Computational basis of decision-making impairment in multiple sclerosis

Background: Multiple sclerosis (MS) is commonly associated with decision-making, neurocognitive impairments, and mood and motivational symptoms. However, their relationship may be obscured by traditional scoring methods. Objectives: To study the computational basis underlying decision-making impairm...

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Autores:
Fernández, Rodrigo S
Crivelli, Lucia
Pedreira, María E
Allegri, Ricardo Francisco
Correale, Jorge
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9394
Acceso en línea:
https://hdl.handle.net/11323/9394
https://doi.org/10.1177/13524585211059308
https://repositorio.cuc.edu.co/
Palabra clave:
Multiple sclerosis
Computational modeling
Decision-making
Cognitive impairment
Rights
openAccess
License
© 2022 by SAGE Publications
id RCUC2_f3807a0b5a3bd4072af286b1293cdfeb
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9394
network_acronym_str RCUC2
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repository_id_str
dc.title.eng.fl_str_mv Computational basis of decision-making impairment in multiple sclerosis
title Computational basis of decision-making impairment in multiple sclerosis
spellingShingle Computational basis of decision-making impairment in multiple sclerosis
Multiple sclerosis
Computational modeling
Decision-making
Cognitive impairment
title_short Computational basis of decision-making impairment in multiple sclerosis
title_full Computational basis of decision-making impairment in multiple sclerosis
title_fullStr Computational basis of decision-making impairment in multiple sclerosis
title_full_unstemmed Computational basis of decision-making impairment in multiple sclerosis
title_sort Computational basis of decision-making impairment in multiple sclerosis
dc.creator.fl_str_mv Fernández, Rodrigo S
Crivelli, Lucia
Pedreira, María E
Allegri, Ricardo Francisco
Correale, Jorge
dc.contributor.author.spa.fl_str_mv Fernández, Rodrigo S
Crivelli, Lucia
Pedreira, María E
Allegri, Ricardo Francisco
Correale, Jorge
dc.subject.proposal.eng.fl_str_mv Multiple sclerosis
Computational modeling
Decision-making
Cognitive impairment
topic Multiple sclerosis
Computational modeling
Decision-making
Cognitive impairment
description Background: Multiple sclerosis (MS) is commonly associated with decision-making, neurocognitive impairments, and mood and motivational symptoms. However, their relationship may be obscured by traditional scoring methods. Objectives: To study the computational basis underlying decision-making impairments in MS and their interaction with neurocognitive and neuropsychiatric measures. Methods: Twenty-nine MS patients and 26 matched control subjects completed a computer version of the Iowa Gambling Task (IGT). Participants underwent neurocognitive evaluation using an expanded version of the Brief Repeatable Battery. Hierarchical Bayesian Analysis was used to estimate three established computational models to compare parameters between groups. Results: Patients showed increased learning rate and reduced loss-aversion during decision-making relative to control subjects. These alterations were associated with: (1) reduced net gains in the IGT; (2) processing speed, executive functioning and memory impairments; and (3) higher levels of depression and current apathy. Conclusion: Decision-making deficits in MS patients could be described by the interplay between latent computational processes, neurocognitive impairments, and mood/motivational symptoms.
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-12-21
dc.date.accessioned.none.fl_str_mv 2022-07-22T12:49:31Z
dc.date.available.none.fl_str_mv 2022-07-22T12:49:31Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Fernández, R. S., Crivelli, L., Pedreira, M. E., Allegri, R. F., & Correale, J. (2022). Computational basis of decision-making impairment in multiple sclerosis. Multiple Sclerosis Journal, 28(8), 1267–1276. https://doi.org/10.1177/13524585211059308
dc.identifier.issn.spa.fl_str_mv 1352-4585
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9394
dc.identifier.url.spa.fl_str_mv https://doi.org/10.1177/13524585211059308
dc.identifier.doi.spa.fl_str_mv 10.1177/13524585211059308
dc.identifier.eissn.spa.fl_str_mv 1477-0970
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Fernández, R. S., Crivelli, L., Pedreira, M. E., Allegri, R. F., & Correale, J. (2022). Computational basis of decision-making impairment in multiple sclerosis. Multiple Sclerosis Journal, 28(8), 1267–1276. https://doi.org/10.1177/13524585211059308
1352-4585
10.1177/13524585211059308
1477-0970
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9394
https://doi.org/10.1177/13524585211059308
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Multiple Sclerosis Journal
dc.relation.references.spa.fl_str_mv 1. Filippi, M, Bar-Or, A, Piehl, F, et al. Multiple sclerosis. Nat Rev Dis Primer 2018; 4(1): 43.
2. Thompson, AJ, Baranzini, SE, Geurts, J, et al. Multiple sclerosis. Lancet 2018; 391(10130): 1622–1636.
3. Dobson, R, Giovannoni, G. Multiple sclerosis—A review. Eur J Neurol 2019; 26(1): 27–40.
4. 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.
