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Learning to diagnose X-rays: a neuroscientific study of practice-related activation changes in the prefrontal cortex

  • Jerome I. Rotgans ORCID logo EMAIL logo
Published/Copyright: December 6, 2021
Diagnosis
From the journal Diagnosis Volume 9 Issue 2

Abstract

Objectives

Medical expertise manifests itself by the ability of a physician to rapidly diagnose patients. How this expertise develops from a neural-activation perspective is not well understood. The objective of the present study was to investigate practice-related activation changes in the prefrontal cortex (PFC) as medical students learn to diagnose chest X-rays.

Methods

The experimental paradigm consisted of a learning and a test phase. During the learning phase, 26 medical students were trained to diagnose four out of eight chest X-rays. These four cases were presented repeatedly and corrective feedback was provided. During the test phase, all eight cases were presented together with near- and far-transfer cases to examine whether participants’ diagnostic learning went beyond simple rote recognition of the trained X-rays. During both phases, participants’ PFC was scanned using functional near-infrared spectroscopy. Response time and diagnostic accuracy were recorded as behavioural indicators. One-way repeated measures ANOVA were conducted to analyse the data.

Results

Results revealed that participants’ diagnostic accuracy significantly increased during the learning phase (F=6.72, p<0.01), whereas their response time significantly decreased (F=16.69, p<0.001). Learning to diagnose chest X-rays was associated with a significant decrease in PFC activity (F=33.21, p<0.001) in the left dorsolateral prefrontal cortex, the orbitofrontal area, the frontopolar area and the frontal eye field. Further, the results of the test phase indicated that participants’ diagnostic accuracy was significantly higher for the four trained cases, second highest for the near-transfer, third highest for the far-transfer cases and lowest for the untrained cases (F=167.20, p<0.001) and response time was lowest for the trained cases, second lowest for the near-transfer, third lowest for the far-transfer cases and highest for the untrained cases (F=9.72, p<0.001). In addition, PFC activity was lowest for the trained and near-transfer cases, followed by the far-transfer cases and highest for the untrained cases (F=282.38, p<0.001).

Conclusions

The results suggest that learning to diagnose X-rays is associated with a significant decrease in PFC activity. In terms of dual-process theory, these findings support the notion that students initially rely more on slow analytical system-2 reasoning. As expertise develops, system-2 reasoning transitions into faster and automatic system-1 reasoning.

Keywords: brain activation changes; diagnostic expertise; dual-process theory; functional near-infrared spectroscopy (fNIRS); neuroscience; radiology
Corresponding author: Jerome I. Rotgans, PhD, Nanyang Technological University, Lee Kong Chian School of Medicine, 11 Mandalay Road, Singapore 308232, Singapore, E-mail:

Acknowledgments

I would like to thank Henk Schmidt for the suggestions he provided for the first draft of the paper. I would also like to thank Gerald Tan for the materials and Juliana Koh for her assistance in collecting the data for the study.

  1. Research funding: None declared.

  2. Author contributions: Authors state no conflict of interest.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: Ethical approval was granted by the University Institutional Review Board.

References

1. Schmidt, HG, Mamede, S. How to improve the teaching of clinical reasoning: a narrative review and a proposal. Med Educ 2015;49:961–73. https://doi.org/10.1111/medu.12775.Search in Google Scholar PubMed

2. Rotgans, JI, Rosby, LV, Low-Beer, N. The arrival of neuroscience to diagnostic reasoning: four issues to keep in mind. Health Prof Educ 2017;3:1–4. https://doi.org/10.1016/j.hpe.2017.01.004.Search in Google Scholar

3. Norman, GR. Education and neuroscience. Adv Health Sci Educ 2016;21:919–20. https://doi.org/10.1007/s10459-016-9721-6.Search in Google Scholar PubMed

4. Evans, J. The heuristic-analytic theory of reasoning: extension and evaluation. Psychon Bull Rev 2006;13:378–95. https://doi.org/10.3758/bf03193858.Search in Google Scholar PubMed

