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Home / Equine Research Bank / Sci Rep / The neurocognitive gains of diagnostic reasoning training us...
Scientific reports2019; 9(1); 19878; doi: 10.1038/s41598-019-56404-z

The neurocognitive gains of diagnostic reasoning training using simulated interactive veterinary cases.

Abstract: The present longitudinal study ascertained training-associated transformations in the neural underpinnings of diagnostic reasoning, using a simulation game named "Equine Virtual Farm" (EVF). Twenty participants underwent structural, EVF/task-based and resting-state MRI and diffusion tensor imaging (DTI) before and after completing their training on diagnosing simulated veterinary cases. Comparing playing veterinarian versus seeing a colorful image across training sessions revealed the transition of brain activity from scientific creativity regions pre-training (left middle frontal and temporal gyrus) to insight problem-solving regions post-training (right cerebellum, middle cingulate and medial superior gyrus and left postcentral gyrus). Further, applying linear mixed-effects modelling on graph centrality metrics revealed the central roles of the creative semantic (inferior frontal, middle frontal and angular gyrus and parahippocampus) and reward systems (orbital gyrus, nucleus accumbens and putamen) in driving pre-training diagnostic reasoning; whereas, regions implicated in inductive reasoning (superior temporal and medial postcentral gyrus and parahippocampus) were the main post-training hubs. Lastly, resting-state and DTI analysis revealed post-training effects within the occipitotemporal semantic processing region. Altogether, these results suggest that simulation-based training transforms diagnostic reasoning in novices from regions implicated in creative semantic processing to regions implicated in improvised rule-based problem-solving.
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Publication Date: 2019-12-27 PubMed ID: 31882714PubMed Central: PMC6934513DOI: 10.1038/s41598-019-56404-zGoogle Scholar: Lookup
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  • Clinical Trial
  • Journal Article
  • Research Support
  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research article explores how training using a simulation game “Equine Virtual Farm” impacts the neural processes related to diagnostic reasoning in participants. MRI and DTI scans were used to observe the brain activity of the participants before and after training, revealing a transition from regions linked with scientific creativity to regions associated with problem-solving.

Overview of the Study

  • The research conducted was a longitudinal study, meaning the researchers collected data over an extended period of time.
  • The study centered around a simulation game named “Equine Virtual Farm” (EVF), used as a tool for training participants in diagnostic reasoning in veterinary cases.
  • Twenty participants took part in the study and underwent structural, task-based and resting-state MRI along with diffusion tensor imaging (DTI) before and after their training, providing the researchers with a detailed look at the brain activity associated with diagnostic reasoning.

Findings of the Study

  • The study found that brain activity transitioned from areas involved in scientific creativity (specifically, the left middle frontal and temporal gyrus) before training, to regions related to insight and problem-solving (right cerebellum, middle cingulate, and medial superior gyrus and left postcentral gyrus) after training.
  • This suggests that the training transitioned the participants from using creativity towards a more structured, rule-based approach to diagnostic thinking.
  • Applying linear mixed-effects modelling further highlighted this shift, indicating centrality within creative semantic and reward system regions (inferior frontal, middle frontal and angular gyrus, and parahippocampus, and the orbital gyrus, nucleus accumbens, and putamen) before training. In contrast, regions involved in inductive reasoning (superior temporal and medial postcentral gyrus and parahippocampus) were the focal points after training.
  • Additionally, resting-state and DTI analysis revealed training effects within the occipitotemporal semantic processing region, again emphasizing the shift towards problem-solving brain regions.

Conclusion

  • The findings suggest that simulation-based training can effectively shift the neural processes involved in diagnostic reasoning from initial creative semantic processing areas, towards structured rule-based problem-solving regions.
  • This research may serve as a foundation for further studies exploring simulation-based training in other cognitive areas or specific applicability within the field of veterinary care,
  • Moreover, this evidence supports the utility of simulation programs such as EVF in dedicated training and education environments.

