Auditory detection learning is accompanied by plasticity in the auditory evoked potential

Wisniewski, Matthew G.; Ball, Natalie J.; Zakrzewski, Alexandria C.; Iyer, Nandini; Thompson, Eric R.; Spencer, Nathan

doi:10.1016/j.neulet.2020.134781

Cited by 12 publications

(28 citation statements)

References 40 publications

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“…Our findings parallel visual category learning where changes in the visual-evoked N1 and late positive component signal successful learning (Perez-Gay Juarez et al, 2019). Our results also align with previous studies using various auditory training tasks including speech (Alain et al, 2010;Alain et al, 2007;Ben-David et al, 2011;Tremblay et al, 2001;Tremblay & Kraus, 2002;Tremblay et al, 2009) and nonspeech sounds (Atienza et al, 2002;Bosnyak et al, 2004;Tong et al, 2009;Wisniewski et al, 2020) suggesting P2 indexes auditory experience that reflects learning success and is not simply a product of the task acquisition process (cf. Tremblay et al, 2014) or repeated stimulus exposure (Ross et al, 2013;Sheehan et al, 2005).…”

Section: Functional Correlates Of Auditory Category Learningsupporting

confidence: 90%

“…In this study, successful learning (i.e., both training accuracy and identification function slopes) was characterized by a reduction in ERP amplitudes after training. The specific direction of P2 modulations varies across experiments with some reporting an increase in evoked responses with learning (Atienza et al, 2002;Bosnyak et al, 2004;Carcagno & Plack, 2011;Ross et al, 2013;Sheehan et al, 2005;Tong et al, 2009;Tremblay et al, 2001;Wisniewski et al, 2020) and others a decrease (Alain et al, 2010;Ben-David et al, 2011;Zhang et al, 2005).…”

Section: Functional Correlates Of Auditory Category Learningmentioning

confidence: 99%

“…As suggested by Alain et al (2010), such discrepancies could be related to the task (e.g., active task vs. passive recording), the stimuli (e.g., speech vs. non-speech), the rate of learning among the participants, or even the rigor of training paradigm. Studies reporting enhanced P2 often included multiple days of training or recorded ERPs during passive listening (Atienza et al, 2002;Bosnyak et al, 2004;Ross et al, 2013;Seppanen et al, 2013;Tremblay et al, 2001;Wisniewski et al, 2020). Long-term auditory experiences (e.g., music training, tone language expertise) have also been associated with enhanced P2 during active sound categorization (Bidelman & Alain, 2015;Bidelman & Lee, 2015;Bidelman et al, 2014) as well as learning (Seppanen et al, 2012(Seppanen et al, , 2013Shahin et al, 2003).…”

Section: Functional Correlates Of Auditory Category Learningmentioning

confidence: 99%

“…Although there are probably some parallels (Liu & Holt, 2011), it remains unclear whether the neuroplastic changes that arise when rapidly learning nonspeech categories (e.g., music) parallels that of speech. More generally, auditory perceptual learning studies have reported changes in both early sensory-evoked (i.e., N1, P2) and late slow-wave ERP responses following training (Alain et al, 2010;Alain et al, 2007;Atienza et al, 2002;Ben-David et al, 2011;Bosnyak et al, 2004;Carcagno & Plack, 2011;Tong et al, 2009;Tremblay et al, 2001;Tremblay & Kraus, 2002;Tremblay et al, 2009;Wisniewski et al, 2020). A true biomarker of learning, however, should vary with learning performance (Tremblay et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Functional plasticity coupled with structural predispositions in auditory cortex shape successful music category learning

Mankel

Shrestha

Tipirneni-Sajja

et al. 2021

Preprint

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Categorizing sounds into meaningful groups helps listeners more efficiently process the auditory scene and is a foundational skill for speech perception and language development. Yet, how auditory categories develop in the brain through learning, particularly for nonspeech sounds, is not well understood. Here, we asked musically naïve listeners to complete a brief (~20 min) training session where they learned to identify sounds from a nonspeech continuum (minor-major 3rd musical intervals). We used multichannel EEG to track behaviorally relevant neuroplastic changes in the auditory event-related potentials (ERPs) pre- to post-training. To rule out mere exposure-induced changes, neural effects were evaluated against a control group of 14 nonmusicians who did not undergo training. We also compared individual categorization performance with structural volumetrics of bilateral primary auditory cortex (PAC) from MRI to evaluate neuroanatomical substrates of learning. Behavioral performance revealed steeper (i.e., more categorical) identification functions in the posttest that correlated with better training accuracy. At the neural level, improvement in learners' behavioral identification was characterized by smaller P2 amplitudes at posttest, particularly over right hemisphere. Critically, learning-related changes in the ERPs were not observed in control listeners, ruling out mere exposure effects. Learners also showed smaller and thinner PAC bilaterally, indicating superior categorization was associated with structural differences in primary auditory brain regions. Collectively, our data suggest successful auditory categorical learning of nonspeech sounds is characterized by short-term functional changes (i.e., greater post-training efficiency) in sensory coding processes superimposed on preexisting structural differences in bilateral auditory cortex.