5. Evans, J. Dual-processing accounts of reasoning, judgment, and social cognition. Annu Rev Psychol 2008;59:255–78. https://doi.org/10.1146/annurev.psych.59.103006.093629.Search in Google Scholar PubMed

6. de Neys, W. Dual processing in reasoning - two systems but one reasoner. Psychol Sci 2006;17:428–33. https://doi.org/10.1111/j.1467-9280.2006.01723.x.Search in Google Scholar PubMed

7. Norman, GR, Sherbino, J, Dore, KL, Wood, TJ, Young, ME, Gaissmaier, W, et al.. The etiology of diagnostic errors: a controlled trial of system 1 versus system 2 reasoning. Acad Med 2014;89:277–84. https://doi.org/10.1097/acm.0000000000000105.Search in Google Scholar PubMed

8. Croskerry, P. From mindless to mindful practice - cognitive bias and clinical decision making. N Engl J Med 2013;368:2445–8. https://doi.org/10.1056/nejmp1303712.Search in Google Scholar PubMed

9. Evans, J. In two minds: dual-process accounts of reasoning. Trends Cogn Sci 2003;7:454–9. https://doi.org/10.1016/j.tics.2003.08.012.Search in Google Scholar PubMed

10. Evans, JSB, Stanovich, KE. Dual-process theories of higher cognition: advancing the debate. Perspect Psychol Sci 2013;8:223–41. https://doi.org/10.1177/1745691612460685.Search in Google Scholar PubMed

11. Norman, G, Young, ME, Brooks, LR. Non‐analytical models of clinical reasoning: the role of experience. Med Educ 2007;41:1140–5. https://doi.org/10.1111/j.1365-2923.2007.02914.x.Search in Google Scholar PubMed

12. Mamede, S, Schmidt, HG, Rikers, RMJP, Penaforte, JC, Coelho-Filho, JM. Breaking down automaticity: case ambiguity and the shift to reflective approaches in clinical reasoning. Med Educ 2007;41:1185–92. https://doi.org/10.1111/j.1365-2923.2007.02921.x.Search in Google Scholar PubMed

13. Mamede, S, Schmidt, HG, Rikers, RMJP, Penaforte, JC, Coelho-Filho, JM. Influence of perceived difficulty of cases on physicians’ diagnostic reasoning. Acad Med 2008;83:1210–6. https://doi.org/10.1097/acm.0b013e31818c71d7.Search in Google Scholar PubMed

14. Monteiro, SM, Norman, G. Diagnostic reasoning: where we’ve been, where we’re going. Teach Learn Med 2013;25:S26–32. https://doi.org/10.1080/10401334.2013.842911.Search in Google Scholar PubMed

15. Chamberland, M, Mamede, S, St-Onge, C, Rivard, MA, Setrakian, J, Levesque, A, et al.. Students’ self-explanations while solving unfamiliar cases: the role of biomedical knowledge. Med Educ 2013;47:1109–16. https://doi.org/10.1111/medu.12253.Search in Google Scholar PubMed

16. Sherbino, J, Dore, KL, Wood, TJ, Young, ME, Gaissmaier, W, Kreuger, S, et al.. The relationship between response time and diagnostic accuracy. Acad Med 2012;87:785–91. https://doi.org/10.1097/acm.0b013e318253acbd.Search in Google Scholar PubMed

17. Rotgans, JI, Schmidt, HG, Rosby, LV, Tan, GJS, Mamede, S, Zwaan, L, et al.. Evidence supporting dual‐process theory of medical diagnosis: a functional near‐infrared spectroscopy study. Med Educ 2019;53:143–52. https://doi.org/10.1111/medu.13681.Search in Google Scholar PubMed

18. Durning, SJ, Graner, J, Artino, ARJr, Pangaro, LN, Beckman, T, Holmboe, E, et al.. Using functional neuroimaging combined with a think-aloud protocol to explore clinical reasoning expertise in internal medicine. Mil Med 2012;177:72–8. https://doi.org/10.7205/milmed-d-12-00242.Search in Google Scholar PubMed

