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Methodological Advances in Leveraging Neuroimaging Datasets in Adolescent Substance Use Research

  • Adriene M. BeltzEmail author
  • Alexander Weigard
Adolescent / Young Adult Addiction (M Heitzeg, Section Editor)
  • 35 Downloads
Part of the following topical collections:
  1. Topical Collection on Adolescent / Young Adult Addiction

Abstract

Purpose of Review

Recent innovations in the statistical analysis of neuroimaging data related to adolescent substance use are highlighted. Going beyond assumptions of homogeneity in small studies of regional localization, the focus is on novel approaches that integrate across regions of the brain and levels of analysis in order to detect individual differences in use along with antecedents and consequences.

Recent Findings

Three analysis approaches are considered. Multimodal approaches like the construct-network framework combine neural, behavioral (including cognitive), and self-report indicators to create comprehensive representations of risk factors for adolescent substance use. Machine learning approaches link adolescent substance use to complex patterns of brain activity detected using prediction-focused algorithms. Person-specific approaches reflect heterogeneity in functional brain connectivity associated with adolescent substance use.

Summary

When applied to specialized datasets, multimodal, machine learning, and person-specific approaches have significant potential to provide unique insights into the neural processes underlying adolescent substance use.

Keywords

Alcohol use Brain structure and function Machine learning Magnetic resonance imaging Multimodal approaches Person-specific analyses 

Notes

Funding Information

Alexander Weigard was supported by NIAAA T32 AA007477 (to Dr. Frederick Blow).

Compliance with Ethical Standards

This article does not contain any studies with human or animal subjects performed by any of the authors.

