References
- Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Varoquaux, G., Thirion, B., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics, 8, 14. https://doi.org/https://doi.org/10.3389/fninf.2014.00014
- Adler, N. E., Boyce, T., Chesney, M. A., Cohen, S., Folkman, S., Kahn, R. L., & Syme, S. L. (1994). Socioeconomic status and health. The challenge of the gradient. The American Psychologist, 49(1), 15–24. https://doi.org/https://doi.org/10.1037//0003-066x.49.1.15
- Alcala-Lopez, D., Smallwood, J., Jefferies, E., Van Overwalle, F., Vogeley, K., Mars, R. B., Turetsky, B. I., Laird, A. R., Fox, P. T., Eickhoff, S. B., & Bzdok, D. (2018). Computing the social brain connectome across systems and states. Cerebral Cortex, 28(7), 2207–2232. https://doi.org/https://doi.org/10.1093/cercor/bhx121
- Alfaro-Almagro, F., Jenkinson, M., Bangerter, N. K., Andersson, J. L. R., Griffanti, L., Douaud, G., Sotiropoulos, S. N., Jbabdi, S., Hernandez-Fernandez, M., Vallee, E., Vidaurre, D., Webster, M., McCarthy, P., Rorden, C., Daducci, A., Alexander, D. C., Zhang, H., Dragonu, I., Matthews, P. M., & Smith, S. M. (2018). Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage, 166(3), 400–424. https://doi.org/https://doi.org/10.1016/j.neuroimage.2017.10.034
- Andersson, J. L., Jenkinson, M., & Smith, S. (2007). Non-linear registration aka Spatial normalisation FMRIB technical report TR07JA2. FMRIB Analysis Group of the University of Oxford 2(1), 1–22. https://www.fmrib.ox.ac.uk/datasets/techrep/tr07ja2/tr07ja2.pdf
- Ansell, E. B., Rando, K., Tuit, K., Guarnaccia, J., & Sinha, R. (2012). Cumulative adversity and smaller gray matter volume in medial prefrontal, anterior cingulate, and insula regions. Biological Psychiatry, 72(1), 57–64. https://doi.org/https://doi.org/10.1016/j.biopsych.2011.11.022
- Bickart, K. C., Wright, C. I., Dautoff, R. J., Dickerson, B. C., & Barrett, L. F. (2011). Amygdala volume and social network size in humans. Nature Neuroscience, 14(2), 163–164. https://doi.org/https://doi.org/10.1038/nn.2724
- Brito, N. H., & Noble, K. G. (2014). Socioeconomic status and structural brain development. Frontiers in Neuroscience, 8, 276. https://doi.org/https://doi.org/10.3389/fnins.2014.00276
- Butterworth, P., Cherbuin, N., Sachdev, P., & Anstey, K. J. (2012). The association between financial hardship and amygdala and hippocampal volumes: Results from the PATH through life project. Social Cognitive and Affective Neuroscience, 7(5), 548–556. https://doi.org/https://doi.org/10.1093/scan/nsr027
- Bzdok, D., Eickenberg, M., Varoquaux, G., & Thirion, B. (2017). Hierarchical region-network sparsity for high-dimensional inference in brain imaging. Information Processing in Medical Imaging: Proceedings of the . Conference, 10265, 323–335. https://doi.org/https://doi.org/10.1007/978-3-319-59050-9_26
- Bzdok, D., Floris, D. L., & Marquand, A. F. (2020). Analysing brain networks in population neuroscience: A case for the Bayesian philosophy. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1796), 20190661. https://doi.org/https://doi.org/10.1098/rstb.2019.0661
- Eisenberger, N. I., Taylor, S. E., Gable, S. L., Hilmert, C. J., & Lieberman, M. D. (2007). Neural pathways link social support to attenuated neuroendocrine stress responses. Neuroimage, 35(4), 1601–1612. https://doi.org/https://doi.org/10.1016/j.neuroimage.2007.01.038
- Eisenberger, N. I., Inagaki, T. K., Muscatell, K. A., Byrne Haltom, K. E., & Leary, M. R. (2011). The neural sociometer: Brain mechanisms underlying state self-esteem. Journal of Cognitive Neuroscience, 23(11), 3448–3455. https://doi.org/https://doi.org/10.1162/jocn_a_00027
- Farah, M. J. (2017). The neuroscience of socioeconomic status: Correlates, causes, and consequences. Neuron, 96(1), 56–71. https://doi.org/https://doi.org/10.1016/j.neuron.2017.08.034
- Foubert-Samier, A., Catheline, G., Amieva, H., Dilharreguy, B., Helmer, C., Allard, M., & Dartigues, J. F. (2012). Education, occupation, leisure activities, and brain reserve: A population-based study. Neurobiology of Aging, 33(2), 423–e15. https://doi.org/https://doi.org/10.1016/j.neurobiolaging.2010.09.023
- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. CRC press.
