Human brainstem plasticity: The interaction of stimulus probability and auditory learning

Skoe, Erika; Chandrasekaran, Bharath; Spitzer, Emily R.; Wong, Patrick C. M.; Kraus, Nina

doi:10.1016/j.nlm.2013.11.011

Cited by 40 publications

(49 citation statements)

References 65 publications

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“…Although this finding is consistent with observations from animal models in which commonly occurring sound combinations leads to SSA (Malmierca et al, 2009;PerezGonzalez et al, 2005), it is seemingly at odds with recent reports from our group and others showing that statistically predictable stimulus conditions give rise to enhanced cABRs in humans (Skoe, et al, 2014;Gnanateja et al, 2013;Slabu et al, 2012;Parbery-Clark et al, 2011;Skoe & Kraus, 2010b;Chandrasekaran et al, 2009). This diversity of findings warrants discussion.…”

Section: Comparisons With Previous Findingssupporting

confidence: 90%

“…In support of this hypothesis, brainstem and midbrain structures are known to be sensitive to statistical regularities within novel sound streams (Gnanateja, Ranjan, Firdose, Sinha, & Maruthy, 2013;Skoe et al, 2013;Parbery-Clark, Strait, & Kraus, 2011;Chandrasekaran, Hornickel, Skoe, Nicol, & Kraus, 2009;Malmierca et al, 2009;Perez-Gonzalez et al, 2005). For example, using an oddball paradigm, we recently demonstrated that the frequency of a soundʼs occurrence modulates the encoding of novel pitch contours within the complex auditory brainstem response (cABR; Skoe, Chandrasekaran, Spitzer, Wong, & Kraus, 2014). The alternative, Bayesian Brainstem Hypothesis, posits that the auditory brainstem is a subjective sound processor that factors in prior experience when calculating the probability of sounds co-occurring within the current sensory environment.…”

Section: Introductionmentioning

confidence: 96%

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Prior Experience Biases Subcortical Sensitivity to Sound Patterns

Skoe¹,

Krizman²,

Spitzer³

et al. 2015

Journal of Cognitive Neuroscience

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To make sense of our ever-changing world, our brains search out patterns. This drive can be so strong that the brain imposes patterns when there are none. The opposite can also occur: The brain can overlook patterns because they do not conform to expectations. In this study, we examined this neural sensitivity to patterns within the auditory brainstem, an evolutionarily ancient part of the brain that can be fine-tuned by experience and is integral to an array of cognitive functions. We have recently shown that this auditory hub is sensitive to patterns embedded within a novel sound stream, and we established a link between neural sensitivity and behavioral indices of learning [Skoe, E., Krizman, J., Spitzer, E., & Kraus, N. The auditory brainstem is a barometer of rapid auditory learning. Neuroscience, 243, 104-114, 2013]. We now ask whether this sensitivity to stimulus statistics is biased by prior experience and the expectations arising from this experience. To address this question, we recorded complex auditory brainstem responses (cABRs) to two patterned sound sequences formed from a set of eight repeating tones. For both patterned sequences, the eight tones were presented such that the transitional probability (TP) between neighboring tones was either 33% (low predictability) or 100% (high predictability). Although both sequences were novel to the healthy young adult listener and had similar TP distributions, one was perceived to be more musical than the other. For the more musical sequence, participants performed above chance when tested on their recognition of the most predictable two-tone combinations within the sequence (TP of 100%); in this case, the cABR differed from a baseline condition where the sound sequence had no predictable structure. In contrast, for the less musical sequence, learning was at chance, suggesting that listeners were "deaf" to the highly predictable repeating two-tone combinations in the sequence. For this condition, the cABR also did not differ from baseline. From this, we posit that the brainstem acts as a Bayesian sound processor, such that it factors in prior knowledge about the environment to index the probability of particular events within ever-changing sensory conditions.

