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Fast construction of voxel-level functional connectivity graphs

机译：快速构建体素级功能连接图

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Background Graph-based analysis of fMRI data has recently emerged as a promising approach to study brain networks. Based on the assessment of synchronous fMRI activity at separate brain sites, functional connectivity graphs are constructed and analyzed using graph-theoretical concepts. Most previous studies investigated region-level graphs, which are computationally inexpensive, but bring along the problem of choosing sensible regions and involve blurring of more detailed information. In contrast, voxel-level graphs provide the finest granularity attainable from the data, enabling analyses at superior spatial resolution. They are, however, associated with considerable computational demands, which can render high-resolution analyses infeasible. In response, many existing studies investigating functional connectivity at the voxel-level reduced the computational burden by sacrificing spatial resolution. Methods Here, a novel, time-efficient method for graph construction is presented that retains the original spatial resolution. Performance gains are instead achieved through data reduction in the temporal domain based on dichotomization of voxel time series combined with tetrachoric correlation estimation and efficient implementation. Results By comparison with graph construction based on Pearson’s r , the technique used by the majority of previous studies, we find that the novel approach produces highly similar results an order of magnitude faster. Conclusions Its demonstrated performance makes the proposed approach a sensible and efficient alternative to customary practice. An open source software package containing the created programs is freely available for download.
机译：基于背景图的fMRI数据分析最近成为研究脑网络的一种有前途的方法。基于在单独的大脑部位的同步功能磁共振成像活动的评估，使用图论概念构建和分析功能连接图。以前的大多数研究都对区域级图进行了研究，这些区域图在计算上不昂贵，但是却带来了选择合理区域的问题，并且涉及到更详细信息的模糊化。相反，体素级图提供了可从数据中获得的最细粒度，从而能够以较高的空间分辨率进行分析。但是，它们与相当大的计算需求相关联，这可能使高分辨率分析不可行。作为回应，许多现有的研究在体素级别的功能连接的研究通过牺牲空间分辨率来减轻了计算负担。方法在这里，提出了一种新颖，省时的图构建方法，该方法保留了原始的空间分辨率。取而代之的是，通过基于体素时间序列的二分法并结合四项相关估计和有效实现，通过在时域中进行数据缩减来实现性能提升。结果通过与大多数先前研究使用的基于Pearson r的图构造进行比较，我们发现该新方法产生的高度相似结果快了一个数量级。结论结论所证明的性能使所提出的方法成为习惯做法的明智而有效的替代方法。包含创建的程序的开源软件包可以免费下载。

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