- Gianaros, P. J., Kuan, D. C., Marsland, A. L., Sheu, L. K., Hackman, D. A., Miller, K. G., & Manuck, S. B. (2017). Community socioeconomic disadvantage in midlife relates to cortical morphology via neuroendocrine and cardiometabolic pathways. Cerebral Cortex (New York, N.Y.: 1991), 27(1), 460–473. https://doi.org/https://doi.org/10.1093/cercor/bhv233
- Gwatkin, D. R. (2017). Trends in health inequalities in developing countries. The Lancet Global Health, 5(4), e371–e372. https://doi.org/https://doi.org/10.1016/S2214-109X(17)30080-3
- Hackman, D. A., & Farah, M. J. (2009). Socioeconomic status and the developing brain. Trends in Cognitive Sciences, 13(2), 65–73. https://doi.org/https://doi.org/10.1016/j.tics.2008.11.003
- Holland, E., Wolf, E. B., Looser, C., & Cuddy, A. (2017). Visual attention to powerful postures: People avert their gaze from nonverbal dominance displays. Journal of Experimental Social Psychology, 68(1), 60–67. https://doi.org/https://doi.org/10.1016/j.jesp.2016.05.001
- Inagaki, T. K., Muscatell, K. A., Irwin, M. R., Cole, S. W., & Eisenberger, N. I. (2012). Inflammation selectively enhances amygdala activity to socially threatening images. Neuroimage, 59(4), 3222–3226. https://doi.org/https://doi.org/10.1016/j.neuroimage.2011.10.090
- Izuma, K., Saito, D. N., & Sadato, N. (2008). Processing of social and monetary rewards in the human striatum. Neuron, 58(2), 284–294. https://doi.org/https://doi.org/10.1016/j.neuron.2008.03.020
- Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2), 143–156. https://doi.org/https://doi.org/10.1016/s1361-8415(01)00036-6
- Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825–841. https://doi.org/https://doi.org/10.1016/s1053-8119(02)91132-8
- Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature Neuroscience, 10(12), 1625–1633. https://doi.org/https://doi.org/10.1038/nn2007
- Kanai, R., & Rees, G. (2011). The structural basis of inter-individual differences in human behaviour and cognition. Nature Reviews. Neuroscience, 12(4), 231–242. https://doi.org/https://doi.org/10.1038/nrn3000
- Kernbach, J. M., Yeo, B. T. T., Smallwood, J., Margulies, D. S., Thiebaut de Schotten, M., Walter, H., Sabuncu, M. R., Holmes, A. J., Gramfort, A., Varoquaux, G., Thirion, B., & Bzdok, D. (2018). Subspecialization within default mode nodes characterized in 10,000 UK Biobank participants. Proceedings of the National Academy of Sciences of the United States of America, 115(48), 12295–12300. https://doi.org/https://doi.org/10.1073/pnas.1804876115
- Kiesow, H., Dunbar, R. I. M., Kable, J. W., Kalenscher, T., Vogeley, K., Schilbach, L., Marquand, A. F., Wiecki, T. V., & Bzdok, D. (2020). 10,000 social brains: Sex differentiation in human brain anatomy. Science Advances, 6(12), eaaz1170. https://doi.org/https://doi.org/10.1126/sciadv.aaz1170
- Kiesow, H., Uddin, L. Q., Bernhardt, B. C., Kable, J., & Bzdok, D. (2021). Dissecting the midlife crisis: Disentangling social, personality and demographic determinants in social brain anatomy. Communications Biology, 4(1), 1–17. https://doi.org/https://doi.org/10.1038/s42003-021-02206-x
- Kraus, M. W., Cote, S., & Keltner, D. (2010). Social class, contextualism, and empathic accuracy. Psychological Science, 21(11), 1716–1723. https://doi.org/https://doi.org/10.1177/0956797610387613
- Kruschke, J. (2011). Doing Bayesian data analysis: A tutorial with R and BUGS. Academic Press/Elsevier.