Cite This Article

APA
Nassar M. (2019). The neurocognitive gains of diagnostic reasoning training using simulated interactive veterinary cases. Sci Rep, 9(1), 19878. https://doi.org/10.1038/s41598-019-56404-z

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 9
Issue: 1
Pages: 19878
PII: 19878

Researcher Affiliations

Nassar, Maaly
  • European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK. maaly13@yahoo.com.
  • Freie Universität Berlin, Center for Digital Systems, Berlin, 14195, Germany. maaly13@yahoo.com.
  • Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, Berlin, 10117, Germany. maaly13@yahoo.com.

MeSH Terms

  • Adult
  • Brain / diagnostic imaging
  • Brain / physiology
  • Brain Mapping
  • Creativity
  • Diffusion Tensor Imaging
  • Female
  • Humans
  • Male
  • Middle Aged
  • Problem Solving / physiology

Conflict of Interest Statement

The author declares no competing interests.

References

This article includes 61 references
  1. Elstein AS. Thinking about diagnostic thinking: a 30-year perspective.. Adv Health Sci Educ Theory Pract 2009 Sep;14 Suppl 1:7-18.
    doi: 10.1007/s10459-009-9184-0pubmed: 19669916google scholar: lookup
  2. Bordage G, Lemieux M. Semantic structures and diagnostic thinking of experts and novices.. Acad Med 1991 Sep;66(9 Suppl):S70-2.
    doi: 10.1097/00001888-199109000-00045pubmed: 1930535google scholar: lookup
  3. Bordage G, Connell KJ, Chang RW, Gecht MR, Sinacore JM. Assessing the semantic content of clinical case presentations: studies of reliability and concurrent validity.. Acad Med 1997 Oct;72(10 Suppl 1):S37-9.
    doi: 10.1097/00001888-199710001-00013pubmed: 9347733google scholar: lookup
  4. Ericsson KA, Krampe RT, Tesch-Römer C. The role of deliberate practice in the acquisition of expert performance. Psychol. Rev. 1993;100:363–406.
    doi: 10.1037/0033-295X.100.3.363google scholar: lookup
  5. Norman G. Dual processing and diagnostic errors.. Adv Health Sci Educ Theory Pract 2009 Sep;14 Suppl 1:37-49.
    doi: 10.1007/s10459-009-9179-xpubmed: 19669921google scholar: lookup
  6. Croskerry P. A universal model of diagnostic reasoning.. Acad Med 2009 Aug;84(8):1022-8.
    doi: 10.1097/ACM.0b013e3181ace703pubmed: 19638766google scholar: lookup
  7. Westbury CF. Bayes' rule for clinicians: an introduction.. Front Psychol 2010;1:192.
    doi: 10.3389/fpsyg.2010.00192pmc: PMC3153801pubmed: 21833252google scholar: lookup
  8. Norman G. Research in clinical reasoning: past history and current trends.. Med Educ 2005 Apr;39(4):418-27.
    doi: 10.1111/j.1365-2929.2005.02127.xpubmed: 15813765google scholar: lookup
  9. Dumas D, Torre DM, Durning SJ. Using Relational Reasoning Strategies to Help Improve Clinical Reasoning Practice.. Acad Med 2018 May;93(5):709-714.
    doi: 10.1097/ACM.0000000000002114pubmed: 29280755google scholar: lookup
  10. Custers EJ. Thirty years of illness scripts: Theoretical origins and practical applications.. Med Teach 2015 May;37(5):457-62.
    doi: 10.3109/0142159X.2014.956052pubmed: 25180878google scholar: lookup
  11. Dumas D, Alexander PA, Baker LM, Jablansky S, Dunbar KN. Relational reasoning in medical education: Patterns in discourse and diagnosis. J. Educ. Psychol. 2014;106:1021–1035.
    doi: 10.1037/a0036777google scholar: lookup
