Intracranial Recordings and Computational Modeling of Music Reveal the Time Course of Prediction Error Signaling in Frontal and Temporal Cortices
Diana OmigieMarcus T. PearceKatia LehongreDominique HasbounVincent NavarroClaude AdamSéverine Samson
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Prediction is held to be a fundamental process underpinning perception, action, and cognition. To examine the time course of prediction error signaling, we recorded intracranial EEG activity from nine presurgical epileptic patients while they listened to melodies whose information theoretical predictability had been characterized using a computational model. We examined oscillatory activity in the superior temporal gyrus (STG), the middle temporal gyrus (MTG), and the pars orbitalis of the inferior frontal gyrus, lateral cortical areas previously implicated in auditory predictive processing. We also examined activity in anterior cingulate gyrus (ACG), insula, and amygdala to determine whether signatures of prediction error signaling may also be observable in these subcortical areas. Our results demonstrate that the information content (a measure of unexpectedness) of musical notes modulates the amplitude of low-frequency oscillatory activity (theta to beta power) in bilateral STG and right MTG from within 100 and 200 msec of note onset, respectively. Our results also show this cortical activity to be accompanied by low-frequency oscillatory modulation in ACG and insula-areas previously associated with mediating physiological arousal. Finally, we showed that modulation of low-frequency activity is followed by that of high-frequency (gamma) power from approximately 200 msec in the STG, between 300 and 400 msec in the left insula, and between 400 and 500 msec in the ACG. We discuss these results with respect to models of neural processing that emphasize gamma activity as an index of prediction error signaling and highlight the usefulness of musical stimuli in revealing the wide-reaching neural consequences of predictive processing.Keywords:
Inferior frontal gyrus
Time perception
A single session of heart rate variability (HRV) biofeedback in apparently healthy young people and adolescents aged 14-17 years in order to increase vagal effects on heart rhythm and also electroencephalograms were carried out. Different variants of EEG spectral power during the successful HRV biofeedback session were identified. In the case of I variant of EEG activity the increase of power spectrum of alpha-, betal-, theta-components takes place in all parts of the brain. In the case of II variant of EEG activity the reduction of power spectrum of alpha-, betal-, theta-activity in all parts of the brain was observed. I and II variants of EEG activity cause more intensive regime of cortical-subcortical interactions. During the III variant of EEG activity the successful biofeedback is accompanied by increase of alpha activity in the central, front and anteriofrontal brain parts and so indicates the formation of thalamocortical relations of neural network in order to optimize the vegetal regulation of heart function. There was an increase in alpha- and beta1-activity in the parietal, central, frontal and temporal brain parts during the IV variant of EEG activity and so that it provides the relief of neural networks communication for information processing. As a result of V variance of EEG activity there was the increase of power spectrum of theta activity in the central and frontal parts of both cerebral hemispheres, so it was associated with the cortical-hippocampal interactions to achieve a successful biofeedback.
Biofeedback
Alpha (finance)
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Prior neuroimaging studies have supported the idea that the human insular cortex plays an important role in processing and representing internal bodily states, also termed "interoception." According to recent theoretical studies, interoception includes several aspects such as attention and accuracy. However, there is no consensus on the laterality and location of the insula to support each aspect of interoception. Thus, we aimed to identify the anatomical insular subdivisions involved in interoceptive attention and accuracy; we examined 28 healthy volunteers who completed the behavioral heartbeat counting task and interoceptive attention paradigm using functional magnetic resonance imaging. First, interoceptive attention induced significant activation in the bilateral frontal operculum, precentral gyrus, middle insula, middle cingulate cortex, and supplementary motor area. Then, we compared the activation in anatomically predefined insular subdivisions during interoceptive attention. The highest activation of the middle short gyrus was noted within the insular cortex, followed by the anterior short gyrus and posterior short gyrus, while no significant hemispheric differences were observed. Finally, the interoceptive accuracy index, measured using the heartbeat counting task, strongly correlated with the activity of the right dorsal anterior insula/frontal operculum. These findings suggest that interoceptive attention is associated with the bilateral dorsal mid-anterior insula, which supports the processing and representation of bodily signals. In contrast, the more dorsal anterior portion of the right insula plays a key role in obtaining accurate interoception.
Interoception
Insular cortex
Operculum (bryozoa)
Inferior frontal gyrus
Middle temporal gyrus
Middle frontal gyrus
Superior temporal gyrus
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Inferior frontal gyrus
Superior frontal gyrus
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EEG is a brain imaging called electroencephalography (EEG) that measures and displays brain activities with signals. Brain-Computer Interface (BCI), on the other hand, are devices that allow their users to interact with computers only through brain activity. This activity is usually measured by EEG. Brain Computer Interface applications base their functionality either on observing the user's state or allowing the user to submit their ideas. EEG signals are gathered from the different brain regions with the international 10-20 electrode system as multichannel and the structure of them is very complex. Therefore, the EEG signals need to be analyzed for understanding and recognizing. In this study, the time-frequency characteristics of the EEG signal for the realization of a specific thought or action related to palm opening and closing were investigated. The EEG frequency band and the EEG electrodes (brain region) where the activity can be strongly traced were identified.
