Discrete Dynamic Bayesian Network Analysis of fMRI Data
Discrete Dynamic Bayesian Network Analysis of fMRI Data
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DOI：
10.1002/hbm.20490
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发表时间：
2009-01-01
影响因子：
4.8
通讯作者：
Clark, Vincent P.
中科院分区：
文献类型：
--
作者：
Burge, John;Lane, Terran;Clark, Vincent P.
我们研究了使用离散动态贝叶斯网络（dDBNs）的有效性，这是一种用于机器学习的数据驱动建模技术，用于识别感兴趣的神经解剖区域之间的功能相关性。与许多神经成像分析技术不同，该方法不受线性和/或高斯噪声假设的限制。它通过将神经解剖区域的时间序列建模为离散的，而不是连续的，具有多项式分布的随机变量来实现这一点。我们使用从健康和痴呆老年受试者收集的fMRI数据集证明了这种方法（Buckner等人，[2000]：J Cogn Neurosci 12：24-34），并基于痴呆的诊断识别相关性。结果在三个方面进行了验证。首先，引出的相关性是强大的留一交叉验证，并通过傅立叶自举方法，他们不太可能是由于随机的机会。第二，dDBN识别出了在实验范式下预期的相关性。第三，dDBN预测痴呆症的能力与两种常用的机器学习分类器：支持向量机和高斯朴素贝叶斯网络具有竞争力。我们还验证了dDBN选择相关的非线性标准的基础上。最后，我们提供了一个简短的分析，从巴克纳等人得出的相关性。的数据表明，与健康老年人相比，老年痴呆症受试者的内嗅和枕叶皮质参与程度降低，顶叶和杏仁核参与程度增加（通过BOLD测量之间的功能相关性进行测量）。dDBN方法的局限性和扩展进行了讨论。Hurry Brain Mapp 30：122-137，2009. (c)2007 Wiley-Liss，Inc.
We examine the efficacy of using discrete Dynamic Bayesian Networks (dDBNs), a data-driven modeling technique employed in machine learning, to identify functional correlations among neuroanatomical regions of interest. Unlike many neuroimaging analysis techniques, this method is not limited by linear and/or Gaussian noise assumptions. It achieves this by modeling the time series of neuroanatomical regions as discrete, as opposed to continuous, random variables with multinomial distributions. We demonstrated this method using an fMRI dataset collected from healthy and demented elderly subjects (Buckner, et al., [2000]: J Cogn Neurosci 12:24-34) and identify correlates based on a diagnosis of dementia. The results are validated in three ways. First, the elicited correlates are shown to be robust over leave-one-out cross-validation and, via a Fourier bootstrapping method, that they were not likely due to random chance. Second, the dDBNs identified correlates that would be expected given the experimental paradigm. Third, the dDBN's ability to predict dementia is competitive with two commonly employed machine-learning classifiers: the support vector machine and the Gaussian naive Bayesian network. We also verify that the dDBN selects correlates based on non-linear criteria. Finally, we provide a brief analysis of the correlates elicited from Buckner et al.'s data that suggests that demented elderly subjects have reduced involvement of entorhinal and occipital cortex and greater involvement of the parietal lobe and amygdala in brain activity compared with healthy elderly (as measured via functional correlations among BOLD measurements). Limitations and extensions to the dDBN method are discussed. Hurry Brain Mapp 30:122-137, 2009. (c) 2007 Wiley-Liss, Inc.