:10.1126/science.aaa8954Google Scholar
Ghiani, C. A., & Faundez, V. (2017). Cellular and molecular mechanisms of neurodevelopmental disorders. Journal of Neuroscience Research, 95, 1093–1096. doi:10.1002/jnr.24041Google Scholar
Gluckman, P. D., Hanson, M. A., Cooper, C., & Thornburg, K. L. (2008). Effect of in utero and early-life conditions on adult health and disease. New England Journal of Medicine, 359, 61–73. doi:10.1056/NEJMra0708473Google Scholar
Gupta, K. K., Gupta, V. K., & Shirasaka, T. (2016). An update on fetal alcohol syndrome—Pathogenesis, risks, and treatment. Alcoholism: Clinical and Experimental Research, 40, 1594–1602. doi:10.1111/acer.13135Google Scholar
Habas, P. A., Kim, K., Rousseau, F., Glenn, O. A., Barkovich, A. J., & Studholme, C. (2010). Atlas-based segmentation of developing tissues in the human brain with quantitative validation in young fetuses. Human Brain Mapping, 31, 1348–1358. doi:10.1002/hbm.20935Google Scholar
Habes, M., Erus, G., Toledo, J. B., Zhang, T., Bryan, N., Launer, L. J., … Davatzikos, C. (2016). White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain, 139(Pt. 4), 1164–1179. doi:10.1093/brain/aww008Google Scholar
Hagmann, P. (2005). From diffusion MRI to brain connectomics (PhD dissertation, Ecole Polytechnique Fédérale de Lausanne).Google Scholar
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLOS Biology, 6, e159. doi:10.1371/journal.pbio.0060159Google Scholar
Hagmann, P., Grant, P., & Fair, D. (2012). MR connectomics: A conceptual framework for studying the developing brain. Frontiers in Systems Neuroscience, 6. doi:10.3389/fnsys.2012.00043Google Scholar
Han, K., Chapman, S. B., & Krawczyk, D. C. (2016). Disrupted intrinsic connectivity among default, dorsal attention, and frontoparietal control networks in individuals with chronic traumatic brain injury. Journal of the International Neuropsychological Society, 22, 263–279. doi:10.1017/S1355617715001393Google Scholar
Hanson, M., & Gluckman, P. (2011). Developmental origins of noncommunicable disease: Population and public health implications. American Journal of Clinical Nutrition, 94(Suppl.), 1754S–1758S. doi:10.3945/ajcn.110.001206Google Scholar
Haroutunian, V., Katsel, P., Roussos, P., Davis, K. L., Altshuler, L. L., & Bartzokis, G. (2014). Myelination, oligodendrocytes, and serious mental illness. Glia, 62, 1856–1877. doi:10.1002/glia.22716Google Scholar
Harvison, K. W., Molfese, D. L., Woodruff-Borden, J., & Weigel, R. A. (2009). Neonatal auditory evoked responses are related to perinatal maternal anxiety. Brain and Cognition, 71, 369–374. doi:10.1016/j.bandc.2009.06.004Google Scholar
Hong, S.-B., Zalesky, A., Fornito, A., Park, S., Yang, Y.-H., Park, M.-H., … Kim, J.-W. (2014). Connectomic disturbances in attention-deficit/hyperactivity disorder: A whole-brain tractography analysis. Biological Psychiatry, 76, 656–663. doi:10.1016/j.biopsych.2013.12.013Google Scholar
Howard, L. M., Molyneaux, E., Dennis, C.-L., Rochat, T., Stein, A., & Milgrom, J. (2014). Non-psychotic mental disorders in the perinatal period. Lancet, 384, 1775–1788. doi:10.1016/S0140-6736(14)61276-9Google Scholar
Howard, L. M., Piot, P., & Stein, A. (2014). No health without perinatal mental health. Lancet, 384, 1723–1724. doi:10.1016/S0140-6736(14)62040-7Google Scholar
Huang, A. C., Hu, L., Kauffman, S. A., Zhang, W., & Shmulevich, I. (2009). Using cell fate attractors to uncover transcriptional regulation of HL60 neutrophil differentiation. BMC Systems Biology, 3, 20. doi:10.1186/1752-0509-3-20Google Scholar
Hunter, S. K., Mendoza, J. H., D'Anna, K., Zerbe, G. O., McCarthy, L., Hoffman, C., … Ross, R. G. (2012). Antidepressants may mitigate the effects of prenatal maternal anxiety on infant auditory sensory gating. American Journal Psychiatry, 169, 616–624.Google Scholar
Hyde, L. W. (2015). Developmental psychopathology in an era of molecular genetics and neuroimaging: A developmental neurogenetics approach. Development and Psychopathology, 27, 587–613. doi:10.1017/S0954579415000188Google Scholar
Irimia, M., Weatheritt, R. J., Ellis, J., Parikshak, N. N., Gonatopoulos-Pournatzis, T., Babor, M., … Blencowe, B. J. (2014). A highly conserved program of neuronal microexons is misregulated in autistic brains. Cell, 159, 1511–1523. doi:10.1016/j.cell.2014.11.035Google Scholar
Jakab, A., Kasprian, G., Schwartz, E., Gruber, G. M., Mitter, C., Prayer, D., … Langs, G. (2015). Disrupted developmental organization of the structural connectome in fetuses with corpus callosum agenesis. NeuroImage, 111, 277–288. doi:10.1016/j.neuroimage.2015.02.038Google Scholar
Jakab, A., Schwartz, E., Kasprian, G., Gruber, G. M., Prayer, D., Schöpf, V., & Langs, G. (2014). Fetal functional imaging portrays heterogeneous development of emerging human brain networks. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00852Google Scholar
