Prospective associations, longitudinal patterns of childhood socioeconomic status, and white matter organization in adulthood

Abstract The association between childhood socioeconomic status (SES) and brain development is an emerging area of research. The primary focus to date has been on SES and variations in gray matter structure with much less known about the relation between childhood SES and white matter structure. Using a longitudinal study of SES, with measures of income‐to‐needs ratio (INR) at age 9, 13, 17, and 24, we examined the prospective relationship between childhood SES (age 9 INR) and white matter organization in adulthood using diffusion tensor imaging. We also examined how changes in INR from childhood through young adulthood are associated with white matter organization in adult using a latent growth mixture model. Using tract‐based spatial statistics (TBSS) we found that there is a significant prospective positive association between childhood INR and white matter organization in the bilateral uncinate fasciculus, bilateral cingulum bundle, bilateral superior longitudinal fasciculus, and corpus callosum (p < .05, FWE corrected). The probability that an individual was in the high‐increasing INR profile across development compared with the low‐increasing INR profile was positively associated with white matter organization in the bilateral uncinate fasciculus, left cingulum, and bilateral superior longitudinal fasciculus. The results of the current study have potential implications for interventions given that early childhood poverty may have long‐lasting associations with white matter structure. Furthermore, trajectories of socioeconomic status during childhood are important—with individuals that belong to the latent profile that had high increases in INR having greater regional white matter organization in adulthood.

[1]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[2]  C. Lebel,et al.  A review of diffusion MRI of typical white matter development from early childhood to young adulthood , 2019, NMR in biomedicine.

[3]  Martin Styner,et al.  Maternal Interleukin-6 concentration during pregnancy is associated with variation in frontolimbic white matter and cognitive development in early life , 2019, NeuroImage.

[4]  Alexander J. Dufford,et al.  Socioeconomic disadvantage, brain morphometry, and attentional bias to threat in middle childhood , 2018, Cognitive, Affective, & Behavioral Neuroscience.

[5]  E. Mandonnet,et al.  The Nomenclature of Human White Matter Association Pathways: Proposal for a Systematic Taxonomic Anatomical Classification , 2018, Front. Neuroanat..

[6]  O. Bueno,et al.  Latent class analysis of attention and white matter correlation in children with attention-deficit/hyperactivity disorder , 2018, Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas.

[7]  John P. Aggleton,et al.  The cingulum bundle: Anatomy, function, and dysfunction , 2018, Neuroscience & Biobehavioral Reviews.

[8]  Cassidy L. McDermott,et al.  Longitudinally Mapping Childhood Socioeconomic Status Associations with Cortical and Subcortical Morphology , 2018, The Journal of Neuroscience.

[9]  Craig K Enders,et al.  A Fully Conditional Specification Approach to Multilevel Imputation of Categorical and Continuous Variables , 2018, Psychological methods.

[10]  Bruce Fischl,et al.  False positive rates in surface-based anatomical analysis , 2018, NeuroImage.

[11]  A. Daugherty,et al.  Socioeconomic status and hippocampal volume in children and young adults. , 2018, Developmental science.

[12]  L. Nyberg,et al.  Neurocognitive Profiles of Older Adults with Working-Memory Dysfunction , 2018, Cerebral cortex.

[13]  N. Tottenham,et al.  Socioeconomic Status, Amygdala Volume, and Internalizing Symptoms in Children and Adolescents , 2018, Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53.

[14]  David G. Weissman,et al.  Income change alters default mode network connectivity for adolescents in poverty , 2018, Developmental Cognitive Neuroscience.

[15]  Alexander J. Dufford,et al.  Family Income, Cumulative Risk Exposure, and White Matter Structure in Middle Childhood , 2017, Front. Hum. Neurosci..

[16]  Josiah K. Leong,et al.  Effects of sensitivity to life stress on uncinate fasciculus segments in early adolescence , 2017, Social cognitive and affective neuroscience.

