A hierarchical bayesian model for differential connectivity in multi-trial brain signals

Hu, Lechuan; Guindani, Michele; Fortin, Norbert J.; Ombao, Hernando

doi:10.1016/j.ecosta.2020.03.009

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…In this section, we use the proposed method to analyze an LFP dataset obtained from a memory coding experiment on nonspatial events (Allen et al ., 2016; Hu et al ., 2020). In that experiment, rats were trained to identify a series of five odors during the experiment.…”

Section: Analysis Of Odor Memory Data In a Rat Neurobiology Experimentsmentioning

confidence: 99%

Regularized matrix data clustering and its application to image analysis

Shen

Zhang

et al. 2020

Biometrics

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We propose a novel regularized mixture model for clustering matrix-valued data. The proposed method assumes a separable covariance structure for each cluster and imposes a sparsity structure (e.g., low rankness, spatial sparsity) for the mean signal of each cluster. We formulate the problem as a finite mixture model of matrix-normal distributions with regularization terms, and then develop an EM-type of algorithm for efficient computation. In theory, we show that the proposed estimators are strongly consistent for various choices of penalty functions. Simulation and two applications on brain signal studies confirm the excellent performance of the proposed method including a better prediction accuracy than the competitors and the scientific interpretability of the solution.

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Section: Analysis Of Odor Memory Data In a Rat Neurobiology Experimentsmentioning

confidence: 99%

Regularized matrix data clustering and its application to image analysis

Shen

Zhang

et al. 2020

Biometrics

Self Cite

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“…Hierarchical time-domain models such as autoregressions (ARs) and vector autoregressions (VARs) -as well as versions of these models that allow for changes in the parameters over time to capture non-stationary behavior, such as time-varying ARs (TVARs) and time-varying VARs (TV-VAR) -have been used to infer latent structure from multiple brain signals [e.g., 16,18,13,12,5,6]. Some of these approaches consider a hierarchical structure in the AR or VAR coefficients, while others consider latent factor models within a Bayesian framework coupled with sophisticated and flexible prior structures.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these approaches consider a hierarchical structure in the AR or VAR coefficients, while others consider latent factor models within a Bayesian framework coupled with sophisticated and flexible prior structures. Approaches involving multivariate models are often used to infer features related to the behavior of individual time series, as well as those that provide information about relationships across multiple time series such as coherence, partial coherence and/or partial directed coherence [e.g., 5,6]. Other modeling frameworks such as those based on factor models, focus on discovering the latent structure underlying multiple time series [e.g., 18,13].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical dynamic PARCOR models for analysis of multiple brain signals

Zhao

Prado

2023

Statistics and Its Interface

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We present an efficient hierarchical model for inferring latent structure underlying multiple non-stationary time series. The proposed model describes the time-varying behavior of multiple time series in the partial autocorrelation domain, which results in a lower dimensional representation, and consequently computationally faster inference, than those required by models in the time and/or frequency domains, such as time-varying autoregressive models, which are commonly used in practice. We illustrate the performance of the proposed hierarchical dynamic PARCOR models and corresponding Bayesian inferential procedures in the context of analyzing multiple brain signals recorded simultaneously during specific experimental settings or clinical studies. The proposed approach allows us to efficiently obtain posterior summaries of the time-frequency characteristics of the multiple time series, as well as those summarizing their common underlying structure.

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