Jelescu, I. O., Veraart, J., Adisetiyo, V., Milla, S. S., Novikov, D. S., & Fieremans, E. (2015). One diffusion acquisition and different white matter models: How does microstructure change in human early development based on WMTI and NODDI? NeuroImage, 107, 242–256. doi:10.1016/j.neuroimage.2014.12.009Google Scholar
Jiang, X., & Nardelli, J. (2016). Cellular and molecular introduction to brain development. Neurobiology of Disease, 92 (Pt. A), 3–17. doi:10.1016/j.nbd.2015.07.007Google Scholar
Johnson, M. H., Jones, E. J. H., & Gliga, T. (2015). Brain adaptation and alternative developmental trajectories. Development and Psychopathology, 27, 425–442. doi:10.1017/S0954579415000073Google Scholar
Jones, I., Chandra, P. S., Dazzan, P., & Howard, L. M. (2014). Bipolar disorder, affective psychosis, and schizophrenia in pregnancy and the post-partum period. Lancet, 384, 1789–1799. doi:10.1016/S0140-6736(14)61278-2Google Scholar
Kang, H. J., Kawasawa, Y. I., Cheng, F., Zhu, Y., Xu, X., Li, M., … Sestan, N. (2011). Spatio-temporal transcriptome of the human brain. Nature, 478, 483–489.Google Scholar
Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311.Google Scholar
Karst, A. T., & Hutsler, J. J. (2016). Two-dimensional analysis of the supragranular layers in autism spectrum disorder. Research in Autism Spectrum Disorders, 32, 96–105. doi:10.1016/j.rasd.2016.09.004Google Scholar
Khan, S., Gramfort, A., Shetty, N. R., Kitzbichler, M. G., Ganesan, S., Moran, J. M., … Kenet, T. (2013). Local and long-range functional connectivity is reduced in concert in autism spectrum disorders. Proceedings of the National Academy of Sciences, 110, 3107–3112. doi:10.1073/pnas.1214533110Google Scholar
Kipping, J. A., Tuan, T. A., Foiiter, M. V., & Qiu, A. (2016). Asynchronous development of cerebellar, cerebello-cortical, and cortico-cortical functional networks in infancy, childhood, and adulthood. Cerebral Cortex, 12, 1–15.Google Scholar
Klein, A., & Tourville, J. (2012). 101 labeled brain images and a consistent human cortical labeling protocol. Frontiers in Neuroscience, 6. doi:10.3389/fnins.2012.00171Google Scholar
Kofink, D., Boks, M. P. M., Timmers, H. T. M., & Kas, M. J. (2013). Epigenetic dynamics in psychiatric disorders: Environmental programming of neurodevelopmental processes. Neuroscience and Biobehavioral Reviews, 37, 831–845. doi:10.1016/j.neubiorev.2013.03.020Google Scholar
Kostović, I., Jovanov-Milošević, N., Radoš, M., Sedmak, G., Benjak, V., Kostović-Srzentić, M., … Judaš, M. (2014). Perinatal and early postnatal reorganization of the subplate and related cellular compartments in the human cerebral wall as revealed by histological and MRI approaches. Brain Structure and Function, 219, 231–253. doi:10.1007/s00429-012-0496-0Google Scholar
Kostović, I., Judaš, M., & Sedmak, G. (2011). Developmental history of the subplate zone, subplate neurons and interstitial white matter neurons: Relevance for schizophrenia. International Journal of Developmental Neuroscience, 29, 193–205. doi:10.1016/j.ijdevneu.2010.09.005Google Scholar
Kostović, I., Sedmak, G., Vukšić, M., & Judaš, M. (2015). The relevance of human fetal subplate zone for developmental neuropathology of neuronal migration disorders and cortical dysplasia. CNS Neuroscience and Therapeutics, 21, 74–82. doi:10.1111/cns.12333Google Scholar
Koyama, M. S., Di Martino, A., Castellanos, F. X., Ho, E. J., Marcelle, E., Leventhal, B., & Milham, M. P. (2016). Imaging the “at-risk” brain: Future directions. Journal of the International Neuropsychological Society, 22, 164–179. doi:10.1017/S1355617715001356Google Scholar
Kunz, N., Zhang, H., Vasung, L., O'Brien, K. R., Assaf, Y., Lazeyras, F., … Hüppi, P. S. (2014). Assessing white matter microstructure of the newborn with multi-shell diffusion MRI and biophysical compartment models. NeuroImage, 96, 288–299. doi:10.1016/j.neuroimage.2014.03.057Google Scholar
Kushnerenko, E. V., Van den Bergh, B. R. H., & Winkler, I. (2013). Separating acoustic deviance from novelty during the first year of life: A review of event-related potential evidence. Frontiers in Psychology, 4, 595. doi:10.3389/fpsyg.2013.00595Google Scholar
Lebel, C., Gee, M., Camicioli, R., Wieler, M., Martin, W., & Beaulieu, C. (2012). Diffusion tensor imaging of white matter tract evolution over the lifespan. NeuroImage, 60, 340–352. doi:10.1016/j.neuroimage.2011.11.094Google Scholar
Lebel, C., Walton, M., Letourneau, N., Giesbrecht, G. F., Kaplan, B. J., & Dewey, D. (2016). Prepartum and postpartum maternal depressive symptoms are related to children's brain structure in preschool. Biological Psychiatry, 80, 859–868. doi:10.1016/j.biopsych.2015.12.004Google Scholar
Lewis, A. J., Galbally, M., Gannon, T., & Symeonides, C. (2014). Early life programming as a target for prevention of child and adolescent mental disorders. BMC Medicine, 12. doi:10.1186/1741-7015-12-33Google Scholar
