Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-10-11 eCollection Date: 2024-10-01 DOI:10.1371/journal.pcbi.1011986
Jessica L Gaines, Kwang S Kim, Ben Parrell, Vikram Ramanarayanan, Alvincé L Pongos, Srikantan S Nagarajan, John F Houde
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Abstract

Behavioral speech tasks have been widely used to understand the mechanisms of speech motor control in typical speakers as well as in various clinical populations. However, determining which neural functions differ between typical speakers and clinical populations based on behavioral data alone is difficult because multiple mechanisms may lead to the same behavioral differences. For example, individuals with cerebellar ataxia (CA) produce atypically large compensatory responses to pitch perturbations in their auditory feedback, compared to typical speakers, but this pattern could have many explanations. Here, computational modeling techniques were used to address this challenge. Bayesian inference was used to fit a state feedback control (SFC) model of voice fundamental frequency (fo) control to the behavioral pitch perturbation responses of speakers with CA and typical speakers. This fitting process resulted in estimates of posterior likelihood distributions for five model parameters (sensory feedback delays, absolute and relative levels of auditory and somatosensory feedback noise, and controller gain), which were compared between the two groups. Results suggest that the speakers with CA may proportionally weight auditory and somatosensory feedback differently from typical speakers. Specifically, the CA group showed a greater relative sensitivity to auditory feedback than the control group. There were also large group differences in the controller gain parameter, suggesting increased motor output responses to target errors in the CA group. These modeling results generate hypotheses about how CA may affect the speech motor system, which could help guide future empirical investigations in CA. This study also demonstrates the overall proof-of-principle of using this Bayesian inference approach to understand behavioral speech data in terms of interpretable parameters of speech motor control models.

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用贝叶斯方法推断小脑共济失调的扰动反应的状态反馈控制参数。
行为言语任务已被广泛用于了解典型说话者和各种临床人群的言语运动控制机制。然而,仅凭行为数据来确定典型说话者和临床人群的哪些神经功能存在差异是很困难的,因为多种机制可能会导致相同的行为差异。例如,与典型说话者相比,小脑共济失调(CA)患者对听觉反馈中的音高扰动会产生非典型的巨大补偿反应,但这种模式可能有多种解释。在这里,计算建模技术被用来解决这一难题。贝叶斯推理被用于将语音基频(fo)控制的状态反馈控制(SFC)模型拟合到 CA 说话者和典型说话者的行为音高扰动反应中。拟合过程得出了五个模型参数(感觉反馈延迟、听觉和体感反馈噪声的绝对水平和相对水平以及控制器增益)的后似然分布估计值,并对两组参数进行了比较。结果表明,患有 CA 的说话者对听觉和体觉反馈的权重比例可能与典型说话者不同。具体来说,CA 组对听觉反馈的相对敏感度高于对照组。控制器增益参数也存在较大的组间差异,这表明 CA 组对目标错误的运动输出反应增强。这些建模结果提出了 CA 如何影响言语运动系统的假设,有助于指导未来的 CA 实证研究。这项研究还证明了使用这种贝叶斯推理方法从言语运动控制模型的可解释参数角度理解言语行为数据的总体原理。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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