Conflict of Interest

Adriene Beltz and Alexander Weigard declare that they have no conflict of interest.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Ruiter RAC, Kessels LTE, Peters GJY, Kok G. Sixty years of fear appeal research: current state of the evidence. Int J Psychol. 2014;49(2):63–70.  https://doi.org/10.1002/ijop.12042.CrossRefGoogle Scholar
  2. 2.
    Flynn AB, Falco M, Hocini S. Independent evaluation of middle school-based drug prevention curricula: a systematic review. JAMA Pediatr. 2015;169(11):1046–52.  https://doi.org/10.1001/jamapediatrics.2015.1736.CrossRefGoogle Scholar
  3. 3.
    Johnston LD, Miech RA, O’Malley PM, Bachman JG, Schulenberg JE, Patrick ME. Monitoring the future national survey results on drug use 1975-2018: overview, key findings on adolescent drug use. Ann Arbor: Institute for Social Research, University of Michigan; 2019.Google Scholar
  4. 4.
    Lisdahl KM, Sher KJ, Conway KP, Gonzalez R, Ewing SWF, Nixon SJ, et al. Adolescent brain cognitive development (ABCD) study: overview of substance use assessment methods. Dev Cogn Neurosci. 2018;32:80–96.  https://doi.org/10.1016/j.dcn.2018.02.007.CrossRefGoogle Scholar
  5. 5.
    Hines LA, Morley KI, Mackie C, Lynskey M. Genetic and environmental interplay in adolescent substance use disorders. Curr Addict Rep. 2015;2(2):122–9.  https://doi.org/10.1007/s40429-015-0049-8.CrossRefGoogle Scholar
  6. 6.
    Motley R, Sewell W, Chen YC. Community violence exposure and risk taking behaviors among black emerging adults: a systematic review. J Community Health. 2017;42(5):1069–78.  https://doi.org/10.1007/s10900-017-0353-4.CrossRefGoogle Scholar
  7. 7.
    Groenman AP, Janssen TWP, Oosterlaan J. Childhood psychiatric disorders as risk factor for subsequent substance abuse: a meta-analysis. J Am Acad Child Adolesc Psychiatry. 2017;56(7):556–69.  https://doi.org/10.1016/j.jaac.2017.05.004.CrossRefGoogle Scholar
  8. 8.
    Patrick ME, Schulenberg JE. Prevalence and predictors of adolescent alcohol use and binge drinking in the United States. Alcohol Res Curr Rev. 2013;35(2):193–200.  https://doi.org/10.1002/9780470479193.Google Scholar
  9. 9.
    Ernst M. The triadic model perspective for the study of adolescent motivated behavior. Brain Cogn. 2014;89:104–11.  https://doi.org/10.1016/j.bandc.2014.01.006.CrossRefGoogle Scholar
  10. 10.
    Shulman EP, Smith AR, Silva K, Icenogle G, Duell N, Chein J, et al. The dual systems model: review, reappraisal, and reaffirmation. Dev Cogn Neurosci. 2016;17:103–17.  https://doi.org/10.1016/j.dcn.2015.12.010.CrossRefGoogle Scholar
  11. 11.
    Sharma A, Morrow JD. Neurobiology of adolescent substance use disorders. Child Adolesc Psychiatr Clin N Am. 2016;25(3):367–75.  https://doi.org/10.1016/j.chc.2016.02.001.CrossRefGoogle Scholar
  12. 12.
    Spear LP. Adolescents and alcohol: acute sensitivities, enhanced intake, and later consequences. Neurotoxicol Teratol. 2014;41:51–9.  https://doi.org/10.1016/j.ntt.2013.11.006.CrossRefGoogle Scholar
  13. 13.
    Spear LP. Effects of adolescent alcohol consumption on the brain and behaviour. Nat Rev Neurosci. 2018;19(4):197–214.  https://doi.org/10.1038/nrn.2018.10.CrossRefGoogle Scholar
  14. 14.
    Luciana M, Feldstein SW. Introduction to the special issue: substance use and the adolescent brain: developmental impacts, interventions, and longitudinal outcomes. Dev Cogn Neurosci. 2015;16:1–4.  https://doi.org/10.1016/j.dcn.2015.10.005.CrossRefGoogle Scholar
  15. 15.
    • Silveri MM, Dager AD, Cohen-Gilbert JE, Sneider JT. Neurobiological signatures associated with alcohol and drug use in the human adolescent brain. Neurosci Biobehav Rev. 2016;70:244–59.  https://doi.org/10.1016/j.neubiorev.2016.06.042. Comprehensive review of magnetic resonance imaging studies on adolescent substance use, focusing on alcohol and marijuana use and highlighting limitations and opportunities for future work.CrossRefGoogle Scholar
  16. 16.
    Lorenzetti V, Alonso-Lana S, Youssef GJ, Verdejo-Garcia A, Suo C, Cousijn J, et al. Adolescent cannabis use: what is the evidence for functional brain alteration? Curr Pharm Des. 2016;22(42):6353–65.  https://doi.org/10.2174/1381612822666160805155922.CrossRefGoogle Scholar
  17. 17.
    Squeglia LM, Jacobus J, Tapert SF. The effect of alcohol use on human adolescent brain structures and systems. Handb Clin Neurol. 2014;125:501–10.  https://doi.org/10.1016/b978-0-444-62619-6.00028-8.CrossRefGoogle Scholar
  18. 18.
    Schumann G, Loth E, Banaschewski T, Barbot A, Barker G, Buchel C, et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol Psychiatry. 2010;15(12):1128–39.  https://doi.org/10.1038/mp.2010.4.CrossRefGoogle Scholar
  19. 19.
    Nees F, Tzschoppe J, Patrick CJ, Vollstadt-Klein S, Steiner S, Poustka L, et al. Determinants of early alcohol use in healthy adolescents: the differential contribution of neuroimaging and psychological factors. Neuropsychopharmacology. 2012;37(4):986–95.  https://doi.org/10.1038/npp.2011.282.CrossRefGoogle Scholar