Lewitus, E., Kelava, I., & Huttner, W. B. (2013). Conical expansion of the outer subventricular zone and the role of neocortical folding in evolution and development. Frontiers in Human Neuroscience, 7. doi:10.3389/fnhum.2013.00424Google Scholar
Li, G., Nie, J., Wang, L., Shi, F., Lyall, A. E., Lin, W., … Shen, D. (2014). Mapping longitudinal hemispheric structural asymmetries of the human cerebral cortex from birth to 2 years of age. Cerebral Cortex, 24, 1289–1300. doi:10.1093/cercor/bhs413Google Scholar
Li, G., Wang, L., Shi, F., Lyall, A. E., Lin, W., Gilmore, J. H., & Shen, D. (2014). Mapping longitudinal development of local cortical gyrification in infants from birth to 2 years of age. Journal of Neuroscience, 34, 4228–4238. doi:10.1523/JNEUROSCI.3976-13.2014Google Scholar
Loomans, E. M., van Dijk, A. E., Vrijkotte, T. G. M., van Eijsden, M., Stronks, K., Gemke, R. J. B. J., & Van den Bergh, B. R. H. (2013). Psychosocial stress during pregnancy is related to adverse birth outcomes: Results from a large multi-ethnic community-based birth cohort. European Journal of Public Health, 23, 485–491.Google Scholar
Lupien, S. J., Parent, S., Evans, A. C., Tremblay, R. E., Zelazo, P. D., Corbo, V., … Séguin, J. R. (2011). Larger amygdala but no change in hippocampal volume in 10-year-old children exposed to maternal depressive symptomatology since birth. Proceedings of the National Academy of Sciences, 108, 14324–14329. doi:10.1073/pnas.1105371108Google Scholar
Lusby, C. M., Goodman, S. H., Bell, M. A., & Newport, D. J. (2014). Electroencephalogram patterns in infants of depressed mothers. Developmental Psychobiology, 56, 459–473. doi:10.1002/dev.21112Google Scholar
Lusby, C. M., Goodman, S. H., Yeung, E. W., Bell, M. A., & Stowe, Z. N. (2016). Infant EEG and temperament negative affectivity: Coherence of vulnerabilities to mothers' perinatal depression. Development and Psychopathology, 28(4, Pt. 1), 895–911. doi:10.1017/S0954579416000614Google Scholar
Lussier, S. J., & Stevens, H. E. (2016). Delays in GABAergic interneuron development and behavioral inhibition after prenatal stress. Developmental Neurobiology, 76, 1078–1091. doi:10.1002/dneu.22376Google Scholar
Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. J., & Frith, C. D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the United States of America, 97, 4398–4403. doi:10.1073/pnas.070039597Google Scholar
Marami, B., Mohseni Salehi, S. S., Afacan, O., Scherrer, B., Rollins, C. K., Yang, E., … Gholipour, A. (2017). Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis. NeuroImage, 156(Suppl. C), 475–488. doi:10.1016/j.neuroimage.2017.04.033Google Scholar
Margulies, D. S., Böttger, J., Long, X., Lv, Y., Kelly, C., Schäfer, A., … Villringer, A. (2010). Resting developments: A review of fMRI post-processing methodologies for spontaneous brain activity. Magnetic Resonance Materials in Physics, Biology and Medicine, 23, 289–307. doi:10.1007/s10334-010-0228-5Google Scholar
Margulies, D. S., Vincent, J. L., Kelly, C., Lohmann, G., Uddin, L. Q., Biswal, B. B., … Petrides, M. (2009). Precuneus shares intrinsic functional architecture in humans and monkeys. Proceedings of the National Academy of Sciences of the United States of America, 106, 20069–20074. doi:10.1073/pnas.0905314106Google Scholar
Mascheretti, S., De Luca, A., Trezzi, V., Peruzzo, D., Nordio, A., Marino, C., & Arrigoni, F. (2017). Neurogenetics of developmental dyslexia: From genes to behavior through brain neuroimaging and cognitive and sensorial mechanisms. Translational Psychiatry, 7, e987. doi:10.1038/tp.2016.240Google Scholar
Mennes, M., Stiers, P., Lagae, L., & Van den Bergh, B. R. H. (2006). Long-term cognitive sequelae of antenatal maternal anxiety: Involvement of the orbitofrontal cortex. Neuroscience and Biobehavioral Reviews, 30, 1078–1086. doi:10.1016/j.neubiorev.2006.04.003Google Scholar
Mennes, M., Van den Bergh, B. R. H., Lagae, L., & Stiers, P. (2009). Developmental brain alterations in 17 year old boys are related to antenatal maternal anxiety. Clinical Neurophysiology, 120, 1116–1122. doi:10.1016/j.clinph.2009.04.003Google Scholar
Mennes, M., Van den Bergh, B. R. H., Sunaert, S. S., Lagae, L., & Stiers, P. (2016). Antenatal maternal anxiety modulates the BOLD response in 20-year old adolescents during an endogenous cognitive control task. bioRxiv. doi:10.1101/087817Google Scholar
Menon, V. (2013). Developmental pathways to functional brain networks: Emerging principles. Trends in Cognitive Sciences, 17, 627–640. doi:10.1016/j.tics.2013.09.015Google Scholar
Meredith, R. M. (2015). Sensitive and critical periods during neurotypical and aberrant neurodevelopment: A framework for neurodevelopmental disorders. Neuroscience and Biobehavioral Reviews, 50, 180–188. doi:10.1016/j.neubiorev.2014.12.001Google Scholar
