Association between income and the hippocampus

Jamie L Hanson, Amitabh Chandra, Barbara L Wolfe, Seth D Pollak, Jamie L Hanson, Amitabh Chandra, Barbara L Wolfe, Seth D Pollak

Abstract

Facets of the post-natal environment including the type and complexity of environmental stimuli, the quality of parenting behaviors, and the amount and type of stress experienced by a child affects brain and behavioral functioning. Poverty is a type of pervasive experience that is likely to influence biobehavioral processes because children developing in such environments often encounter high levels of stress and reduced environmental stimulation. This study explores the association between socioeconomic status and the hippocampus, a brain region involved in learning and memory that is known to be affected by stress. We employ a voxel-based morphometry analytic framework with region of interest drawing for structural brain images acquired from participants across the socioeconomic spectrum (n = 317). Children from lower income backgrounds had lower hippocampal gray matter density, a measure of volume. This finding is discussed in terms of disparities in education and health that are observed across the socioeconomic spectrum.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Hippocampal and amygdala region of…
Figure 1. Hippocampal and amygdala region of interest drawings.
The top left brain slice shows a sagittal brain slice with the hippocampus highlighted in yellow and the amygdala in turquoise, while the top right brain image shows an axial slice (with the hippocampus again highlighted in yellow and the amygdala in turquoise). The bottom left brain picture shows a coronal slice with the amygdala in turquoise and the hippocampus in yellow.
Figure 2. Scatterplot of Total Hippocampal Gray…
Figure 2. Scatterplot of Total Hippocampal Gray Matter and Income.
This scatterplot shows the association between total hippocampal gray matter probability and income. Total hippocampal gray matter shown on the vertical axis is displayed as a standardized residual controlling for child's age (in months), gender (dummy-coded), and whole brain volume, while log-transformed income is displayed on the horizontal axis. Higher income is associated with greater gray matter probability.
Figure 3. Scatterplot of Left Hippocampal Gray…
Figure 3. Scatterplot of Left Hippocampal Gray Matter and Income.
This scatterplot shows the association between left hippocampal gray matter probability and income. Left hippocampal gray matter shown on the vertical axis is displayed as a standardized residual controlling for child's age (in months), gender (dummy-coded), and whole brain volume, while log-transformed income is displayed on the horizontal axis. Higher income is associated with greater gray matter probability.
Figure 4. Scatterplot of Right Hippocampal Gray…
Figure 4. Scatterplot of Right Hippocampal Gray Matter and Income.
This scatterplot shows the association between right hippocampal gray matter probability and income. Right hippocampal gray matter shown on the vertical axis is displayed as a standardized residual controlling for child's age (in months), gender (dummy-coded), and whole brain volume, while log-transformed income is displayed on the horizontal axis. Higher income is associated with greater gray matter probability.

