The human brain functions as an efficient system where signals arising from gray matter are transported via white matter tracts to other regions of the brain to facilitate human behavior. However, with a few exceptions, functional and structural neuroimaging data are typically optimized to maximize the quantification of signals arising from a single source. For example, functional magnetic resonance imaging (FMRI) is typically used as an index of gray matter functioning whereas diffusion tensor imaging (DTI) is typically used to determine white matter properties. While it is likely that these signals arising from different tissue sources contain complementary information, the signal processing algorithms necessary for the fusion of neuroimaging data across imaging modalities are still in a nascent stage. In the current paper we present a data-driven method for combining measures of functional connectivity arising from gray matter sources (FMRI resting state data) with different measures of white matter connectivity (DTI). Specifically, a joint independent component analysis (J-ICA) was used to combine these measures of functional connectivity following intensive signal processing and feature extraction within each of the individual modalities. Our results indicate that one of the most predominantly used measures of functional connectivity (activity in the default mode network) is highly dependent on the integrity of white matter connections between the two hemispheres (corpus callosum) and within the cingulate bundles. Importantly, the discovery of this complex relationship of connectivity was entirely facilitated by the signal processing and fusion techniques presented herein and could not have been revealed through separate analyses of both data types as is typically performed in the majority of neuroimaging experiments. We conclude by discussing future applications of this technique to other areas of neuroimaging and examining potential limitations of the methods.

中文翻译：

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联合独立分量分析揭示的多模态和多组织连通性测量

人类大脑作为一个有效系统发挥作用，其中来自灰质的信号通过白质束传输到大脑的其他区域，以促进人类行为。然而，除了少数例外，功能和结构神经影像数据通常经过优化，以最大限度地量化来自单一来源的信号。例如，功能性磁共振成像 (FMRI) 通常用作灰质功能的指标，而扩散张量成像 (DTI) 通常用于确定白质特性。虽然这些来自不同组织来源的信号可能包含互补信息，但跨成像模式融合神经影像数据所需的信号处理算法仍处于初期阶段。在当前的论文中，我们提出了一种数据驱动的方法，用于将灰质源（FMRI 静息状态数据）产生的功能连接性测量与白质连接性 (DTI) 的不同测量相结合。具体来说，联合独立分量分析 (J-ICA) 用于在每个单独的模态内进行密集的信号处理和特征提取后，将这些功能连接的测量结合起来。我们的结果表明，最常用的功能连接性测量之一（默认模式网络中的活动）高度依赖于两个半球（胼胝体）之间和扣带束内白质连接的完整性。重要的，此处介绍的信号处理和融合技术完全促进了这种复杂连接关系的发现，并且无法像大多数神经成像实验中通常进行的那样通过对两种数据类型的单独分析来揭示。我们最后讨论了这种技术在神经影像学的其他领域的未来应用，并检查了这些方法的潜在局限性。