Milgrom, J., Holt, C., Holt, C. J., Ross, J., Ericksen, J., & Gemmill, A. W. (2015). Feasibility study and pilot randomised trial of an antenatal depression treatment with infant follow-up. Archives of Women's Mental Health, 18, 717–730. doi:10.1007/s00737-015-0512-5Google Scholar
Miller, K. L., Stagg, C. J., Douaud, G., Jbabdi, S., Smith, S. M., Behrens, T. E. J., … McNab, J. A. (2011). Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner. NeuroImage, 57, 167–181. doi:10.1016/j.neuroimage.2011.03.070Google Scholar
Mills, K. L., & Tamnes, C. K. (2014). Methods and considerations for longitudinal structural brain imaging analysis across development. Developmental Cognitive Neuroscience, 9, 172–190. doi:10.1016/j.dcn.2014.04.004Google Scholar
Molfese, D. L. (2000). Predicting dyslexia at 8 years of age using neonatal brain responses. Brain Language, 72, 238–245.Google Scholar
Mori, S., Kaufmann, W. E., Davatzikos, C., Stieltjes, B., Amodei, L., Fredericksen, K., … van Zijl, P. C. M. (2002). Imaging cortical association tracts in the human brain using diffusion-tensor-based axonal tracking. Magnetic Resonance in Medicine, 47, 215–223.Google Scholar
Mulkey, S. B., Yap, V. L., Bai, S., Ramakrishnaiah, R. H., Glasier, C. M., Bornemeier, R. A., … Bhutta, A. T. (2015). Amplitude-integrated EEG in newborns with critical congenital heart disease predicts preoperative brain magnetic resonance imaging findings. Pediatric Neurology, 52, 599–605. doi:10.1016/j.pediatrneurol.2015.02.026Google Scholar
Müller, B. C. N., Kühn-Popp, N., Meinhardt, J., Sodian, B., & Paulus, M. (2015). Long-term stability in children's frontal EEG alpha asymmetry between 14-months and 83-months. International Journal of Developmental Neuroscience, 41, 110–114. doi:10.1016/j.ijdevneu.2015.01.002Google Scholar
O'Connor, T. G., Monk, C., & Fitelson, E. M. (2014). Practitioner Review: Maternal mood in pregnancy and child development—Implications for child psychology and psychiatry. Journal of Child Psychology and Psychiatry, 55, 99–111. doi:10.1111/jcpp.12153Google Scholar
Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H., & Ugurbil, K. (1992). Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences of the United States of America, 89, 5951–5955.Google Scholar
Otte, R. A., Donkers, F. C. L., Braeken, M. A. K. A., & Van den Bergh, B. R. H. (2015). Multimodal processing of emotional information in 9-month-old infants: II. Prenatal exposure to maternal anxiety. Brain and Cognition, 95, 107–117. doi:10.1016/j.bandc.2014.12.001Google Scholar
Oubel, E., Koob, M., Studholme, C., Dietemann, J.-L., & Rousseau, F. (2010). Reconstruction of scattered data in fetal diffusion MRI. In Jiang, T., Navab, N., Pluim, J. P. W., & Viergever, M. A. (Eds.), Medical image computing and computer-assisted intervention—MICCAI 2010: 13th International Conference, Beijing, China, September 20–24, 2010, Proceedings, Part I (pp. 574–581). Berlin: Springer.Google Scholar
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015). Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nature Reviews Genetics, 16, 441–458. doi:10.1038/nrg3934Google Scholar
Peltola, M. J., Bakermans-Kranenburg, M. J., Alink, L. R. A., Huffmeijer, R., Biro, S., & van IJzendoorn, M. H. (2014). Resting frontal EEG asymmetry in children: Meta-analyses of the effects of psychosocial risk factors and associations with internalizing and externalizing behavior. Developmental Psychobiology, 56, 1377–1389. doi:10.1002/dev.21223Google Scholar
Pletikos, M., Sousa, A. M. M., Sedmak, G., Meyer, K. A., Zhu, Y., Cheng, F., … Šestan, N. (2014). Temporal specification and bilaterality of human neocortical topographic gene expression. Neuron, 81, 321–332. doi:10.1016/j.neuron.2013.11.018Google Scholar
Poldrack, R. A. (2012). The future of fMRI in cognitive neuroscience. NeuroImage, 62, 1216–1220. doi:10.1016/j.neuroimage.2011.08.007Google Scholar
Posner, J., Cha, J., Roy, A. K., Peterson, B. S., Bansal, R., Gustafsson, H. C., … Monk, C. (2016). Alterations in amygdala–prefrontal circuits in infants exposed to prenatal maternal depression. Translational Psychiatry, 6, e935. doi:10.1038/tp.2016.146Google Scholar
Power, J. D., Fair, D. A., Schlaggar, B. L., & Petersen, S. E. (2010). The development of human functional brain networks. Neuron, 67, 735–748. doi:10.1016/j.neuron.2010.08.017Google Scholar
Qin, S., Duan, X., Supekar, K., Chen, H., Chen, T., & Menon, V. (2016). Large-scale intrinsic functional network organization along the long axis of the human medial temporal lobe. Brain Structure and Function, 221, 3237–3258. doi:10.1007/s00429-015-1098-4Google Scholar
Qiu, A., Anh, T. T., Li, Y., Chen, H., Rifkin-Graboi, A., Broekman, B. F. P., … Meaney, M. J. (2015). Prenatal maternal depression alters amygdala functional connectivity in 6-month-old infants. Translational Psychiatry, 5, e508. doi:10.1038/tp.2015.3Google Scholar
Qiu, A., Rifkin-Graboi, A., Chen, H., Chong, Y. S., Kwek, K., Gluckman, P. D., … Meaney, M. J. (2013). Maternal anxiety and infants' hippocampal development: Timing matters. Translational Psychiatry, 3, e306. doi:10.1038/tp.2013.79Google Scholar
