Prenatal Familial Income Volatility and Infant Subcortical Brain Volumes
Poster No:
1258
Submission Type:
Abstract Submission
Authors:
Genevieve Patterson1, Alexander Dufford2, Sun Hyung Kim3, Martin Styner4, Pilyoung Kim1
Institutions:
1University of Denver, Denver, CO, 2Oregon Health & Science University, Portland, OR, 3The University of North Carolina at Chapel Hill, Chapel Hill, NC, 4Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC
First Author:
Co-Author(s):
Introduction:
Unpredictable environments can be a source of stress influencing the developing brain, including during the prenatal period. Subcortical regions including the amygdala and hippocampus have been previously shown to be especially sensitive to stress exposure, in part due to the high number of glucocorticoid receptors present in these regions (Badihian et al., 2020). Emerging evidence suggests that socioeconomic status (SES) is associated with child brain development including in infancy (Betancourt et al. 2016; Ramphal et al., 2020; Gao et al., 2015). Prior studies have consistently reported associations between lower SES and smaller hippocampal volumes with more mixed results for amygdala volume (Rakesh & Whittle, 2021). Household chaos and unpredictability have also been associated with child development at a behavioral (Evans et al. 2005; Davis et al., 2019), physiological (Tarullo et al., 2020; Noroña-Zhou et al. 2020), and neural (Granger et al., 2021) level. Income volatility, specifically income losses, has been associated with both externalizing and internalizing symptoms throughout development (Miller et al. 2021). However, little is known about the role of income instability and infant brain development. Here we examine the association between familial income instability and subcortical brain volumes in early infancy.
Methods:
The current study includes 63 infants from a prospective longitudinal study of pregnant individuals and their infants from diverse socioeconomic backgrounds (31.7% low income, prenatal income to needs ratio <=2)). Arc Percent Change (APC) in household earned income was calculated for each month compared to the previous month for the time period covering conception to the month of the child's birth. An income shock was defined as APC > 25% for a given month, with decreases defined as a negative shock. The number of negative income shocks were summed across the prenatal time period. Infants completed an MRI after birth during natural sleep (Mean Age = 34.9 days, SD = 19.9 days; 52.4% female). Infant amygdala and hippocampus as well as tissue segmentation were individually segmented using a multimodality, multi-template-based automatic method combining T1- and T2-weighted MR images via the MultiSegPipeline v2.2.1 tool (Cherel et al., 2015), which employs deformable registration and label fusion from the ANTs toolset (Tustison et al., 2021). Bivariate correlations were conducted to test the following as potential covariates: total intercranial volume (ICV), postconceptional age at scan (sum of gestational age at birth and age at scan), sex at birth, birthweight, average prenatal income to needs ratio, and NICU stay. Covariates significantly associated with hippocampus and amygdala volumes were included in further multiple regression analysis predicting infant right and left hippocampus and amygdala volume from the total number of negative income shocks.
Results:
The number of negative income shocks was significantly associated with smaller right hippocampal volume (β = -33.60, p = .01) covarying for ICV and birthweight. The number of negative income shocks was significantly associated with smaller right amygdala volume (β = -11.55, p = .008) covarying for ICV and postconceptional age at scan.
Conclusions:
We found evidence that familial income volatility during the perinatal period is associated with infant brain structure after birth. These early differences in brain structure may potentially influence infant's developmental trajectories with implications for later brain structure, function, and associated behavior. These findings provide support for the development of public programs that prioritize consistency, especially for families and during sensitive periods like pregnancy.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Segmentation and Parcellation
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures 2
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Development
PEDIATRIC
STRUCTURAL MRI
Structures
Sub-Cortical
Other - infant; environment; stress
1|2Indicates the priority used for review
Provide references using author date format
Betancourt, L. M. (2016). Effect of socioeconomic status (SES) disparity on neural development in female African‐American infants at age 1 month. Developmental Science, 19(6), 947-956.
Cherel, M. (2015). Automatic tissue segmentation of neonate brain MR Images with subject-specific atlases. SPIE Medical Imaging, 941311-941311–11.
Davis, E. P. (2019). Across continents and demographics, unpredictable maternal signals are associated with children’s cognitive function. EBioMedicine, 46, 256–263.
Evans, G. W. (2005). The Role of Chaos in Poverty and Children’s Socioemotional Adjustment. Psychological Science, 16(7), 560–565.
Gao, W. (2015). Functional Network Development During the First Year: Relative Sequence and Socioeconomic Correlations. Cerebral Cortex 25(9), 2919-2928.
Granger, S.J. (2021). Aberrant Maturation of the Uncinate Fasciculus Follows Exposure to Unpredictable Patterns of Maternal Signals. Journal of Neuroscience, 41(6), 1242-1250.
Miller, P. (2021). Income dynamics and behavior problems in early childhood, middle childhood, and the transition to adolescence. Journal of Applied Developmental Psychology, 77, 101345.
Noroña-Zhou, A. N. (2020). Unpredictable maternal behavior is associated with a blunted infant cortisol response. Developmental Psychobiology, 62(6), 882–888.
Rakesh, D. (2021). Socioeconomic status and the developing brain–A systematic review of neuroimaging findings in youth. Neuroscience & Biobehavioral Reviews, 13
Ramphal, B. (2020). Brain connectivity and socioeconomic status at birth and externalizing symptoms at age 2 years. Developmental Cognitive Neuroscience, 45, 100811.
Tarullo, A. R., (2020). Cortisol and socioeconomic status in early childhood: A multidimensional assessment. Development and Psychopathology, 32(5), 1876–1887.
Tustison, N. J. (2021). The ANTsX ecosystem for quantitative biological and medical imaging. Scientific Reports, 11(1), 9068.