Shifting uncertainty intolerance: methylphenidate and attention-deficit hyperactivity disorder

0
156

  • 1.

    Schultz, W. Neuronal reward and decision signals: from theories to data. Physiol. Rev. 95, 853–951 (2015).

    CAS 
    Article 

    Google Scholar
     

  • 2.

    Rubia, K. In Oxford Textbook of Attention Deficit Hyperactivity Disorder. 64–72 (Oxford University press, Oxford, 2018).

  • 3.

    Verdejo-García, A., Lawrence, A. J. & Clark, L. Impulsivity as a vulnerability marker for substance-use disorders: review of findings from high-risk research, problem gamblers and genetic association studies. Neurosci. Biobehav. Rev. 32, 777–810 (2008).

    Article 

    Google Scholar
     

  • 4.

    Breyer, J. L. et al. Young adult gambling behaviors and their relationship with the persistence of ADHD. J. Gambl. Stud. 25, 227–238 (2009).

    Article 

    Google Scholar
     

  • 5.

    Dekkers, T. J., Popma, A., van Rentergem, J. A. A., Bexkens, A. & Huizenga, H. M. Risky decision making in attention-deficit/hyperactivity disorder: a meta-regression analysis. Clin. Psychol. Rev. 45, 1–16 (2016).

    Article 

    Google Scholar
     

  • 6.

    Huys, Q. J., Maia, T. V. & Frank, M. J. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat. Neurosci. 19, 404–13. (2016).

    CAS 
    Article 

    Google Scholar
     

  • 7.

    Mandali, A., Weidacker, K., Kim, S.-G. & Voon, V. The ease and sureness of a decision: evidence accumulation of conflict and uncertainty. Brain 142, 1471–1482 (2019).

    Article 

    Google Scholar
     

  • 8.

    Sethi, A., Voon, V., Critchley, H. D., Cercignani, M. & Harrison, N. A. A neurocomputational account of reward and novelty processing and effects of psychostimulants in attention deficit hyperactivity disorder. Brain 141, 1545–1557 (2018).

    Article 

    Google Scholar
     

  • 9.

    Forstmann, B. U., Ratcliff, R., & Wagenmakers, E.-J. Sequential sampling models in cognitive neuroscience: Advantages, applications, and extensions. Annu. Rev. Psychol. 67, 641–666 (2016).

  • 10.

    Ziegler, S., Pedersen, M. L., Mowinckel, A. M. & Biele, G. Modelling ADHD: a review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning. Neurosci. Biobehav. Rev. 71, 633–656 (2016).

    Article 

    Google Scholar
     

  • 11.

    Forstmann, B. U., Ratcliff, R. & Wagenmakers, E.-J. Sequential sampling models in cognitive neuroscience: advantages, applications, and extensions. Annu. Rev. Psychol. 67, 641–66 (2016).

    CAS 
    Article 

    Google Scholar
     

  • 12.

    Mulder, M., Van Maanen, L. & Forstmann, B. J. N. Perceptual decision neurosciences—a model-based review. Neuroscience 277, 872–884 (2014).

  • 13.

    Aron, A. R., Dowson, J. H. Sahakian, B. J. & Robbins, T. W. Methylphenidate improves response inhibition in adults with attention-deficit/hyperactivity disorder. Biol. Psychiatry 54,1465–1468 (2003).

  • 14.

    DeVito, E. E. et al. The effects of methylphenidate on decision making in attention-deficit/hyperactivity disorder. Biol. Psychiatry 64, 636–639 (2008).

    CAS 
    Article 

    Google Scholar
     

  • 15.

    Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P. & Dolan, R. J. Model-based influences on humans’ choices and striatal prediction errors. Neuron 69, 1204–1215 (2011).

    CAS 
    Article 

    Google Scholar
     

  • 16.

    Gillan, C. M., Kosinski, M., Whelan, R., Phelps, E. A. & Daw, N. D. Characterizing a psychiatric symptom dimension related to deficits in goal-directed control. Elife 5, e11305 (2016).

    Article 

    Google Scholar
     

  • 17.

    Schultz, W. et al. Explicit neural signals reflecting reward uncertainty. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 363, 3801–3811 (2008).

    Article 

    Google Scholar
     

  • 18.

    Preuschoff, K., Bossaerts, P. & Quartz, S. R. Neural differentiation of expected reward and risk in human subcortical structures. Neuron 51, 381–390 (2006).

    CAS 
    Article 

    Google Scholar
     

  • 19.

    Wiecki, T.V., Sofer, I. & Frank, M. J. HDDM: Hierarchical Bayesian estimation of the drift-diffusion model in Python. Front. Neuroinform. 7, 14 (2013).

  • 20.

    Krypotos, A.-M., Beckers, T., Kindt, M. & Wagenmakers, E.-J. A Bayesian hierarchical diffusion model decomposition of performance in Approach–Avoidance Tasks. Cognition Emot. 29, 1424–1444 (2015).

    Article 

    Google Scholar
     

  • 21.

    Frank, M. et al. Computational Psychiatry: New Perspectives on Mental Illness. Vol. 20 (MIT Press, 2016).

  • 22.

    Advokat, C. What are the cognitive effects of stimulant medications? Emphasis on adults with attention-deficit/hyperactivity disorder (ADHD). Neurosci. Biobehav. Rev. 34, 1256–1266 (2010).

    CAS 
    Article 

    Google Scholar
     

  • 23.

    Pietras, C. J., Cherek, D. R., Lane, S. D., Tcheremissine, O. V. & Steinberg, J. L. Effects of methylphenidate on impulsive choice in adult humans. Psychopharmacology 170, 390–398 (2003).