5. Duque, B, Sepulcre, J, Bejarano, B, et al. Memory decline evolves independently of disease activity in MS. Mult Scler 2008; 14(7): 947–953.
6. Arnett, PA, Barwick, FH, Beeney, JE. Depression in multiple sclerosis: Review and theoretical proposal. J Int Neuropsychol Soc 2008; 14(5): 691–724.
7. 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.
8. Macías Islas, MÁ, Ciampi, E. Assessment and impact of cognitive impairment in multiple sclerosis: An overview. Biomedicines 2019; 7(1): 22.
9. 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.
10. 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.
11. Neuhaus, M, Calabrese, P, Annoni, J-M. Decision-making in multiple sclerosis patients: A systematic review. Mult Scler Int 2018; 2018: 7835952.
12. 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.
13. 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.
14. 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.
15. Steingroever, H, Wetzels, R, Wagenmakers, E-J. Bayes factors for reinforcement-learning models of the Iowa gambling task. Decision 2016; 3(2): 115–131.
16. Simioni, S, Ruffieux, C, Kleeberg, J, et al. Preserved decision making ability in early multiple sclerosis. J Neurol 2008; 255(11): 1762–1769.
17. 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 | Medline
18. Steingroever, H, Wetzels, R, Wagenmakers, E-J. Absolute performance of reinforcement-learning models for the Iowa Gambling Task. Decision 2014; 1(3): 161–183.
19. 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.
20. 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.
21. 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.
22. Rao, SM, Leo, GJ, Bernardin, L, et al. Cognitive dysfunction in multiple sclerosis: I. Frequency, patterns, and prediction. Neurology 1991; 41(5): 685–691.
23. 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.
24. 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.
25. Schönbrodt, FD, Wagenmakers, EJ. Bayes factor design analysis: Planning for compelling evidence. Psychon Bull Rev 2018; 25(1): 128–142.
26. 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.
27. Lee, MD . How cognitive modeling can benefit from hierarchical Bayesian models. J Math Psychol 2011; 55(1): 1–7.
28. Maia, TV, Frank, MJ. From reinforcement learning models to psychiatric and neurological disorders. Nat Neurosci 2011; 14(2): 154–162.
29. Bennett, D, Silverstein, SM, Niv, Y. The two cultures of computational psychiatry. JAMA Psychiatry 2019; 76(6): 563–564.
30. Collins, AG, Ciullo, B, Frank, MJ, et al. Working memory load strengthens reward prediction errors. J Neurosci 2017; 37(16): 4332–4342.
31. Vikbladh, OM, Meager, MR, King, J, et al. Hippocampal contributions to model-based planning and spatial memory. Neuron 2019; 102(3): 683–693.
32. Rmus, M, McDougle, SD, Collins, AG. The role of executive function in shaping reinforcement learning. Curr Opin Behav Sci 2021; 38: 66–73.
33. 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.
34. 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.
35. O’Doherty, JP, Cockburn, J, Pauli, WM. Learning, reward, and decision making. Annu Rev Psychol 2017; 68: 73–100.
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spelling Fernández, Rodrigo SCrivelli, LuciaPedreira, María EAllegri, Ricardo FranciscoCorreale, Jorge2022-07-22T12:49:31Z2022-07-22T12:49:31Z2021-12-21Fernández, R. S., Crivelli, L., Pedreira, M. E., Allegri, R. F., & Correale, J. (2022). Computational basis of decision-making impairment in multiple sclerosis. Multiple Sclerosis Journal, 28(8), 1267–1276. https://doi.org/10.1177/135245852110593081352-4585https://hdl.handle.net/11323/9394https://doi.org/10.1177/1352458521105930810.1177/135245852110593081477-0970Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Background: Multiple sclerosis (MS) is commonly associated with decision-making, neurocognitive impairments, and mood and motivational symptoms. However, their relationship may be obscured by traditional scoring methods. Objectives: To study the computational basis underlying decision-making impairments in MS and their interaction with neurocognitive and neuropsychiatric measures. Methods: Twenty-nine MS patients and 26 matched control subjects completed a computer version of the Iowa Gambling Task (IGT). Participants underwent neurocognitive evaluation using an expanded version of the Brief Repeatable Battery. Hierarchical Bayesian Analysis was used to estimate three established computational models to compare parameters between groups. Results: Patients showed increased learning rate and reduced loss-aversion during decision-making relative to control subjects. These alterations were associated with: (1) reduced net gains in the IGT; (2) processing speed, executive functioning and memory impairments; and (3) higher levels of depression and current apathy. Conclusion: Decision-making deficits in MS patients could be described by the interplay between latent computational processes, neurocognitive impairments, and mood/motivational symptoms.1 páginaapplication/pdfengSAGE Publications LtdUnited Kingdom© 2022 by SAGE PublicationsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Computational basis of decision-making impairment in multiple sclerosisArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_b1a7d7d4d402bccehttps://journals.sagepub.com/doi/10.1177/13524585211059308Multiple Sclerosis Journal1. Filippi, M, Bar-Or, A, Piehl, F, et al. Multiple sclerosis. Nat Rev Dis Primer 2018; 4(1): 43.2. Thompson, AJ, Baranzini, SE, Geurts, J, et al. Multiple sclerosis. Lancet 2018; 391(10130): 1622–1636.3. Dobson, R, Giovannoni, G. Multiple sclerosis—A review. Eur J Neurol 2019; 26(1): 27–40.4. 