Family Income, Cumulative Risk Exposure, and White Matter Integrity in Middle Childhood

Alexander Dufford, MAUniversity of Denver

Family and Child Neuroscience LabINRICH Meeting

Background• Socioeconomic disadvantage has been associated with altered

brain development. – Grey matter: reduced volume in the hippocampus and prefrontal

cortex (for review see Johnson et al, 2016).

– White matter: reduced white matter integrity in the right parahippocampal cingulum and right superior corticostriate tract (Ursache& Noble, 2016)

• DTI assesses Fractional Anisotropy (FA) of white matter tracts in which greater values indicated more integrity (mature) white matter.

• However, little is known about which aspects of socioeconomic disadvantage are associated with white matter development in a specific developmental period.

What are possible pathways between family income and white matter integrity?

• Children in families experiencing low income, more exposure to:

Physical stressors: NoiseCrowdingPoor Housing Quality

Psychosocial risk factors: Family turmoilViolence (in home or in neighborhood)Family separation

• Additionally it is likely these stressors are co-occurring and have an additive effect on child development

• Conceptualized as the Cumulative Risk Model (Burchinal, Roberts, Hooper, & Zeisel, 2000; Evans, 2003; Evans & Kim, 2010; Evans, Li, & Whipple, 2013; Felner et al., 1995; Greenberg et al., 1999; Herrenkohl, Herrenkohl, & Egolf, 2003).

• Cumulative risk has been a stress pathway of interest in other studies interested in allostatic load (Evans, 2003), gray matter volume (Evans et al., 2015), and brain function (Kim et al.,

2013; Evans et al., 2015); however the relationship with white matter integrity is unclear.

The uncinate fasciculus UF - emotion regulation (Ladouceur et al., 2015).

The cingulum bundle CB - cognitive functions including executive functioning and attentional control (Schermuly et al., 2010)

The superior longitudinal fasciculus SLF - working memory (Vestergaard et al., 2011) and language (Mandonnet, Nouet, Gatignol, Capelle, & Duffau, 2007).

Children growing up in families experiencing low family income show differences in emotion regulation (Raver et al., 2016), executive control (Ursache et al., 2015), working memory (Farah et al., 2006), and language (Perkins et al., 2013).

Functions of the UF, CB, and SLF

UF CB SLF

Hypotheses• Lower family income would be associated with lower integrity in the

white matter tracts involved in executive function and emotion regulation including the uncinate fasciculus (UF), cingulum bundle (CB), and superior longitudinal fasciculus (SLF).

• Lower family income would be associated with higher levels of cumulative risk exposure, which would further be associated with lower integrity in these tracts.

Family Income

Cumulative Risk

White Matter Integrity

Results for Income-to-Needs Ratio (INR): Whole Brain Analysis (n=27)

We used income-to-needs ratio (INR) to measure family income, income information was collected from each parent about the last 12 months of child’s life

Age range was 8-10 (M=8.6 SD=0.67), 59% female, 55.6% White/Caucasian, 25.9% Hispanic/Latino, 7.4% African American, 11.4% Multi-

Right CST Left CST Left CB

INR and FA Values for the UF, CB, and SLF

Family Income

Cumulative Risk

White Matter Integrity

Income to Needs Ratio Income to Needs Ratio

Income to Needs Ratio

Left

Unc

inat

e Fa

scic

ulus

FA

Left

Cin

gulu

m B

undl

e FA

Left

Sup

erio

r Lon

g. F

asci

culu

s FA

White Matter Integrity and Cumulative Risk Is INR associated with cumulative risk exposure?

Calculated cumulative risk score from 0-6 for each domain, scored dichotomously based upon if score was in the top quartile, based up previous studies (Evans & Kim, 2007, 2012; Evans et al., 2007; Evans et al., 2013; Kim et al., 2013).

Cumulative risk was associated with the:left cingulum bundle (β = -0.48, p < 0.05)left superior longitudinal fasciculus (β = -0.47, p < 0.05)

Cumulative Risk

White Matter Integrity

Family Income

r = -0.538, p < 0.01

Cumulative Risk Exposure Cumulative Risk Exposure

Left

Cin

gulu

m B

undl

e FA

Left

SLF

FA

Discussion

We provide evidence of a relationship between family income and structural connectivity across several white matter tracts in the brain. These tracts are involved in a multitude of cognitive and affective processes.