  12. Pelaccia T, Tardif J, Triby E, Charlin B. An analysis of clinical reasoning through a recent and comprehensive approach: the dual-process theory.. Med Educ Online 2011 Mar 14;16.
    pmc: PMC3060310pubmed: 21430797doi: 10.3402/meo.v16i0.5890google scholar: lookup
  13. Hall S, Phang SH, Schaefer JP, Ghali W, Wright B, McLaughlin K. Estimation of post-test probabilities by residents: Bayesian reasoning versus heuristics?. Adv Health Sci Educ Theory Pract 2014 Aug;19(3):393-402.
    doi: 10.1007/s10459-013-9485-1pubmed: 24449125google scholar: lookup
  14. Rottman BM, Prochaska MT, Deaño RC. Bayesian reasoning in residents' preliminary diagnoses.. Cogn Res Princ Implic 2016;1(1):5.
    doi: 10.1186/s41235-016-0005-8pmc: PMC5256430pubmed: 28180156google scholar: lookup
  15. Evans JS, Stanovich KE. Dual-Process Theories of Higher Cognition: Advancing the Debate.. Perspect Psychol Sci 2013 May;8(3):223-41.
    doi: 10.1177/1745691612460685pubmed: 26172965google scholar: lookup
  16. Alexander PA. Relational thinking and relational reasoning: harnessing the power of patterning.. NPJ Sci Learn 2016;1:16004.
    doi: 10.1038/npjscilearn.2016.4pmc: PMC6380381pubmed: 30792891google scholar: lookup
  17. Durning SJ, Dong T, Artino AR, van der Vleuten C, Holmboe E, Schuwirth L. Dual processing theory and experts' reasoning: exploring thinking on national multiple-choice questions.. Perspect Med Educ 2015 Aug;4(4):168-175.
    doi: 10.1007/s40037-015-0196-6pmc: PMC4530528pubmed: 26243535google scholar: lookup
  18. Lesgold A. Expertise in a complex skill: Diagnosing x-ray pictures. In The nature of expertise. 311–342 (Lawrence Erlbaum Associates, Inc, 1988).
  19. Joseph GM, Patel VL. Domain knowledge and hypothesis generation in diagnostic reasoning.. Med Decis Making 1990 Jan-Mar;10(1):31-46.
    doi: 10.1177/0272989X9001000107pubmed: 2182962google scholar: lookup
  20. Durning SJ, Graner J, Artino AR Jr, Pangaro LN, Beckman T, Holmboe E, Oakes T, Roy M, Riedy G, Capaldi V, Walter R, van der Vleuten C, Schuwirth L. Using functional neuroimaging combined with a think-aloud protocol to explore clinical reasoning expertise in internal medicine.. Mil Med 2012 Sep;177(9 Suppl):72-8.
    doi: 10.7205/MILMED-D-12-00242pubmed: 23029866google scholar: lookup
  21. Durning SJ, Costanzo ME, Artino AR, Graner J, van der Vleuten C, Beckman TJ, Wittich CM, Roy MJ, Holmboe ES, Schuwirth L. Neural basis of nonanalytical reasoning expertise during clinical evaluation.. Brain Behav 2015 Mar;5(3):e00309.
    doi: 10.1002/brb3.309pmc: PMC4356847pubmed: 25798328google scholar: lookup
  22. Hruska P, Hecker KG, Coderre S, McLaughlin K, Cortese F, Doig C, Beran T, Wright B, Krigolson O. Hemispheric activation differences in novice and expert clinicians during clinical decision making.. Adv Health Sci Educ Theory Pract 2016 Dec;21(5):921-933.
    doi: 10.1007/s10459-015-9648-3pubmed: 26530736google scholar: lookup
  23. Russo JE, Johnson EJ, Stephens DL. The validity of verbal protocols.. Mem Cognit 1989 Nov;17(6):759-69.
    doi: 10.3758/BF03202637pubmed: 2811673google scholar: lookup
  24. Ericsson KA, Simon HA. Verbal reports as data. Psychol. Rev. 1980;87:215–251.
    doi: 10.1037/0033-295X.87.3.215google scholar: lookup
