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24 - The Triadic Neural Systems Model through a Machine-Learning Mill

from Subpart II.2 - Childhood and Adolescence: The Development of Human Thinking

Published online by Cambridge University Press:  24 February 2022

By
Monique Ernst ,
Josh Gowin  and
Claudie Gaillard
Edited by
Olivier Houdé  and
Grégoire Borst
Olivier Houdé
Affiliation:
Université de Paris V
Grégoire Borst
Affiliation:
Université de Paris V
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Summary

Fifteen years ago, the triadic neural systems model was proposed as a heuristic tool to study and clarify the neural mechanisms accounting for distinct typical adolescent behaviors. Whereas aspects of the models have been validated, the overall theory has not been comprehensively tested, mainly because of the lack of appropriate samples (i.e., large pediatric sample with follow-ups) and sufficiently powerful analytic tools. This situation can be remediated now, thanks to the availability of large longitudinal datasets and the emergence of machine learning (ML) tools in the neuroscience field. This chapter describes a “vision” of how the triadic neural systems model could be tested using ML methodology. In the name of clarity, fictitious concrete examples are presented, together with a concerted effort to keep this essay quite simple and accessible to the majority of clinical researchers. This comes at the expense of a critical review of assumptions and limitations that accompanies any analytical strategy. To mitigate this caveat, references to comprehensive publications are provided.

Type
Chapter
Information
The Cambridge Handbook of Cognitive Development , pp. 516 - 534
Publisher: Cambridge University Press
Print publication year: 2022