19. Rotgans, JI, Low-Beer, N, Rosby, LV. The relevance of neuroscientific research for understanding clinical reasoning. Health Prof Educ 2016;1:1–2. https://doi.org/10.1016/j.hpe.2016.02.001.Search in Google Scholar

20. Durning, SJ, Costanzo, ME, Beckman, TJ, Artino, ARJr, Roy, MJ, Van Der Vleuten, C, et al.. Functional neuroimaging correlates of thinking flexibility and knowledge structure in memory: exploring the relationships between clinical reasoning and diagnostic thinking. Med Teach 2016;38:570–7. https://doi.org/10.3109/0142159x.2015.1047755.Search in Google Scholar PubMed

21. Rourke, L, Cruikshank, LC, Shapke, L, Singhal, A. A neural marker of medical visual expertise: implications for training. Adv Health Sci Educ 2016;21:953–66. https://doi.org/10.1007/s10459-016-9712-7.Search in Google Scholar PubMed

22. Hruska, P, Krigolson, O, Coderre, S, McLaughlin, K, Cortese, F, Doig, C, et al.. Working memory, reasoning, and expertise in medicine—insights into their relationship using functional neuroimaging. Adv Health Sci Educ 2016;21:935–52. https://doi.org/10.1007/s10459-015-9649-2.Search in Google Scholar PubMed

23. Hruska, P, Hecker, KG, Coderre, S, McLaughlin, K, Cortese, F, Doig, C, et al.. Hemispheric activation differences in novice and expert clinicians during clinical decision making. Adv Health Sci Educ 2016;21:921–33. https://doi.org/10.1007/s10459-015-9648-3.Search in Google Scholar PubMed

24. Chang, H-J, Kang, J, Ham, B-J, Lee, Y-M. A functional neuroimaging study of the clinical reasoning of medical students. Adv Health Sci Educ 2016;21:969–82. https://doi.org/10.1007/s10459-016-9685-6.Search in Google Scholar PubMed

25. Hill, NM, Schneider, W. Brain changes in the development of expertise: neuroanatomical and neurophysiological evidence about skill-based adaptations. In: Ericsson, KA, Charness, N, Feltovich, PJ, Hoffman, RR, editors. The Cambridge handbook of expertise and expert performance. New York, NY: Cambridge University Press; 2006. pp. 653–82.10.1017/CBO9780511816796.037Search in Google Scholar

26. Bilalić, M, Campitelli, G. Studies of the activation and structural changes of the brain associated with expertise. In: Ericsson, KA, Hoffman, RR, Kozbelt, A, Williams, AM, editors. Cambridge handbooks in psychology the Cambridge handbook of expertise and expert performance. Cambridge University Press; 2018. pp. 233–53.10.1017/9781316480748.014Search in Google Scholar

27. Corbetta, M, Shulman, GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 2002;3:201–15. https://doi.org/10.1038/nrn755.Search in Google Scholar PubMed

28. Shulman, GL, McAvoy, MP, Cowan, MC, Astafiev, SV, Tansy, AP, d’Avossa, G, et al.. Quantitative analysis of attention and detection signals during visual search. J Neurophysiol 2003;90:3384–97. https://doi.org/10.1152/jn.00343.2003.Search in Google Scholar PubMed

29. Fox, MD, Corbetta, M, Snyder, AZ, Vincent, JL, Raichle, ME. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A 2006;103:10046–51. https://doi.org/10.1073/pnas.0604187103.Search in Google Scholar PubMed PubMed Central

30. Raichle, ME, MacLeod, AM, Snyder, AZ, Powers, WJ, Gusnard, DA, Shulman, GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001;98:676–82. https://doi.org/10.1073/pnas.98.2.676.Search in Google Scholar PubMed PubMed Central

31. Vincent, JL, Snyder, AZ, Fox, MD, Shannon, BJ, Andrews, JR, Raichle, ME, et al.. Coherent spontaneous activity identifies a hippocampal-parietal memory network. J Neurophysiol 2006;96:3517–31. https://doi.org/10.1152/jn.00048.2006.Search in Google Scholar PubMed

