Memory-Efficient Analysis of Dense Functional Connectomes.
Memory-Efficient Analysis of Dense Functional Connectomes.
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DOI：
10.3389/fninf.2016.00050
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发表时间：
2016
影响因子：
3.5
通讯作者：
Borgelt C
中科院分区：
文献类型：
--
作者：
Loewe K;Donohue SE;Schoenfeld MA;Kruse R;Borgelt C
人类大脑的功能依赖于复杂网络中众多个体单元的相互作用和整合。为了识别特定认知任务或精神疾病的网络配置特征，可以基于对不同大脑部位的同步fMRI活动的评估来构建功能连接体，然后使用图论概念进行分析。在大多数以前的研究中，相对粗糙的大脑包裹被用来定义区域作为图形节点。这种包裹的连接体高度依赖于包裹质量，因为区域和功能边界需要相对一致，结果才能解释。相比之下，密集的连接体不受此限制，因为数据固有的分组被用来定义图形节点，也允许连接模式的更详细的空间映射。然而，密集的连接体与相当大的计算需求，在时间和内存的要求。在主内存中显式存储密集连接体所需的内存可能会使其分析变得不可行，特别是在考虑高分辨率数据或跨多个主题或条件的分析时。在这里，我们提出了一个基于对象的矩阵表示，通过按需计算矩阵元素，而不是显式存储它们，实现了非常低的内存占用。这样做，密集连接体所需的内存减少到存储底层时间序列数据所需的量。基于理论考虑和基准测试，不同的矩阵对象实现和附加程序（基于可用的Matlab函数和基于Matlab的第三方软件）的计算效率进行了比较。基于按需计算的矩阵实现具有非常低的内存需求，从而使得能够进行由于内存不足而无法进行的分析。包含创建的程序的开源软件包可供下载。
The functioning of the human brain relies on the interplay and integration of numerous individual units within a complex network. To identify network configurations characteristic of specific cognitive tasks or mental illnesses, functional connectomes can be constructed based on the assessment of synchronous fMRI activity at separate brain sites, and then analyzed using graph-theoretical concepts. In most previous studies, relatively coarse parcellations of the brain were used to define regions as graphical nodes. Such parcellated connectomes are highly dependent on parcellation quality because regional and functional boundaries need to be relatively consistent for the results to be interpretable. In contrast, dense connectomes are not subject to this limitation, since the parcellation inherent to the data is used to define graphical nodes, also allowing for a more detailed spatial mapping of connectivity patterns. However, dense connectomes are associated with considerable computational demands in terms of both time and memory requirements. The memory required to explicitly store dense connectomes in main memory can render their analysis infeasible, especially when considering high-resolution data or analyses across multiple subjects or conditions. Here, we present an object-based matrix representation that achieves a very low memory footprint by computing matrix elements on demand instead of explicitly storing them. In doing so, memory required for a dense connectome is reduced to the amount needed to store the underlying time series data. Based on theoretical considerations and benchmarks, different matrix object implementations and additional programs (based on available Matlab functions and Matlab-based third-party software) are compared with regard to their computational efficiency. The matrix implementation based on on-demand computations has very low memory requirements, thus enabling analyses that would be otherwise infeasible to conduct due to insufficient memory. An open source software package containing the created programs is available for download.
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DOI：
10.1073/pnas.0911855107
发表时间：
2010-03-09
影响因子：
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作者：
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