A Hierarchical Bayesian Model for Differential Connectivity in Multi-trial Brain Signals.
A Hierarchical Bayesian Model for Differential Connectivity in Multi-trial Brain Signals.
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DOI：
10.1016/j.ecosta.2020.03.009
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发表时间：
2020-07
影响因子：
1.9
通讯作者：
Ombao H
中科院分区：
文献类型：
--
作者：
Hu L;Guindani M;Fortin NJ;Ombao H
神经科学界对测量大脑连通性和开发方法有浓厚的兴趣，这些方法可以区分不同患者组和不同实验刺激的连通性。这些统计工具的发展对于理解支持记忆编码和检索的大脑结构之间功能关系的动态至关重要。然而，挑战来自于需要在连接建模中结合条件内相似性和条件间异质性，以及如何提供一种自然的方法来对有效连接进行试验和条件级推理。提出了一种贝叶斯层次向量自回归（BH-VAR）模型来表征大脑连通性并推断不同条件下的连通性差异。条件内连接相似性和条件间连接异质性由特定试验模型的先验考虑。除了完全贝叶斯框架之外，还提出了一种替代的两阶段计算方法，该方法仍然允许通过MCMC后验抽样对试验间条件进行直接的不确定性量化，但为估计试验特定VAR参数提供了快速近似过程。该方法的一个新方面是使用特定频率的测量，部分定向相干性（PDC），以表征贝叶斯框架下的有效连通性。更具体地说，PDC可以推断出方向性，并解释相对于大脑网络中所有可能的接收器，当前发送器通道中某一频率的振荡活动对特定接收器通道中未来振荡活动的影响程度。该模型应用于大鼠执行复杂序列记忆任务时收集的大型电生理数据集。这个独特的数据集包括通过海马区CA1的电极阵列记录的局部场电位（LFPs）活动，同时动物在两种主要条件下进行多次试验。所提出的建模方法为记忆表现过程中的海马体连通性提供了新的见解。具体来说，它将CA1分为两个功能单元，一个外侧节段和一个内侧节段，每个功能单元与自身的功能连通性都比与另一个功能单元强。该方法还揭示了信息在试验中主要以横向向内侧方向流动（条件内），并表明这种效应在一个试验条件下比另一个试验条件下更强（条件间效应）。总的来说，这些结果表明，所提出的模型是一种很有前途的方法，可以量化条件内和条件间功能连接的变化，因此应该在神经科学研究中有广泛的应用。
There is a strong interest in the neuroscience community to measure brain connectivity and develop methods that can differentiate connectivity across patient groups and across different experimental stimuli. The development of such statistical tools is critical to understand the dynamics of functional relationships among brain structures supporting memory encoding and retrieval. However, the challenge comes from the need to incorporate within-condition similarity with between-conditions heterogeneity in modeling connectivity, as well as how to provide a natural way to conduct trial- and condition-level inference on effective connectivity. A Bayesian hierarchical vector autoregressive (BH-VAR) model is proposed to characterize brain connectivity and infer differences in connectivity across conditions. Within-condition connectivity similarity and between-conditions connectivity heterogeneity are accounted for by the priors on trial-specific models. In addition to the fully Bayesian framework, an alternative two-stage computation approach is also proposed which still allows straightforward uncertainty quantification of between-trial conditions via MCMC posterior sampling, but provides a fast approximate procedure for the estimation of trial-specific VAR parameters. A novel aspect of the approach is the use of a frequency-specific measure, partial directed coherence (PDC), to characterize effective connectivity under the Bayesian framework. More specifically, PDC allows inferring directionality and explaining the extent to which the present oscillatory activity at a certain frequency in a sender channel influences the future oscillatory activity in a specific receiver channel relative to all possible receivers in the brain network. The proposed model is applied to a large electrophysiological dataset collected as rats performed a complex sequence memory task. This unique dataset includes local field potentials (LFPs) activity recorded from an array of electrodes across hippocampal region CA1 while animals were presented with multiple trials from two main conditions. The proposed modeling approach provided novel insights into hippocampal connectivity during memory performance. Specifically, it separated CA1 into two functional units, a lateral and a medial segment, each showing stronger functional connectivity to itself than to the other. This approach also revealed that information primarily flowed in a lateral-to-medial direction across trials (within-condition), and suggested this effect was stronger on one trial condition than the other (between-conditions effect). Collectively, these results indicate that the proposed model is a promising approach to quantify the variation of functional connectivity, both within- and between-conditions, and thus should have broad applications in neuroscience research.
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影响因子：
3.2
作者：
Gorrostieta C;Fiecas M;Ombao H;Burke E;Cramer S
通讯作者：
Cramer S
影响因子：
5.7
作者：
O'Keefe, J
通讯作者：
O'Keefe, J
影响因子：
3.7
作者：
MITCHELL, TJ;BEAUCHAMP, JJ
通讯作者：
BEAUCHAMP, JJ
影响因子：
0.9
作者：
Gorrostieta, Cristina;Ombao, Hernando;Von Sachs, Rainer
通讯作者：
Von Sachs, Rainer
影响因子：
2.7
作者：
Allen, Leila M.;Lesyshyn, Rachel A.;Fortin, Norbert J.
通讯作者：
Fortin, Norbert J.