  20. 20.
    Paulus MP, Squeglia LM, Bagot K, Jacobus J, Kuplicki R, Breslin FJ, et al. Screen media activity and brain structure in youth: evidence for diverse structural correlation networks from the ABCD study. Neuroimage. 2019;185:140–53.  https://doi.org/10.1016/j.neuroimage.2018.10.040.CrossRefGoogle Scholar
  21. 21.
    Thompson WK, Barch DM, Bjork JM, Gonzalez R, Nagel BJ, Nixon SJ et al. The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: findings from the ABCD study’s baseline neurocognitive battery. Dev Cogn Neurosci. 2018:100606-.  https://doi.org/10.1016/j.dcn.2018.12.004.
  22. 22.
    Heinrich A, Muller KU, Banaschewski T, Barker GJ, Bokde ALW, Bromberg U, et al. Prediction of alcohol drinking in adolescents: personality-traits, behavior, brain responses, and genetic variations in the context of reward sensitivity. Biol Psychol. 2016;118:79–87.  https://doi.org/10.1016/j.biopsycho.2016.05.002.CrossRefGoogle Scholar
  23. 23.
    Campbell DT, Fiske DW. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol Bull. 1959;56(2):81–105.  https://doi.org/10.1037/h0046016.CrossRefGoogle Scholar
  24. 24.
    Huys QJM, Maia TV, Frank MJ. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci. 2016;19(3):404–13.  https://doi.org/10.1038/nn.4238.CrossRefGoogle Scholar
  25. 25.
    Gray KM, Squeglia LM. What have we learned about adolescent substance use? J Child Psychol Psychiatry. 2018;59:618–27.  https://doi.org/10.1111/jcpp.12783.CrossRefGoogle Scholar
  26. 26.
    Beltz AM, Wright AGC, Sprague BN, Molenaar PCM. Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment. 2016;23(4):447–58.  https://doi.org/10.1177/1073191116648209.CrossRefGoogle Scholar
  27. 27.
    •• Paulus MP, Thompson WK. The challenges and opportunities of small effects the new normal in academic psychiatry. JAMA Psychiatry. 2019;76(4):353–4.  https://doi.org/10.1001/jamapsychiatry.2018.4540. Thoughtful commentary on recent indications that clinical neuroscience may be limited by small effects, which may, in turn, make generalizable causal explanations elusive and translational work challenging.CrossRefGoogle Scholar
  28. 28.
    Patrick CJ, Venables NC, Yancey JR, Hicks BM, Nelson LD, Kramer MD. A construct-network approach to bridging diagnostic and physiological domains: application to assessment of externalizing psychopathology. J Abnorm Psychol. 2013;122(3):902–16.  https://doi.org/10.1037/a0032807.CrossRefGoogle Scholar
  29. 29.
    Nelson LD, Patrick CJ, Bernat EM. Operationalizing proneness to externalizing psychopathology as a multivariate psychophysiological phenotype. Psychophysiology. 2011;48(1):64–73.  https://doi.org/10.1111/j.1469-8986.2010.01047.x.CrossRefGoogle Scholar
  30. 30.
    Venables NC, Hicks BM, Yancey JR, Kramer MD, Nelson LD, Strickland CM, et al. Evidence of a prominent genetic basis for associations between psychoneurometric traits and common mental disorders. Int J Psychophysiol. 2017;115:4–12.  https://doi.org/10.1016/j.ijpsycho.2016.09.011.CrossRefGoogle Scholar
  31. 31.
    • Venables NC, Foell J, Yancey JR, Kane MJ, Engle RW, Patrick CJ. Quantifying inhibitory control as externalizing proneness: a cross-domain model. Clin Psychol Sci. 2018;6(4):561–80.  https://doi.org/10.1177/2167702618757690. Outlines how the construct-network approach can be used to integrate neural, behavioral (including cognitive), and self-report indicators to form a multimodal factor that predicts substance use.CrossRefGoogle Scholar
  32. 32.
    • Brislin SJ, Patrick CJ, Flor H, Nees F, Heinrich A, Drislane LE, et al. Extending the construct network of trait disinhibition to the neuroimaging domain: validation of a bridging scale for use in the European IMAGEN project. Assessment. 2019;26(4):567–81.  https://doi.org/10.1177/1073191118759748. Outlines the development and validation of a dimensional measure of trait disinhibition in IMAGEN, facilitating the application of future multimodal factors that include neuroimaging indicators in the dataset. CrossRefGoogle Scholar
  33. 33.
    • Yarkoni T, Westfall J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect Psychol Sci. 2017;12(6):1100–22.  https://doi.org/10.1177/1745691617693393. Discusses the tradeoffs between predictive and explanatory approaches, while advocating for increased emphasis on a predictive approach rooted in the long-held principles of machine learning, and provides an excellent overview of the principles and implementation of machine learning methods. CrossRefGoogle Scholar
  34. 34.
    •• Whelan R, Watts R, Orr CA, Althoff RR, Artiges E, Banaschewski T, et al. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature. 2014;512(7513):185–9.  https://doi.org/10.1038/nature13402. Empirical application of a machine learning algorithm for the prediction of adolescent binge drinking behavior to multimodal data in the IMAGEN dataset; models successfully predicted binge drinking at baseline and future time points and generalized to novel data.CrossRefGoogle Scholar
