scholarly journals Glutamatergic modulation of auditory cortex connectivity with attentional brain networks in unpredictable perceptual environment

2019 ◽  
Author(s):  
K. Kompus ◽  
V. Volehaugen ◽  
A. Craven ◽  
K. Specht
Keyword(s):  
Predictive Model ◽  
Prediction Error ◽  
Model Building ◽  
Signal Propagation ◽  
Auditory Stimulation ◽  
Perceptual Inference ◽  
Auditory Oddball ◽  
Frontoparietal Network ◽  
Young Subjects ◽  
The Brain

AbstractIn a stable environment the brain can minimize processing required for sensory input by forming a predictive model of the surrounding world and suppressing neural response to predicted stimuli. Unpredicted stimuli lead to a prediction error signal propagation through the perceptual network, and resulting adjustment to the predictive model. The inter-regional plasticity which enables the model-building and model-adjustment is hypothesized to be mediated via glutamatergic receptors. While pharmacological challenge studies with glutamate receptor ligands have demonstrated impact on prediction-error indices, it is not clear how inter-individual differences in the glutamate system affect the prediction-error processing in non-medicated state. In the present study we examined 20 healthy young subjects with resting-state proton MRS spectroscopy to characterize glutamate+glutamine (rs-Glx) levels in their Heschl’s gyrus (HG), and related this to HG functional connectivity during a roving auditory oddball protocol. No rs-Glx effects were found within the frontotemporal prediction-error network. Larger rs-Glx signal was related to stronger connectivity between HG and bilateral inferior parietal lobule during unpredictable auditory stimulation. We also found effects of rs-Glx on the coherence of default mode network (DMN) and frontoparietal network (FPN) during unpredictable auditory stimulation. Our results demonstrate the importance of Glx in modulating long-range connections and wider networks in the brain during perceptual inference.

scholarly journals Glutamatergic modulation of auditory cortex connectivity with attentional brain networks in unpredictable perceptual environment

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kristiina Kompus ◽  
Vegard Volehaugen ◽  
Alex Craven ◽  
Karsten Specht
Keyword(s):  
Predictive Model ◽  
Prediction Error ◽  
Model Building ◽  
Signal Propagation ◽  
Auditory Stimulation ◽  
Perceptual Inference ◽  
Auditory Oddball ◽  
Frontoparietal Network ◽  
Young Subjects ◽  
The Brain

Abstract In a stable environment the brain can minimize processing required for sensory input by forming a predictive model of the surrounding world and suppressing neural response to predicted stimuli. Unpredicted stimuli lead to a prediction error signal propagation through the perceptual network, and resulting adjustment to the predictive model. The inter-regional plasticity which enables the model-building and model-adjustment is hypothesized to be mediated via glutamatergic receptors. While pharmacological challenge studies with glutamate receptor ligands have demonstrated impact on prediction-error indices, it is not clear how inter-individual differences in the glutamate system affect the prediction-error processing in non-medicated state. In the present study we examined 20 healthy young subjects with resting-state proton MRS spectroscopy to characterize glutamate + glutamine (rs-Glx) levels in their Heschl’s gyrus (HG), and related this to HG functional connectivity during a roving auditory oddball protocol. No rs-Glx effects were found within the frontotemporal prediction-error network. Larger rs-Glx signal was related to stronger connectivity between HG and bilateral inferior parietal lobule during unpredictable auditory stimulation. We also found effects of rs-Glx on the coherence of default mode network and frontoparietal network during unpredictable auditory stimulation. Our results demonstrate the importance of Glx in modulating long-range connections and wider networks in the brain during perceptual inference.

Predictive Control of a Cable-Stayed Bridge under Multiple-Support Excitations

2011 ◽  
Vol 66-68 ◽  
pp. 268-272
Author(s):  
Gui Yun Yan ◽  
Zheng Zhang
Keyword(s):  
Predictive Model ◽  
Predictive Control ◽  
Control Strategy ◽  
Prediction Error ◽  
Actual System ◽  
Time Step ◽  
Cable Stayed Bridge ◽  
System Output ◽  
Multiple Support ◽  
Active Control Strategy