Qiu, A., Tuan, T. A., Ong, M. L., Li, Y., Chen, H., Rifkin-Graboi, A., … Gluckman, P. D. (2015). COMT haplotypes modulate associations of antenatal maternal anxiety and neonatal cortical morphology. American Journal of Psychiatry, 172, 163–172. doi:10.1176/appi.ajp.2014.14030313Google Scholar
Rakers, F., Rupprecht, S., Dreiling, M., Bergmeier, C., Witte, O. W., & Schwab, M. (2017). Transfer of maternal psychosocial stress to the fetus. Neuroscience and Biobehavioral Reviews. Advance online publication. doi:10.1016/j.neubiorev.2017.02.019Google Scholar
Reid, L. B., Sale, M. V., Cunnington, R., Mattingley, J. B., & Rose, S. E. (2017). Brain changes following four weeks of unimanual motor training: Evidence from fMRI-guided diffusion MRI tractography. Human Brain Mapping, 9, 4302–4312 doi:10.1002/hbm.23514Google Scholar
Reuter, M., Tisdall, M. D., Qureshi, A., Buckner, R. L., van der Kouwe, A. J. W., & Fischl, B. R. (2015). Head motion during MRI acquisition reduces gray matter volume and thickness estimates. NeuroImage, 107, 107–115. doi:10.1016/j.neuroimage.2014.12.006Google Scholar
Richmond, S., Johnson, K. A., Seal, M. L., Allen, N. B., & Whittle, S. (2016). Development of brain networks and relevance of environmental and genetic factors: A systematic review. Neuroscience and Biobehavioral Reviews, 71, 215–239. doi:10.1016/j.neubiorev.2016.08.024Google Scholar
Rifkin, L., Lewis, S., Jones, P., Toone, B., & Murray, R. (1994). Low birth weight and schizophrenia. British Journal of Psychiatry, 165, 357–362. doi:10.1192/bjp.165.3.357Google Scholar
Rifkin-Graboi, A., Bai, J., Chen, H., Hameed, W. B. R., Sim, L. W., Tint, M. T., … Qiu, A. (2013). Prenatal maternal depression associates with microstructure of right amygdala in neonates at birth. Biological Psychiatry, 74, 837–844. doi:10.1016/j.biopsych.2013.06.019Google Scholar
Rifkin-Graboi, A., Meaney, M. J., Chen, H., Bai, J., Hameed, W. B. R., Tint, M. T., … Qiu, A. (2015). Antenatal maternal anxiety predicts variations in neural structures implicated in anxiety disorders in newborns. Journal of the American Academy of Child & Adolescent Psychiatry, 54, 313–321. doi:10.1016/j.jaac.2015.01.013Google Scholar
Roalf, D. R., Quarmley, M., Elliott, M. A., Satterthwaite, T. D., Vandekar, S. N., Ruparel, K., … Gur, R. E. (2016). The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort. NeuroImage, 125, 903–919. doi:10.1016/j.neuroimage.2015.10.068Google Scholar
Robinson, E. C., Jbabdi, S., Glasser, M. F., Andersson, J., Burgess, G. C., Harms, M. P., … Jenkinson, M. (2014). MSM: A new flexible framework for Multimodal Surface Matching. NeuroImage, 100, 414–426. doi:10.1016/j.neuroimage.2014.05.069Google Scholar
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059–1069. doi:10.1016/j.neuroimage.2009.10.003Google Scholar
Rutkowski, T. P., Schroeder, J. P., Gafford, G. M., Warren, S. T., Weinshenker, D., Caspary, T., & Mulle, J. G. (2017). Unraveling the genetic architecture of copy number variants associated with schizophrenia and other neuropsychiatric disorders. Journal of Neuroscience Research, 95, 1144–1160. doi:10.1002/jnr.23970Google Scholar
Sandman, C. A., Buss, C., Head, K., & Davis, E. P. (2015). Fetal exposure to maternal depressive symptoms is associated with cortical thickness in late childhood. Biological Psychiatry, 77, 324–334. doi:10.1016/j.biopsych.2014.06.025Google Scholar
Sarkar, S., Craig, M. C., Dell'Acqua, F., O'Connor, T. G., Catani, M., Deeley, Q., … Murphy, D. G. M. (2014). Prenatal stress and limbic-prefrontal white matter microstructure in children aged 6–9 years: A preliminary diffusion tensor imaging study. World Journal of Biological Psychiatry, 15, 346–352. doi:10.3109/15622975.2014.903336Google Scholar
Schaer, M., Cuadra, M. B., Tamarit, L., Lazeyras, F., Eliez, S., & Thiran, J.-P. (2008). A surface-based approach to quantify local cortical gyrification. IEEE Transactions on Medical Imaging, 27, 161–170. doi:10.1109/TMI.2007.903576Google Scholar
Scheinost, D., Kwon, S. H., Lacadie, C., Sze, G., Sinha, R., Constable, R. T., & Ment, L. R. (2016). Prenatal stress alters amygdala functional connectivity in preterm neonates. NeuroImage: Clinical, 12, 381–388. doi:10.1016/j.nicl.2016.08.010Google Scholar
Scheinost, D., Sinha, R., Cross, S. N., Kwon, S. H., Sze, G., Constable, R. T., & Ment, L. R. (2016). Does prenatal stress alter the developing connectome? Pediatric Research. Advance online publication. doi:10.1038/pr.2016.197Google Scholar
Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., … Mühlau, M. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage, 59, 3774–3783. doi:10.1016/j.neuroimage.2011.11.032Google Scholar
Schöpf, V., Kasprian, G., Brugger, P. C., & Prayer, D. (2012). Watching the fetal brain at “rest.” International Journal of Developmental Neuroscience, 30, 11–17. doi:10.1016/j.ijdevneu.2011.10.006Google Scholar
