Longitudinal pathways linking family risk, neural risk processing, delay discounting, and adolescent substance use

Jungmeen Kim-Spoon, Nina Lauharatanahirun, Kristin Peviani, Alexis Brieant, Kirby Deater-Deckard, Warren K Bickel, Brooks King-Casas, Jungmeen Kim-Spoon, Nina Lauharatanahirun, Kristin Peviani, Alexis Brieant, Kirby Deater-Deckard, Warren K Bickel, Brooks King-Casas

Abstract

Background: Current theories in neuroscience emphasize the crucial role of individual differences in the brain contributing to the development of risk taking during adolescence. Yet, little is known about developmental pathways through which family risk factors are related to neural processing of risk during decision making, ultimately contributing to health risk behaviors. Using a longitudinal design, we tested whether neural risk processing, as affected by family multi-risk index, predicted delay discounting and substance use.

Method: One hundred and fifty-seven adolescents (aged 13-14 years at Time 1, 52% male) were assessed annually three times. Family multi-risk index was measured by socioeconomic adversity, household chaos, and family risk-taking behaviors. Delay discounting was assessed by a computerized task, substance use by questionnaire data, and risk-related neural processing by blood-oxygen-level-dependent (BOLD) responses in the amygdala during a lottery choice task.

Results: Family multi-risk index at Time 1 was related to adolescent substance use at Time 3 (after controlling for baseline substance use) indirectly through heightened amygdala sensitivity to risks and greater delay discounting.

Conclusions: Our results elucidate the crucial role of neural risk processing in the processes linking family multi-risk index and the development of substance use. Furthermore, risk-related amygdala activation and delay discounting are important targets in the prevention and treatment of substance use among adolescents growing up in high-risk family environments.

Keywords: Family multi-risk index; delay discounting; functional neuroimaging; risk processing; substance use.

Conflict of interest statement

The authors have declared that they have no competing or potential conflicts of interest.

Conflicts of interest statement: No conflicts declared.

© 2019 Association for Child and Adolescent Mental Health.

Figures

Figure 1.
Figure 1.
A) In the lottery choice task, adolescents were asked to choose between pairs of uncertain gambles. For each gamble, there was a high and low monetary outcome, each associated with a specific probability. The associations between outcomes and probabilities are represented with corresponding colors (orange or blue), B) Each trial consisted of a decision phase, a fixation phase, an outcome phase, and an inter-trial-interval (ITI), C) During the decision phase of the economic lottery choice task, adolescents with higher (relative to lower) levels of family multi-risk index scores exhibited increased BOLD responses in the right amygdala to chosen gambles that were of higher relative to lower levels of risk (i.e., coefficient of variation; CV), t(133) = 4.09, p(cluster family-wise error correction) < .05).
Figure 2.
Figure 2.
Summarized model fitting results of the path model of longitudinal associations among family multi-risk index, neural risk processing, delay discounting, and substance use among adolescents. Standardized estimates are presented. *p < .05; **p < .001.

