BackgroundVarious neuroimaging studies, both structural and functional, have provided
support for the proposal that a distributed brain network is likely to be
the neural basis of intelligence. The theory of Distributed Intelligent
Processing Systems (DIPS), first developed in the field of Artificial
Intelligence, was proposed to adequately model distributed neural
intelligent processing. In addition, the neural efficiency
hypothesis suggests that individuals with higher intelligence
display more focused cortical activation during cognitive performance,
resulting in lower total brain activation when compared with individuals who
have lower intelligence. This may be understood as a property of the
DIPS.Methodology and Principal FindingsIn our study, a new EEG brain mapping technique, based on the neural
efficiency hypothesis and the notion of the brain as a
Distributed Intelligence Processing System, was used to investigate the
correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence
Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain
activity associated with visual and verbal processing, in order to test the
validity of a distributed neural basis for intelligence.ConclusionThe present results support these claims and the neural efficiency
hypothesis.
Patients decided to undergo the treatment because they were already considering it (54%) or because they were dissatisfied with their lips or nasolabial folding (52%). The fact that the treatment was free of charge solidified the decision. Patients consider themselves as good-looking and they wanted to preserve such a condition.
BackgroundDespite 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 AnalysisResultsThe 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.ConclusionsPCA 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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