Seckl, J. R. (2007). Glucocorticoids, developmental “programming” and the risk of affective dysfunction. In De Kloet, E. R., Oitzl, M. S., & Vermetten, E. (Eds.), Progress in brain research (Vol. 167, pp. 17–34). New York: Elsevier.Google Scholar
Seiradake, E., Jones, E. Y. J., & Klein, R. (2016). Structural perspectives on axon guidance. Annual Review of Cell and Developmental Biology, 32, 577–608. doi:10.1146/annurev-cellbio-111315-125008Google Scholar
Silbereis, J. C., Pochareddy, S., Zhu, Y., Li, M., & Sestan, N. (2016). The cellular and molecular landscapes of the developing human central nervous system. Neuron, 89, 248–268. doi:10.1016/j.neuron.2015.12.008Google Scholar
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., … Beckmann, C. F. (2009). Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences, 106, 13040–13045. doi:10.1073/pnas.0905267106Google Scholar
Smyser, C. D., Inder, T. E., Shimony, J. S., Hill, J. E., Degnan, A. J., Snyder, A. Z., & Neil, J. J. (2010). Longitudinal analysis of neural network development in preterm infants. Cerebral Cortex, 20, 2852–2862. doi:10.1093/cercor/bhq035Google Scholar
Soe, N. N., Wen, D. J., Poh, J. S., Li, Y., Broekman, B. F. P., Chen, H., … Qiu, A. (2016). Pre- and post-natal maternal depressive symptoms in relation with infant frontal function, connectivity, and behaviors. PLOS ONE, 11, e0152991. doi:10.1371/journal.pone.0152991Google Scholar
Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8, 418–425. doi:10.1016/j.tics.2004.07.008Google Scholar
Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: A structural description of the human brain. PLOS Computational Biology, 1, e42. doi:10.1371/journal.pcbi.0010042Google Scholar
Sroufe, L. A., & Rutter, M. (1984). The domain of developmental psychopathology. Child Development, 55, 17–19.Google Scholar
Stam, C. J., Tewarie, P., van Dellen, E., van Straaten, E. C. W., Hillebrand, A., & van Mieghem, P. (2014). The trees and the forest: Characterization of complex brain networks with minimum spanning trees. International Journal of Psychophysiology, 92, 129–138. doi:10.1016/j.ijpsycho.2014.04.001Google Scholar
Stam, C. J., & van Straaten, E. C. W. (2012). The organization of physiological brain networks. Clinical Neurophysiology, 123, 1067–1087. doi:10.1016/j.clinph.2012.01.011Google Scholar
Stein, A., Pearson, R. M., Goodman, S. H., Rapa, E., Rahman, A., McCallum, M., … Pariante, C. M. (2014). Effects of perinatal mental disorders on the fetus and child. Lancet, 384, 1800–1819. doi:10.1016/S0140-6736(14)61277-0Google Scholar
Striedter, G. F., Srinivasan, S., & Monuki, E. S. (2015). Cortical folding: When, where, how, and why? Annual Review of Neuroscience, 38, 291–307. doi:10.1146/annurev-neuro-071714-034128Google Scholar
Suárez-Solá, M. L., González-Delgado, F. J., Pueyo-Morlans, M., Medina-Bolívar, O. C., Hernández-Acosta, N. C., González-Gómez, M., & Meyer, G. (2009). Neurons in the white matter of the adult human neocortex. Frontiers in Neuroanatomy, 3, 7. doi:10.3389/neuro.05.007.2009Google Scholar
Swanson, L. W., & Lichtman, J. W. (2016). From Cajal to connectome and beyond. Annual Review of Neuroscience, 39. doi:10.1146/annurev-neuro-071714-033954Google Scholar
Tallinen, T., & Biggins, J. S. (2015). Mechanics of invagination and folding: Hybridized instabilities when one soft tissue grows on another. Physical Review E, 92, 022720. doi:10.1103/PhysRevE.92.022720Google Scholar
Tallinen, T., Chung, J. Y., Rousseau, F., Girard, N., Lefèvre, J., & Mahadevan, L. (2016). On the growth and form of cortical convolutions. Nature Physics. Advance online publication. doi:10.1038/nphys3632Google Scholar
Tardif, C. L., Schäfer, A., Waehnert, M., Dinse, J., Turner, R., & Bazin, P.-L. (2015). Multi-contrast multi-scale surface registration for improved alignment of cortical areas. NeuroImage, 111, 107–122. doi:10.1016/j.neuroimage.2015.02.005Google Scholar
Teipel, S. J., Bokde, A. L. W., Meindl, T., Amaro, E., Soldner, J., Reiser, M. F., … Hampel, H. (2010). White matter microstructure underlying default mode network connectivity in the human brain. NeuroImage, 49, 2021–2032. doi:10.1016/j.neuroimage.2009.10.067Google Scholar
Thapar, A., Cooper, M., & Rutter, M. (2017). Neurodevelopmental disorders. Lancet Psychiatry, 4, 339–346. doi:10.1016/S2215-0366(16)30376-5Google Scholar
Thayer, J. F., Åhs, F., Fredrikson, M., Sollers, J. J., & Wager, T. D. (2012). A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience and Biobehavioral Reviews, 36, 747–756. doi:10.1016/j.neubiorev.2011.11.009Google Scholar
Thomason, M. E., Brown, J. A., Dassanayake, M. T., Shastri, R., Marusak, H. A., Hernandez-Andrade, E., … Romero, R. (2014). Intrinsic functional brain architecture derived from graph theoretical analysis in the human fetus. PLOS ONE, 9, e94423. doi:10.1371/journal.pone.0094423Google Scholar
