Dataset Information

Bayesian Modeling of the Dynamics of Phase Modulations and their Application to Auditory Event Related Potentials at Different Loudness Scales.

ABSTRACT: We study the effect of long-term habituation signatures of auditory selective attention reflected in the instantaneous phase information of the auditory event-related potentials (ERPs) at four distinct stimuli levels of 60, 70, 80, and 90 dB SPL. The analysis is based on the single-trial level. The effect of habituation can be observed in terms of the changes (jitter) in the instantaneous phase information of ERPs. In particular, the absence of habituation is correlated with a consistently high phase synchronization over ERP trials. We estimate the changes in phase concentration over trials using a Bayesian approach, in which the phase is modeled as being drawn from a von Mises distribution with a concentration parameter which varies smoothly over trials. The smoothness assumption reflects the fact that habituation is a gradual process. We differentiate between different stimuli based on the relative changes and absolute values of the estimated concentration parameter using the proposed Bayesian model.

SUBMITTER: Mortezapouraghdam Z

PROVIDER: S-EPMC4730906 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Bayesian Modeling of the Dynamics of Phase Modulations and their Application to Auditory Event Related Potentials at Different Loudness Scales.

Mortezapouraghdam Zeinab Z Wilson Robert C RC Schwabe Lars L Strauss Daniel J DJ

Frontiers in computational neuroscience 20160128

We study the effect of long-term habituation signatures of auditory selective attention reflected in the instantaneous phase information of the auditory event-related potentials (ERPs) at four distinct stimuli levels of 60, 70, 80, and 90 dB SPL. The analysis is based on the single-trial level. The effect of habituation can be observed in terms of the changes (jitter) in the instantaneous phase information of ERPs. In particular, the absence of habituation is correlated with a consistently high ...[more]

PMID: 26858631

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Project description:ObjectiveThe present study was performed to investigate neonatal auditory perception function by quantitative electroencephalography (QEEG) and auditory event-related potentials (aERPs) and identify the characteristics of auditory perception development in newborns.MethodsFifty-three normal full-term neonates were divided into three groups according their age in days. An auditory oddball paradigm was used. QEEG (resting state and task state) and aERPs were performed. EEG δ power in the resting and task states and at different ages was respectively analyzed. The N2 area and latency of aERPs at different ages were also compared.ResultsThe four main findings of this study are as follows. First, the increase in the EEG δ power was significantly greater in the task than resting state in Group 3 at the Fz lead (t = -3.371, P = 0.004) and in Groups 2 and 3 at the Cz lead (Group 2: t = -3.149, P = 0.005; Group 3: t = -3.609, P = 0.002). Second, the δ power gradually increased from 1 to 10 days of age (Group 1), peaked at 11 to 20 days (Group 2), and gradually decreased from 21 to 28 days (Group 3). The data in the Fz lead during the task state and in the Cz lead during the resting and task states were statistically significant (F = 5.875, P = 0.005; F = 5.523, P = 0.007; and F = 5.402, P = 0.008, respectively). Third, the N2 area significantly increased with age by presentation of target stimuli (F = 5.26, P = 0.01). The N2 area increased most significantly from 21 to 28 days (Group 3). Finally, the N2 latency significantly decreased with age (Fz lead: F = 4.66, P = 0.023; Cz lead: F = 7.18, P = 0.005). The N2 latency decreased most significantly from 11 to 20 days of age (Group 2).ConclusionRapid cognitive development occurs during the neonatal period. In the first several days after birth, the EEG δ power and N2 area manifested the characteristic performance of identifying task information. QEEG and aERP measurement can be used as objective indices with which to evaluate auditory perception development in neonates.

Project description:Learning to anticipate future states of the world based on statistical regularities in the environment is a key component of perception and is vital for the survival of many organisms. Such statistical learning and prediction are crucial for acquiring language and music appreciation. Importantly, learned expectations can be implicitly derived from exposure to sensory input, without requiring explicit information regarding contingencies in the environment. Whereas many previous studies of statistical learning have demonstrated larger neuronal responses to unexpected versus expected stimuli, the neuronal bases of the expectations themselves remain poorly understood. Here we examined behavioral and neuronal signatures of learned expectancy via human scalp-recorded event-related brain potentials (ERPs). Participants were instructed to listen to a series of sounds and press a response button as quickly as possible upon hearing a target noise burst, which was either reliably or unreliably preceded by one of three pure tones in low-, mid-, and high-frequency ranges. Participants were not informed about the statistical contingencies between the preceding tone 'cues' and the target. Over the course of a stimulus block, participants responded more rapidly to reliably cued targets. This behavioral index of learned expectancy was paralleled by a negative ERP deflection, designated as a neuronal contingency response (CR), which occurred immediately prior to the onset of the target. The amplitude and latency of the CR were systematically modulated by the strength of the predictive relationship between the cue and the target. Re-averaging ERPs with respect to the latency of behavioral responses revealed no consistent relationship between the CR and the motor response, suggesting that the CR represents a neuronal signature of learned expectancy or anticipatory attention. Our results demonstrate that statistical regularities in an auditory input stream can be implicitly learned and exploited to influence behavior. Furthermore, we uncover a potential 'prediction signal' that reflects this fundamental learning process.

Dataset Information

Bayesian Modeling of the Dynamics of Phase Modulations and their Application to Auditory Event Related Potentials at Different Loudness Scales.

Publications

Bayesian Modeling of the Dynamics of Phase Modulations and their Application to Auditory Event Related Potentials at Different Loudness Scales.

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