This paper presents a predictive control strategy for seismic protection of a benchmark cable-stayed bridge with consideration of multiple-support excitations. In this active control strategy, a multi-step predictive model is built to estimate the seismic dynamics of cable-stayed bridge and the effects of some complicated factors such as time-varying, model mismatching, disturbances and uncertainty of controlled system, are taken into account by the prediction error feedback in the multi-step predictive model. The prediction error is that the actual system output is compared to the model prediction at each time step. Numerical simulation is carried out for analyzing the seismic responses of the controlled cable-stayed bridge and the results show that the developed predictive control strategy can reduce the seismic response of benchmark cable-stayed bridge efficiently.

scholarly journals Monitoring Migraine Cycle Dynamics with an Easy-to-Use Electrophysiological Marker—A Pilot Study

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3918 ◽  
Author(s):  
Goded Shahaf ◽  
Pora Kuperman ◽  
Yuval Bloch ◽  
Shahak Yariv ◽  
Yelena Granovsky
Keyword(s):  
Pilot Study ◽  
Stress Level ◽  
Event Related Potentials ◽  
Healthy Controls ◽  
Preliminary Results ◽  
Auditory Oddball ◽  
Control Subjects ◽  
Related Potentials ◽  
Auditory Oddball Task ◽  
The Brain

Migraine attacks can cause significant discomfort and reduced functioning for days at a time, including the pre-ictal and post-ictal periods. During the inter-ictsal period, however, migraineurs seem to function normally. It is puzzling, therefore, that event-related potentials of migraine patients often differ in the asymptomatic and inter-ictal period. Part of the electrophysiological dynamics demonstrated in the migraine cycle are attention related. In this pilot study we evaluated an easy-to-use new marker, the Brain Engagement Index (BEI), for attention monitoring during the migraine cycle. We sampled 12 migraine patients for 20 days within one calendar month. Each session consisted of subjects’ reports of stress level and migraine-related symptoms, and a 5 min EEG recording, with a 2-electrode EEG device, during an auditory oddball task. The first minute of the EEG sample was analyzed. Repetitive samples were also obtained from 10 healthy controls. The brain engagement index increased significantly during the pre-ictal (p ≈ 0.001) and the ictal (p ≈ 0.020) periods compared with the inter-ictal period. No difference was observed between the pre-ictal and ictal periods. Control subjects demonstrated intermediate Brain Engagement Index values, that is, higher than inter-ictal, yet lower than pre-ictal. Our preliminary results demonstrate the potential advantage of the use of a simple EEG system for improved prediction of migraine attacks. Further study is required to evaluate the efficacy of the Brain Engagement Index in monitoring the migraine cycle and the possible effects of interventions.

scholarly journals Attention controls multisensory perception via 2 distinct mechanisms at different levels of the cortical hierarchy

PLoS Biology ◽  
2021 ◽  
Vol 19 (11) ◽  
pp. e3001465
Author(s):  
Ambra Ferrari ◽  
Uta Noppeney
Keyword(s):  
Causal Inference ◽  
Causal Structure ◽  
Multisensory Perception ◽  
Spatial Representations ◽  
Perceptual Inference ◽  
Sensory Signals ◽  
Cortical Hierarchy ◽  
Parietal Cortices ◽  
The Brain ◽  
Different Levels

To form a percept of the multisensory world, the brain needs to integrate signals from common sources weighted by their reliabilities and segregate those from independent sources. Previously, we have shown that anterior parietal cortices combine sensory signals into representations that take into account the signals’ causal structure (i.e., common versus independent sources) and their sensory reliabilities as predicted by Bayesian causal inference. The current study asks to what extent and how attentional mechanisms can actively control how sensory signals are combined for perceptual inference. In a pre- and postcueing paradigm, we presented observers with audiovisual signals at variable spatial disparities. Observers were precued to attend to auditory or visual modalities prior to stimulus presentation and postcued to report their perceived auditory or visual location. Combining psychophysics, functional magnetic resonance imaging (fMRI), and Bayesian modelling, we demonstrate that the brain moulds multisensory inference via 2 distinct mechanisms. Prestimulus attention to vision enhances the reliability and influence of visual inputs on spatial representations in visual and posterior parietal cortices. Poststimulus report determines how parietal cortices flexibly combine sensory estimates into spatial representations consistent with Bayesian causal inference. Our results show that distinct neural mechanisms control how signals are combined for perceptual inference at different levels of the cortical hierarchy.

scholarly journals Prefrontal solution to the bias-variance tradeoff during reinforcement learning