Thomason, M. E., Dassanayake, M. T., Shen, S., Katkuri, Y., Alexis, M., Anderson, A. L., … Romero, R. (2013). Cross-hemispheric functional connectivity in the human fetal brain. Science Translational Medicine, 5, 173ra124–173ra124. doi:10.1126/scitranslmed.3004978Google Scholar
Thomason, M. E., Grove, L. E., Lozon, T. A. Jr., Vila, A. M., Ye, Y., Nye, M. J., … Romero, R. (2015). Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero. Developmental Cognitive Neuroscience, 11, 96–104. doi:10.1016/j.dcn.2014.09.001Google Scholar
Thomason, M. E., Scheinost, D., Manning, J. H., Grove, L. E., Hect, J., Marshall, N., … Romero, R. (2017). Weak functional connectivity in the human fetal brain prior to preterm birth. Scientific Reports, 7, 39286. doi:10.1038/srep39286Google Scholar
Thompson, C., Syddall, H., Rodin, I., Omond, C., & Barker, D. J. P. (2001). Birth weight and the risk of depressive disorder in late life. British Journal of Psychiatry, 179, 450–455. doi:10.1192/bjp.179.5.450Google Scholar
Toro, R. (2012). On the possible shapes of the brain. Evolutionary Biology, 39, 600–612. doi:10.1007/s11692-012-9201-8Google Scholar
Tóth, B., Urbán, G., Háden, G. P., Márk, M., Török, M., Stam, C. J., & Winkler, I. (2017). Large-scale network organization of EEG functional connectivity in newborn infants. Human Brain Mapping, 38, 4019–4033. doi:10.1002/hbm.23645Google Scholar
Tourbier, S., Velasco-Annis, C., Taimouri, V., Hagmann, P., Meuli, R., Warfield, S. K., … Gholipour, A. (2017). Automated template-based brain localization and extraction for fetal brain MRI reconstruction. NeuroImage, 155(Suppl. C), 460–472. doi:10.1016/j.neuroimage.2017.04.004Google Scholar
Turner, B. M., Forstmann, B. U., Love, B. C., Palmeri, T. J., & van Maanen, L. (2017). Approaches to analysis in model-based cognitive neuroscience. Journal of Mathematical Psychology, 76(Pt. B), 65–79. doi:10.1016/j.jmp.2016.01.001Google Scholar
Turner, B. M., Rodriguez, C. A., Norcia, T. M., McClure, S. M., & Steyvers, M. (2016). Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data. NeuroImage, 128, 96–115. doi:10.1016/j.neuroimage.2015.12.030Google Scholar
Van den Bergh, B. R. H. (2011). Developmental programming of early brain and behaviour development and mental health: A conceptual framework. Developmental Medicine and Child Neurology, 53, 19–23. doi:10.1111/j.1469-8749.2011.04057.xGoogle Scholar
Van den Bergh, B. R. H. (2016). Maternal anxiety, mindfulness, and heart rate variability during pregnancy influence fetal and infant development. In Reissland, N. & Kisilevsky, B. S. (Eds.), Fetal development: Research on brain and behavior, environmental influences, and emerging technologies (pp. 267–292). Cham, Switzerland: Springer.Google Scholar
Van den Bergh, B. R. H., & Marcoen, A. (2004). High antenatal maternal anxiety is related to ADHD symptoms, externalizing problems, and anxiety in 8- and 9-year-olds. Child Development, 75, 1085–1097. doi:10.1111/j.1467-8624.2004.00727.xGoogle Scholar
Van den Bergh, B. R. H., Mennes, M., Oosterlaan, J., Stevens, V., Stiers, P., Marcoen, A., & Lagae, L. (2005). High antenatal maternal anxiety is related to impulsivity during performance on cognitive tasks in 14- and 15-year-olds. Neuroscience and Biobehavioral Reviews, 29, 259–269. doi:10.1016/j.neubiorev.2004.10.010Google Scholar
Van den Bergh, B. R. H., Mennes, M., Stevens, V., van der Meere, J., Borger, N., Stiers, P., … Lagae, L. (2006). ADHD deficit as measured in adolescent boys with a continuous performance task is related to antenatal maternal anxiety. Pediatric Reserch, 59, 78–82.Google Scholar
Van den Bergh, B. R. H., Mulder, E. J. H., Visser, G. H. A., Poelmann-Weesjes, G., Bekedam, D. J., & Prechtl, H. F. R. (1989). The effect of (induced) maternal emotions on fetal behaviour: A controlled study. Early Human Development, 19, 9–19. doi:10.1016/0378-3782(89)90100-XGoogle Scholar
Van den Bergh, B. R. H., van den Heuvel, M. I., Lahti, M., Braeken, M., de Rooij, S. R., Entringer, S., … Schwab, M. (2017). Prenatal developmental origins of behavior and mental health: The influence of maternal stress in pregnancy. Neuroscience and Biobehavioral Reviews. Advance online publication. doi:10.1016/j.neubiorev.2017.07.003Google Scholar
van den Heuvel, M. I., Donkers, F. C., Winkler, I., Otte, R. A., & Van den Bergh, B. R. H. (2015). Maternal mindfulness and anxiety during pregnancy affect infants' neural responses to sounds. Social Cognitive Affective Neurosciience, 10, 453–460. doi:10.1093/scan/nsu075Google Scholar
van den Heuvel, M. I., Henrichs, J., Donkers, F. C., & Van den Bergh, B. R. H. (in press). Children prenatally exposed to maternal anxiety devote more attentional resources to neutral pictures. Developmental Science. doi:10.1111/desc.12612Google Scholar