[17]  Koraly Pérez-Edgar,et al.  Frontolimbic functioning during threat-related attention: Relations to early behavioral inhibition and anxiety in children , 2017, Biological Psychology.

[18]  Daniel McNeish,et al.  Missing data methods for arbitrary missingness with small samples , 2017 .

[19]  C. Sripada,et al.  Childhood poverty is associated with altered hippocampal function and visuospatial memory in adulthood , 2016, Developmental Cognitive Neuroscience.

[20]  Kimberly G. Noble,et al.  Socioeconomic status, white matter, and executive function in children , 2016, Brain and behavior.

[21]  Israel Liberzon,et al.  Childhood Cumulative Risk Exposure and Adult Amygdala Volume and Function , 2016, Journal of neuroscience research.

[22]  Sara B. Johnson,et al.  State of the Art Review: Poverty and the Developing Brain , 2016, Pediatrics.

[23]  Andrew T. Drysdale,et al.  Individual differences in frontolimbic circuitry and anxiety emerge with adolescent changes in endocannabinoid signaling across species , 2016, Proceedings of the National Academy of Sciences.

[24]  G. Evans,et al.  Poverty and the Development of Psychopathology , 2016 .

[25]  I. Liberzon,et al.  Childhood social inequalities influences neural processes in young adult caregiving. , 2015, Developmental psychobiology.

[26]  Annchen R Knodt,et al.  Lower structural integrity of the uncinate fasciculus is associated with a history of child maltreatment and future psychological vulnerability to stress , 2015, Development and Psychopathology.

[27]  I. Liberzon,et al.  Childhood poverty and recruitment of adult emotion regulatory neurocircuitry. , 2015, Social cognitive and affective neuroscience.

[28]  Kylie H. Alm,et al.  Development of the uncinate fasciculus: Implications for theory and developmental disorders , 2015, Developmental Cognitive Neuroscience.

[29]  M. Angstadt,et al.  Childhood Poverty Predicts Adult Amygdala and Frontal Activity and Connectivity in Response to Emotional Faces , 2015, Front. Behav. Neurosci..

[30]  B. J. Casey,et al.  Family Income, Parental Education and Brain Structure in Children and Adolescents , 2015, Nature Neuroscience.

[31]  Steven D Chen,et al.  The Superior Longitudinal Fasciculus in Typically Developing Children and Adolescents , 2015, Journal of child neurology.

[32]  Kimberly G. Noble,et al.  Socioeconomic status and structural brain development , 2014, Front. Neurosci..

[33]  Haitao Wang,et al.  The Nerve Growth Factor Signaling and Its Potential as Therapeutic Target for Glaucoma , 2014, BioMed research international.

[34]  David C. Geary,et al.  Hippocampal-neocortical functional reorganization underlies children's cognitive development , 2014, Nature Neuroscience.

[35]  Junling Gao,et al.  Sex Differences of Uncinate Fasciculus Structural Connectivity in Individuals with Conduct Disorder , 2014, BioMed research international.

[36]  Michael Angstadt,et al.  Volitional regulation of emotions produces distributed alterations in connectivity between visual, attention control, and default networks , 2014, NeuroImage.

[37]  C. Kilts,et al.  Childhood maltreatment is associated with a sex‐dependent functional reorganization of a brain inhibitory control network , 2014, Human brain mapping.

[38]  I. Liberzon,et al.  Childhood Poverty and Stress Reactivity Are Associated with Aberrant Functional Connectivity in Default Mode Network , 2014, Neuropsychopharmacology.

[39]  G. Parra,et al.  An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses. , 2014, Journal of pediatric psychology.

[40]  Martin Styner,et al.  DTIPrep: quality control of diffusion-weighted images , 2014, Front. Neuroinform..

[41]  John H. Gilmore,et al.  Correction: Family Poverty Affects the Rate of Human Infant Brain Growth , 2015, PloS one.