References

    1. Aranovich GJ, McClure SM, Fryer S, & Mathalon DH (2016). The effect of cognitive challenge on delay discounting. NeuroImage, 124, 733–739. 10.1016/j.neuroimage.2015.09.027
    1. Arnsten AFT (2009). Stress signalling pathways that impair prefrontal cortex structure and function. Nature Reviews Neuroscience, 10, 410–422. 10.1038/nrn2648
    1. Arthur MW, Briney JS, Hawkins JD, Abbott RD, Brooke-Weiss BL, & Catalano RF (2007). Measuring risk and protection in communities using the Communities That Care Youth Survey. Evaluation and Program Planning, 30, 197–211. 10.1016/j.evalprogplan.2007.01.009
    1. Asbury K, Dunn JF, Pike A, & Plomin R (2003). Nonshared environmental influences on individual differences in early behavioral development: A monozygotic twin differences study. Child Development, 74, 933–943. 10.1111/1467-8624.00577
    1. Bechara A, & Damasio AR (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52, 336–372. 10.1016/j.geb.2004.06.010
    1. Bickel WK, Miller ML, Yi R, Kowal BP, Lindquist DM, & Pitcock JA (2007). Behavioral and neuroeconomics of drug addiction: Competing neural systems and temporal discounting processes. Drug and Alcohol Dependence, 90(Supplement 1), S85–S91. 10.1016/j.drugalcdep.2006.09.016
    1. Bickel WK, Moody L, Quisenberry AJ, Ramey CT, & Sheffer CE (2014). A competing neurobehavioral decision systems model of SES-related health and behavioral disparities. Preventive Medicine, 68, 37–43. 10.1016/j.ypmed.2014.06.032
    1. Birn RM, Roeber BJ, & Pollak SD (2017). Early childhood stress exposure, reward pathways, and adult decision making. Proceedings of the National Academy of Sciences of the United States of America, 114 (51), 13549–13554. 10.1073/pnas.1708791114
    1. Bridgett DJ, Burt NM, Edwards ES, & Deater-Deckard K (2015). Intergenerational transmission of self-regulation: A multidisciplinary review and integrative conceptual framework. Psychological Bulletin, 141(3), 602–654. 10.1037/a0038662
    1. Deater-Deckard K, Dodge KA, Bates JE & Pettit GS (1998). Multiple-risk factors in the development of externalizing behavior problems: Group and individual differences. Development and Psychopathology, 10, 469–493.
    1. Ernst M, Pine DS, & Hardin M (2006). Triadic model of the neurobiology of motivated behavior in adolescence. Psychological Medicine, 36, 299–312. 10.1017/S0033291705005891
    1. Evans GW, Gonnella C, Marcynyszyn LA, Gentile L, & Salpekar N (2005). The role of chaos in poverty and children’s socioemotional adjustment. Psychological Science, 16, 560–565. 10.1111/j.0956-7976.2005.01575.x
    1. Farah MJ (2017). The neuroscience of socioeconomic status: Correlates, causes, and consequences. Neuron, 96, 56–71. 10.1016/j.neuron.2017.08.034
    1. Friston KJ,Rotshtein P,Geng JJ, Sterzer P,and Henson RN(2006). A critique of functional localisers. NeuroImage, 30, 1077–1087. 10.1016/j.neuroimage.2005.08.012
    1. Hamilton CM, Strader LC, Pratt JG, Maiese D, Hendershot T, Kwok RK, … Hake J (2011). The PhenX Toolkit: Get the most from your measures. American Journal of Epidemiology, 174, 253–60. 10.1093/aje/kwr193
    1. Hanson JL, Hariri AR, & Williamson DE (2015). Blunted ventral striatum development in adolescence reflects emotional neglect and predicts depressive symptoms. Biological Psychiatry, 78, 598–605. 10.1016/j.biopsych.2015.05.010
    1. Hill KG, Hawkins JD, Catalano RF, Abbott RD, & Guo J (2005). Family influences on the risk of daily smoking initiation. Journal of Adolescent Health, 37, 202–210. 10.1016/j.jadohealth.2004.08.014
    1. Holt CA, & Laury SK (2002). Risk aversion and incentive effects. American Economic Review, 92, 1644–1655. 10.1257/000282802762024700
    1. Huettel SA, Stowe CJ, Gordon EM, Warner BT, & Platt ML (2006). Neural signatures of economic preferences for risk and ambiguity. Neuron, 49, 765–775. 10.1016/j.neuron.2006.01.024
    1. Johnson MW, & Bickel WK (2002). Within-subject comparison of real and hypothetical money rewards in delay discounting. Journal of the Experimental Analysis of Behavior, 77, 129–146. 10.1901/jeab.2002.77-129
    1. Johnson MW, Bickel WK, Baker F, Moore BA, Badger GJ, & Budney AJ (2010). Delay discounting in current and former marijuana-dependent individuals. Experimental and Clinical Psychopharmacology, 18, 99–107. 10.1037/a0018333
    1. Kann L, Kinchen S, Shanklin SL, Flint KH, Hawkins J, Harris WA, … Zaza S (2014). Youth risk behavior surveillance-United States, 2013. Morbidity and Mortality Weekly Report: Surveillance Summaries, 63, 1–168.
    1. Kim-Spoon J, Deater-Deckard K, Lauharatanahirun N, Farley JP, Chiu PH, Bickel WK, & King-Casas B (2017). Neural interaction between risk sensitivity and cognitive control predicting health risk behaviors among late adolescents. Journal of Research on Adolescence, 27, 674–682. 10.1111/jora.12295
    1. Kim-Spoon J, Kahn RE, Lauharatanahirun N, Deater-Deckard K, Bickel WK, Chiu PH, & King-Casas B (2017). Executive functioning and substance use in adolescence: Neurobiological and behavioral perspectives. Neuropsychologia, 100, 79–92. 10.1016/j.neuropsychologia.2017.04.020
    1. Kim-Spoon J, McCullough ME, Bickel WK, Farley JP, & Longo GS (2015). Longitudinal associations among religiousness, delay discounting, and substance use initiation in early adolescence. Journal of Research on Adolescence, 25, 36–43. 10.1111/jora.12104
