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      A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia

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          Abstract

          The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists. We propose that placebo analgesia is a facet of pain perception, others being the modulating effects of emotions, cognition and past experience, and we suggest that a computational understanding of pain may provide a unifying explanation of these phenomena. Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data. Our model not only agrees with placebo analgesia, but also predicts that learning can affect pain perception in other unexpected ways, which experimental evidence supports. Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.

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          Most cited references50

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          Noise in the nervous system.

          Noise--random disturbances of signals--poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.
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            A comprehensive review of the placebo effect: recent advances and current thought.

            Our understanding and conceptualization of the placebo effect has shifted in emphasis from a focus on the inert content of a physical placebo agent to the overall simulation of a therapeutic intervention. Research has identified many types of placebo responses driven by different mechanisms depending on the particular context wherein the placebo is given. Some placebo responses, such as analgesia, are initiated and maintained by expectations of symptom change and changes in motivation/emotions. Placebo factors have neurobiological underpinnings and actual effects on the brain and body. They are not just response biases. Other placebo responses result from less conscious processes, such as classical conditioning in the case of immune, hormonal, and respiratory functions. The demonstration of the involvement of placebo mechanisms in clinical trials and routine clinical practice has highlighted interesting considerations for clinical trial design and opened up opportunities for ethical enhancement of these mechanisms in clinical practice.
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              Learning and inference in the brain.

              This article is about how the brain data mines its sensory inputs. There are several architectural principles of functional brain anatomy that have emerged from careful anatomic and physiologic studies over the past century. These principles are considered in the light of representational learning to see if they could have been predicted a priori on the basis of purely theoretical considerations. We first review the organisation of hierarchical sensory cortices, paying special attention to the distinction between forward and backward connections. We then review various approaches to representational learning as special cases of generative models, starting with supervised learning and ending with learning based upon empirical Bayes. The latter predicts many features, such as a hierarchical cortical system, prevalent top-down backward influences and functional asymmetries between forward and backward connections that are seen in the real brain. The key points made in article are: (i). hierarchical generative models enable the learning of empirical priors and eschew prior assumptions about the causes of sensory input that are inherent in non-hierarchical models. These assumptions are necessary for learning schemes based on information theory and efficient or sparse coding, but are not necessary in a hierarchical context. Critically, the anatomical infrastructure that may implement generative models in the brain is hierarchical. Furthermore, learning based on empirical Bayes can proceed in a biologically plausible way. (ii). The second point is that backward connections are essential if the processes generating inputs cannot be inverted, or the inversion cannot be parameterised. Because these processes involve many-to-one mappings, are non-linear and dynamic in nature, they are generally non-invertible. This enforces an explicit parameterisation of generative models (i.e. backward connections) to afford recognition and suggests that forward architectures, on their own, are not sufficient for perception. (iii). Finally, non-linearities in generative models, mediated by backward connections, require these connections to be modulatory, so that representations in higher cortical levels can interact to predict responses in lower levels. This is important in relation to functional asymmetries in forward and backward connections that have been demonstrated empirically.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2015
                9 February 2015
                : 10
                : 2
                : e0117270
                Affiliations
                [001]Department of Medical and Biological Sciences, Universit degli Studi di Udine, Udine, Italy
                Brain and Spine Institute (ICM), FRANCE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DA MZ. Performed the experiments: DA MZ. Analyzed the data: DA MZ. Wrote the paper: DA MZ.

                [¤]

                Current address: Centro Studi e Ricerche in Neuroscienze Cognitive, Universit di Bologna, Campus di Cesena, Cesena, Italy

                Article
                PONE-D-13-40246
                10.1371/journal.pone.0117270
                4321992
                25664586
                1b7fa113-594e-431e-95fb-30179bdd565e
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 1 October 2013
                : 22 December 2014
                Page count
                Figures: 6, Tables: 1, Pages: 20
                Funding
                These authors have no support or funding to report.
                Categories
                Research Article

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                Uncategorized

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