[论文解读] Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data

Biwei Huang, Kun Zhang+4 more|arXiv (Cornell University)|Jan 27, 2019

Functional Brain Connectivity Studies被引用 7

一句话总结

本文提出一种基于静息态fMRI数据的两步因果发现方法，利用受限函数因果模型识别自闭症谱系障碍（ASD）中脑区间直接因果影响。通过学习因果影响强度——尤其是从上额回到远距离区域的连接减弱——该方法在诊断准确率上超越基于相关性的方法，揭示了默认模式网络中信息流受损及ASD大脑小世界特性降低。

ABSTRACT

Autism spectrum disorder (ASD) is one of the major developmental disorders affecting children. Recently, it has been hypothesized that ASD is associated with atypical brain connectivities. A substantial body of researches use Pearson's correlation coefficients, mutual information, or partial correlation to investigate the differences in brain connectivities between ASD and typical controls from functional Magnetic Resonance Imaging (fMRI). However, correlation or partial correlation does not directly reveal causal influences - the information flow - between brain regions. Comparing to correlation, causality pinpoints the key connectivity characteristics and removes redundant features for diagnosis. In this paper, we propose a two-step method for large-scale and cyclic causal discovery from fMRI. It can identify brain causal structures without doing interventional experiments. The learned causal structure, as well as the causal influence strength, provides us the path and effectiveness of information flow. With the recovered causal influence strength as candidate features, we then perform ASD diagnosis by further doing feature selection and classification. We apply our methods to three datasets from Autism Brain Imaging Data Exchange (ABIDE). From experimental results, it shows that with causal connectivities, the diagnostic accuracy largely improves. A closer examination shows that information flows starting from the superior front gyrus to default mode network and posterior areas are largely reduced. Moreover, all enhanced information flows are from posterior to anterior or in local areas. Overall, it shows that long-range influences have a larger proportion of reductions than local ones, while local influences have a larger proportion of increases than long-range ones. By examining the graph properties of brain causal structure, the group of ASD shows reduced small-worldness.

研究动机与目标

• 为解决自闭症谱系障碍（ASD）行为诊断的局限性，其依赖主观临床判断且缺乏对潜在生物学机制的洞察。
• 克服基于相关性的脑连接度量的不足，后者无法捕捉信息流动方向和因果关系。
• 从静息态fMRI数据中开发一种可扩展的、支持循环结构的因果发现方法，识别脑区间直接因果影响，无需干预实验。
• 评估所学习的因果影响强度是否在ASD诊断性能上优于传统统计依赖性方法（如相关性或偏相关性）。
• 利用因果建模研究ASD相关的结构与功能脑连接模式——特别是信息流动方向与强度。

提出的方法

• 应用受限函数因果模型（CFM）将每个脑区的活动建模为直接原因的线性函数加上独立噪声，实现因果结构恢复。
• 采用两步算法实现大规模、循环因果发现，从fMRI时间序列数据中识别因果顺序并估计因果影响强度。
• 施加条件独立性约束以识别直接因果关系，并将其与间接或虚假关联区分开来。
• 对学习到的因果影响强度执行特征选择，以降低维度并提升分类性能。
• 利用所选因果特征进行分类以诊断ASD，并与基于相关性或偏相关性的方法进行性能比较。
• 分析图属性（如小世界性）以表征ASD与神经典型对照组之间脑网络组织的差异。

实验结果

研究问题

• RQ1 从静息态fMRI数据中学习的因果影响强度是否能显著提升ASD诊断准确率，相较基于相关性的方法？
• RQ2 在ASD个体中，哪些脑区的因果影响强度显著改变，特别是在信息流方向与强度方面？
• RQ3 ASD与典型对照组在脑区间长程与局部因果影响方面有何差异？
• RQ4 与对照组相比，ASD因果脑网络的网络级属性（如小世界性）如何？
• RQ5 是否存在信息流受损的一致性模式，例如ASD中来自上额回对后部及默认模式网络区域的影响减弱？

主要发现

• 从fMRI数据中学习的因果影响强度显著提升了ASD诊断准确率，优于相关性与偏相关性度量。
• ASD中来自上额回到其他脑区（尤其是默认模式网络与后部区域）的信息流显著减弱。
• ASD中增强的因果影响主要为局部或后向至前向，而长程影响则表现出更高的减弱比例而非增强。
• 长程因果影响比局部影响更常减弱，而局部影响则表现出更高的增强比例。
• ASD的因果脑网络结构表现出小世界性降低，表明其全局信息整合效率低于神经典型对照组。
• 因果模型揭示了相关性或偏相关性无法检测到的方向性信息流模式，凸显了因果性在理解ASD神经生物学中的重要性。