Bayesian inference of a spectral graph model for brain oscillations

Jin, Huaqing; Verma, Parul; Jiang, Fei; Nagarajan, Srikantan S.; Raj, Ashish

doi:10.1016/j.neuroimage.2023.120278

Cited by 1 publication

(3 citation statements)

References 69 publications

(118 reference statements)

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“…The output simulated a time course of voice f o in response to a -100 cent, 400 ms, mid-utterance perturbation of f o feedback. Random noise was added to model output during training as Jin et al [36] found that this increased reconstruction accuracy. Uniform noise distributions of increasing width were tested and since the quality of model fit stopped improving for training noise distribution wider than 7 cents, noise with distribution ∼ U ( − 3.5, 3.5) was added to the simulator output during training.…”

Section: Methodsmentioning

confidence: 99%

“…Inference 10 5 simulations were used to train the neural density estimator [36]. The posterior was then sampled 10 4 times for each group.…”

Section: Empirical Observationmentioning

confidence: 99%

“…This idea of exploring relative levels of feedback noise between different sensory modalities was also used by Crevecoeur et al[23] in their state feedback control model of arm reaching. The output simulated a time course of voice f o in response to a -100 cent, 400 ms, mid-utterance perturbation of f o feedback.Random noise was added to model output during training as Jin et al[36] found that this increased reconstruction accuracy. Uniform noise distributions of increasing width were tested and since the quality of model fit stopped improving for training noise…”

mentioning

confidence: 99%

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Bayesian inference of state feedback control parameters forf_operturbation responses in cerebellar ataxia

Gaines,

Kim,

Parrell

et al. 2024

Preprint

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Behavioral speech tasks have been widely used to understand the mechanisms of speech motor control in healthy speakers as well as in various clinical populations. However, determining which neural functions differ between healthy 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 abnormally large compensatory responses to pitch perturbations in their auditory feedback, compared to controls, 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 individuals with CA and healthy controls. This fitting process resulted in estimates of posterior likelihood distributions of 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 CA group may proportionally weight auditory and somatosensory feedback differently from the control group. 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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Section: Methodsmentioning

confidence: 99%

“…Inference 10 5 simulations were used to train the neural density estimator [36]. The posterior was then sampled 10 4 times for each group.…”

Section: Empirical Observationmentioning

confidence: 99%

mentioning

confidence: 99%

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Bayesian inference of state feedback control parameters forf_operturbation responses in cerebellar ataxia

Gaines,

Kim,

Parrell

et al. 2024

Preprint

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Bayesian inference of a spectral graph model for brain oscillations

Cited by 1 publication

References 69 publications

Bayesian inference of state feedback control parameters forf_operturbation responses in cerebellar ataxia

Bayesian inference of state feedback control parameters forf_operturbation responses in cerebellar ataxia

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Bayesian inference of a spectral graph model for brain oscillations

Cited by 1 publication

References 69 publications

Bayesian inference of state feedback control parameters forfoperturbation responses in cerebellar ataxia

Bayesian inference of state feedback control parameters forfoperturbation responses in cerebellar ataxia

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Bayesian inference of state feedback control parameters forf_operturbation responses in cerebellar ataxia

Bayesian inference of state feedback control parameters forf_operturbation responses in cerebellar ataxia