2020 ◽  
Author(s):  
Dongjae Kim ◽  
Jaeseung Jeong ◽  
Sang Wan Lee
Keyword(s):  
Adaptive Control ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Brain Regions ◽  
Decision Task ◽  
Prediction Errors ◽  
Model Based ◽  
Model Free ◽  
Bias Variance ◽  
The Brain

AbstractThe goal of learning is to maximize future rewards by minimizing prediction errors. Evidence have shown that the brain achieves this by combining model-based and model-free learning. However, the prediction error minimization is challenged by a bias-variance tradeoff, which imposes constraints on each strategy’s performance. We provide new theoretical insight into how this tradeoff can be resolved through the adaptive control of model-based and model-free learning. The theory predicts the baseline correction for prediction error reduces the lower bound of the bias–variance error by factoring out irreducible noise. Using a Markov decision task with context changes, we showed behavioral evidence of adaptive control. Model-based behavioral analyses show that the prediction error baseline signals context changes to improve adaptability. Critically, the neural results support this view, demonstrating multiplexed representations of prediction error baseline within the ventrolateral and ventromedial prefrontal cortex, key brain regions known to guide model-based and model-free learning.One sentence summaryA theoretical, behavioral, computational, and neural account of how the brain resolves the bias-variance tradeoff during reinforcement learning is described.

scholarly journals Using the past to estimate sensory uncertainty

2019 ◽  
Author(s):  
Ulrik Beierholm ◽  
Tim Rohe ◽  
Ambra Ferrari ◽  
Oliver Stegle ◽  
Uta Noppeney
Keyword(s):  
Temporal Dynamics ◽  
Visual Signals ◽  
Perceptual Inference ◽  
Reliability Estimates ◽  
Uncertainty Estimates ◽  
Classical Models ◽  
Past Experiences ◽  
Combine Information ◽  
Sensory Uncertainty ◽  
The Brain

AbstractTo form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.In a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discountingOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

scholarly journals EVALUATION OF THE CORRELATION BETWEEN FACIAL MUSCLE AND BRAIN ACTIVITIES IN AUDITORY STIMULATION

Fractals ◽  
2021 ◽  
Vol 29 (01) ◽  
pp. 2150100
Author(s):  
MIRRA SOUNDIRARAJAN ◽  
MARTIN AUGUSTYNEK ◽  
ONDREJ KREJCAR ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Human Body ◽  
Strong Correlation ◽  
Rock Music ◽  
Sample Entropy ◽  
Auditory Stimulation ◽  
Auditory Stimuli ◽  
Facial Muscle ◽  
Eeg Signals ◽  
Facial Muscles ◽  
The Brain

Evaluation of the correlation of the activities of various organs is an important area of research in physiology. In this paper, we evaluated the correlation among the brain and facial muscles’ reactions to various auditory stimuli. We played three different music (relaxing, pop, and rock music) to 13 subjects and accordingly analyzed the changes in complexities of EEG and EMG signals by calculating their fractal exponent and sample entropy. Based on the results, EEG and EMG signals experienced more significant changes by presenting relaxing, pop, and rock music, respectively. A strong correlation was observed among the alterations of the complexities of EMG and EEG signals, which indicates the coupling of the activities of facial muscles and brain. This method could be further applied to investigate the coupling of the activities of the brain and other organs of the human body.

5 Models of the world: from perception to hallucination

2020 ◽  
Vol 91 (8) ◽  
pp. e2.3-e2
Author(s):  
Paul Fletcher
Keyword(s):  
Model Building ◽  
Processing System ◽  
Human Perception ◽  
Predictive Processing ◽  
External Reality ◽  
Consultant Psychiatrist ◽  
University Of Cambridge ◽  
The World ◽  
The University ◽  
The Brain