    CAS 
    Article 

    Google Scholar
     

  • 24.

    Shiels, K. et al. Effects of methylphenidate on discounting of delayed rewards in attention deficit/hyperactivity disorder. Exp. Clin. Psychopharmacol. 17, 291–301 (2009).

    CAS 
    Article 

    Google Scholar
     

  • 25.

    Lopez-Persem, A., Domenech, P. & Pessiglione, M. How prior preferences determine decision-making frames and biases in the human brain. Elife 5, e20317 (2016).

    Article 

    Google Scholar
     

  • 26.

    Schonberg, T. et al. Decreasing ventromedial prefrontal cortex activity during sequential risk-taking: an fMRI investigation of the balloon analog risk task. Front. Neurosci. 6, 80 (2012).

    Article 

    Google Scholar
     

  • 27.

    Domenech, P., Redout, J., Koechlin, E. & Dreher, J.-C. The neuro-computational architecture of value-based selection in the human brain. Cereb. Cortex 28, 585–601 (2017).


    Google Scholar
     

  • 28.

    Christakou, A., Brammer, M., Giampietro, V. & Rubia, K.J.J.O.N. Right ventromedial and dorsolateral prefrontal cortices mediate adaptive decisions under ambiguity by integrating choice utility and outcome evaluation. J. Neurosci. 29, 11020–11028 (2009).

    CAS 
    Article 

    Google Scholar
     

  • 29.

    Hartstra, E., Oldenburg, J., Van Leijenhorst, L., Rombouts, S. & Crone, E. A. Brain regions involved in the learning and application of reward rules in a two-deck gambling task. Neuropsychologia 48, 1438–1446 (2010).

    CAS 
    Article 

    Google Scholar
     

  • 30.

    Bechara, A., Damasio, H., Damasio, A.R. & Lee, G.P.J.J.O.N. Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. J. Neurosci. 19, 5473–5481 (1999).

    CAS 
    Article 

    Google Scholar
     

  • 31.

    Studer, B., Manes, F., Humphreys, G., Robbins, T. W. & Clark, L. Risk-sensitive decision-making in patients with posterior parietal and ventromedial prefrontal cortex injury. Cereb. Cortex 25, 1–9 (2013).

    Article 

    Google Scholar
     

  • 32.

    Hulvershorn, L.A. et al. Neural activation during risky decision-making in youth at high risk for substance use disorders. Psychiatry Res. 233, 102–111 (2015).

    Article 

    Google Scholar
     

  • 33.

    Spencer, R. C., Devilbiss, D. M. & Berridge, C. W. The cognition-enhancing effects of psychostimulants involve direct action in the prefrontal cortex. Biol. Psychiatry 77, 940–950 (2015).

    CAS 
    Article 

    Google Scholar
     

  • 34.

    Ojala, K.E. et al., Dopaminergic drug effects on probability weighting during risky decision making. eNeuro 5, ENEUEO.0330-18 (2018).

  • 35.

    Rigoli, F. et al. Dopamine increases a value-independent gambling propensity. Neuropsycholpharmacology 41, 2658–2667 (2016).

    CAS 
    Article 

    Google Scholar
     

  • 36.

    Lopez-Guzman, S., Konova, A. B. & Glimcher, P. W. Computational psychiatry of impulsivity and risk: how risk and time preferences interact in health and disease. Philos. Trans. R. Soc. B 374, 20180135 (2019).

    Article 

    Google Scholar
     

  • 37.

    Wallsten, T. S., Pleskac, T. J. & Lejuez, C. W. Modeling behavior in a clinically diagnostic sequential risk-taking task. Psychological Rev. 112, 862 (2005).

    Article 

    Google Scholar
     

  • 38.

    Campbell-Meiklejohn, D. et al. In for a penny, in for a pound: methylphenidate reduces the inhibitory effect of high stakes on persistent risky choice. J. Neurosci. 32, 13032–13038 (2012).

    CAS 
    Article 

    Google Scholar
     

  • 39.

    Costa, A. et al. Methylphenidate effects on neural activity during response inhibition in healthy humans. Cereb. Cortex 23, 1179–1189 (2012).

    Article 

    Google Scholar
     

  • 40.

    Schmidt, A. et al. Comparative effects of methylphenidate, modafinil, and MDMA on response inhibition neural networks in healthy subjects. Int. J. Neuropsychopharmacol. 20, 712–720 (2017).

    CAS 
    Article 

    Google Scholar
     

  • 41.

    Pauls, A. M. et al. Methylphenidate effects on prefrontal functioning during attentional-capture and response inhibition. Biol. Psychiatry. 72, 142–149 (2012).

    CAS 
    Article 

    Google Scholar
     

  • 42.

    DeVito, E.E. et al. Methylphenidate improves response inhibition but not reflection-impulsivity in children with attention deficit hyperactivity disorder (ADHD). Psychopharmacology (Berl). 202, 531–539 (2009).

    CAS 
    Article 

    Google Scholar
     

  • 43.

    Shalev, L., Gross-Tsur, V. & Pollak, Y. Single dose methylphenidate does not impact on attention and decision making in healthy medical students. J. Neurol. Res. 2, 227–234 (2013).


    Google Scholar
     

  • 44.

    Gvirts, H. Z. et al. The effect of methylphenidate on decision making in patients with borderline personality disorder and attention-deficit/hyperactivity disorder. Int. Clin. Psychopharmacol. 33, 233–237 (2018).

    Article 

    Google Scholar
     

  • 45.

    Voon, V. et al. Waiting impulsivity: the influence of acute methylphenidate and feedback. Int. J. Neuropsychopharmacol. 19, pyv074 (2015).

    Article 

    Google Scholar
     

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