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Brain activity and medical diagnosis: an EEG study.

Ribas LM, Rocha FT, Ortega NR, da Rocha AF, Massad E - BMC Neurosci (2013)

Bottom Line: We investigated brain electroencephalography (EEG) activity associated with diagnostic decision-making in the realm of veterinary medicine using X-rays as a fundamental auxiliary test.Two of these patterns are proposed to be associated with visual processing and the executive control of the task.PCA analysis was successful in disclosing the different patterns of brain activity associated with hypothesis triggering and handling (pattern P1); identification uncertainty and prevalence assessment (pattern P3), and hypothesis plausibility calculation (pattern P2); Logistic regression analysis was successful in disclosing the brain activity associated with clinical reasoning success, and together with regression analysis showed that clinical practice reorganizes the neural circuits supporting clinical reasoning.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Medicine, University of São Paulo and LIM 01-HCMFMUSP, Dr, Arnaldo 455, 01246-903, São Paulo, Brazil. edmassad@usp.br.

ABSTRACT

Background: Despite new brain imaging techniques that have improved the study of the underlying processes of human decision-making, to the best of our knowledge, there have been very few studies that have attempted to investigate brain activity during medical diagnostic processing. We investigated brain electroencephalography (EEG) activity associated with diagnostic decision-making in the realm of veterinary medicine using X-rays as a fundamental auxiliary test. EEG signals were analysed using Principal Components (PCA) and Logistic Regression Analysis

Results: The principal component analysis revealed three patterns that accounted for 85% of the total variance in the EEG activity recorded while veterinary doctors read a clinical history, examined an X-ray image pertinent to a medical case, and selected among alternative diagnostic hypotheses. Two of these patterns are proposed to be associated with visual processing and the executive control of the task. The other two patterns are proposed to be related to the reasoning process that occurs during diagnostic decision-making.

Conclusions: PCA analysis was successful in disclosing the different patterns of brain activity associated with hypothesis triggering and handling (pattern P1); identification uncertainty and prevalence assessment (pattern P3), and hypothesis plausibility calculation (pattern P2); Logistic regression analysis was successful in disclosing the brain activity associated with clinical reasoning success, and together with regression analysis showed that clinical practice reorganizes the neural circuits supporting clinical reasoning.

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Related in: MedlinePlus

The brain mappings of the regression between expertise and H(ei) calculated for each of the electrodes (independent variables). Color-coding is similar to that in Figure 6.
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Figure 7: The brain mappings of the regression between expertise and H(ei) calculated for each of the electrodes (independent variables). Color-coding is similar to that in Figure 6.

Mentions: Table 5 shows the results of the logistic regression analysis between the expertise (as measured by the years of practice) and the H(ei) calculated for each of the electrodes. This regression explains approximately 34% of the observed data. The regression brain mapping calculated from the normalized values in Table 5 is shown in Figure 7.


Brain activity and medical diagnosis: an EEG study.

Ribas LM, Rocha FT, Ortega NR, da Rocha AF, Massad E - BMC Neurosci (2013)

The brain mappings of the regression between expertise and H(ei) calculated for each of the electrodes (independent variables). Color-coding is similar to that in Figure 6.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3852492&req=5

Figure 7: The brain mappings of the regression between expertise and H(ei) calculated for each of the electrodes (independent variables). Color-coding is similar to that in Figure 6.
Mentions: Table 5 shows the results of the logistic regression analysis between the expertise (as measured by the years of practice) and the H(ei) calculated for each of the electrodes. This regression explains approximately 34% of the observed data. The regression brain mapping calculated from the normalized values in Table 5 is shown in Figure 7.

Bottom Line: We investigated brain electroencephalography (EEG) activity associated with diagnostic decision-making in the realm of veterinary medicine using X-rays as a fundamental auxiliary test.Two of these patterns are proposed to be associated with visual processing and the executive control of the task.PCA analysis was successful in disclosing the different patterns of brain activity associated with hypothesis triggering and handling (pattern P1); identification uncertainty and prevalence assessment (pattern P3), and hypothesis plausibility calculation (pattern P2); Logistic regression analysis was successful in disclosing the brain activity associated with clinical reasoning success, and together with regression analysis showed that clinical practice reorganizes the neural circuits supporting clinical reasoning.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Medicine, University of São Paulo and LIM 01-HCMFMUSP, Dr, Arnaldo 455, 01246-903, São Paulo, Brazil. edmassad@usp.br.

ABSTRACT

Background: Despite new brain imaging techniques that have improved the study of the underlying processes of human decision-making, to the best of our knowledge, there have been very few studies that have attempted to investigate brain activity during medical diagnostic processing. We investigated brain electroencephalography (EEG) activity associated with diagnostic decision-making in the realm of veterinary medicine using X-rays as a fundamental auxiliary test. EEG signals were analysed using Principal Components (PCA) and Logistic Regression Analysis

Results: The principal component analysis revealed three patterns that accounted for 85% of the total variance in the EEG activity recorded while veterinary doctors read a clinical history, examined an X-ray image pertinent to a medical case, and selected among alternative diagnostic hypotheses. Two of these patterns are proposed to be associated with visual processing and the executive control of the task. The other two patterns are proposed to be related to the reasoning process that occurs during diagnostic decision-making.

Conclusions: PCA analysis was successful in disclosing the different patterns of brain activity associated with hypothesis triggering and handling (pattern P1); identification uncertainty and prevalence assessment (pattern P3), and hypothesis plausibility calculation (pattern P2); Logistic regression analysis was successful in disclosing the brain activity associated with clinical reasoning success, and together with regression analysis showed that clinical practice reorganizes the neural circuits supporting clinical reasoning.

Show MeSH
Related in: MedlinePlus