Identification
SIGNAL (programming language)
Interface (matter)
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많은 연구자들은 인간의 사고를 functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG)등을 이용해서 두뇌 활동 영역을 연구하고 있다. 주로 의학 분야와 심리학의 영역에서 두뇌의 활동을 연구하여 간질이나 발작을 알아내고 거짓말 탐지 분야에서도 사용된다. 본 논문에서는 사람의 두뇌활동을 측정하여 인간의 감정을 인식하는 연구에 중점을 두었다. 특히, fMRI와 TRS 그리고 EEG를 이용해서 사람의 두뇌 활동을 측정하는 연구를 하였다. 많은 연구자들이 한 가지 측정 장치만을 사용하여서 측정하거나 fMRI와 EEG를 동시에 측정하는 연구를 진행하고 있다. 현재에는 단순히 두뇌의 활동을 측정하거나 측정 시 발생하는 잡음들을 제거하는 연구들에 중점을 두고 진행되고 있다. 본 연구에서는 fMRI와 TRS를 동시에 측정하여 얻은 두뇌 활동 데이터를 가지고 감정에 따른 활동영역의 EEG 신호를 측정하였다. EEG 신호분석에 있어서 기존의 뇌파만을 가지고 특징을 찾아내는 것을 넘어서 각각의 채널에서 기록되는 뇌파의 파형을 주파수에 따라서 분류하고 정확한 측정을 위해 낮은 주파수를 제거하고 연구자가 필요한 부분의 뇌파를 분석하였다. 【Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.】
EEG-fMRI
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Previous studies have reported the effect of emotion regulation (ER) strategies on both individual and social decision-making, however, the effect of regulation on socially driven emotions independent of decisions is still unclear. In the present study, we investigated the neural effects of using reappraisal to both up- and down-regulate socially driven emotions. Participants played the Dictator Game (DG) in the role of recipient while undergoing fMRI, and concurrently applied the strategies of either up-regulation (reappraising the proposer's intentions as more negative), down-regulation (reappraising the proposer's intentions as less negative), as well as a baseline "look" condition. Results showed that regions responding to the implementation of reappraisal (effect of strategy, that is, "regulating regions") were the inferior and middle frontal gyrus, temporo parietal junction and insula bilaterally. Importantly, the middle frontal gyrus activation correlated with the frequency of regulatory strategies in daily life, with the insula activation correlating with the perceived ability to reappraise the emotions elicited by the social situation. Regions regulated by reappraisal (effect of regulation, that is, "regulated regions") were the striatum, the posterior cingulate and the insula, showing increased activation for the up-regulation and reduced activation for down-regulation, both compared to the baseline condition. When analyzing the separate effects of partners' behavior, selfish behavior produced an activation of the insula, not observed when subjects were treated altruistically. Here we show for the first time that interpersonal ER strategies can strongly affect neural responses when experiencing socially driven emotions. Clinical implications of these findings are also discussed to understand how the way we interpret others' intentions may affect the way we emotionally react.
Inferior frontal gyrus
Middle frontal gyrus
Superior temporal gyrus
Temporoparietal junction
Social decision making
Medial frontal gyrus
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EEG stands for Electroencephalogram. EEG is used to record signals from brain; signals are recorded from the scalp or cortex of brain. EEG used for both clinically purpose as well as for scientifically purpose. Hence measurement of EEG signals plays an important role in mind/brain studies. Reorganization of EEG signals from brain is one of the most overriding approaches to extract the data/knowledge from mind/brain dynamics. Analyzing Electrical activity of brain through EEG provide medical science to examine different brain diseases. Electrical activity of brain can easily be classified as normal brain waves or abnormal brain waves. Normal brain waves used to study various states of mind where as abnormal brain waves used to indicate medical problems. Classification of EEG signals play important role in medical science, some important applications for EEG wave classification are diagnosis of sleep disorders and construction of BCI to assist disabled person. Reorganization of EEG signals from brain is one of the most overriding approaches to extract the data/knowledge from mind/brain dynamics. Analyzing Electrical activity of brain through EEG provide medical science to examine different brain diseases.
Brain waves
EEG-fMRI
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Abstract Abnormal decision making can result in detrimental outcomes of clinical importance, and decision making is strongly linked to neural prediction error signalling. Activation likelihood estimation (ALE) meta‐analyses were used to examine the neural correlates of prediction error signals of individuals taking different types of substances and healthy controls with contrast and conjunction analyses. Twenty‐eight studies were included in the meta‐analysis, representing 424 substance users' individuals and 834 healthy control individuals. Robust brain activity associated with prediction error signals in substance users was found for the bilateral striatum and insula. Healthy control subjects also activated bilateral striatum, midbrain, right insula and right medial‐inferior frontal gyrus. Compared with healthy controls, substance users showed blunted activity in the bilateral putamen, right medial‐inferior frontal gyrus and insula. The current meta‐analysis of cross‐sectional findings investigated neural prediction error signals in substance users. PE abnormalities in substance users might be related to poor decision making. In conclusion, the present study helps identify the pathophysiological underpinnings of maladaptive decision making in substance users.
Inferior frontal gyrus
Putamen
Medial frontal gyrus
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Time perception
Brain mapping
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EEG-fMRI
Human brain
Neurophysiology
Brain Function
Eyes open
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