  35. 35.
    Squeglia LM, Ball TM, Jacobus J, Brumback T, McKenna BS, Nguyen-Louie TT, et al. Neural predictors of initiating alcohol use during adolescence. Am J Psychiatr. 2017;174(2):172–85.  https://doi.org/10.1176/appi.ajp.2016.15121587.CrossRefGoogle Scholar
  36. 36.
    Rosenberg MD, Casey BJ, Holmes AJ. Prediction complements explanation in understanding the developing brain. Nat Commun. 2018;9:589.  https://doi.org/10.1038/s41467-018-02887-9.CrossRefGoogle Scholar
  37. 37.
    Gabrieli JDE, Ghosh SS, Whitfield-Gabrieli S. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron. 2015;85(1):11–26.  https://doi.org/10.1016/j.neuron.2014.10.047.CrossRefGoogle Scholar
  38. 38.
    Tomczyk S, Isensee B, Hanewinkel R. Latent classes of polysubstance use among adolescents: a systematic review. Drug Alcohol Depend. 2016;160:12–29.  https://doi.org/10.1016/j.drugalcdep.2015.11.035.CrossRefGoogle Scholar
  39. 39.
    Molenaar PCM. A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever. Meas Interdiscip Res Persp. 2004;2(4):201–18.  https://doi.org/10.1207/s15366359mea0204_1.CrossRefGoogle Scholar
  40. 40.
    Chadi N, Bagley SM, Hadland SE. Addressing adolescents’ and young adults’ substance use disorders. Med Clin N Am. 2018;102(4):603–20.  https://doi.org/10.1016/j.mcna.2018.02.015.CrossRefGoogle Scholar
  41. 41.
    Silvers JA, Squeglia LM, Thomsen KR, Hudson KA, Ewing SWF. Hunting for what works: adolescents in addiction treatment. Alcohol Clin Exp Res. 2019;43(4):578–92.  https://doi.org/10.1111/acer.13984.CrossRefGoogle Scholar
  42. 42.
    Gates KM, Molenaar PCM. Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. Neuroimage. 2012;63(1):310–9.  https://doi.org/10.1016/j.neuroimage.2012.06.026.CrossRefGoogle Scholar
  43. 43.
    Gates KM, Molenaar PCM, Hillary FG, Ram N, Rovine MJ. Automatic search for fMRI connectivity mapping: an alternative to granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM. Neuroimage. 2010;50(3):1118–25.  https://doi.org/10.1016/j.neuroimage.2009.12.117.CrossRefGoogle Scholar
  44. 44.
    Gates KM, Molenaar PCM, Hillary FG, Slobounov S. Extended unified SEM approach for modeling event-related fMRI data. Neuroimage. 2011;54(2):1151–8.  https://doi.org/10.1016/j.neuroimage.2010.08.051.CrossRefGoogle Scholar
  45. 45.
    •• Beltz AM, Gates KM. Network mapping with GIMME. Multivar Behav Res. 2017;52(6):789–804.  https://doi.org/10.1080/00273171.2017.1373014. Tutorial on one person-specific approach to the analysis of intensive longitudinal data, such as functional neuroimaging data: group iterative multiple model estimation (GIMME).CrossRefGoogle Scholar
  46. 46.
    Foster KT, Beltz AM. Advancing statistical analysis of ambulatory assessment data in the study of addictive behavior: a primer on three person-oriented techniques. Addict Behav. 2018;83:25–34.  https://doi.org/10.1016/j.addbeh.2017.12.018.CrossRefGoogle Scholar
  47. 47.
    Zelle SL, Gates KM, Fiez JA, Sayette MA, Wilson SJ. The first day is always the hardest: functional connectivity during cue exposure and the ability to resist smoking in the initial hours of a quit attempt. Neuroimage. 2017;151:24–32.  https://doi.org/10.1016/j.neuroimage.2016.03.015.CrossRefGoogle Scholar
  48. 48.
    • Beltz AM, Gates KM, Engels AS, Molenaar PCM, Pulido C, Turrisi R, et al. Changes in alcohol-related brain networks across the first year of college: a prospective pilot study using fMRI effective connectivity mapping. Addict Behav. 2013;38(4):2052–9.  https://doi.org/10.1016/j.addbeh.2012.12.023. Empirical application of a person-specific approach (i.e., GIMME) to the analysis of alcohol task-related neuroimaging data from adolescents across the transition to college.CrossRefGoogle Scholar
  49. 49.
    Beltz AM, Molenaar PCM. Dealing with multiple solutions in structural vector autoregressive models. Multivar Behav Res. 2016;51(2–3):357–73.  https://doi.org/10.1080/00273171.2016.1151333.CrossRefGoogle Scholar
  50. 50.
    Gates KM, Lane ST, Varangis E, Giovanello K, Guiskewicz K. Unsupervised classification during time-series model building. Multivar Behav Res. 2017;52(2):129–48.  https://doi.org/10.1080/00273171.2016.1256187.CrossRefGoogle Scholar
  51. 51.
    Insel TR. The NIMH Research Domain Criteria (RDoC) project: precision medicine for psychiatry. Am J Psychiatr. 2014;171(4):395–7.  https://doi.org/10.1176/appi.ajp.2014.14020138.CrossRefGoogle Scholar
  52. 52.
    Litten RZ, Ryan ML, Falk DE, Reilly M, Fertig JB, Koob GF. Heterogeneity of alcohol use disorder: understanding mechanisms to advance personalized treatment. Alcohol Clin Exp Res. 2015;39(4):579–84.  https://doi.org/10.1111/acer.12669.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of MichiganAnn ArborUSA
  2. 2.Department of PsychiatryUniversity of MichiganAnn ArborUSA

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