van den Heuvel, M. I., Mandl, R., & Hulshoff Pol, H. (2008). Normalized cut group clustering of resting-state fMRI data. PLOS ONE, 3, e2001. doi:10.1371/journal.pone.0002001Google Scholar
van den Heuvel, M. I., & Thomason, M. E. (2016). Functional connectivity of the human brain in utero. Trends in Cognitive Sciences. Advance online publication. doi:10.1016/j.tics.2016.10.001Google Scholar
van Essen, D. C., & Barch, D. M. (2015). The human connectome in health and psychopathology. World Psychiatry, 14, 154–157. doi:10.1002/wps.20228Google Scholar
van Essen, D. C., & Maunsell, J. H. R. (1980). Two-dimensional maps of the cerebral cortex. Journal of Comparative Neurology, 191, 255–281. doi:10.1002/cne.901910208Google Scholar
Vértes, P. E., & Bullmore, E. T. (2015). Annual Research Review: Growth connectomics—The organization and reorganization of brain networks during normal and abnormal development. Journal of Child Psychology and Psychiatry, 56, 299–320. doi:10.1111/jcpp.12365Google Scholar
von Leupoldt, A., Mangelschots, E., Niederstrasser, N. G., Braeken, M., Billiet, T., & Van den Bergh, B. R. H. (2017). Prenatal stress exposure is associated with increased dyspnea perception in adulthood. European Respiratory Journal. Advance online publication.Google Scholar
Walhovd, K. B., Fjell, A. M., Giedd, J., Dale, A. M., & Brown, T. T. (2016). Through thick and thin: A need to reconcile contradictory results on trajectories in human cortical development. Cerebral Cortex. Advance online publication. doi:10.1093/cercor/bhv301Google Scholar
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442. doi:10.1038/30918Google Scholar
Wegiel, J., Kuchna, I., Nowicki, K., Imaki, H., Wegiel, J., Marchi, E., … Wisniewski, T. (2010). The neuropathology of autism: Defects of neurogenesis and neuronal migration, and dysplastic changes. Acta Neuropathologica, 119, 755–770. doi:10.1007/s00401-010-0655-4Google Scholar
Weiskopf, N., Suckling, J., Williams, G. B., Correia, M. M., Inkster, B., Tait, R., … Lutti, A. (2013). Quantitative multi-parameter mapping of R1, PD, MT, and R2 at 3T: A multi-center validation. Frontiers in Neuroscience, 7, 95. doi:10.3389/fnins.2013.00095Google Scholar
Wen, D. J., Poh, J. S., Ni, S. N., Chong, Y. S., Chen, H., Kwek, K., … Qiu, A. (2017). Influences of prenatal and postnatal maternal depression on amygdala volume and microstructure in young children. Translational Psychiatry, 7, e1103. doi:10.1038/tp.2017.74Google Scholar
Wen, Z. (2017). Modeling neurodevelopmental and psychiatric diseases with human iPSCs. Journal of Neuroscience Research, 95, 1097–1109. doi:10.1002/jnr.24031Google Scholar
Williams, V. J., Juranek, J., Cirino, P., & Fletcher, J. M. (2017). Cortical thickness and local gyrification in children with developmental dyslexia. Cerebral Cortex. Advance online publication.Google Scholar
Winkler, A. M., Kochunov, P. V., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., … Glahn, D. C. (2010). Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. NeuroImage, 53, 1135–1146. doi: 10.1016/j.neuroimage.2009.12.028Google Scholar
Winston, G. P., Micallef, C., Symms, M. R., Alexander, D. C., Duncan, J. S., & Zhang, H. (2014). Advanced diffusion imaging sequences could aid assessing patients with focal cortical dysplasia and epilepsy. Epilepsy Research, 108, 336–339. doi:10.1016/j.eplepsyres.2013.11.004Google Scholar
Wright, R., Kyriakopoulou, V., Ledig, C., Rutherford, M. A., Hajnal, J. V., Rueckert, D., & Aljabar, P. (2014). Automatic quantification of normal cortical folding patterns from fetal brain MRI. NeuroImage, 91(Suppl. C), 21–32. doi:10.1016/j.neuroimage.2014.01.034Google Scholar
Wright, R., Makropoulos, A., Kyriakopoulou, V., Patkee, P. A., Koch, L. M., Rutherford, M. A., … Aljabar, P. (2015). Construction of a fetal spatio-temporal cortical surface atlas from in utero MRI: Application of spectral surface matching. NeuroImage, 120(Suppl. C), 467–480. doi:10.1016/j.neuroimage.2015.05.087Google Scholar
Yu, Q., Ouyang, A., Chalak, L., Jeon, T., Chia, J., Mishra, V., … Huang, H. (2015). Structural development of human fetal and preterm brain cortical plate based on population-averaged templates. Cerebral Cortex. Advance online publication. doi:10.1093/cercor/bhv201Google Scholar
Zhan, J., Dinov, I. D., Li, J., Zhang, Z., Hobel, S., Shi, Y., … Liu, S. (2013). Spatial–temporal atlas of human fetal brain development during the early second trimester. NeuroImage, 82, 115–126. doi:10.1016/j.neuroimage.2013.05.063Google Scholar
Ziats, M. N., Grosvenor, L. P., & Rennert, O. M. (2015). Functional genomics of human brain development and implications for autism spectrum disorders. Translational Psychiatry, 5, e665. doi:10.1038/tp.2015.153Google Scholar
Ziegler, G., Ridgway, G. R., Dahnke, R., Gaser, C., & Alzheimer's Disease Neuroimaging Initiative. (2014). Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects. NeuroImage, 97(Suppl. C), 333–348. doi:10.1016/j.neuroimage.2014.04.01Google Scholar