Limits...
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.

Show MeSH

Related in: MedlinePlus

The entropy brain maps associated with the clinical history reading (CH), X-ray analysis (XR) and diagnosis decision-making (DG). The normalized entropy values obtained for each electrode were color coded such that the dark-blue areas were associated with the highest entropy values and the dark-red areas were associated with the lowest entropy values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3852492&req=5

Figure 3: The entropy brain maps associated with the clinical history reading (CH), X-ray analysis (XR) and diagnosis decision-making (DG). The normalized entropy values obtained for each electrode were color coded such that the dark-blue areas were associated with the highest entropy values and the dark-red areas were associated with the lowest entropy values.

Mentions: Table 1 shows the entropy values H(ei) calculated for each electrode ei and each stage of the diagnostic decision-making process. The normalized values were obtained for each electrode and for each stage (CH, XR or DG) and were used to generate the entropy brain maps shown in Figure 3. The Pearson correlation coefficients calculated for the entropy values and the different stages (CH, XR and DG) are shown in Table 1.


Brain activity and medical diagnosis: an EEG study.

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

The entropy brain maps associated with the clinical history reading (CH), X-ray analysis (XR) and diagnosis decision-making (DG). The normalized entropy values obtained for each electrode were color coded such that the dark-blue areas were associated with the highest entropy values and the dark-red areas were associated with the lowest entropy values.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: The entropy brain maps associated with the clinical history reading (CH), X-ray analysis (XR) and diagnosis decision-making (DG). The normalized entropy values obtained for each electrode were color coded such that the dark-blue areas were associated with the highest entropy values and the dark-red areas were associated with the lowest entropy values.
Mentions: Table 1 shows the entropy values H(ei) calculated for each electrode ei and each stage of the diagnostic decision-making process. The normalized values were obtained for each electrode and for each stage (CH, XR or DG) and were used to generate the entropy brain maps shown in Figure 3. The Pearson correlation coefficients calculated for the entropy values and the different stages (CH, XR and DG) are shown in Table 1.

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