Computational basis of decision-making impairment in multiple sclerosis

1. Filippi, M, Bar-Or, A, Piehl, F, et al. Multiple sclerosis. Nat Rev Dis Primer 2018; 4(1): 43.
Google Scholar | Crossref | Medline2. Thompson, AJ, Baranzini, SE, Geurts, J, et al. Multiple sclerosis. Lancet 2018; 391(10130): 1622–1636.
Google Scholar | Crossref | Medline3. Dobson, R, Giovannoni, G. Multiple sclerosis—A review. Eur J Neurol 2019; 26(1): 27–40.
Google Scholar | Crossref | Medline4. Sepulcre, J, Vanotti, S, Hernández, R, et al. Cognitive impairment in patients with multiple sclerosis using the Brief Repeatable Battery-Neuropsychology test. Mult Scler 2006; 12(2): 187–195.
Google Scholar | SAGE Journals | ISI5. Duque, B, Sepulcre, J, Bejarano, B, et al. Memory decline evolves independently of disease activity in MS. Mult Scler 2008; 14(7): 947–953.
Google Scholar | SAGE Journals | ISI6. Arnett, PA, Barwick, FH, Beeney, JE. Depression in multiple sclerosis: Review and theoretical proposal. J Int Neuropsychol Soc 2008; 14(5): 691–724.
Google Scholar | Crossref | Medline7. Raimo, S, Spitaleri, D, Trojano, L, et al. Apathy as a herald of cognitive changes in multiple sclerosis: A 2-year follow-up study. Mult Scler 2020; 26(3): 363–371.
Google Scholar | SAGE Journals | ISI8. Macías Islas, MÁ, Ciampi, E. Assessment and impact of cognitive impairment in multiple sclerosis: An overview. Biomedicines 2019; 7(1): 22.
Google Scholar | Crossref9. Farez, MF, Crivelli, L, Leiguarda, R, et al. Decision-making impairment in patients with multiple sclerosis: A case-control study. BMJ Open 2014; 4(7): e004918.
Google Scholar | Crossref | Medline10. Sepúlveda, M, Fernández-Diez, B, Martínez-Lapiscina, EH, et al. Impairment of decision-making in multiple sclerosis: A neuroeconomic approach. Mult Scler 2017; 23(13): 1762–1771.
Google Scholar | SAGE Journals | ISI11. Neuhaus, M, Calabrese, P, Annoni, J-M. Decision-making in multiple sclerosis patients: A systematic review. Mult Scler Int 2018; 2018: 7835952.
Google Scholar | Medline12. Muhlert, N, Sethi, V, Cipolotti, L, et al. The grey matter correlates of impaired decision-making in multiple sclerosis. J Neurol Neurosurg Psychiatry 2015; 86(5): 530–536.
Google Scholar | Crossref | Medline13. Kleeberg, J, Bruggimann, L, Annoni, J-M, et al. Altered decision-making in multiple sclerosis: A sign of impaired emotional reactivity? Ann Neurol 2004; 56(6): 787–795.
Google Scholar | Crossref | Medline14. Bechara, A, Damasio, H, Tranel, D, et al. Dissociation of working memory from decision making within the human prefrontal cortex. J Neurosci 1998; 18(1): 428–437.
Google Scholar | Crossref | Medline | ISI15. Steingroever, H, Wetzels, R, Wagenmakers, E-J. Bayes factors for reinforcement-learning models of the Iowa gambling task. Decision 2016; 3(2): 115–131.
Google Scholar | Crossref16. Simioni, S, Ruffieux, C, Kleeberg, J, et al. Preserved decision making ability in early multiple sclerosis. J Neurol 2008; 255(11): 1762–1769.
Google Scholar | Crossref | Medline17. Azcárraga-Guirola, E, Rodríguez-Agudelo, Y, Velázquez-Cardoso, J, et al. Electrophysiological correlates of decision making impairment in multiple sclerosis. Eur J Neurosci 2017; 45(2): 321–329.