[42]  D. Barch,et al.  The effects of poverty on childhood brain development: the mediating effect of caregiving and stressful life events. , 2013, JAMA pediatrics.

[43]  Israel Liberzon,et al.  Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood , 2013, Proceedings of the National Academy of Sciences.

[44]  Stefany Coxe,et al.  Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis , 2013, Structural equation modeling : a multidisciplinary journal.

[45]  G. Evans,et al.  Childhood poverty, chronic stress, and young adult working memory: the protective role of self-regulatory capacity. , 2013, Developmental science.

[46]  Peter J Gianaros,et al.  Inflammatory pathways link socioeconomic inequalities to white matter architecture. , 2013, Cerebral cortex.

[47]  Y. Nikolova,et al.  Uncinate fasciculus fractional anisotropy correlates with typical use of reappraisal in women but not men. , 2013, Emotion.

[48]  I. Olson,et al.  Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis. , 2013, Brain : a journal of neurology.

[49]  G. Evans,et al.  Childhood Poverty, Chronic Stress, Self-Regulation, and Coping , 2013 .

[50]  Derek K. Jones,et al.  Cingulum White Matter in Young Women at Risk of Depression: The Effect of Family History and Anhedonia , 2012, Biological Psychiatry.

[51]  Franck Ramus,et al.  The Influence of Socioeconomic Status on Children’s Brain Structure , 2012, PloS one.

[52]  G. Evans,et al.  Childhood Poverty and Young Adults’ Allostatic Load , 2012, Psychological science.

[53]  Silvia A. Bunge,et al.  Developmental Cognitive Neuroscience Neural changes underlying the development of episodic memory during middle childhood , 2022 .

[54]  J. Viikari,et al.  Socioeconomic status and the development of depressive symptoms from childhood to adulthood: a longitudinal analysis across 27 years of follow-up in the Young Finns study. , 2012, Social science & medicine.

[55]  C. Lebel,et al.  Diffusion tensor imaging of white matter tract evolution over the lifespan , 2012, NeuroImage.

[56]  Alex D. Leow,et al.  Quantitative Tract-Specific Measures of Uncinate and Cingulum in Major Depression Using Diffusion Tensor Imaging , 2012, Neuropsychopharmacology.

[57]  L. Jäncke,et al.  Volumetric associations between uncinate fasciculus, amygdala, and trait anxiety , 2012, BMC Neuroscience.

[58]  A. May.  Experience-dependent structural plasticity in the adult human brain , 2011, Trends in Cognitive Sciences.

[59]  Olaf B. Paulson,et al.  White Matter Microstructure in Superior Longitudinal Fasciculus Associated with Spatial Working Memory Performance in Children , 2011, Journal of Cognitive Neuroscience.

[60]  C. Lebel,et al.  Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood , 2011, The Journal of Neuroscience.

[61]  Patrick C M Wong,et al.  White Matter Anisotropy in the Ventral Language Pathway Predicts Sound-to-Word Learning Success , 2011, The Journal of Neuroscience.

[62]  S. Pollak,et al.  Association between Income and the Hippocampus , 2011, PloS one.

[63]  Xin Fan,et al.  White Matter Changes in Healthy Adolescents at Familial Risk for Unipolar Depression: A Diffusion Tensor Imaging Study , 2011, Neuropsychopharmacology.

[64]  Gail M. Williams,et al.  Family poverty over the early life course and recurrent adolescent and young adult anxiety and depression: a longitudinal study. , 2010, American journal of public health.

[65]  Kathryn R. Cullen,et al.  Altered white matter microstructure in adolescents with major depression: a preliminary study. , 2010, Journal of the American Academy of Child and Adolescent Psychiatry.

[66]  Jonathan D. Power,et al.  Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..

[67]  G. Evans,et al.  Childhood poverty, chronic stress, and adult working memory , 2009, Proceedings of the National Academy of Sciences.

[68]  Stephen M. Smith,et al.  Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.