  25. Fox MC, Ericsson KA, Best R. Do procedures for verbal reporting of thinking have to be reactive? A meta-analysis and recommendations for best reporting methods.. Psychol Bull 2011 Mar;137(2):316-44.
    doi: 10.1037/a0021663pubmed: 21090887google scholar: lookup
  26. Charlin B, Boshuizen HP, Custers EJ, Feltovich PJ. Scripts and clinical reasoning.. Med Educ 2007 Dec;41(12):1178-84.
    doi: 10.1111/j.1365-2923.2007.02924.xpubmed: 18045370google scholar: lookup
  27. Ericsson KA. An expert-performance perspective of research on medical expertise: the study of clinical performance.. Med Educ 2007 Dec;41(12):1124-30.
    doi: 10.1111/j.1365-2923.2007.02946.xpubmed: 18045365google scholar: lookup
  28. Ericsson KA, Nandagopal K, Roring RW. Toward a science of exceptional achievement: attaining superior performance through deliberate practice.. Ann N Y Acad Sci 2009 Aug;1172:199-217.
    doi: 10.1196/annals.1393.001pubmed: 19743555google scholar: lookup
  29. Iwata N, Fujiwara M, Kodera Y, Tanaka C, Ohashi N, Nakayama G, Koike M, Nakao A. Construct validity of the LapVR virtual-reality surgical simulator.. Surg Endosc 2011 Feb;25(2):423-8.
    doi: 10.1007/s00464-010-1184-xpubmed: 20585960google scholar: lookup
  30. Law B, Atkins MS, Kirkpatrick AE, Lomax AJ. Eye gaze patterns differentiate novice and experts in a virtual laparoscopic surgery training environment. Proceedings of the Eye tracking research & applications symposium on Eye tracking research & applications - ETRA’2004 41–48, 10.1145/968363.968370 (ACM Press, 2004).
  31. Usón-Gargallo J, Usón-Casaús JM, Pérez-Merino EM, Soria-Gálvez F, Morcillo E, Enciso S, Sánchez-Margallo FM. Validation of a realistic simulator for veterinary gastrointestinal endoscopy training.. J Vet Med Educ 2014 Autumn;41(3):209-17.
    doi: 10.3138/jvme.0913-127Rpubmed: 24947679google scholar: lookup
  32. Williamson JA. Construct validation of a small-animal thoracocentesis simulator.. J Vet Med Educ 2014 Winter;41(4):384-9.
    doi: 10.3138/jvme.0314-037Rpubmed: 25148881google scholar: lookup
  33. Elarbi MM, Ragle CA, Fransson BA, Farnsworth KD. Face, construct, and concurrent validity of a simulation model for laparoscopic ovariectomy in standing horses.. J Am Vet Med Assoc 2018 Jul 1;253(1):92-100.
    doi: 10.2460/javma.253.1.92pubmed: 29911940google scholar: lookup
  34. Nassar M. Equine virtual farm: A novel interdisciplinary simulation for learning veterinary physiology within clinical context. (Mensch und buch verlag, Berlin, 2011).
  35. Brewe E, Bartley JE, Riedel MC, Sawtelle V, Salo T, Boeving ER, Bravo EI, Odean R, Nazareth A, Bottenhorn KL, Laird RW, Sutherland MT, Pruden SM, Laird AR. Toward a Neurobiological Basis for Understanding Learning in University Modeling Instruction Physics Courses.. Front ICT 2018 May;5.
    doi: 10.3389/fict.2018.00010pmc: PMC6519462pubmed: 31106219google scholar: lookup
  36. Draganski B, Gaser C, Kempermann G, Kuhn HG, Winkler J, Büchel C, May A. Temporal and spatial dynamics of brain structure changes during extensive learning.. J Neurosci 2006 Jun 7;26(23):6314-7.
    pmc: PMC6675198pubmed: 16763039doi: 10.1523/jneurosci.4628-05.2006google scholar: lookup