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): 187–195.5. Duque, B, Sepulcre, J, Bejarano, B, et al. Memory decline evolves independently of disease activity in MS. Mult Scler 2008; 14(7): 947–953.6. Arnett, PA, Barwick, FH, Beeney, JE. Depression in multiple sclerosis: Review and theoretical proposal. J Int Neuropsychol Soc 2008; 14(5): 691–724.7. 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): 363–371.8. Macías Islas, MÁ, Ciampi, E. Assessment and impact of cognitive impairment in multiple sclerosis: An overview. Biomedicines 2019; 7(1): 22.9. 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): e004918.10. 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): 1762–1771.11. Neuhaus, M, Calabrese, P, Annoni, J-M. Decision-making in multiple sclerosis patients: A systematic review. Mult Scler Int 2018; 2018: 7835952.12. 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): 530–536.13. 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): 787–795.14. 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): 428–437.15. Steingroever, H, Wetzels, R, Wagenmakers, E-J. Bayes factors for reinforcement-learning models of the Iowa gambling task. Decision 2016; 3(2): 115–131.16. Simioni, S, Ruffieux, C, Kleeberg, J, et al. Preserved decision making ability in early multiple sclerosis. J Neurol 2008; 255(11): 1762–1769.17. 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): 161–183.19. 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: 849.20. 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: 640.21. Polman, CH, Reingold, SC, Edan, G, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria.” Ann Neurol 2005; 58(6): 840–846.22. Rao, SM, Leo, GJ, Bernardin, L, et al. Cognitive dysfunction in multiple sclerosis: I. Frequency, patterns, and prediction. Neurology 1991; 41(5): 685–691.23. Benedict, RH, Fishman, I, McClellan, MM, et al. Validity of the beck depression inventory-fast screen in multiple sclerosis. Mult Scler 2003; 9(4): 393–396.24. 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): 54–67.25. Schönbrodt, FD, Wagenmakers, EJ. Bayes factor design analysis: Planning for compelling evidence. Psychon Bull Rev 2018; 25(1): 128–142.26. Ahn, W-Y, Haines, N, Zhang, L. Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Comput Psychiatr 2017; 1: 24–57.27. Lee, MD . How cognitive modeling can benefit from hierarchical Bayesian models. J Math Psychol 2011; 55(1): 1–7.28. Maia, TV, Frank, MJ. From reinforcement learning models to psychiatric and neurological disorders. Nat Neurosci 2011; 14(2): 154–162.29. Bennett, D, Silverstein, SM, Niv, Y. The two cultures of computational psychiatry. JAMA Psychiatry 2019; 76(6): 563–564.30. Collins, AG, Ciullo, B, Frank, MJ, et al. Working memory load strengthens reward prediction errors. J Neurosci 2017; 37(16): 4332–4342.31. Vikbladh, OM, Meager, MR, King, J, et al. Hippocampal contributions to model-based planning and spatial memory. Neuron 2019; 102(3): 683–693.32. Rmus, M, McDougle, SD, Collins, AG. The role of executive function in shaping reinforcement learning. Curr Opin Behav Sci 2021; 38: 66–73.33. 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: 3419871.34. 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): 1163–1173.35. O’Doherty, JP, Cockburn, J, Pauli, WM. Learning, reward, and decision making. Annu Rev Psychol 2017; 68: 73–100.828Multiple sclerosisComputational modelingDecision-makingCognitive impairmentPublicationORIGINALComputational basis of decision-making impairment in multiple sclerosis.pdfComputational basis of decision-making impairment in multiple sclerosis.pdfapplication/pdf55357https://repositorio.cuc.edu.co/bitstreams/33f931ba-2284-4bd6-bc67-7a34809b1d9b/download8413dd82bba07a6e6717bdb43200ceccMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/7e80b808-3597-4dda-8623-8c40d23f07e3/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTComputational basis of decision-making impairment in multiple sclerosis.pdf.txtComputational basis of decision-making impairment in multiple sclerosis.pdf.txttext/plain1561https://repositorio.cuc.edu.co/bitstreams/8b4db432-330e-4f80-ae3a-0bd12ebca452/downloadc49ba3183c33be1f0e5eaddbe8e24a97MD53THUMBNAILComputational basis of decision-making impairment in multiple 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