32. Seeley, WW. The salience network: a neural system for perceiving and responding to homeostatic demands. J Neurosci 2019;39:9878–82. https://doi.org/10.1523/jneurosci.1138-17.2019.Search in Google Scholar

33. Chand, GB, Dhamala, M. The salience network dynamics in perceptual decision-making. Neuroimage 2016;134:85–93. https://doi.org/10.1016/j.neuroimage.2016.04.018.Search in Google Scholar PubMed

34. Vincent, JL, Kahn, I, Snyder, AZ, Raichle, ME, Buckner, RL. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J Neurophysiol 2008;100:3328–42. https://doi.org/10.1152/jn.90355.2008.Search in Google Scholar PubMed PubMed Central

35. Hellyer, PJ, Shanahan, M, Scott, G, Wise, RJ, Sharp, DJ, Leech, R. The control of global brain dynamics: opposing actions of frontoparietal control and default mode networks on attention. J Neurosci 2014;34:451–61. https://doi.org/10.1523/jneurosci.1853-13.2014.Search in Google Scholar PubMed PubMed Central

36. Dixon, ML, De La Vega, A, Mills, C, Andrews-Hanna, J, Spreng, RN, Cole, MW, et al.. Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc Natl Acad Sci U S A 2018;115:E1598–607. https://doi.org/10.1073/pnas.1715766115.Search in Google Scholar PubMed PubMed Central

37. Siegel, M, Engel, AK, Donner, TH. Cortical network dynamics of perceptual decision-making in the human brain. Front Hum Neurosci 2011;5:1–12. https://doi.org/10.3389/fnhum.2011.00021.Search in Google Scholar PubMed PubMed Central

38. Heekeren, HR, Marrett, S, Ungerleider, LG. The neural systems that mediate human perceptual decision making. Nat Rev Neurosci 2008;9:467–79. https://doi.org/10.1038/nrn2374.Search in Google Scholar PubMed

39. Leff, DR, Yongue, G, Vlaev, I, Orihuela-Espina, F, James, D, Taylor, MJ, et al.. Contemplating the next maneuver. Ann Surg 2017;265:320–30. https://doi.org/10.1097/sla.0000000000001651.Search in Google Scholar

40. Büchel, C, Coull, J, Friston, K. The predictive value of changes in effective connectivity for human learning. Science 1999;283:1538–41.10.1126/science.283.5407.1538Search in Google Scholar PubMed

41. Noah, JA, Ono, Y, Nomoto, Y, Shimada, S, Tachibana, A, Zhang, X, et al.. fMRI validation of fNIRS measurements during a naturalistic task. J Vis Exp 2015;100:52116. https://doi.org/10.3791/52116.Search in Google Scholar

42. Pierro, ML, Hallacoglu, B, Sassaroli, A, Kainerstorfer, JM, Fantini, S. Validation of a novel hemodynamic model for coherent hemodynamics spectroscopy (CHS) and functional brain studies with fNIRS and fMRI. Neuroimage 2014;85:222–33. https://doi.org/10.1016/j.neuroimage.2013.03.037.Search in Google Scholar

43. Heinzel, S, Haeussinger, FB, Hahn, T, Ehlis, A-C, Plichta, MM, Fallgatter, AJ. Variability of (functional) hemodynamics as measured with simultaneous fNIRS and fMRI during intertemporal choice. Neuroimage 2013;71:125–34. https://doi.org/10.1016/j.neuroimage.2012.12.074.Search in Google Scholar

44. Rosby, LV, Schmidt, HG, Tan, GJ, Low-Beer, N, Mamede, S, Zwaan, L, et al.. Promotion of knowledge transfer and retention in year 2 medical students using an online training exercise. Adv Health Sci Educ 2021;26:1059–74. https://doi.org/10.1007/s10459-021-10037-y.Search in Google Scholar

45. Forster, KI, Davis, C. Repetition priming and frequency attenuation in lexical access. J Exp Psychol Learn Mem Cogn 1984;10:680–98. https://doi.org/10.1037/0278-7393.10.4.680.Search in Google Scholar