  37. Zaitsev M, Hennig J, Speck O. Point spread function mapping with parallel imaging techniques and high acceleration factors: fast, robust, and flexible method for echo-planar imaging distortion correction.. Magn Reson Med 2004 Nov;52(5):1156-66.
    doi: 10.1002/mrm.20261pubmed: 15508146google scholar: lookup
  38. Ashburner J, Friston KJ. Voxel-based morphometry--the methods.. Neuroimage 2000 Jun;11(6 Pt 1):805-21.
    doi: 10.1006/nimg.2000.0582pubmed: 10860804google scholar: lookup
  39. Lohmann G, Margulies DS, Horstmann A, Pleger B, Lepsien J, Goldhahn D, Schloegl H, Stumvoll M, Villringer A, Turner R. Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain.. PLoS One 2010 Apr 27;5(4):e10232.
    doi: 10.1371/journal.pone.0010232pmc: PMC2860504pubmed: 20436911google scholar: lookup
  40. Fletcher JM, Wennekers T. From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity.. Int J Neural Syst 2018 Mar;28(2):1750013.
    doi: 10.1142/S0129065717500137pubmed: 28076982google scholar: lookup
  41. Bernal-Rusiel JL, Greve DN, Reuter M, Fischl B, Sabuncu MR. Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models.. Neuroimage 2013 Feb 1;66:249-60.
    doi: 10.1016/j.neuroimage.2012.10.065pmc: PMC3586747pubmed: 23123680google scholar: lookup
  42. Chen G, Taylor PA, Shin YW, Reynolds RC, Cox RW. Untangling the relatedness among correlations, Part II: Inter-subject correlation group analysis through linear mixed-effects modeling.. Neuroimage 2017 Feb 15;147:825-840.
    doi: 10.1016/j.neuroimage.2016.08.029pmc: PMC5303634pubmed: 27751943google scholar: lookup
  43. Wilson MD, Sethi S, Lein PJ, Keil KP. Valid statistical approaches for analyzing sholl data: Mixed effects versus simple linear models.. J Neurosci Methods 2017 Mar 1;279:33-43.
    doi: 10.1016/j.jneumeth.2017.01.003pmc: PMC5346342pubmed: 28104486google scholar: lookup
  44. Binder JR, Desai RH, Graves WW, Conant LL. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies.. Cereb Cortex 2009 Dec;19(12):2767-96.
    doi: 10.1093/cercor/bhp055pmc: PMC2774390pubmed: 19329570google scholar: lookup
  45. Beaty RE, Christensen AP, Benedek M, Silvia PJ, Schacter DL. Creative constraints: Brain activity and network dynamics underlying semantic interference during idea production.. Neuroimage 2017 Mar 1;148:189-196.
    doi: 10.1016/j.neuroimage.2017.01.012pmc: PMC6083214pubmed: 28082106google scholar: lookup
  46. Zhou X, Li M, Li L, Zhang Y, Cui J, Liu J, Chen C. The semantic system is involved in mathematical problem solving.. Neuroimage 2018 Feb 1;166:360-370.
    doi: 10.1016/j.neuroimage.2017.11.017pubmed: 29129671google scholar: lookup
  47. Kounios J, Frymiare JL, Bowden EM, Fleck JI, Subramaniam K, Parrish TB, Jung-Beeman M. The prepared mind: neural activity prior to problem presentation predicts subsequent solution by sudden insight.. Psychol Sci 2006 Oct;17(10):882-90.
    doi: 10.1111/j.1467-9280.2006.01798.xpubmed: 17100789google scholar: lookup
  48. Tian F, Tu S, Qiu J, Lv JY, Wei DT, Su YH, Zhang QL. Neural correlates of mental preparation for successful insight problem solving.. Behav Brain Res 2011 Jan 20;216(2):626-30.
    doi: 10.1016/j.bbr.2010.09.005pubmed: 20837067google scholar: lookup
  49. Shi B, Cao X, Chen Q, Zhuang K, Qiu J. Different brain structures associated with artistic and scientific creativity: a voxel-based morphometry study.. Sci Rep 2017 Feb 21;7:42911.