[69]  T. Paus,et al.  Why do many psychiatric disorders emerge during adolescence? , 2008, Nature Reviews Neuroscience.

[70]  Chun-Hung Yeh,et al.  Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data , 2008, NeuroImage.

[71]  R. Fields,et al.  White matter in learning, cognition and psychiatric disorders , 2008, Trends in Neurosciences.

[72]  Alexander Leemans,et al.  Microstructural maturation of the human brain from childhood to adulthood , 2008, NeuroImage.

[73]  S. Petersen,et al.  Development of distinct control networks through segregation and integration , 2007, Proceedings of the National Academy of Sciences.

[74]  A. Alexander,et al.  Diffusion tensor imaging of the brain , 2007, Neurotherapeutics.

[75]  Bruce D. McCandliss,et al.  Socioeconomic gradients predict individual differences in neurocognitive abilities. , 2007, Developmental science.

[76]  Alan Connelly,et al.  Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.

[77]  M. Farah,et al.  Childhood poverty: Specific associations with neurocognitive development , 2006, Brain Research.

[78]  K. Mccartney,et al.  Within-child associations between family income and externalizing and internalizing problems. , 2006, Developmental psychology.

[79]  Christian Beaulieu,et al.  Diffusion tensor imaging of neurodevelopment in children and young adults , 2005, NeuroImage.

[80]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[81]  T. Klingberg,et al.  Maturation of White Matter is Associated with the Development of Cognitive Functions during Childhood , 2004, Journal of Cognitive Neuroscience.

[82]  G. Evans.  The environment of childhood poverty. , 2004, The American psychologist.

[83]  G. Evans,et al.  A multimethodological analysis of cumulative risk and allostatic load among rural children. , 2003, Developmental psychology.

[84]  G. Evans,et al.  The environment of poverty: multiple stressor exposure, psychophysiological stress, and socioemotional adjustment. , 2002, Child development.

[85]  R. Kikinis,et al.  Uncinate fasciculus findings in schizophrenia: a magnetic resonance diffusion tensor imaging study. , 2002, The American journal of psychiatry.

[86]  G. Fitzmaurice,et al.  Socioeconomic status in childhood and the lifetime risk of major depression. , 2002, International journal of epidemiology.

[87]  K. Mccartney,et al.  Change in family income-to-needs matters more for children with less. , 2001, Child development.

[88]  Katherine E. Masyn,et al.  General growth mixture modeling for randomized preventive interventions. , 2001, Biostatistics.

[89]  D. Le Bihan,et al.  Diffusion tensor imaging: Concepts and applications , 2001, Journal of magnetic resonance imaging : JMRI.

[90]  T. DiPrete,et al.  Family Change, Employment Transitions, and the Welfare State: Household Income Dynamics in the United States and Germany , 2000, American Sociological Review.

[91]  B. Muthén,et al.  Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. , 2000, Alcoholism, clinical and experimental research.

[92]  A. Baum,et al.  Socioeconomic Status and Chronic Stress: Does Stress Account for SES Effects on Health? , 1999, Annals of the New York Academy of Sciences.

[93]  C. Cohen,et al.  Poverty and the course of schizophrenia: implications for research and policy. , 1993, Hospital & community psychiatry.

[94]  D. Cicchetti,et al.  Developmental approaches to depression , 1992, Development and Psychopathology.

[95]  P. Goldman-Rakic,et al.  Dual pathways connecting the dorsolateral prefrontal cortex with the hippocampal formation and parahippocampal cortex in the rhesus monkey , 1984, Neuroscience.

[96]  W. Gibson.  Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis , 1959 .

[97]  M. Farah,et al.  Neurocognitive correlates of socioeconomic status in kindergarten children. , 2005, Developmental science.

[98]  H. Aronen,et al.  [Functional magnetic resonance imaging of the brain]. , 1997, Duodecim; laaketieteellinen aikakauskirja.