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      Placebo Analgesia Changes Alpha Oscillations Induced by Tonic Muscle Pain: EEG Frequency Analysis Including Data during Pain Evaluation

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          Abstract

          Placebo exhibits beneficial effects on pain perception in human experimental studies. Most of these studies demonstrate that placebo significantly decreased neural activities in pain modulatory brain regions and pain-evoked potentials. This study examined placebo analgesia-related effects on spontaneous brain oscillations. We examined placebo effects on four order-fixed 20-min conditions in two sessions: isotonic saline-induced control conditions (with/without placebo) followed by hypertonic saline-induced tonic muscle pain conditions (with/without placebo) in 19 subjects using continuous electroencephalography (EEG) recording. Placebo treatment exerted significant analgesic effects in 14 placebo responders, as subjective intensity of pain perception decreased. Frequency analyses were performed on whole continuous EEG data, data during pain perception rating and data after rating. The results in the first two cases revealed that placebo induced significant increases and a trend toward significant increases in the amplitude of alpha oscillation during tonic muscle pain compared to control conditions in frontal-central regions of the brain, respectively. Placebo-induced decreases in the subjective intensity of pain perception significantly and positively correlated with the increases in the amplitude of alpha oscillations during pain conditions. In conclusion, the modulation effect of placebo treatment was captured when the pain perception evaluating period was included. The strong correlation between the placebo effect on reported pain perception and alpha amplitude suggest that alpha oscillations in frontal-central regions serve as a cortical oscillatory basis of the placebo effect on tonic muscle pain. These results provide important evidence for the investigation of objective indicators of the placebo effect.

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          Most cited references60

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            Development and validation of brief measures of positive and negative affect: The PANAS scales.

            In recent studies of the structure of affect, positive and negative affect have consistently emerged as two dominant and relatively independent dimensions. A number of mood scales have been created to measure these factors; however, many existing measures are inadequate, showing low reliability or poor convergent or discriminant validity. To fill the need for reliable and valid Positive Affect and Negative Affect scales that are also brief and easy to administer, we developed two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS). The scales are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period. Normative data and factorial and external evidence of convergent and discriminant validity for the scales are also presented.
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              Nonparametric statistical testing of EEG- and MEG-data.

              In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.

                Author and article information

                Contributors
                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Media S.A.
                1662-5188
                10 May 2016
                2016
                : 10
                : 45
                Affiliations
                [1] 1Research Center for Neural Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Shenzhen, China
                [2] 2Department of Pain, Shenzhen Sixth People’s Hospital (Nanshan Hospital), Guangdong Medical College Shenzhen, China
                Author notes

                Edited by: Hong Qiao, Chinese Academy of Sciences, China

                Reviewed by: Meng Liang, Tianjin Medical University, China; Weiwei Peng, Southwest University, China

                *Correspondence: Yunhai Qiu yh.qiu@ 123456siat.ac.cn

                These authors have contributed equally to this work.

                Article
                10.3389/fncom.2016.00045
                4861840
                27242501
                d39d5e3c-b3dc-45f2-b74d-2c812b6cbb0b
                Copyright © 2016 Li, Wang, Ke, Liu, Yuan, Zhang, Xiong and Qiu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 January 2016
                : 25 April 2016
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 58, Pages: 9, Words: 7106
                Categories
                Neuroscience
                Original Research

                Neurosciences
                placebo,eeg,tonic muscle pain,pain perception,alpha oscillation
                Neurosciences
                placebo, eeg, tonic muscle pain, pain perception, alpha oscillation

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