Google Scholar | Crossref | Medline18. Steingroever, H, Wetzels, R, Wagenmakers, E-J. Absolute performance of reinforcement-learning models for the Iowa Gambling Task. Decision 2014; 1(3): 161–183.
Google Scholar | Crossref19. Ahn, W-Y, Vasilev, G, Lee, S-H, et al. Decision-making in stimulant and opiate addicts in protracted abstinence: Evidence from computational modeling with pure users. Front Psychol 2014; 5: 849.
Google Scholar | Crossref | Medline20. Worthy, DA, Pang, B, Byrne, KA. Decomposing the roles of perseveration and expected value representation in models of the Iowa Gambling Task. Front Psychol 2013; 4: 640.
Google Scholar | Crossref | Medline21. Polman, CH, Reingold, SC, Edan, G, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria.” Ann Neurol 2005; 58(6): 840–846.
Google Scholar | Crossref | Medline | ISI22. Rao, SM, Leo, GJ, Bernardin, L, et al. Cognitive dysfunction in multiple sclerosis: I. Frequency, patterns, and prediction. Neurology 1991; 41(5): 685–691.
Google Scholar | Crossref | Medline | ISI23. Benedict, RH, Fishman, I, McClellan, MM, et al. Validity of the beck depression inventory-fast screen in multiple sclerosis. Mult Scler 2003; 9(4): 393–396.
Google Scholar | SAGE Journals | ISI24. Chiaravalloti, ND, DeLuca, J. Assessing the behavioral consequences of multiple sclerosis: An application of the Frontal Systems Behavior Scale (FrSBe). Cogn Behav Neurol 2003; 16(1): 54–67.
Google Scholar | Crossref | Medline25. Schönbrodt, FD, Wagenmakers, EJ. Bayes factor design analysis: Planning for compelling evidence. Psychon Bull Rev 2018; 25(1): 128–142.
Google Scholar | Crossref | Medline26. Ahn, W-Y, Haines, N, Zhang, L. Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Comput Psychiatr 2017; 1: 24–57.
Google Scholar | Crossref | Medline27. Lee, MD . How cognitive modeling can benefit from hierarchical Bayesian models. J Math Psychol 2011; 55(1): 1–7.
Google Scholar | Crossref28. Maia, TV, Frank, MJ. From reinforcement learning models to psychiatric and neurological disorders. Nat Neurosci 2011; 14(2): 154–162.
Google Scholar | Crossref | Medline | ISI29. Bennett, D, Silverstein, SM, Niv, Y. The two cultures of computational psychiatry. JAMA Psychiatry 2019; 76(6): 563–564.
Google Scholar | Crossref | Medline30. Collins, AG, Ciullo, B, Frank, MJ, et al. Working memory load strengthens reward prediction errors. J Neurosci 2017; 37(16): 4332–4342.
Google Scholar | Crossref | Medline31. Vikbladh, OM, Meager, MR, King, J, et al. Hippocampal contributions to model-based planning and spatial memory. Neuron 2019; 102(3): 683–693.
Google Scholar | Crossref | Medline32. Rmus, M, McDougle, SD, Collins, AG. The role of executive function in shaping reinforcement learning. Curr Opin Behav Sci 2021; 38: 66–73.
Google Scholar | Crossref33. Laura, DG, Silvia, T, Nikolaos, P, et al. The role of fMRI in the assessment of neuroplasticity in MS: A systematic review. Neural Plast 2018; 2018: 3419871.
Google Scholar | Crossref | Medline34. Weygandt, M, Wakonig, K, Behrens, J, et al. Brain activity, regional gray matter loss, and decision-making in multiple sclerosis. Mult Scler 2018; 24(9): 1163–1173.
Google Scholar | SAGE Journals | ISI35. O’Doherty, JP, Cockburn, J, Pauli, WM. Learning, reward, and decision making. Annu Rev Psychol 2017; 68: 73–100.
Google Scholar | Crossref | Medline

Comments (0)

No login
gif