- Li, S., Zhang, Q., & Muennig, P. (2018). Subjective assessments of income and social class on health and survival: An enigma. SSM - Population Health, 6(3), 295–300. https://doi.org/https://doi.org/10.1016/j.ssmph.2017.10.005
- Mackenbach, J. P., Bos, V., Andersen, O., Cardano, M., Costa, G., Harding, S., Reid, A., Hemström, Ö., Valkonen, T., & Kunst, A. E. (2003). Widening socioeconomic inequalities in mortality in six Western European countries. International Journal of Epidemiology, 32(5), 830–837. https://doi.org/https://doi.org/10.1093/ije/dyg209
- Marmot, M. G., Rose, G., Shipley, M., & Hamilton, P. J. (1978). Employment grade and coronary heart disease in British civil servants. Journal of Epidemiology & Community Health , 32(4), 244–249. https://doi.org/https://doi.org/10.1136/jech.32.4.244
- Mason, M., Magee, J. C., & Fiske, S. T. (2014). Neural substrates of social status inference: Roles of medial prefrontal cortex and superior temporal sulcus. Journal of Cognitive Neuroscience, 26(5), 1131–1140. https://doi.org/https://doi.org/10.1162/jocn_a_00553
- Mesulam, M. M. (1998). From sensation to cognition. Brain: a Journal of Neurology, 121(6), 1013–1052. https://doi.org/https://doi.org/10.1093/brain/121.6.1013
- Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., Griffanti, L., Douaud, G., Okell, T. W., Weale, P., Dragonu, I., Garratt, S., Hudson, S., Collins, R., Jenkinson, M., & Smith, S. M. (2016). Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience, 19(11), 1523–1536. https://doi.org/https://doi.org/10.1038/nn.4393
- Mitchell, J. P. (2009). Social psychology as a natural kind. Trends in Cognitive Sciences, 13(6), 246–251. https://doi.org/https://doi.org/10.1016/j.tics.2009.03.008
- Muscatell, K. A. (2018). Socioeconomic influences on brain function: Implications for health. Annals of the New York Academy of Sciences, 1428(1), 14–32. https://doi.org/https://doi.org/10.1111/nyas.13862
- Mutz, J., Roscoe, C. J., & Lewis, C. M. (2021). Exploring health in the UK Biobank: Associations with sociodemographic characteristics, psychosocial factors, lifestyle and environmental exposures. BMC Medicine, 19(1), 1–18. https://doi.org/https://doi.org/10.1186/s12916-021-02097-z
- Patel, J. A., Nielsen, F. B. H., Badiani, A. A., Assi, S., Unadkat, V. A., Patel, B., Ravindrane, R., & Wardle, H. (2020). Poverty, inequality and COVID-19: The forgotten vulnerable. Public Health, 183(6), 110–111. https://doi.org/https://doi.org/10.1016/j.puhe.2020.05.006
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., and Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of Machine Learning Research, 12(85), 2825–2830. http://jmlr.org/papers/v12/pedregosa11a.html
- Pelphrey, K. A., Viola, R. J., & McCarthy, G. (2004). When strangers pass: Processing of mutual and averted social gaze in the superior temporal sulcus. Psychological Science, 15(9), 598–603. https://doi.org/https://doi.org/10.1111/j.0956-7976.2004.00726.x
- Rademacher, L., Salama, A., Grunder, G., & Spreckelmeyer, K. N. (2014). Differential patterns of nucleus accumbens activation during anticipation of monetary and social reward in young and older adults. Social Cognitive and Affective Neuroscience, 9(6), 825–831. https://doi.org/https://doi.org/10.1093/scan/nst047
- Sapolsky, R. M. (2004). Social status and health in humans and other animals. Annual Review of Anthropology, 33(1), 393–418. https://doi.org/https://doi.org/10.1146/annurev.anthro.33.070203.144000
- Sapolsky, R. M. (2005). The influence of social hierarchy on primate health. Science, 308(5722), 648–652. https://doi.org/https://doi.org/10.1126/science.1106477