An identical whole-brain model for maternal education revealed no significant results.

Cumulative risk exposure was inversely structural integrity across several white matter tracts involved in multiple neurocognitive processes. Limitations: the Indirect effect model was not significant due to a relatively

small sample.

More comprehensive picture of cumulative risk and the brain: grey matter, functional activity, and structural connectivity

Future direction: The prospective associations among socioeconomic disadvantages, white matter development, and cognitive and emotional outcomes.

Thank you for your attention!

Special thanks to my mentor, Dr. Pilyoung Kim, and FCN’s amazing team of research coordinators, research assistants, and graduate students for their support!

This work was supported by the National Institute of Child Health and Human Development [R21HD078797; R01 HD090068]; the Professional Research Opportunity for Faculty (PROF) and Faculty Research Fund (FRF), University of Denver; and the Victoria S. Levin Award For

Early Career Success in Young Children's Mental Health Research, Society for Research in Child Development (SRCD).

Supplementary Slides

Measuring Cumulative Risk• 3 psychosocial risk factors measured by the Life Events and Circumstances Checklist

(Work, Cowen, Parker, & Wyman, 1990; Wyman, Cowen, Work, & Parker, 1991)

• Family turmoil, violence, and child separation from the family

• Mothers are interviewed, asked multiple questions in each domain

Risk Factor Mean SD Range Quartile

Cumulative Risk (total) 1.7 1.46 0.00 - 5.00 NA

Crowding

Noise (Leq)

Housing Quality

Family Turmoil

Violence

Family Separation

0.4454.67

0.48

2.66

1.03

2.11

0.206.14

0.29

1.83

1.01

1.01

0.00 – 1.00 31.8 – 71.40

0.00 – 1.08

0.00 – 6.00

0.00 – 3.00

0.00 – 5.00

0.5457.2

0.73

3.00

1.00

2.00

Group Analysis

Conducted a whole-brain, voxelwise regression with INR as a regressor

FSL’s Randomise conducted a nonparametric permutation test and was corrected for multiple comparisons using Threshold-Free Cluster Enhancement (TFCE; Smith & Nichols)

Clusters that reach significance at p < 0.05 FWE corrected were localized using the JHU White-Matter Tractography Atlas

Other regions included: bilateral superior longitudinal fasciculus, bilateral corticospinal tracts, left inferior longitudinal fasciculus, left anterior thalamic radiation, a portion of the forceps minor, and a portion of the body of the corpus callosum

Cluster # Voxels X Y Z JHU Label

1 938 109 161 96 Forceps minor

2 771 132 127 92 L Superior Longitudinal Fasciculus

3 759 91 102 47 R Corticospinal Tract

4 660 68 109 113 R Superior Corona Radiata

5 556 83 102 60 R Corticospinal Tract

6 483 123 107 62 Fornix

7 457 112 108 124 L Corticospinal Tract

8 420 124 117 37 L Cingulum (hippocampus)

9 417 135 108 64 L Inferior Longitudinal Fasciculus

10 227 133 82 103 L Superior Longitudinal Fasciculus

11 181 133 154 80 L Uncinate Fasciculus

12 129 100 113 101 Body of Corpus Callosum

Other Tracts from Whole-brain Analysis

White Matter Integrity and Cumulative Risk

What is currently known about family income and white matter integrity?

• In adulthood, lower SES (income) was associated with reduced FA in the uncinate fasciculus, superior longitudinal fasciculus, corona radiata, and pontine crossing tract

• Adiposity, smoking, and CRP partially mediated these relationships

• Other studies have found no relationship between income and FA, specifically in childhood (Jednorog et al., 2012).

Participants and Measures• 31 healthy children from ages 8-10 participated in the

study

• Comprised of a home visit and a MRI scan visit

• Family income was measured using income-to-needs ratio (accounts for family size)

• Cumulative risk measured based up previous studies (Evans & Kim, 2007, 2012; Evans et al., 2007; Evans et al., 2013; Kim et al., 2013).