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References

Balleine, B. W., & Killcross, S. (2006). Parallel incentive processing: An integrated view of amygdala function. Trends in Neuroscience, 29, 272–279.CrossRefGoogle ScholarPubMed
Balleine, B. W., & O’Doherty, J. P. (2010). Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology, 35, 48–69.CrossRefGoogle ScholarPubMed
Banich, M. T. (2009). Executive function: The search for an integrated account. Current Directions in Psychological Science, 18, 89–94.CrossRefGoogle Scholar
Berridge, K. C., & Kringelbach, M. L. (2013). Neuroscience of affect: Brain mechanisms of pleasure and displeasure. Current Opinions in Neurobiology, 23, 294–303.CrossRefGoogle ScholarPubMed
Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neuroscience, 26, 507–513.CrossRefGoogle ScholarPubMed
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010). Dopamine in motivational control: Rewarding, aversive, and alerting. Neuron, 68, 815–834.CrossRefGoogle ScholarPubMed
Campese, V. D., Sears, R. M., Moscarello, J. M., Diaz-Mataix, L., Cain, C. K., & LeDoux, J. E. (2016). The neural foundations of reaction and action in aversive motivation. In Simpson, E. H., & Balsam, P. D. (eds.), Behavioral Neuroscience of Motivation (pp. 171–195). Cham: Springer International Publishing.Google Scholar
Casey, B. J., Jones, R. M., & Hare, T. A. (2008). The adolescent brain. Annals of the New York Academy of Sciences, 1124, 111–126.CrossRefGoogle ScholarPubMed
Christoff, K., & Gabrieli, J. D. E. (2000). The frontopolar cortex and human cognition: Evidence for a rostrocaudal hierarchical organization within the human prefrontal cortex. Psychobiology, 28, 168–186.CrossRefGoogle Scholar
Delgado, M. R., Jou, R. L., & Phelps, E. A. (2011). Neural systems underlying aversive conditioning in humans with primary and secondary reinforcers. Frontiers in Neuroscience, 5, 71.CrossRefGoogle ScholarPubMed
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14, 91–118.CrossRefGoogle ScholarPubMed
Elliot, A. J., & Covington, M. V. (2001). Approach and avoidance motivation. Educational Psychology Review, 13, 73–92.CrossRefGoogle Scholar
Ernst, M. (2014). The triadic model perspective for the study of adolescent motivated behavior. Brain and Cognition, 89, 104–111.CrossRefGoogle Scholar
Ernst, M., Daniele, T., & Frantz, K. (2011). New perspectives on adolescent motivated behavior: Attention and conditioning. Developmental Cognitive Neuroscience, 1, 377–389.CrossRefGoogle ScholarPubMed
Ernst, M., Pine, D. S., & Hardin, M. (2006). Triadic model of the neurobiology of motivated behavior in adolescence. Psychological Medicine, 36, 299–312.CrossRefGoogle ScholarPubMed
Ernst, M., & Spear, L. P. (2009). Reward systems. In de Haan, M. & Gunnar, M. R. (eds.), Handbook of Developmental Social Neuroscience (pp. 324–341). New York: The Guilford Press.Google Scholar
Gleason, P. M., Boushey, C. J., Harris, J. E., & Zoellner, J. (2015). Publishing nutrition research: A review of multivariate techniques – Part 3: Data reduction methods. Journal of the Academy of Nutrition & Dietetics, 115, 1072–1082.CrossRefGoogle ScholarPubMed
Goode, T. D., & Maren, S. (2017). Role of the bed nucleus of the stria terminalis in aversive learning and memory. Learning & Memory, 24, 480–491.CrossRefGoogle ScholarPubMed
Hare, T. A., Tottenham, N., Galvan, A., Voss, H. U., Glover, G. H., & Casey, B. J. (2008). Biological substrates of emotional reactivity and regulation in adolescence during an emotional go–nogo task. Biological Psychiatry, 63, 927–934.CrossRefGoogle ScholarPubMed
Iniesta, R., Stahl, D., & McGuffin, P. (2016). Machine learning, statistical learning and the future of biological research in psychiatry. Psychological Medicine, 46, 2455–2465.CrossRefGoogle ScholarPubMed
Jernigan, T. L., & Brown, S. A. (2018). Introduction. Developmental Cognitive Neuroscience, 32, 1–3.CrossRefGoogle ScholarPubMed
Kouneiher, F., Charron, S., & Koechlin, E. (2009). Motivation and cognitive control in the human prefrontal cortex. Nature Neuroscience, 12, 939–945.CrossRefGoogle ScholarPubMed
Kragel, P. A., & LaBar, K. S. (2016). Decoding the nature of emotion in the brain. Trends in Cognitive Sciences, 20, 444–455.CrossRefGoogle ScholarPubMed
LeDoux, J., & Daw, N. D. (2018). Surviving threats: Neural circuit and computational implications of a new taxonomy of defensive behaviour. Nature Reviews Neuroscience, 19, 269–282.CrossRefGoogle ScholarPubMed
Maren, S. (2016). Parsing reward and aversion in the amygdala. Neuron, 90, 209–211.CrossRefGoogle ScholarPubMed
Mirenowicz, J., & Schultz, W. (1996). Preferential activation of midbrain dopamine neurons by appetitive rather than aversive stimuli. Nature, 379, 449–451.CrossRefGoogle ScholarPubMed
Murphy, F. C., Nimmo-Smith, I., & Lawrence, A. D. (2003). Functional neuroanatomy of emotions: A meta-analysis. Cognitive, Affective, & Behavioral Neuroscience, 3, 207–233.CrossRefGoogle ScholarPubMed
Petrides, M. (2005). Lateral prefrontal cortex: Architectonic and functional organization. Philosophical Transactions of the Royal Society B: Biological Sciences, 360, 781–795.CrossRefGoogle ScholarPubMed
Quevedo, K. M., Benning, S. D., Gunnar, M. R., & Dahl, R. E. (2009). The onset of puberty: Effects on the psychophysiology of defensive and appetitive motivation. Development and Psychopathology, 21, 27–45.CrossRefGoogle ScholarPubMed
Sanford, C. A., Soden, M. E., Baird, M. A., Miller, S. M., Schulkin, J., Palmiter, R. D., … Zweifel, L. S. (2017). A central amygdala CRF circuit facilitates learning about weak threats. Neuron, 93, 164–178.CrossRefGoogle ScholarPubMed
Silvers, J. A., McRae, K., Gabrieli, J. D. E., Gross, J. J., Remy, K. A., & Ochsner, K. N. (2012). Age-related differences in emotional reactivity, regulation, and rejection sensitivity in adolescence. Emotion, 12, 1235–1247.CrossRefGoogle ScholarPubMed
Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28, 78–106.CrossRefGoogle ScholarPubMed
Stuss, D. T., & Alexander, M. P. (2007). Is there a dysexecutive syndrome? Philosophical Transactions of the Royal Society of London B: Biological Science, 362, 901–915.CrossRefGoogle Scholar
Tarca, A. L., Carey, V. J., Chen, X. W., Romero, R., & Drăghici, S. (2007). Machine learning and its applications to biology. PLoS Computational Biology, 3, e116.CrossRefGoogle ScholarPubMed
Tsutsui-Kimura, I., Bouchekioua, Y., Mimura, M., & Tanaka, K. F. (2017). A new paradigm for evaluating avoidance/escape motivation. International Journal of Neuropsychopharmacology, 20, 593–601.CrossRefGoogle ScholarPubMed
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.CrossRefGoogle Scholar
Varoquaux, G., & Craddock, R. C. (2013). Learning and comparing functional connectomes across subjects. Neuroimage, 80, 405–415.CrossRefGoogle ScholarPubMed
Varoquaux, G., Raamana, P. R., Engemann, D. A., Hoyos-Idrobo, A., Schwartz, Y., & Thirion, B. (2017). Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage, 145, 166–179.CrossRefGoogle ScholarPubMed
Wassum, K. M., & Izquierdo, A. (2015). The basolateral amygdala in reward learning and addiction. Neuroscience& Biobehavioral Reviews, 57, 271–283.CrossRefGoogle ScholarPubMed

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