[论文解读] Explicitly Linking Regional Activation and Function Connectivity: Community Structure of Weighted Networks with Continuous Annotation

Andrew C. Murphy, Shi Gu+5 more|arXiv (Cornell University)|Nov 23, 2016

Functional Brain Connectivity Studies参考文献 59被引用 22

一句话总结

该论文提出了一种用于带连续节点注释的加权网络社区检测的生成概率模型，实现了对功能连接与区域激活的联合分析。研究发现，更快的运动学习与功能连接模式和激活幅度变化之间的差异性增强相关，表明这两者是独立但互补的神经生理学维度。

ABSTRACT

A major challenge in neuroimaging is understanding the mapping of neurophysiological dynamics onto cognitive functions. Traditionally, these maps have been constructed by examining changes in the activity magnitude of regions related to task performance. Recently, network neuroscience has produced methods to map connectivity patterns among many regions to certain cognitive functions by drawing on tools from network science and graph theory. However, these two different views are rarely addressed simultaneously, largely because few tools exist that account for patterns between nodes while simultaneously considering activation of nodes. We address this gap by solving the problem of community detection on weighted networks with continuous (non-integer) annotations by deriving a generative probabilistic model. This model generates communities whose members connect densely to nodes within their own community, and whose members share similar annotation values. We demonstrate the utility of the model in the context of neuroimaging data gathered during a motor learning paradigm, where edges are task-based functional connectivity and annotations to each node are beta weights from a general linear model that encoded a linear decrease in blood-oxygen-level-dependent signal with practice. Interestingly, we observe that individuals who learn at a faster rate exhibit the greatest dissimilarity between functional connectivity and activation magnitudes, suggesting that activation and functional connectivity are distinct dimensions of neurophysiology that track behavioral change. More generally, the tool that we develop offers an explicit, mathematically principled link between functional activation and functional connectivity, and can readily be applied to a other similar problems in which one set of imaging data offers network data, and a second offers a regional attribute.

研究动机与目标

• 为解决神经影像学中缺乏能同时建模区域间连接与区域激活幅度的工具的问题。
• 开发一种在加权网络中具有连续节点注释的社区检测的数学上严谨的方法。
• 研究功能连接与区域激活整合如何影响行为结果，特别是运动任务中的学习速率。
• 提供一个可推广的框架，适用于多模态神经影像及其他节点属性与网络结构共现的系统。

提出的方法

• 为带连续注释的加权网络的社区检测制定一种生成概率模型。
• 将社区建模为内部连接紧密且注释值相似的节点组。
• 采用基于似然的推断方法，识别在观察到的网络结构和注释值下概率最大的社区。
• 将该模型应用于运动学习任务的fMRI数据，以功能连接作为边权重，以GLM中的beta权重作为节点注释。
• 采用基于置换的零模型检验注释与社区结构之间关系的显著性。
• 通过注释值与社区归属之间的互信息量化注释对社区结构的贡献。

实验结果

研究问题

• RQ1 在运动学习过程中，大脑网络中的功能连接模式与区域激活幅度在多大程度上共同变化？
• RQ2 激活幅度与连接结构的整合在多大程度上影响学习表现？
• RQ3 注释在多大程度上塑造社区结构与个体学习速率之间是否存在系统性关系？
• RQ4 观察到的激活与连接之间的关系是否特异于学习过程，还是可由随机网络结构解释？

主要发现

• 更快的学习者表现出激活幅度对社区结构贡献与学习速率之间显著的负相关性。
• 真实数据中注释值与社区归属之间的互信息显著高于基于置换的零模型。
• 学习速率较快的个体在不同脑区的功能连接模式与激活变化幅度之间表现出更大的差异性。
• 结果表明，功能连接与激活幅度在学习过程中代表了独立且非冗余的神经生理动态维度。
• 所提出的方法成功识别出在功能上连接紧密且具有相似激活特征的社区，其统计显著性通过置换检验得到验证。
• 该框架可推广至涉及网络结构与区域属性的其他多模态神经影像问题。