46. Horner, A, Henson, R. Priming, response learning and repetition suppression. Neuropsychologia 2008;46:1979–91. https://doi.org/10.1016/j.neuropsychologia.2008.01.018.Search in Google Scholar

47. Logan, GD. Repetition priming and automaticity: common underlying mechanisms? Cogn Psychol 1990;22:1–35. https://doi.org/10.1016/0010-0285(90)90002-l.Search in Google Scholar

48. Lucas, M. Semantic priming without association: a meta-analytic review. Psychon Bull Rev 2000;7:618–30. https://doi.org/10.3758/bf03212999.Search in Google Scholar PubMed

49. Shelton, JR, Martin, RC. How semantic is automatic semantic priming? J Exp Psychol Learn Mem Cogn 1992;18:1191–210. https://doi.org/10.1037/0278-7393.18.6.1191.Search in Google Scholar

50. Dosenbach, NU, Fair, DA, Miezin, FM, Cohen, AL, Wenger, KK, Dosenbach, RA, et al.. Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci U S A 2007;104:11073–8. https://doi.org/10.1073/pnas.0704320104.Search in Google Scholar PubMed PubMed Central

51. Kelly, AC, Garavan, H. Human functional neuroimaging of brain changes associated with practice. Cereb Cortex 2005;15:1089–102. https://doi.org/10.1093/cercor/bhi005.Search in Google Scholar PubMed

52. Ramsey, NF, Jansma, JM, Jager, G, Van Raalten, T, Kahn, R. Neurophysiological factors in human information processing capacity. Brain 2004;127:517–25. https://doi.org/10.1093/brain/awh060.Search in Google Scholar PubMed

53. Poldrack, RA, Gabrieli, JD. Characterizing the neural mechanisms of skill learning and repetition priming: evidence from mirror reading. Brain 2001;124:67–82. https://doi.org/10.1093/brain/124.1.67.Search in Google Scholar PubMed

54. Petersen, SE, Van Mier, H, Fiez, JA, Raichle, ME. The effects of practice on the functional anatomy of task performance. Proc Natl Acad Sci U S A 1998;95:853–60. https://doi.org/10.1073/pnas.95.3.853.Search in Google Scholar PubMed PubMed Central

55. Plichta, MM, Heinzel, S, Ehlis, AC, Pauli, P, Fallgatter, AJ. Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study. Neuroimage 2007;35:625–34. https://doi.org/10.1016/j.neuroimage.2006.11.028.Search in Google Scholar PubMed

56. Mamede, S, van Gog, T, van den Berge, K, Rikers, RM, van Saase, JL, van Guldener, C, et al.. Effect of availability bias and reflective reasoning on diagnostic accuracy among internal medicine residents. J Am Med Assoc 2010;304:1198–203. https://doi.org/10.1001/jama.2010.1276.Search in Google Scholar PubMed

57. Croskerry, P, Singhal, G, Mamede, S. Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ Qual Saf 2013;22:58–64. https://doi.org/10.1136/bmjqs-2012-001712.Search in Google Scholar PubMed PubMed Central

58. Elstein, AS. Thinking about diagnostic thinking: a 30-year perspective. Adv Health Sci Educ 2009;14:7–18. https://doi.org/10.1007/s10459-009-9184-0.Search in Google Scholar PubMed

59. Croskerry, P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med 2003;78:775–80. https://doi.org/10.1097/00001888-200308000-00003.Search in Google Scholar PubMed

Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/dx-2021-0104).