- Saxe, R., & Kanwisher, N. (2003). People thinking about thinking people. The role of the temporo-parietal junction in “theory of mind”. Neuroimage, 19(4), 1835–1842. https://doi.org/https://doi.org/10.1016/s1053-8119(03)00230-1
- Shaked, D., Millman, Z. B., Moody, D. L. B., Rosenberger, W. F., Shao, H., Katzel, L. I., & Waldstein, S. R. (2019). Sociodemographic disparities in corticolimbic structures. PloS one, 14(5), e0216338. https://doi.org/https://doi.org/10.1371/journal.pone.0216338
- Shweikh, Y., Ko, F., Chan, M. P. Y., Patel, P. J., Muthy, Z., Khaw, P. T., Foster, P. J., Strouthidis, N., & Foster, P. J. (2015). Measures of socioeconomic status and self-reported glaucoma in the UK Biobank cohort. Eye, 29(10), 1360–1367. https://doi.org/https://doi.org/10.1038/eye.2015.157
- Singh-Manoux, A., Marmot, M. G., & Adler, N. E. (2005). Does subjective social status predict health and change in health status better than objective status? Psychosomatic Medicine, 67(6), 855–861. https://doi.org/https://doi.org/10.1097/01.psy.0000188434.52941.a0
- Singh-Manoux, A., Gueguen, A., Martikainen, P., Ferrie, J., Marmot, M., & Shipley, M. (2007). Self-rated health and mortality: Short- and long-term associations in the Whitehall II study. Psychosomatic Medicine, 69(2), 138–143. https://doi.org/https://doi.org/10.1097/PSY.0b013e318030483a
- Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155. https://doi.org/https://doi.org/10.1002/hbm.10062
- Staff, R. T., Murray, A. D., Ahearn, T. S., Mustafa, N., Fox, H. C., & Whalley, L. J. Childhood socioeconomic status and adult brain size: Childhood socioeconomic status influences adult hippocampal size. (2012). Annals of Neurology, 71(5), 653–660. association between childhood SES and hippocampal volume. https://doi.org/https://doi.org/10.1002/ana.22631
- Suárez, L. E., Markello, R. D., Betzel, R. F., & Misic, B. (2020). Linking structure and function in macroscale brain networks. Trends in Cognitive Sciences, 24(4), 302–315. https://doi.org/https://doi.org/10.1016/j.tics.2020.01.008
- Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12(3), e1001779. https://doi.org/https://doi.org/10.1371/journal.pmed.1001779
- Valk, S. L., Bernhardt, B. C., Trautwein, F. M., Bockler, A., Kanske, P., Guizard, N., & Singer, T. (2017). Structural plasticity of the social brain: Differential change after socio-affective and cognitive mental training. Science Advances, 3(10), e1700489. https://doi.org/https://doi.org/10.1126/sciadv.1700489
- Weill, J. A., Stigler, M., Deschenes, O., & Springborn, M. R. (2020). Social distancing responses to COVID-19 emergency declarations strongly differentiated by income. Proceedings of the National Academy of Sciences of the United States of America, 117(33), 19658–19660. https://doi.org/https://doi.org/10.1073/pnas.2009412117
- Yang, J., Liu, H., Wei, D., Liu, W., Meng, J., Wang, K., Hao, L., & Qiu, J. (2016). Regional gray matter volume mediates the relationship between family socioeconomic status and depression-related trait in a young healthy sample. Cognitive, Affective, & Behavioral Neuroscience, 16(1), 51–62. https://doi.org/https://doi.org/10.3758/s13415-015-0371-6
- Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45–57. https://doi.org/https://doi.org/10.1109/42.906424
- Zink, C. F., Tong, Y., Chen, Q., Bassett, D. S., Stein, J. L., & Meyer-Lindenberg, A. (2008). Know your place: Neural processing of social hierarchy in humans. Neuron, 58(2), 273–283. https://doi.org/https://doi.org/10.1016/j.neuron.2008.01.025