Risk Factor Mean SD Range Quartile

Cumulative Risk (total) 1.7 1.46 0.00 - 5.00 NA

Crowding

Noise (Leq)

Housing Quality

Family Turmoil

Violence

Family Separation

0.4454.67

0.48

2.66

1.03

2.11

0.206.14

0.29

1.83

1.01

1.01

0.00 – 1.00 31.8 – 71.40

0.00 – 1.08

0.00 – 6.00

0.00 – 3.00

0.00 – 5.00

0.5457.2

0.73

3.00

1.00

2.00

Cumulative Risk Scores for the Sample

Why should we care about white matter?• White matter makes up over half of the tissue in the brain

(Fields, 2010).

• The major white matter tracts undergo significant development in childhood (Paus et al., 2009; Schmithorst et al., 2002), therefore may be susceptible to environmental influence.

• Maturation of white matter in childhood has been associated with increases in cognitive functioning (Mabbott, Noseworthy, Bouffet,

Laughlin, & Rockel, 2006; Nagy, Westerberg, & Klingberg, 2004), language development (Urger et al., 2015; Wong, Chandrasekaran, Garibaldi, & Wong, 2011), and emotion regulation (Versace et al., 2015).

• Children in families experiencing low income have shown lower performance in these domains (Hackman et al., 2010; Raver et al., 2017).

Limitations of the Present Study

Measurement of family income was based upon the last 12 months, did not measure income since birth

Results need to be replicated in larger sample

Study was cross-sectional

Our study focused on stress risk factors, future studies need to considered such as exposure to toxins (Gray et al., 2013), nutritional deprivation (Kant et al., 2013), and lack of cognitive stimulation (Lipina et al., 2013)

Other Fiber Tracts?

Fornix: major output fiber of the hippocampus, involved in memory (Gaffan, 1974)

Superior longitudinal fasciculus: connects frontal lobe to parieto-temporal association areas (Makris et al., 2005)

SLF is implicated in both language processing (Mandonnet et al., 2007) and working memory (Vestergaard et al., 2011)

Both of these domains have been linked to reduced performance in children experiencing low family income (Hackman & Farah, 2009; Hackman et al., 2010)

Body of the corpus callosum (major hemispheric connection)

Future Studies

Examine if cumulative risk mediates the relationship between family income and integrity using a voxel-wise mediation model

Extend the current findings to include measurements of mood (internalizing symptoms) or social/emotional information behavioral perfomance

Further examination of the UF and CB using deterministic tractography and examine FA along the fiber tracts

DTI Processing

DWI data was acquired with 71 directions and corrected for eddy currents and motion (4 participants excluded)

A diffusion tensor model was fit at each voxel and FA images for each participant and standardized into FMRIB58_FA standard space FA template

Calculating Cumulative Risk

• For each of the 6 domains, risk is defined dichotomously as risk or no-risk (0 or 1)

• Participants are given a score of 1 for each domain if their value is in the top quartile of the sample (Evans & Cassells, 2014; Evans, Fuller-Rowell, & Doan, 2012; Evans & Kim, 2012; Wells, Evans, Beavis, & Ong, 2010)

• Total cumulative risk is measured by summing the values for each domain (0-6)

Measuring Cumulative Risk• Uses 6 environmental risk factors (scores range from 0

to 6 in which 6 indicates the highest levels of exposure)

• 3 physical risk factors: Crowding: divide number of individuals living in the

household by the number of rooms (including bathrooms)

Noise (Leq): assessed using a decibel meter in primary social space for 2 hours (during home visit)

Housing quality: assessed by researcher using a standardized scale (Evans, Wells, Chan, & Salzman, 2000); measures structural defects, maintenance, cleanliness

N (%) Mean ± SD Range

Child age (years)

Child sex (female)16

8.66 ± 067 8 - 10

Child race/ethnicity

White/Caucasian 15

Black/African American 7

Hispanic 2

Multi-racial 3

Income-to-needs ratio 2.16 ± 1.40 0.00 – 4.95

Table 1. Demographic information for the sample.

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