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Section: Comparisons With Previous Findingssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 96%

Prior Experience Biases Subcortical Sensitivity to Sound Patterns

Skoe¹,

Krizman²,

Spitzer³

et al. 2015

Journal of Cognitive Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, ABRs can become more difficult to collect in older/larger animals due to increased fat to muscle ratios (Zhou et al, 2006). Lastly, while it is known that ABRs can assess neural plastic changes that occur in the auditory brainstem (Skoe et al, 2013; Skoe et al, 2014), it is less certain if they directly measure neural computations above the level of the inferior colliculus. Sensorimotor gating experiments utilizing PPI of the acoustic startle reflex can also monitor neural plastic changes in these lower auditory structures but importantly, also have the ability to monitor cortical structures which are critically involved in modulating prepulse inhibition (Davis, 1984; Du et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Prepulse inhibition of the acoustic startle reflex vs. auditory brainstem response for hearing assessment

et al. 2016

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The high prevalence of noise-induced and age-related hearing loss in the general population has warranted the use of animal models to study the etiology of these pathologies. Quick and accurate auditory threshold determination is a prerequisite for experimental manipulations targeting hearing loss in animal models. The standard auditory brainstem response (ABR) measurement is fairly quick and translational across species, but is limited by the need for anesthesia and a lack of perceptual assessment. The goal of this study was to develop a new method of hearing assessment utilizing prepulse inhibition (PPI) of the acoustic startle reflex, a commonly used tool that measures detection thresholds in awake animals, and can be performed on multiple animals simultaneously. We found that in control mice PPI audiometric functions are similar to both ABR and traditional operant conditioning audiograms. The hearing thresholds assessed with PPI audiometry in sound exposed mice were also similar to those detected by ABR thresholds one day after exposure. However, three months after exposure PPI threshold shifts were still evident at and near the frequency of exposure whereas ABR thresholds recovered to the pre-exposed level. In contrast, PPI audiometry and ABR wave one amplitudes detected similar losses. PPI audiometry provides a high throughput automated behavioral screening tool of hearing in awake animals. Overall, PPI audiometry and ABR assessments of the auditory system are robust techniques with distinct advantages and limitations, which when combined, can provide ample information about the functionality of the auditory system.

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“…Although this idea received support from early seminal studies suggesting subcortical generators to the MMN (Kraus et al, 1994; Csépe, 1995), it has been largely unexplored until recently (Escera and Malmierca, 2014). Yet, recent human studies have suggested that subcortical auditory stations may undergoo substantial experience-dependent plasticity (Chandrasekaran and Kraus, 2010; Kraus and Skoe, 2010; Chandrasekaran et al, 2013; Skoe et al, 2013, 2014). For example, Kraus et al have found that the brainstem activity is enhanced when stimulus sequences contain predictable sounds compared to more random (i.e., unpredictable) sequences that is infrequent and unpredictable.…”

Section: Stimulus-specific Adaptation Appears Subcorticallymentioning

confidence: 99%

Neuronal adaptation, novelty detection and regularity encoding in audition

Malmierca¹,

Sanchez-Vives

Escera³

et al. 2014

Front. Syst. Neurosci.

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The ability to detect unexpected stimuli in the acoustic environment and determine their behavioral relevance to plan an appropriate reaction is critical for survival. This perspective article brings together several viewpoints and discusses current advances in understanding the mechanisms the auditory system implements to extract relevant information from incoming inputs and to identify unexpected events. This extraordinary sensitivity relies on the capacity to codify acoustic regularities, and is based on encoding properties that are present as early as the auditory midbrain. We review state-of-the-art studies on the processing of stimulus changes using non-invasive methods to record the summed electrical potentials in humans, and those that examine single-neuron responses in animal models. Human data will be based on mismatch negativity (MMN) and enhanced middle latency responses (MLR). Animal data will be based on the activity of single neurons at the cortical and subcortical levels, relating selective responses to novel stimuli to the MMN and to stimulus-specific neural adaptation (SSA). Theoretical models of the neural mechanisms that could create SSA and novelty responses will also be discussed.

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Human brainstem plasticity: The interaction of stimulus probability and auditory learning

Cited by 40 publications

References 65 publications

Prior Experience Biases Subcortical Sensitivity to Sound Patterns

Prior Experience Biases Subcortical Sensitivity to Sound Patterns

Prepulse inhibition of the acoustic startle reflex vs. auditory brainstem response for hearing assessment

Neuronal adaptation, novelty detection and regularity encoding in audition

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