Paul Fletcher is Wellcome Investigator and Bernard Wolfe Professor of Health Neuroscience at the University of Cambridge. He is also Director of Studies for Preclinical Medicine at Clare College and Honorary Consultant Psychiatrist with the Cambridgeshire and Peterborough NHS Foundation Trust. He studied Medicine, before carrying out specialist training in Psychiatry and taking a PhD in cognitive neuroscience. He researches human perception, learning and decision-making in health and mental illness.We do not have direct contact with external reality. We must rely on messages from the sense organs, conveying information about the state of the world and our bodies. These messages are not easy to decipher, being noisy and ambiguous, but from them we have to construct models of the world. I will discuss this challenge and how we are very adept at creating a model of reality based on achieving a balance between what our senses are telling us and our expectations of what should be the case. This is often referred to as the predictive processing framework.Relying on this balance comes at a cost, rendering us vulnerable to illusions and biases and, in more extreme cases, to creating a reality that diverges from that experienced by others. This can arise for a variety of reasons but, at the root, I suggest, lies the nature of the brain as a model-building organ. Though this divergence from reality – psychosis – often seems inexplicable and incomprehensible, I suggest that a few core principles can help us to understand it and offers ways of thinking about how phenomena like hallucinations can be understood. Interestingly, the framework suggests ways in which apparently similar phenomena like hallucinations can arise from distinct alterations to the function of a predictive processing system.

scholarly journals Signal propagation via cortical hierarchies

2020 ◽  
Vol 4 (4) ◽  
pp. 1072-1090 ◽  
Author(s):  
Bertha Vézquez-Rodríguez ◽  
Zhen-Qi Liu ◽  
Patric Hagmann ◽  
Bratislav Misic
Keyword(s):  
Shortest Paths ◽  
Signal Propagation ◽  
Brain Regions ◽  
Hierarchical Organization ◽  
Attention Networks ◽  
Diffusion Spectrum Imaging ◽  
Cortical Hierarchy ◽  
Spectrum Imaging ◽  
Structural Networks ◽  
The Brain

The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here we investigate how this intrinsic hierarchical organization of the brain shapes the transmission of information among regions. The hierarchical positioning of individual regions was quantified by applying diffusion map embedding to resting-state functional MRI networks. Structural networks were reconstructed from diffusion spectrum imaging and topological shortest paths among all brain regions were computed. Sequences of nodes encountered along a path were then labeled by their hierarchical position, tracing out path motifs. We find that the cortical hierarchy guides communication in the network. Specifically, nodes are more likely to forward signals to nodes closer in the hierarchy and cover a range of unimodal and transmodal regions, potentially enriching or diversifying signals en route. We also find evidence of systematic detours, particularly in attention networks, where communication is rerouted. Altogether, the present work highlights how the cortical hierarchy shapes signal exchange and imparts behaviorally relevant communication patterns in brain networks.

scholarly journals Examining the optimal timing for closed-loop auditory stimulation of slow-wave sleep in young and older adults

SLEEP ◽  
2019 ◽  
Vol 43 (6) ◽  
Author(s):  
Miguel Navarrete ◽  
Jules Schneider ◽  
Hong-Viet V Ngo ◽  
Mario Valderrama ◽  
Alexander J Casson ◽  
...  
Keyword(s):  
Slow Wave ◽  
Closed Loop ◽  
Slow Wave Sleep ◽  
Auditory Stimulation ◽  
Optimal Time ◽  
Optimal Timing ◽  
Experimental Conditions ◽  
Young Subjects ◽  
The Impact ◽  
Specific Phase

Abstract Study Objectives Closed-loop auditory stimulation (CLAS) is a method for enhancing slow oscillations (SOs) through the presentation of auditory clicks during sleep. CLAS boosts SOs amplitude and sleep spindle power, but the optimal timing for click delivery remains unclear. Here, we determine the optimal time to present auditory clicks to maximize the enhancement of SO amplitude and spindle likelihood. Methods We examined the main factors predicting SO amplitude and sleep spindles in a dataset of 21 young and 17 older subjects. The participants received CLAS during slow-wave-sleep in two experimental conditions: sham and auditory stimulation. Post-stimulus SOs and spindles were evaluated according to the click phase on the SOs and compared between and within conditions. Results We revealed that auditory clicks applied anywhere on the positive portion of the SO increased SO amplitudes and spindle likelihood, although the interval of opportunity was shorter in the older group. For both groups, analyses showed that the optimal timing for click delivery is close to the SO peak phase. Click phase on the SO wave was the main factor determining the impact of auditory stimulation on spindle likelihood for young subjects, whereas for older participants, the temporal lag since the last spindle was a better predictor of spindle likelihood. Conclusions Our data suggest that CLAS can more effectively boost SOs during specific phase windows, and these differ between young and older participants. It is possible that this is due to the fluctuation of sensory inputs modulated by the thalamocortical networks during the SO.

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