Received: 2021-07-27
Accepted: 2021-10-29
Published Online: 2021-12-06

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Reviews
  3. Fujirebio Lumipulse SARS-CoV-2 antigen immunoassay: pooled analysis of diagnostic accuracy
  4. Potential prognostic value of miRNAs as biomarker for progression and recurrence after nephrectomy in renal cell carcinoma: a literature review
  5. Consensus Paper
  6. A call to action: next steps to advance diagnosis education in the health professions
  7. Opinion Papers
  8. Narrowing the mindware gap in medicine
  9. From principles to practice: embedding clinical reasoning as a longitudinal curriculum theme in a medical school programme
  10. Is body temperature mass screening a reliable and safe option for preventing COVID-19 spread?
  11. Original Articles
  12. Investigating cognitive factors and diagnostic error in a presentation of complicated multisystem disease
  13. “Sick or not sick?” A mixed methods study evaluating the rapid determination of illness severity in a pediatric emergency department
  14. Evaluation of feedback modalities and preferences regarding feedback on decision-making in a pediatric emergency department
  15. Emergency medicine physicians’ perspectives on diagnostic accuracy in neurology: a qualitative study
  16. The impact of medical scribes on emergency physician diagnostic testing and diagnosis charting
  17. Automated identification of diagnostic labelling errors in medicine
  18. How patients describe their diagnosis compared to clinical documentation
  19. Learning to diagnose X-rays: a neuroscientific study of practice-related activation changes in the prefrontal cortex
  20. A clinical reasoning curriculum for medical students: an interim analysis
  21. Improvements and limits of anti SARS-CoV-2 antibodies assays by WHO (NIBSC 20/136) standardization
  22. Letters to the Editor
  23. From the amyloid hypothesis to the autoimmune hypothesis of Alzheimer’s disease
  24. What we cannot see in virtual diagnosis: the potential pitfalls of telediagnosis related to teamwork
  25. Virucidal effects of mouthwashes or mouth rinses: a world of caution for molecular detection of SARS-CoV-2 in saliva
  26. Case Report – Lessons in Clinical Reasoning
  27. Lessons in clinical reasoning ‒ pitfalls, myths and pearls: a case of recurrent pancreatitis
  28. Congress Abstracts
  29. The Diagnostic Error in Medicine 14th Annual International Conference
https://doi.org/10.1515/dx-2021-0104
Keywords for this article
brain activation changes; diagnostic expertise; dual-process theory; functional near-infrared spectroscopy (fNIRS); neuroscience; radiology

Articles in the same Issue

  1. Frontmatter
  2. Reviews
  3. Fujirebio Lumipulse SARS-CoV-2 antigen immunoassay: pooled analysis of diagnostic accuracy
  4. Potential prognostic value of miRNAs as biomarker for progression and recurrence after nephrectomy in renal cell carcinoma: a literature review
  5. Consensus Paper
  6. A call to action: next steps to advance diagnosis education in the health professions
  7. Opinion Papers
  8. Narrowing the mindware gap in medicine
  9. From principles to practice: embedding clinical reasoning as a longitudinal curriculum theme in a medical school programme
  10. Is body temperature mass screening a reliable and safe option for preventing COVID-19 spread?
  11. Original Articles
  12. Investigating cognitive factors and diagnostic error in a presentation of complicated multisystem disease
  13. “Sick or not sick?” A mixed methods study evaluating the rapid determination of illness severity in a pediatric emergency department
  14. Evaluation of feedback modalities and preferences regarding feedback on decision-making in a pediatric emergency department
  15. Emergency medicine physicians’ perspectives on diagnostic accuracy in neurology: a qualitative study
  16. The impact of medical scribes on emergency physician diagnostic testing and diagnosis charting
  17. Automated identification of diagnostic labelling errors in medicine
  18. How patients describe their diagnosis compared to clinical documentation
  19. Learning to diagnose X-rays: a neuroscientific study of practice-related activation changes in the prefrontal cortex
  20. A clinical reasoning curriculum for medical students: an interim analysis
  21. Improvements and limits of anti SARS-CoV-2 antibodies assays by WHO (NIBSC 20/136) standardization
  22. Letters to the Editor
  23. From the amyloid hypothesis to the autoimmune hypothesis of Alzheimer’s disease
  24. What we cannot see in virtual diagnosis: the potential pitfalls of telediagnosis related to teamwork
  25. Virucidal effects of mouthwashes or mouth rinses: a world of caution for molecular detection of SARS-CoV-2 in saliva
  26. Case Report – Lessons in Clinical Reasoning
  27. Lessons in clinical reasoning ‒ pitfalls, myths and pearls: a case of recurrent pancreatitis
  28. Congress Abstracts
  29. The Diagnostic Error in Medicine 14th Annual International Conference
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