The Time Course of the Amplitude and Latency in the Auditory Late Response Evoked by Repeated Tone Bursts

Zhang, Fawen; Eliassen, James C.; Anderson, Jill M.; Scheifele, Peter M.; Brown, David K.

doi:10.3766/jaaa.20.4.4

Cited by 26 publications

(25 citation statements)

References 57 publications

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“…The finding of less prominent adaptive pattern in CI users compared to NH listeners in the current study is consistent with that reported in Zhang et al [56]. One explanation for the reduced amount of adaptation in CI users is that neural degeneration may have altered the adaptation/refractory features of neural responses [58]. This explanation is supported by a significant correlation between AI LAEP and the age at implantation, which is a factor affecting the degree of neural degeneration.…”

Section: Discussionsupporting

confidence: 92%

Neural Adaptation and Behavioral Measures of Temporal Processing and Speech Perception in Cochlear Implant Recipients

et al. 2013

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The objective was to determine if one of the neural temporal features, neural adaptation, can account for the across-subject variability in behavioral measures of temporal processing and speech perception performance in cochlear implant (CI) recipients. Neural adaptation is the phenomenon in which neural responses are the strongest at the beginning of the stimulus and decline following stimulus repetition (e.g., stimulus trains). It is unclear how this temporal property of neural responses relates to psychophysical measures of temporal processing (e.g., gap detection) or speech perception. The adaptation of the electrical compound action potential (ECAP) was obtained using 1000 pulses per second (pps) biphasic pulse trains presented directly to the electrode. The adaptation of the late auditory evoked potential (LAEP) was obtained using a sequence of 1-kHz tone bursts presented acoustically, through the cochlear implant. Behavioral temporal processing was measured using the Random Gap Detection Test at the most comfortable listening level. Consonant nucleus consonant (CNC) word and AzBio sentences were also tested. The results showed that both ECAP and LAEP display adaptive patterns, with a substantial across-subject variability in the amount of adaptation. No correlations between the amount of neural adaptation and gap detection thresholds (GDTs) or speech perception scores were found. The correlations between the degree of neural adaptation and demographic factors showed that CI users having more LAEP adaptation were likely to be those implanted at a younger age than CI users with less LAEP adaptation. The results suggested that neural adaptation, at least this feature alone, cannot account for the across-subject variability in temporal processing ability in the CI users. However, the finding that the LAEP adaptive pattern was less prominent in the CI group compared to the normal hearing group may suggest the important role of normal adaptation pattern at the cortical level in speech perception.

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Section: Discussionsupporting

confidence: 92%

Neural Adaptation and Behavioral Measures of Temporal Processing and Speech Perception in Cochlear Implant Recipients

et al. 2013

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“…Second, to assess the amplitude attenuation profile in response to increasing RAS frequencies, repeated measures ANOVAs were performed for peak-to-peak amplitudes of the P1/ N1-complex (Papanicolaou et al, 1984;Lim et al, 2005). This peak-to-peak assessment is not influenced by baseline resetting effects due to preceding AEPs and may be considered a more stable measure than absolute amplitude of individual peak components (Zhang et al, 2009). ANOVAs were performed in PASW Statistics (PASW Statistics for Mac, version 18.0, SPSS Inc., Chicago, IL, USA) in two ways: (1) with the within-subjects factors RAS (from 1 Hz to 6 Hz) and the between-subjects factor Group (controls vs. experimental conditions of the patient cohort) for differences between patients and controls; and (2) with the within-subjects factors RAS (from 1 Hz to 6 Hz) and Treatment (DOPA-ON, DOPA-OFF, ON-DBS, OFF-DBS).…”

Section: Discussionmentioning

confidence: 99%

Subthalamic deep brain stimulation improves auditory sensory gating deficit in Parkinson’s disease

Gulberti

Hamel

Buhmann

et al. 2015

Clinical Neurophysiology

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“…In the absence of such a phase reset or if stimuli occur in a non-ideal phase, behavioral responses are suppressed or slowed down. (c) When the timing of a sound is correctly anticipated (right panel), a decrease in evoked auditory responses (N1) is observed [22,23]. This schematic compares the amplitude of an event-related response (N1, averaged across 10 trials) to unexpected (left panel) and expected (right panel) auditory stimuli.…”

Section: Predictive Timing and Delta-theta Oscillationsmentioning

confidence: 98%

“…The predictive alignment of delta-theta oscillations in an ideal excitability phase speeds up stimulus detection (Figure 1b) [20,21]. Furthermore, when stimuli are implicitly expected based on their temporal regularity, early sensory responses are reduced (Figure 1c) [22,23]. The magnitude of neural responses, however, depends on whether temporal predictions are tested in the presence or absence of explicit attention to the nature or location of those events (Box 1 and [24][25][26][27]).…”

Section: Predictive Timing and Delta-theta Oscillationsmentioning

confidence: 99%

Cortical oscillations and sensory predictions

Arnal

Giraud

2012

Trends in Cognitive Sciences

906

856

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Predictive coding: the idea that the brain generates hypotheses about the possible causes of forthcoming sensory events and that these hypotheses are compared with incoming sensory information. The difference between topdown expectation and incoming sensory inputs, that is, prediction error, is propagated forward throughout the cortical hierarchy. Predictive timing (temporal expectations): an extension of the notion of predictive coding to the exploitation of temporal regularities (such as a beat) or associative contingencies (for instance, temporal relation between two inputs) to infer the occurrence of future sensory events. Top-down processing: efferent neural operations that convey the internal goals or states of the observer. This notion generally includes different cognitive processes, such as attention and expectations (Box 1). Neural oscillations: neurophysiological electromagnetic signals [from LocalField Potentials (LFP), electroencephalographic (EEG) and magnetoencephalographic recordings (MEG)] that reflect coherent neuronal population behavior at different spatial scales. These signals have been labeled as a function of their frequency from human surface EEG: delta (2-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), and gamma bands (30-100 Hz). The mechanistic properties of oscillations are computationally interesting as a means of explaining various aspects of perception and cognition, for example, longdistance communication across brain regions, unification of various attributes of the same object, segmentation of the sensory input, memory etc.

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The Time Course of the Amplitude and Latency in the Auditory Late Response Evoked by Repeated Tone Bursts

Cited by 26 publications

References 57 publications

Neural Adaptation and Behavioral Measures of Temporal Processing and Speech Perception in Cochlear Implant Recipients

Neural Adaptation and Behavioral Measures of Temporal Processing and Speech Perception in Cochlear Implant Recipients

Subthalamic deep brain stimulation improves auditory sensory gating deficit in Parkinson’s disease

Cortical oscillations and sensory predictions

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