An Integrative Model of Subcortical Auditory Plasticity

Chandrasekaran, Bharath; Skoe, Erika; Kraus, Nina

doi:10.1007/s10548-013-0323-9

Cited by 58 publications

(47 citation statements)

References 134 publications

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“…Our results demonstrate the feasibility of machine learning approaches to characterize the FFR, which is considered a promising biomarker of auditory function (Chandrasekaran & Kraus, 2010; Chandrasekaran et al, 2012; Chandrasekaran., Skoe, & Kraus, 2014). Importantly, our study shows that the machine learning derived metrics can reflect biologically-relevant influences on auditory processing.…”

Section: Discussionmentioning

confidence: 57%

“…Following this line of research, recent studies have documented a more faithful subcortical encoding of lexical tones (e.g., Mandarin tones) in native speakers of tonal languages (e.g., Chinese), relative to native speakers of languages that are not tonal (e.g., English) (Krishnan et al, 2010; Krishnan, Gandour, Bidelman, & Swaminathan, 2009; Krishnan et al, 2005; Xie et al, 2017). These results are theoretically interesting because they demonstrate that subcortical structures are not just passive relay stations, but relate to individual differences in auditory processing (Chandrasekaran, Skoe, & Kraus, 2014). …”

Section: Introductionmentioning

confidence: 97%

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Hidden Markov modeling of frequency-following responses to Mandarin lexical tones

Llanos

Xie

Chandrasekaran

2017

Journal of Neuroscience Methods

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Background The frequency-following response (FFR) is a scalp-recorded electrophysiological potential reflecting phase-locked activity from neural ensembles in the auditory system. The FFR is often used to assess the robustness of subcortical pitch processing. Due to low signal-to-noise ratio at the single-trial level, FFRs are typically averaged across thousands of stimulus repetitions. Prior work using this approach has shown that subcortical encoding of linguistically-relevant pitch patterns is modulated by long-term language experience. New method We examine the extent to which a machine learning approach using hidden Markov modeling (HMM) can be utilized to decode Mandarin tone-categories from scalp-record electrophysiolgical activity. We then assess the extent to which the HMM can capture biologically-relevant effects (language experience-driven plasticity). To this end, we recorded FFRs to four Mandarin tones from 14 adult native speakers of Chinese and 14 of native English. We trained a HMM to decode tone categories from the FFRs with varying size of averages. Results and comparisons with existing methods Tone categories were decoded with above-chance accuracies using HMM. The HMM derived metric (decoding accuracy) revealed a robust effect of language experience, such that FFRs from native Chinese speakers yielded greater accuracies than native English speakers. Critically, the language experience-driven plasticity was captured with average sizes significantly smaller than those used in the extant literature. Conclusions Our results demonstrate the feasibility of HMM in assessing the robustness of neural pitch. Machine-learning approaches can complement extant analytical methods that capture auditory function and could reduce the number of trials needed to capture biological phenomena.

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Section: Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 97%

Hidden Markov modeling of frequency-following responses to Mandarin lexical tones

Llanos

Xie

Chandrasekaran

2017

Journal of Neuroscience Methods

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“…It is likely that similar changes in the strength of the corticofugal inputs to the IC with changes in pitch salience may account for the experience-dependent enhancement of the subcortical responses. It has been proposed that the corticofugal inputs to the IC provide continuous online modulation of processing of pitch-relevant information based on a predictive algorithm wherein robust representation of behaviorally relevant features suggest smaller predictive error (Chandrasekaran et al, 2014). …”

Section: Discussionmentioning

confidence: 99%

Language-experience plasticity in neural representation of changes in pitch salience

2016

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Neural representation of pitch-relevant information at the brainstem and cortical levels of processing is influenced by language experience. A well-known attribute of pitch is its salience. Brainstem frequency following responses and cortical pitch specific responses, recorded concurrently, were elicited by a pitch salience continuum spanning weak to strong pitch of a dynamic, iterated rippled noise pitch contour—homolog of a Mandarin tone. Our aims were to assess how language experience (Chinese, English) affects i) enhancement of neural activity associated with pitch salience at brainstem and cortical levels, ii) the presence of asymmetry in cortical pitch representation, and iii) patterns of relative changes in magnitude along the pitch salience continuum. Peak latency (Fz: Na, Pb, Nb) was shorter in the Chinese than the English group across the continuum. Peak-to-peak amplitude (Fz: Na-Pb, Pb-Nb) of the Chinese group grew larger with increasing pitch salience, but an experience-dependent advantage was limited to the Na-Pb component. At temporal sites (T7/T8), the larger amplitude of the Chinese group across the continuum was both limited to the Na-Pb component and the right temporal site. At the brainstem level, F0 magnitude gets larger as you increase pitch salience, and it too reveals Chinese superiority. A direct comparison of cortical and brainstem responses for the Chinese group reveals different patterns of relative changes in magnitude along the pitch salience continuum. Such differences may point to a transformation in pitch processing at the cortical level presumably mediated by local sensory and/or extrasensory influence overlaid on the brainstem output.

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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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An Integrative Model of Subcortical Auditory Plasticity

Cited by 58 publications

References 134 publications

Hidden Markov modeling of frequency-following responses to Mandarin lexical tones

Hidden Markov modeling of frequency-following responses to Mandarin lexical tones

Language-experience plasticity in neural representation of changes in pitch salience

Neuronal adaptation, novelty detection and regularity encoding in audition

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