Evaluating Functional Autocorrelation within Spatially Distributed Neural Processing Networks.
Evaluating Functional Autocorrelation within Spatially Distributed Neural Processing Networks.
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DOI：
10.4310/sii.2010.v3.n1.a4
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发表时间：
2010
影响因子：
0.8
通讯作者：
Kilts CD
中科院分区：
文献类型：
--
作者：
Derado G;Bowman FD;Ely TD;Kilts CD
数据驱动的统计方法，如聚类分析或独立成分分析，应用于体内功能神经成像数据，有助于识别神经处理网络，表现出类似的任务相关或restingstate模式的活动。理想情况下，这种网络内的体素的测量大脑活动应该表现出高自相关性。一个重要的限制是，算法通常不量化或统计测试体素之间的网络内相关性的强度或性质。为了扩展这种数据驱动的分析给出的结果，我们建议使用Moran's I统计量来测量确定的神经处理网络内的功能自相关程度，并评估所观察到的关联的统计显著性。我们适应传统的定义莫兰的我，适用于神经影像学分析，通过定义全球自相关指数使用基于网络的社区。此外，我们计算网络特定的整体自相关的贡献。我们目前的结果，从自助分析，提供实证支持，使用我们的假设检验框架。我们说明了我们的方法，使用正电子发射断层扫描（PET）的数据，研究精神分裂症和功能性磁共振成像（fMRI）的数据，从一项研究抑郁症的个体之间的工作记忆的神经表征。
Data-driven statistical approaches, such as cluster analysis or independent component analysis, applied to in vivo functional neuroimaging data help to identify neural processing networks that exhibit similar task-related or restingstate patterns of activity. Ideally, the measured brain activity for voxels within such networks should exhibit high autocorrelation. An important limitation is that the algorithms do not typically quantify or statistically test the strength or nature of the within-network relatedness between voxels. To extend the results given by such data-driven analyses, we propose the use of Moran’s I statistic to measure the degree of functional autocorrelation within identified neural processing networks and to evaluate the statistical significance of the observed associations. We adapt the conventional definition of Moran’s I, for applicability to neuroimaging analyses, by defining the global autocorrelation index using network-based neighborhoods. Also, we compute network-specific contributions to the overall autocorrelation. We present results from a bootstrap analysis that provide empirical support for the use of our hypothesis testing framework. We illustrate our methodology using positron emission tomography (PET) data from a study that examines the neural representation of working memory among individuals with schizophrenia and functional magnetic resonance imaging (fMRI) data from a study of depression.