One of the goals of neuroscience is to study interactions between different brain regions during rest and while performing specific cognitive tasks. The Multivariate Bayesian Autoregressive Decomposition (MBMARD) is proposed as an intuitive and novel Bayesian non-parametric model to represent high-dimensional signals as a low-dimensional mixture of univariate uncorrelated latent oscillations. Each latent oscillation captures a specific underlying oscillatory activity and hence will be modeled as a unique second-order autoregressive process due to a compelling property that its spectral density has a shape characterized by a unique frequency peak and bandwidth, which are parameterized by a location and a scale parameter. The posterior distributions of the parameters of the latent oscillations are computed via a metropolis-within-Gibbs algorithm. One of the advantages of MBMARD is its robustness against misspecification of standard models which is demonstrated in simulation studies. The main scientific questions addressed by MBMARD are the effects of long-term abuse of alcohol consumption on memory by analyzing EEG records of alcoholic and non-alcoholic subjects performing a visual recognition experiment. The MBMARD model exhibited novel interesting findings including identifying subject-specific clusters of low and high-frequency oscillations among different brain regions.

翻译：摘要：神经科学的目标之一是研究静息状态及执行特定认知任务时大脑不同区域间的交互作用。本文提出多元贝叶斯自回归分解（MBMARD）作为一种直观且新颖的贝叶斯非参数模型，将高维信号表示为低维混合的单变量不相关潜在振荡。每个潜在振荡捕捉特定的潜在振荡活动，并因其谱密度具有由唯一频率峰值和带宽表征的形状这一显著特性，被建模为独特的二阶自回归过程，其中峰值和带宽分别由位置参数和尺度参数参数化。通过嵌套Metropolis的Gibbs算法计算潜在振荡参数的后验分布。MBMARD的优势之一在于其对标准模型误设的鲁棒性，该特性通过仿真研究得到验证。MBMARD所解决的核心科学问题在于，通过分析酗酒与非酗酒受试者在视觉识别实验中的脑电图记录，探究长期酒精滥用对记忆的影响。该模型揭示了新颖且有意义的发现，包括识别出不同大脑区域中高频与低频振荡的受试者特异性聚类。