    1. Kim P, Evans GW, Angstadt M, Ho SS, Sripada CS, Swain JE, … Phan KL (2013). Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proceedings of the National Academy of Sciences, 110, 18442–18447. 10.1073/pnas.1308240110
    1. Lauharatanahirun N, Maciejewski D, Holmes C, Deater-Deckard K, Kim-Spoon J, & King-Casas B (2018). Neural correlates of risk processing among adolescents: Influences of parental monitoring and household chaos. Child Development, 89, 784–796. 10.1111/cdev.13036.
    1. Maciejewski D, Lauharatanahirun N, Herd T, Lee J, Deater-Deckard K, King-Casas B, & Kim-Spoon J (2018). Neural cognitive control moderates the association between insular risk processing and risk-taking behaviors via perceived stress in adolescents. Developmental Cognitive Neuroscience. 30, 150–158. 10.1016/j.dcn.2018.02.005
    1. Matheny AP, Wachs TD, Ludwig JL, & Phillips K (1995). Bringing order out of chaos: Psychometric characteristics of the confusion, hubbub, and order scale. Journal of Applied Developmental Psychology, 16, 429–444. 10.1016/0193-3973(95)90028-4
    1. Merz EC, Tottenham N, & Noble KG (2018). Socioeconomic status, amygdala volume, and internalizing symptoms in children and adolescents. Journal of Clinical Child & Adolescent Psychology, 47, 312–323. 10.1080/15374416.2017.1326122
    1. Mohr PNC, Biele G, & Heekeren HR (2010). Neural processing of risk. The Journal of Neuroscience, 30, 6613–6619. 10.1523/JNEUROSCI.0003-10.2010
    1. Moss HB, Chen CM, & Yi HY (2014). Early adolescent patterns of alcohol, cigarettes, and marijuana polysubstance use and young adult substance use outcomes in a nationally representative sample. Drug & Alcohol Dependence, 136, 51–62. 10.1016/j.drugalcdep.2013.12.011
    1. Mullainathan S, & Shafir E (2013). Scarcity: Why having too little means so much. New York, NY, US: Times Books/Henry Holt and Co.
    1. Muthén LK, & Muthén BO (1998–2017). Mplus User’s Guide. Eighth Edition: Los Angeles, CA: Muthén & Muthén.
    1. Pessoa L (2010). Emotion and cognition and the amygdala: From “what is it?” to “what’s to be done?” Neuropsychologia, 48, 3416–3429. 10.1016/j.neuropsychologia.2010.06.038
    1. Preacher KJ, & Hayes AF (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891. 10.3758/BRM.40.3.879
    1. Romens SE, Casement MD, McAloon R, Keenan K, Hipwell AE, Guyer AE, & Forbes EE (2015). Adolescent girls’ neural response to reward mediates the relation between childhood financial disadvantage and depression. Journal of Child Psychology & Psychiatry, 56, 1177–1184. 10.1111/jcpp.12410
    1. Roth MC, Humphreys KL, King LS, & Gotlib IH (2018). Self-reported neglect, amygdala volume, and symptoms of anxiety in adolescent boys. Child Abuse & Neglect, 80, 80–89. 10.1016/j.chiabu.2018.03.016
    1. Satorra A, & Bentler PM (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507–514. 10.1007/BF02296192
    1. Saxbe D, Lyden H, Gimbel SI, Sachs M, Del Piero LB, Margolin G, & Kaplan JT (2018). Longitudinal associations between family aggression, externalizing behavior, and the structure and function of the amygdala. Journal of Research on Adolescence, 28, 134–149. 10.1111/jora.12349
    1. Sheridan MA, Sarsour K, Jutte D, D’Esposito M, & Boyce WT (2012). The impact of social disparity on prefrontal function in childhood. PLOS ONE, 7, e35744 10.1371/journal.pone.0035744
    1. Smith VL (1976). Experimental economics: Induced value theory. The American Economic Review, 66, 274–279.
    1. Spielberg JM, Galarce EM, Ladouceur CD, McMakin DL, Olino TM, Forbes EE, … Dahl RE (2015). Adolescent development of inhibition as a function of SES and gender: Converging evidence from behavior and fMRI. Human Brain Mapping, 36, 3194–3203. 10.1002/hbm.22838
    1. Swartz JR, Williamson DE, & Hariri AR (2014). Developmental change in amygdala reactivity during adolescence: Effects of family history of depression and stressful life events. American Journal of Psychiatry, 172, 276–283. 10.1176/appi.ajp.2014.14020195
    1. Tottenham N, & Galván A (2016). Stress and the adolescent brain: Amygdala-prefrontal cortex circuitry and ventral striatum as developmental targets. Neuroscience and Biobehavioral Reviews, 70, 217–227. 10.1016/j.neubiorev.2016.07.030
    1. Trentacosta CJ, Hyde LW, Shaw DS, Dishion TJ, Gardner F, & Wilson M (2008). The relations among cumulative risk, parenting, and behavior problems during early childhood. Journal of Child Psychology and Psychiatry, 49(11), 1211–1219. 10.1111/j.1469-7610.2008.01941.x
    1. van den Bos W, Rodriguez CA, Schweitzer JB, & McClure SM (2014). Connectivity strength of dissociable striatal tracts predict individual differences in temporal discounting. The Journal of Neuroscience, 34, 10298–10310. 10.1523/JNEUROSCI.4105-13.2014
    1. Wierenga L, Langen M, Ambrosino S, van Dijk S, Oranje B, & Durston S (2014). Typical development of basal ganglia, hippocampus, amygdala and cerebellum from age 7 to 24. NeuroImage, 96, 67–72. 10.1016/j.neuroimage.2014.03.072
    1. Whittle S, Vijayakumar N, Simmons JG, Dennison M, Schwartz O, Pantelis C, … Allen NB (2017). Role of positive parenting in the association between neighborhood social disadvantage and brain development across adolescence JAMA Psychiatry, 74, 824–832. 10.1001/jamapsychiatry.2017.1558

Source: PubMed

3
Iratkozz fel