References

    1. Greenough W, Black J. Induction of brain structure by experience: Substrate for cognitive development. In: Gunnar MR, Nelson CA, editors. Minnesota Symposia on Child Psychology 24: Developmental Behavioral Neuroscience. Hillsdale, NJ: Lawrence Erlbaum; 1992. pp. 155–200.
    1. Meaney MJ, Szyf M. Environmental programming of stress responses through DNA methylation: life at the interface between a dynamic environment and a fixed genome. Dialogues Clin Neurosci. 2005;7:103–23.
    1. Raizada RD, Kishiyama MM. Effects of socioeconomic status on brain development, and how cognitive neuroscience may contribute to levelling the playing field. Front Hum Neurosci. 2010;4:3. Available: Accessed 2011 March 18.
    1. Evans GW, Kim P. Childhood Poverty and Health Cumulative Risk Exposure and Stress Dysregulation. Psychological Science. 2007;18:953–957.
    1. McEwen BS. Plasticity of the hippocampus: adaptation to chronic stress and allostatic load. Ann N Y Acad Sci. 2001;933:265–77.
    1. Fiala BA, Joyce JN, Greenough WT. Environmental complexity modulates growth of granule cell dendrites in developing but not adult hippocampus of rats. Exp Neurol. 1978;59:372–83.
    1. Greenough WT, Volkmar FR, Juraska JM. Effects of rearing complexity on dendritic branching in frontolateral and temporal cortex of the rat. Exp Neurol. 1973;41:371–8.
    1. Soffié M, Hahn K, Terao E, Eclancher F. Behavioural and glial changes in old rats following environmental enrichment. Behav Brain Res. 1999;101:37–49.
    1. Green EJ, Greenough WT. Altered synaptic transmission in dentate gyrus of rats reared in complex environments: evidence from hippocampal slices maintained in vitro. J Neurophysiol. 1986;55:739–50.
    1. Kempermann G, Kuhn HG, Gage FH. More hippocampal neurons in adult mice living in an enriched environment. Nature. 1997;386:493–5.
    1. Will BE, Rosenzweig MR, Bennett EL, Hebert M, Morimoto H. Relatively brief environmental enrichment aids recovery of learning capacity and alters brain measures after postweaning brain lesions in rats. J Comp Physiol Psychol. 1977;91:33–50.
    1. Meaney MJ. Maternal care, gene expression, and the transmission of individual differences in stress reactivity across generations. Annu Rev Neurosci. 2001;24:1161–92.
    1. Farah MJ, Betancourt L, Shera DM, Savage JH, Giannetta JM, et al. Environmental stimulation, parental nurturance and cognitive development in humans. Dev Sci. 2008;11:793–801.
    1. Rao H, Betancourt L, Giannetta JM, Brodsky NL, Korczykowski M, et al. Early parental care is important for hippocampal maturation: Evidence from brain morphology in humans. Neuroimage. 2010;49:1144–50.
    1. Adler NE, Boyce T, Chesney MA, Cohen S, Folkman S, et al. Socioeconomic status and health. The challenge of the gradient. Am Psychol. 1994;49:15–24.
    1. Lupien SJ, Maheu F, Tu M, Fiocco A, Schramek TE. The effects of stress and stress hormones on human cognition: Implications for the field of brain and cognition. Brain Cogn. 2007;65:209–37.
    1. McLoyd VC, Wilson L. Maternal behavior, social support, and economic conditions as predictors of distress in children. New Dir Child Dev Winter. 1990;46:49–69.
    1. Moffitt TE, Gabrielli WF, Mednick SA, Schulsinger F. Socioeconomic status, IQ, and delinquency. J Abnorm Psychol. 1981;90:152–6.
    1. Evans AC Brain Development Cooperative Group. The NIH MRI study of normal brain development. Neuroimage. 2006;30:184–202.
    1. Waber DP, De Moor C, Forbes PW, Almli CR, Botteron KN, et al. The NIH MRI study of normal brain development: performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery. J Int Neuropsychol Soc. 2007;13:729–746.
    1. Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage. 2000;11:805–821.
    1. Busatto GF, Diniz BS, Zanetti MV. Voxel-based morphometry in Alzheimer's disease. Expert Rev Neurother. 2008;8:1691–702.
    1. Williams LM. Voxel-based morphometry in schizophrenia: implications for neurodevelopmental connectivity models, cognition and affect. Expert Rev Neurother. 2008;8:1049–65.
    1. Whitwell JL, Josephs KA. Voxel-based morphometry and its application to movement disorders. Parkinsonism Relat Disord. 2007;13(Suppl 3):S406–16.
    1. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38:95–113.
    1. Bergouignan L, Chupin M, Czechowska Y, Kinkingnéhun S, Lemogne C, et al. Can voxel based morphometry, manual segmentation and automated segmentation equally detect hippocampal volume differences in acute depression? Neuroimage. 2009;45:29–37.
    1. Yassa MA, Stark CE. A quantitative evaluation of cross-participant registration techniques for MRI studies of the medial temporal lobe. Neuroimage. 2009;44:319–27.
    1. Wilke M, Holland SK, Altaye M, Gaser C. Template-O-Matic: a toolbox for creating customized pediatric templates. Neuroimage. 2008;41:903–13.
    1. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–89.
    1. Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage. 2009;46:786–802.
    1. Hackman DA, Farah MJ. Socioeconomic status and the developing brain. Trends Cogn Sci. 2009;13:65–73.
    1. Farah MJ, Shera DM, Savage JH, Betancourt L, Giannetta JM, et al. Childhood poverty: specific associations with neurocognitive development. Brain Res. 2006;1110:166–174.
    1. Squire LR. Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans. Psychol Rev. 1992;99:195–231.
    1. Biegler R, McGregor A, Krebs JR, Healy SD. A larger hippocampus is associated with longer-lasting spatial memory. Proc Natl Acad Sci U S A. 2001;98:6941–4.
    1. Gianaros PJ, Jennings JR, Sheu LK, Greer PJ, Kuller LH, et al. Prospective reports of chronic life stress predict decreased grey matter volume in the hippocampus. Neuroimage. 2007;35:795–803.
    1. McEwen BS. Stress, sex, and neural adaptation to a changing environment: mechanisms of neuronal remodeling. Ann N Y Acad Sci. 2010;1204(Suppl):E38–59.
    1. Tottenham N, Sheridan MA. A review of adversity, the amygdala and the hippocampus: a consideration of developmental timing. Front Hum Neurosci. 2010;3:68. Available: Accessed 2011 March 18.

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