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Section: Functional Correlates Of Auditory Category Learningsupporting

confidence: 90%

Section: Functional Correlates Of Auditory Category Learningmentioning

confidence: 99%

Section: Functional Correlates Of Auditory Category Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Functional plasticity coupled with structural predispositions in auditory cortex shape successful music category learning

Mankel

Shrestha

Tipirneni-Sajja

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Human electrophysiological studies have also shown enhanced P2 cortical activity in individuals with short-term auditory training (reviewed in Tremblay, 2007;Tremblay et al, 2014), language experience (Wagner et al, 2013), and short/longterm musical training (Atienza et al, 2002;Shahin et al, 2003;Tremblay, 2007;Tong et al, 2009). More recently, results showing increased P2 to trained pitch sounds during passive listening infer that training-induced cortical plasticity is related to permanent perception changes rather than the effect of selective attention (Wisniewski et al, 2020). There is growing neuroimaging evidence to support the notion that the neural representations of complex sounds such as music at the peripheral and central levels are influenced extensively by experience or training (Parbery-Clark et al, 2012;Sankaran et al, 2020).…”

Section: P2 Response Reflecting Musical-training Induced Plasticitymentioning

confidence: 99%

Long-Term Musical Training Alters Auditory Cortical Activity to the Frequency Change

Lee

Han

Lee

2020

Front. Hum. Neurosci.

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Objective: The ability to detect frequency variation is a fundamental skill necessary for speech perception. It is known that musical expertise is associated with a range of auditory perceptual skills, including discriminating frequency change, which suggests the neural encoding of spectral features can be enhanced by musical training. In this study, we measured auditory cortical responses to frequency change in musicians to examine the relationships between N1/P2 responses and behavioral performance/musical training. Methods: Behavioral and electrophysiological data were obtained from professional musicians and age-matched non-musician participants. Behavioral data included frequency discrimination detection thresholds for no threshold-equalizing noise (TEN), +5, 0, and −5 signal-to-noise ratio settings. Auditory-evoked responses were measured using a 64-channel electroencephalogram (EEG) system in response to frequency changes in ongoing pure tones consisting of 250 and 4,000 Hz, and the magnitudes of frequency change were 10%, 25% or 50% from the base frequencies. N1 and P2 amplitudes and latencies as well as dipole source activation in the left and right hemispheres were measured for each condition. Results: Compared to the non-musician group, behavioral thresholds in the musician group were lower for frequency discrimination in quiet conditions only. The scalprecorded N1 amplitudes were modulated as a function of frequency change. P2 amplitudes in the musician group were larger than in the non-musician group. Dipole source analysis showed that P2 dipole activity to frequency changes was lateralized to the right hemisphere, with greater activity in the musician group regardless of the hemisphere side. Additionally, N1 amplitudes to frequency changes were positively related to behavioral thresholds for frequency discrimination while enhanced P2 amplitudes were associated with a longer duration of musical training. Conclusions: Our results demonstrate that auditory cortical potentials evoked by frequency change are related to behavioral thresholds for frequency discrimination in musicians. Larger P2 amplitudes in musicians compared to non-musicians reflects musical training-induced neural plasticity.

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Cortical representation of musical pitch in event-related potentials

et al. 2023

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Neural coding of auditory stimulus frequency is well-documented; however, the cortical signals and perceptual correlates of pitch have not yet been comprehensively investigated. This study examined the temporal patterns of event-related potentials (ERP) in response to single tones of pitch chroma, with an assumption that these patterns would be more prominent in musically-trained individuals than in non-musically-trained individuals. Participants with and without musical training (N = 20) were presented with seven notes on the C major scale (C4, D4, E4, F4, G4, A4, and B4), and whole-brain activities were recorded. A linear regression analysis between the ERP amplitude and the seven notes showed that the ERP amplitude increased or decreased as the frequency of the pitch increased. Remarkably, these linear correlations were anti-symmetric between the hemispheres. Specifically, we found that ERP amplitudes of the left and right frontotemporal areas decreased and increased, respectively, as the pitch frequency increased. Although linear slopes were significant in both groups, the musically-trained group exhibited marginally steeper slope, and their ERP amplitudes were most discriminant for frequency of tone of pitch at earlier latency than in the non-musically-trained group (~ 460 ms vs ~ 630 ms after stimulus onset). Thus, the ERP amplitudes in frontotemporal areas varied according to the pitch frequency, with the musically-trained participants demonstrating a wider range of amplitudes and inter-hemispheric anti-symmetric patterns. Our findings may provide new insights on cortical processing of musical pitch, revealing anti-symmetric processing of musical pitch between hemispheres, which appears to be more pronounced in musically-trained people.

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Auditory detection learning is accompanied by plasticity in the auditory evoked potential

Cited by 12 publications

References 40 publications

Functional plasticity coupled with structural predispositions in auditory cortex shape successful music category learning

Functional plasticity coupled with structural predispositions in auditory cortex shape successful music category learning

Long-Term Musical Training Alters Auditory Cortical Activity to the Frequency Change

Cortical representation of musical pitch in event-related potentials

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