    doi: 10.1038/srep42911pmc: PMC5318918pubmed: 28220826google scholar: lookup
  50. Wu X, Yang W, Tong D, Sun J, Chen Q, Wei D, Zhang Q, Zhang M, Qiu J. A meta-analysis of neuroimaging studies on divergent thinking using activation likelihood estimation.. Hum Brain Mapp 2015 Jul;36(7):2703-18.
    doi: 10.1002/hbm.22801pmc: PMC6869224pubmed: 25891081google scholar: lookup
  51. Kringelbach ML, Rolls ET. The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology.. Prog Neurobiol 2004 Apr;72(5):341-72.
    doi: 10.1016/j.pneurobio.2004.03.006pubmed: 15157726google scholar: lookup
  52. Jahanshahi M, Obeso I, Rothwell JC, Obeso JA. A fronto-striato-subthalamic-pallidal network for goal-directed and habitual inhibition.. Nat Rev Neurosci 2015 Dec;16(12):719-32.
    doi: 10.1038/nrn4038pubmed: 26530468google scholar: lookup
  53. Camara E, Rodriguez-Fornells A, Münte TF. Functional connectivity of reward processing in the brain.. Front Hum Neurosci 2008;2:19.
    doi: 10.3389/neuro.09.019.2008pmc: PMC2647336pubmed: 19242558google scholar: lookup
  54. Saggar M, Quintin EM, Kienitz E, Bott NT, Sun Z, Hong WC, Chien YH, Liu N, Dougherty RF, Royalty A, Hawthorne G, Reiss AL. Pictionary-based fMRI paradigm to study the neural correlates of spontaneous improvisation and figural creativity.. Sci Rep 2015 May 28;5:10894.
    pmc: PMC4446895pubmed: 26018874doi: 10.1038/srep10894google scholar: lookup
  55. Crescentini C, Seyed-Allaei S, De Pisapia N, Jovicich J, Amati D, Shallice T. Mechanisms of rule acquisition and rule following in inductive reasoning.. J Neurosci 2011 May 25;31(21):7763-74.
    doi: 10.1523/JNEUROSCI.4579-10.2011pmc: PMC6633120pubmed: 21613489google scholar: lookup
  56. Bowden EM, Jung-Beeman M. Aha! Insight experience correlates with solution activation in the right hemisphere.. Psychon Bull Rev 2003 Sep;10(3):730-7.
    doi: 10.3758/BF03196539pubmed: 14620371google scholar: lookup
  57. Gazzaniga MS. Cerebral specialization and interhemispheric communication: does the corpus callosum enable the human condition?. Brain 2000 Jul;123 ( Pt 7):1293-326.
    doi: 10.1093/brain/123.7.1293pubmed: 10869045google scholar: lookup
  58. Aminoff EM, Kveraga K, Bar M. The role of the parahippocampal cortex in cognition.. Trends Cogn Sci 2013 Aug;17(8):379-90.
    doi: 10.1016/j.tics.2013.06.009pmc: PMC3786097pubmed: 23850264google scholar: lookup
  59. Kleibeuker SW, Koolschijn PC, Jolles DD, De Dreu CK, Crone EA. The neural coding of creative idea generation across adolescence and early adulthood.. Front Hum Neurosci 2013;7:905.
    doi: 10.3389/fnhum.2013.00905pmc: PMC3874541pubmed: 24416008google scholar: lookup
  60. Shi B, Cao X, Chen Q, Zhuang K, Qiu J. Different brain structures associated with artistic and scientific creativity: a voxel-based morphometry study.. Sci Rep 2017 Feb 21;7:42911.
    doi: 10.1038/srep42911pmc: PMC5318918pubmed: 28220826google scholar: lookup
  61. Hao X, Cui S, Li W, Yang W, Qiu J, Zhang Q. Enhancing insight in scientific problem solving by highlighting the functional features of prototypes: an fMRI study.. Brain Res 2013 Oct 9;1534:46-54.
    doi: 10.1016/j.brainres.2013.08.041pubmed: 23994216google scholar: lookup

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